what sociologists know about the acceptance and diffusion ... · diffusion on innovation within...

What Sociologists Know About the Acceptance and Diffusion of

The Case of Engineering Education

AMERICAN SOCIOLOGICAL ASSOCIATION

CENTER FOR THE ADVANCEMENT OF SCHOLARSHIP ON ENGINEERING EDUCATION

INNOVATION:

About the American Sociological Association

Founded in 1905, the American Sociological Association (ASA) is a non-profit membership association dedicated to advancing sociology asa scientific discipline and profession serving the public good. With morethan 14,000 members, ASA encompasses sociologists who are facultymembers at colleges and universities, researchers, policy analysts,practitioners, and students. Members of specific sections of theAssociation including those concerned with Science, Knowledge, andTechnology; Organizations, Occupations and Work: and Teaching andLearning engage in research that is relevant to the acceptance anddiffusion on innovation within varying institutional contexts. Within theASA Executive Office, the Research and Development Departmentdevelops and disseminates studies on the science labor force and therelationship among networks, mentoring, curriculum, and career trajectories. Also within the Executive Office, the ASA Academic andProfessional Affairs Program, is responsible for encouraging the bestpractices in education, training, teaching, and evaluation.

About the Center for the Advancement of Scholarship on Engineering Education

Founded in 1992, the Center for the Advancement of Scholarship onEngineering Education (CASEE) of the National Academy of Engineering(NAE) represents a collaborative effort across stakeholder communitiesto improve the alignment of the knowledge and skills possessed byfuture and current engineers with the knowledge and skills sought byvarious stakeholders. This effort is pursued through research, development, and deployment of, innovative policies, practices, and toolsdesigned to enhance the effectiveness and efficiency of systems for theformal, informal, and lifelong education of current and future engineers.CASEE serves to integrate, enhance, and promote engineering educationby building collaborative networks with engineering schools/colleges aswell as employers of engineers, forming cross-institutional/organiza-tional learning communities of individuals exploring specific issues inengineering education. Though clearly focused on engineers andengineering education, CASEE benefits from and contributes toknowledge across a wide spectrum of the natural and social sciencecommunities, and other communities as well.

Frances Fox PivenPresident

Arne L. KallebergPresident-ElectCynthia F. EpsteinPast President

Sally J. HillsmanExecutive Officer

Wm. A. WulfNAE President

Lance DavisNAE Executive Officer

Cite publication as:Spalter-Roth, Roberta, Norman Fortenberry, and Barbara Lovitts. 2007.

What Sociologists Know About the Acceptance and Diffusion of Innovation: The Case of Engineering Education. Washington, DC: American Sociological Association.

For more information:American Sociological Association

1307 New York Avenue, NW, Suite 700Washington, DC 20005-4701

http://www.asanet.orgCopyright © 2007 by the American Sociological Association. All rights reserved.

Partial support for this project was provided to the National Academy of Engineering (NAE) andthe American Sociological Association (ASA) under grant SES-0523255 by the Human and SocialDynamics Program (HSD) of the National Science Foundation (NSF). Without the HSD grant wecould not have undertaken this project.

We'd like especially to express our appreciation to the workshop participants. This cross-disci-plinary workshop would not have been successful without their creativity, hard work, andwillingness to listen, learn, and go the next step. A part of their work can be seen in the whitepapers that they submitted as background to the workshop. The views expressed in the summaryreport and in the white papers are those of the Principal Investigators or the individual authorsand do not necessarily represent those of the NSF. We would also like to thank various staffmembers of the American Sociological Association and the Center for the Advancement ofScholarship on Engineering Education (CASEE) of the NAE for their tireless efforts:

William Erskine, Research Associate, ASANicole Van Vooren, Research Assistant, ASASylvia Pociask, Research Assistant, ASALynette Osborne, ExxonMobil Scholar-in-Residence, NAE/CASEESonja Atkinson, Program Associate, NAE/CASEEJason Williams, Program Assistant, NAE/CASEE

Jill Campbell was the copy editor for this report. Any remaining errors are those of the Principal Investigators.

Roberta Spalter-Roth, Director, Research and Development Department, American Sociological Association [email protected]

Norman Fortenberry, Director, Center for the Advancement of Scholarship on Engineering Education, National Academy of Engineering [email protected]

Barbara Lovitts, Senior Program Officer, Center for the Advancement of Scholarship onEngineering Education, National Academy of Engineering [email protected]

ACKNOWLEDGEMENTS

What Sociologists Know About the Acceptance and Diffusion of Innovation iv

Robin AdamsAssistant Professor ofEngineering EducationPurdue University

Susan AmbroseAssociate Provost for EducationCarnegie Mellon University

Maura BorregoAssistant Professor ofEngineering EducationVirginia Technical Institute

David BowenAssistant Professor of Engineering California State University, East Bay

Lori BreslowDirector, Teaching and Learning LaboratoryMassachusetts Institute ofTechnology

Val BurrisProfessor of SociologyUniversity of Oregon

Jennifer CroissantAssociate Professor ofWomen’s Studies

University of Arizona

Mary Frank FoxNSF Advance Professor of SociologyGeorgia Institute ofTechnology

Jeffrey FroydProfessor of ElectricalEngineeringTexas A&M University

Linda GrantProfessor of SociologyUniversity of Georgia

Sandra HansonProfessor of SociologyCatholic University

Laura KramerDepartment of SociologyMontclair State University

Erin LeaheyAssistant Professor of SociologyUniversity of Arizona

Cheryl LeggonAssociate Professor of Public PolicyGeorgia Institute of Technology

Michael LouiProfessor of Electrical andComputer EngineeringUniversity of Illinois, Urbana Champaign

Peter MeiksinsProfessor of SociologyCleveland State University

Willie Pearson Jr.Professor and Chair of theSchool of History, Science, andTechnologyGeorgia Institute of Technology

Stephen PlankAssistant Professor of Sociology & Center for SocialOrganization of SchoolsJohns Hopkins University

Gerhard SonnertResearch Associate, Department of Physics, and Lecturer, Department of SociologyHarvard University

Edmund TsangAssistant Dean of Engineeringfor Undergraduate ProgramsWestern Michigan University

What Sociologists Know About the Acceptance and Diffusion of Innovation v

WORKSHOP PARTICIPANTS

ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

EXECUTIVE SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

REPORT TITLE PAGE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Opportunity for a Workshop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

The Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Orienting Memos About What We Know and What We Need to Know . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Organizational Context and Faculty Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Faculty Rewards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Diffusion of Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

The Workshop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Plenary Session: What We Know . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Breakout Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Closing Plenary Session and Next Steps . . . . . . . . . . . . . . . . . . . . . 27

Project Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28Areas of Agreement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

APPENDICES

Appendix 1: List of Workshop Participants . . . . . . . . . . . . . . . . . . . 33

Appendix 2: Workshop Agenda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Appendix 3: Papers Presented by Workshop Participants . . . . . . . 37

Table of Contents

Organizational Context and Faculty BehaviorDavid Bowen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38Jennifer Croissant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45Mary Frank Fox . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49Sandra Hanson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55Cheryl Leggon and Willie Pearson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66Michael Loui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71Gerhard Sonnert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

Faculty RewardsSusan Ambrose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .85Lori Breslow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .98Linda Grant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .104Laura Kramer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .113Peter Meiksins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .120Edmund Tsang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .132

Diffusion of InnovationRobin Adams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .135Maura Borrengo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .146Val Burris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .151Jeffrey Froyd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .163James Moody and Erin Leahey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .171Stephen Plank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .178

LIST OF FIGURES

Bachelor’s Degrees Awarded in Engineering to All U.S. Citizens, Women, and Protected Minorities, 1971-2005 . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Average Familiarity with Other Participants Before the Workshop . . . . . . . . 11

Post-Workshop Discussion Rank Scores for Engineers and Sociologists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Figure 1

Figure 2

Figure 3

The curricula and pedagogy of engineering disciplines face mounting pressure tochange in response to the national need for engineers who can compete in the globalworkforce and the need to increase the racial, ethnic, and gender diversity of theprofession. For over two decades, numerous organizations have issued reports encour-aging greater attention to the undergraduate instruction in engineering disciplines,and yet, the curricula and pedagogy have been slow to change. Therefore, a majorquestion is what factors increase the likelihood that the new engineering curricula orpedagogy will be adopted?

In order to understand the social and human dynamics that facilitate and inhibit thediffusion and acceptance of new engineering curricula and pedagogy, the Center forthe Advancement of Scholarship on Engineering Education (CASEE) of the NationalAcademy of Engineering (NAE) collaborated with the American SociologicalAssociation (ASA) convened a workshop in April 2006 to learn what sociologists knowabout the organizational contexts, the reward systems, and the networks that couldincrease the acceptance and diffusion of innovations in engineering education and todevelop potential joint studies to address what is not known.

PROJECT EXPECTATIONS

The Principal Investigators (PIs) had the following expectations as we launched this project.

• Sociologists and engineering faculty could bring complementary insights to under-standing and addressing the problem of diffusion and change.

• Over the course of the workshop participants would develop hypothesesand study designs to answer questions about the relationships amongorganizational contexts, reward systems, and diffusion networks.

• Cross-disciplinary teams of engineers and sociologists would build on theseideas and develop fundable research projects.

• The resulting study designs and systematic evidence go beyond potentiallybiased, context-free, “best practices” and can be applied to other STEM disciplinesfacing problems bringing about curricular and pedagogical change.

The participants were divided among the topic areas to be discussed at theworkshop (Organizational Context and Faculty Behavior, Faculty Rewards, andDiffusion of Innovations), and each wrote a short white paper as background.

EXECUTIVE SUMMARY

What Sociologists Know About the Acceptance and Diffusion of Innovation 1

The bulk of the two-day meeting was spent with the working groups in separatebreakout sessions. Time was allotted in these sessions for the attendees to:

• Review what sociologists and engineers already know from previousresearch in the topic areas.

• Determine the gaps in current knowledge and what engineers and sociologists want to know.

• Develop hypotheses related to the three topic areas.• Prioritize the hypotheses based on their importance and ability to be

effectively studied.• Develop a research agenda for each topic area, and identify methods for

examining the research questions.

PROJECT OUTCOMES

The PIs were hopeful that the workshop would achieve its goal of bringingsociologists and engineering educators together to learn what is known and todevelop studies for what needs to be known in order to increase the acceptanceand diffusion of new engineering curricula and pedagogy. They were pleasantlysurprised when the two groups came together and, after adjusting to somedifferences in language, found common ground and identified problems ofmutual interest. In fact, engineers appeared particularly interested in workingwith sociologists and in using their concepts, theories, and models.

Research AgendasThrough their discussions, each of the three working groups developedresearch questions related to their topic area. Each selected the most promisingand developed a research plan to answer the question.

Organizational Contexts and Faculty Behavior. This group asked, “How does aparticular innovation and its adoption vary by location in the prestigehierarchy of engineering faculty, departments, and institutions?” Theyproposed to examine the adoption of integrated, first-year engineeringcurricula by comparing departments and institutions at different points in theprestige scale (gathered from prestige scores, Accreditation Board for Scienceand Engineering [ABET] self-studies, and faculty websites). A set of hypotheseswould be tested using a multi-method design.

Faculty Rewards. This group asked, “What rewards will mitigate the costs ofinnovative teaching in environments that place highest value on researchproductivity and the acquisition of external funding?” They proposed to assess


THE WORKSHOP

the relationship between a list of specific faculty rewards and measures ofinnovative practices in three engineering gatekeeper courses by surveyingchairs, finding secondary data, and controlling for a variety of factors.

Diffusion of Innovations. This group asked, “How does the rate of adoption ofcurricular innovation, such as capstone courses and design labs, vary by typesof networks?” They proposed to study a variety of networks, collect data from awide variety of sources, and employ multi-method analytic approaches.

Areas of Agreement Throughout the workshop sessions there were strong areas of agreementamong the participants.

• There are structural and cultural contexts that are part of all organizationaland individual decision processes in accepting innovations. Studies of bestpractices frequently do not examine these contexts.

• Measuring the scope and rate of acceptance of innovations requiresstudying a variety of units of analysis, including individuals, departments,and schools of engineering.

• Multi-method studies provide the highest level of rigor, richness, and understanding.

• Engineering faculty need to work with sociologists and other social scien-tists to conduct these studies.

• The more rigorous the study, the more likely the findings are to beaccepted, if disseminated through mixed networks.

Almost all of the participants agreed that they wanted to continue to partic-ipate in the project, expand their research designs, and develop fundable grantproposals. Continued activity could benefit sociologists as well as engineeringeducators. The broader impact of this project could be an increased under-standing of ways in which sociological insights can help innovate and improvethe quality of the Science, Technology, Engineering, and Mathematics (STEM)workforce. Evidence from rigorous joint studies could lead to the greateradoption of new engineering curricula and pedagogy that could increase thenumber of U.S. engineering undergraduate majors and improve their problem-solving abilities. The resulting study designs and systematic evidence gobeyond potentially biased, context-free, “best practices” and can be applied toother STEM disciplines facing problems bringing about curricular andpedagogical change.


THE WORKSHOP

What Sociologists Know About the Acceptance and Diffusion of

INNOVATION:The Case of Engineering Education

ROBERTA M. SPALTER-ROTHAmerican Sociological Association

NORMAN L. FORTENBERRYCenter for the Advancement of Scholarship

on Engineering Education

BARBARA LOVITTSCenter for the Advancement of Scholarship

on Engineering Education

Final Report

2006

This report is based on a two-day national workshop thatbrought together engineering faculty and sociologicalresearchers in a cross-disciplinary working group. Theparticipants developed hypotheses and study designs to inves-tigate what social dynamics would improve the diffusion andacceptance of new engineering curricula and pedagogy. The

workshop was funded by the National Science Foundation (NSF)program on Human and Social Dynamics, grant SES-0523255.

In organizing this conference, the Principal Investigators (PIs) hopedthat engineering faculty and sociologists, working together, woulddevelop a better understanding of what is needed to bring about theacceptance of innovations. They further hoped that this interdisci-plinary venture would be part of the larger science policy project ofunderstanding human and social processes underlying successfulinnovation and diffusion in all the Science, Technology, Engineering,and Mathematics (STEM) disciplines.

The PIs had the following expectations as they launched this project.

• Sociologists and engineering faculty could bring complementaryinsights to understanding and addressing the problem of diffusionand change.

• Over the course of the workshop participants would develophypotheses and study designs to answer questions about the relation-ships among (1) the types of educational institutions, status ofinnovators, and status of acceptors; (2) the type and distribution ofrewards, the values and norms of the engineers, and the acceptanceof innovation; and (3) the type of networks, innovations, and the rateof acceptance.

• The resulting studies would provide systematic evidence rather thanpotentially biased reports of “best practices.”

• Joint teams of engineers and sociologists would build on these ideasand develop fundable research projects.


The PIs recruited sociologists who had developed and tested theoriesabout how scientific and educational innovations occur and are diffusedthrough organizations, institutions, and social networks. Their studiesaddress how factors such as status hierarchies, social networks, andprofessional norms can encourage or impede innovation and thediffusion of science (Burris 2004; Fox 1992, 2001; Meiksins 2002;Meiksins and Watson 1989; Sonnert 1995). Engineering faculty wererecruited who could provide background information on the workingsof engineering departments and schools that few sociologists knew,including information about failed and successful innovations inengineering education.

This report discusses what we know and what we need to know in orderto increase the likelihood that engineering faculty and departments willadopt curricular and pedagogical innovations. It is based on thebackground memos and conversations about research between theengineers and sociologists during the two-day NSF-funded workshop.This report presents the stages of the project, including its background,selection of participants, production of white papers, introductoryplenary session and breakout sessions, the final plenary session, andfuture steps. The proposed research designs can be applied to otherSTEM disciplines that face barriers to educational reform.

The proposedresearch designscan be applied toother science,technology, andmathematics disciplines that face barriers toeducational reform.


Background

In response to the national need for engineers who can compete in theglobal workforce and the need to increase the racial, ethnic, and genderdiversity of the profession, the curriculum and pedagogy of engineeringeducation face mounting pressure to change. As Figure 1 shows, thenumber of engineering degrees earned by women and minorities since1970 is a small portion of the number earned by all U.S. citizens.

For over two decades, numerous organizations have issued reportsencouraging greater attention to the nature and quality of under-graduate instruction in engineering disciplines (Accreditation Board forEngineering and Technology 1995; American Society for EngineeringEducation 1986, 1987, 1994; Hissey 2000; McMasters 2000; NationalResearch Council 1985a, 1985b, 1986a, 1986b, 1995; National ScienceFoundation 1989, 1995). As a result, large-scale efforts have been under-taken to change curriculum and educational practices. Although newpedagogic approaches are beginning to take root, relatively little haschanged in the content and conduct of undergraduate engineeringinstruction (National Academy of Engineering 2004). Therefore, a majorquestion for engineers and engineering educators is, what factorsincrease the likelihood that the new curricula or pedagogy inengineering disciplines will be adopted?


Fig. 1 Bachelor’sDegrees Awarded inEngineering to All USCitizents, Women andProtectedMinorities1971–200

Protected Minorities

80K

70K

60K

50K

40K

30K

20K

10K

0K

Women

All U.S. Citizens

1971 1987 2005

(Number ofEngineeringBachelorsDegrees)

Source: Commission of Professionals in Science and Technology, Professional Womenand Minorities: A Total Human Resource Data Compendium (14th Edition)Washington, DC. CPST, 2006, Table 3_89.

From the perspective of many engineering faculty, the lack of change isbased on individual failure. In Menges’ (2000) words, “Why do facultyfail to use demonstrably effective teaching methods and other data-basedinformation about teaching, and how can the situation be changed?”[italics added]. Rather than blaming individuals, the field would benefitfrom analyzing the impact of structural and cultural contexts that shapeindividual, departmental, and administrative choices. Althoughinnovation in engineering education requires explicit consideration ofindividual faculty and their motivations (Froyd 2001), analysis of socialdynamics, including organizational contexts, reward structures, anddiffusion networks, provides an understanding of these motivations.Studies that examine structural location—that is, whether individualengineers practicing innovative instructional methods are located at thecenter or the margins of different types of higher education institutions,networks, or resource systems (Schneiberg and Clemens 2006)—areneeded. Although engineering educators speak of engineering culture,there is limited systematic analysis of the cultural tensions that occurwithin the modern university, within and between engineering disci-plines, and between professional norms and curricular pedagogy.Engineers know the disciplinary organizations, journals, and membersof their field; however, very few systematically study the paths andstrategies to diffuse and accept innovations in engineering education.

Recently, the engineering accreditation process has changed to focus onstudent learning outcomes rather than educational credits[Accreditation Board for Science and Engineering (ABET)]. This changeprovides a positive context for new approaches to engineeringeducation. Engineering faculty—including faculty on campuses thatparticipated in the NSF Engineering Education Coalitions—have writtenabout the changes that are necessary to implement new engineeringcurricula and pedagogy. Together, these changes suggest that theengineering community is now prepared to benefit substantially from asociological examination of the structural and cultural conditions thatimpede or encourage the diffusion and adoption of its instructionalpractices.

“Why do faculty failto use demonstrablyeffective teachingmethods and other data-basedinformation aboutteaching, and howcan the situation bechanged?”


BACKGROUND


Opportunity for a Workshop

Norman Fortenberry, director of the National Academy ofEngineering’s Center for the Advancement of Scholarship onEngineering Education (CASEE), was interested in takingadvantage of social science research to increase the spread ofinnovation in engineering education. In turn, Roberta Spalter-Roth,research director of the American Sociological Association, wasinterested in the application of sociological theories and methods toexplain the social dynamics that encourage or impede theinnovation and diffusion of scientific findings.

At an NSF workshop on the pathways to STEM careers (Martin andPearson 2005), Fortenberry and Spalter-Roth discussed potentialprojects that could draw their respective communities together. Anopportunity to hold such a workshop was provided by the programon Human and Social Dynamics Program (HSD) sponsored by NSF.As defined by the HSD initiative (National Science Foundation2005), “Aspects of social dynamics include knowledge about organi-zational, cultural, and societal adaptation that increase our abilityto understand…social structures that create, define, and result fromchange…and the dynamics of human and social behavior at alllevels” (p. 2). Fortenberry and Spalter-Roth, along with sociologistBarbara Lovitts, a senior program officer at CASEE, applied for andreceived an HSD grant for a two-day workshop. The goal of theworkshop was to develop models that will allow exploration ofwhether an understanding of social dynamics can increaseacceptance and diffusion of enhanced curricular and pedagogicalmethods.

“Aspects of socialdynamics includeknowledge aboutorganizational,cultural, and societaladaptation thatincrease our abilityto understand…socialstructures thatcreate, define, andresult fromchange…and thedynamics of humanand social behaviorat all levels”


The Process

Bringing engineers and sociologists together in a workshop was in itselfan innovative strategy. The workshop process began with the selectionof 19 participants (9 engineers and engineering educators and 11 sociol-ogists). Applicants were solicited from a variety of sources includingengineering and sociology section members, NSF awardees, andrelevant published scholarship. Engineering applicants were required tosubmit statements on challenges they face in their local environmentsand how they hope to apply what they learn at the workshop to theresolution of those challenges. Sociology applicants were required tosubmit statements on how their empirical findings on structure,rewards systems, and networks could encourage the diffusion andadoption of new engineering curricula and pedagogy (see Appendix I forthe list of participants).

The guiding questions for the workshop were:

• How do new knowledge, curricula, and pedagogical practice spread and become accepted?

• How can we model the social dynamics that impede or facilitate individual and institutional diffusion and acceptance of change?

In other words, what do we know from research and practice, and whatelse do we need to know about the structural, political, economic, andsocio-cultural factors and processes that result in fostering individual,departmental, institutional, and disciplinary change?

Another purpose of the workshop was to build a cross-disciplinarycommunity that would develop joint projects to be pursued after theworkshop. As Figure 2 shows, before the workshop few engineers orsociologists knew each other, with sociologists more likely andengineers less likely to know either sociologists or engineers.

Once selected, the PIs divided the participants into three workinggroups based on the existing literature for understanding the socialcontext for individual acceptance of change. Appendix II contains a

Sociologists Engineers

bibliography of relevant works. These three working groups eachhad to take into account specific aspects of social contexts anddynamics in order to understand individual behavior.

ORGANIZATIONAL CONTEXTS AND FACULTY BEHAVIOR:Despite strong evidence that faculty behavior influences studentretention, most faculty members are uninvolved in activitiesaimed at improved undergraduate retention (Kramer 2001;Massy and Wilger 2000; Wankat et al. 2002). Why is this? Arefaculty members making poor individual choices and resistingchange, or does the organizational context in which faculty aresocialized and teach affect their acceptance of innovation orresistance to change? An individual learning perspective doesnot take systematic account of characteristics such as institu-tional prestige, influence, and networks that affect faculty abilityto accept innovation. In addition, an individual perspective doesnot account for how race and gender hierarchies affect choice orthe changing context of the discipline as a whole. Finally, it doesnot take collective resistance into account.

FACULTY REWARDS: Faculty members, chairs of departments,and deans of schools have many competing demands on theirtime. They must be convinced, therefore, that the benefits ofchange are worth the investment of their time and energy.What set of contradictory demands, norms, resources, power,and rewards affect faculty acceptance of innovation? What setact as barriers to acceptance of innovation? Does acceptancevary by type of institution or department? What are possiblestrategies to increase the likelihood of accepting innovation?What are the power, resource, and cultural shifts necessary forstrategies to work?

DIFFUSING INNOVATIONS: Diffusing innovative practices thatenhance instructional effectiveness and student learning withinundergraduate engineering education has received little explo-ration. What structural and organizational characteristics andprocesses advance the diffusion of innovations in engineeringeducation across many campuses? These can include the typeand prestige of communication patterns and networks that canfacilitate or impede acceptance and diffusion among individuals,departments, and institutions.


THE PROCESS

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k

Fig. 2 Average Familiaritywith other Participantsbefore Conference

LEVEL OF FAMILIARITY SCALE1 = “I do not know this person.”2 = “I have only heard of this person.”3 = “We talk once a year.”4 =“We talk a few more times a year.”5 = “We talk monthly or more.”


Orienting Memos About What We Know and What We Need to Know

All participants were required to submit an orienting memo showinghow available scholarship, along with their own work and experience,bears on the research questions and to suggest new hypotheses for theirworking groups. (See Appendix III for these orienting memos.)

The orienting memos shed light on the problems faced and the reasonsfor resistance to change. The PIs formulated the analyses into a series ofhypotheses about social dynamics or processes in each of the three topicareas for participant discussion.

ORGANIZATIONAL CONTEXT AND FACULTY BEHAVIOR—HYPOTHESES

1. Faculty behavior and organizational contexts cannot be separated.Faculty members respond to cues, rewards, trends, and fadswithin their specific institution. This is especially true inengineering, which is highly dependent on resources andteamwork.

2. Outside social movements and social pressures (e.g., professionalassociations, employers, civil rights movements, state legislators)can push for new curricula. They may be the agents of change inbringing about dissemination and use.

3. Increases in institutional and faculty resources lead to increasesin diffusion, which, in turn, lead to broader acceptance of innova-tions.

4. Institutions of higher education place contradictory demands onfaculty in terms of research, teaching, and service.

5. The division of labor in teaching affects the use of newengineering pedagogy. Large required courses tend to be taught bysenior faculty who do not do research but tend to uphold tradi-tional methods of teaching and traditional curricula.

6. Implementation of new engineering pedagogy will decrease biasand increase diversity.

FACULTY REWARDS—HYPOTHESES

1. Faculty members who get pleasure from doing a good jobteaching and developing colleagues with the same interests will bemore likely to use new engineering pedagogy.

2. Increases in salaries and awards will increase diffusion.3. Fear of losing turf and losing control lead to resistance to the

adoption of new curricula and pedagogy.4. New pedagogy will be disseminated and used to the extent that it

fits with norms of what is good engineering and what makes agood engineer.

5. Collective rather than individual mentoring can be used to bringabout curricular and pedagogical transformation as well asincrease diversity.

6. Especially in research universities, faculty members are stillevaluated by grants and publications, even though teaching hasbecome a higher priority.

DIFFUSION OF INNOVATIONS—HYPOTHESES

1. Endorsement of new curricula and pedagogy by those in higheradministration (e.g., presidents and provosts) and other strongplayers increases the likelihood of acceptance and diffusion.

2. Those in the middle of organizational hierarchies will have themost practical power in implementing technology. Support bydepartment chairs and deans will increase the use of newengineering pedagogy.

3. The pattern of network ties will affect diffusion. For example,structural equivalence may be the most important network factorin explaining dissemination of new engineering instructionaltechnologies.

4. A combination of links within well-populated networks and linksthat bridge isolated networks is needed to diffuse innovations.

5. Dissemination is most likely to occur between similarly placed,central actors in networks.

6. Multiple pathways within networks increase dissemination.7. Greater acceptance by opinion leaders (usually those at the center

of networks) will result in greater dissemination.8. The typical responses to new engineering curricula are neither

acceptance nor rejection, but other responses such as assimilation,partial acceptance, or lip service.

These hypotheses from the orienting memos were carried into theworkshop for further discussion.


ORIENTING MEMOS


The Workshop

PLENARY SESSION: WHAT WE KNOW The workshop began with a series of paired plenary speakers (a sociol-ogist and an engineer) who provided the assembled engineeringeducators and sociologists with an overview of what we know about thecomplexities of change in the social structures, cultural values, andnetworks that could be used to create or impede change in engineeringeducation. (See papers by speakers Ambrose, Burris, Croissant, FrankFox, Froyd, Meiksins, Plank in Appendix III.)

The Structure and Culture of Higher Education. Universities withvarious missions and prestige rankings are part of a larger system ofhigher education. The system communicates its values, beliefs, andpriorities to members by how it structures power and authority and howit allocates rewards and status. The modern research university does thefollowing:

• Emphasizes the academic discipline and the work of the individualfaculty member, especially in terms of bringing in outside honors,prestige, and research dollars.

• Awards internal prestige and offers rewards to individuals whoseresearch brings outside funding.

• Makes graduate education and the training of researchers a priority. • Focuses on specialized rather than general knowledge.• Attempts to be responsive to state legislative demands for student

outcome assessment.

Power and authority within institutions of higher education are highlydecentralized. This allows universities to be flexible and respond rapidlyto issues raised by individual groups. At the same time, it decreasesuniversities’ capacity to forge broad, unifying organizational strategies.Other than department chairs, administrators have relatively littleinfluence over faculty teaching decisions. We need to know more aboutthe size of institutions, autonomy of faculty, style of leadership, level ofimpetus for status-maintenance or mobility, subfields of faculty (olderversus newer, basic versus applied, more research versus less), andfaculty and student composition (by citizenship, gender, race, andethnicity). How do these factors affect innovation?

Factors comparable to those discussed for higher education as a wholeexist within engineering colleges/schools and departments. These unitsoften feel the need to satisfy a number of stakeholders given theirdependence on a positive relationship with industry for employment oftheir graduates and a relatively unique system of national accreditationfor individual engineering programs.

The Complexity of Rewards in Academic Engineering. From the late 19thCentury to the present, engineers’ views on the nature of engineeringand engineering knowledge evolved from that of “shop culture,” withits emphasis on training engineers through a kind of practical appren-ticeship in industrial settings to a “school culture,” with its emphasis oncollege-level training premised on acquiring scientific knowledge. Withthis shift came the occupational identity of “engineering science” andthe achievement of professional status. As a result of the shift, researchand researcher training replaced teaching and internships as the mosthighly valued aspect of the academic engineer’s job.

Contemporary engineering culture and its associated values containelements that are both hostile to and consistent with new approaches toengineering education. Elements that work against change includestatus and prestige concerns of engineers and their views of what consti-tutes “good engineering science.” At the same time, engineers’ valuesalso contain elements that encourage a focus on less “pure” scientificresearch and on the kinds of activities associated with practicalengineering work that engineering curricula seek to encourage—teamwork and solving real-world problems.

Institutional Rewards are powerful determinants of faculty behavior,and the institutional reward structure provides the blueprint for howfaculty spend their time. Most engineering programs are in researchuniversities, where faculty are still evaluated primarily on theirresearch productivity such as proposals generated, research funding,and scholarly publications. However, there has been growing criticismof academics’ narrow focus on traditional research, and universities areunder increasing pressure from state legislatures, accrediting agencies,parents, and students to demonstrate the “value added” from auniversity education. These pressures have led to a noticeable shifttoward holding educators accountable for their students’ achievementof measurable learning outcomes and a resulting resurgence of interestin high-quality teaching at research and comprehensive institutions.However, faculty are receiving multiple and mixed messages. They are

Contemporaryengineering cultureand its associatedvalues containelements that areboth hostile to andconsistent with newapproaches toengineeringeducation.


THE WORKSHOP

simultaneously asked to care more about teaching and to spend lesstime on it, because instructional costs represent a very large portion ofmost universities’ (shrinking) budgets.

Some department chairs, deans, and administrators at research univer-sities have come to view the current balance between research andteaching in the reward structure as inappropriate and in need of modifi-cation. They are starting to place more emphasis on innovative educa-tional practices. This new emphasis on education—both within univer-sities and from external change agents as well—has led to some changesin the rewards offered to university faculty:

• Major foundations have made resources available for research onteaching and learning.

• Many universities have made efforts to reward good teaching (e.g.,teaching awards, small grants for innovative teaching).

• Publishing outlets for research on engineering education and forscholarship of teaching and learning have proliferated.

• Some universities have modified their tenure and promotioncriteria.

Still, many faculty remain skeptical about administrative sincerity inthe absence of material support for curricular and pedagogical change,such as paying for faculty to attend workshops on pedagogy, in light ofunchanging expectations of high research productivity. They perceivethat articles, grants, or awards related to teaching and/or advising meanlittle or nothing for career success.

Faculty Workloads. Because the institutions in which most engineeringdepartments are located favor funded research, many academic depart-ments have adopted a faculty workload model that may support, butoften ends up undermining, the diffusion of innovation in engineeringeducation. Lighter teaching and service loads are given to faculty whosetenure and promotion depend heavily on externally funded research.Heavy teaching and service loads are given to faculty who haveexplicitly expressed no desire to engage in research or are currently notinvolved in research. Under this arrangement, there is an unspokenagreement that the teaching faculty will be left alone because they areresponsible for generating the largest percentage of student credit-hoursin the department.


THE WORKSHOP

The belief system of the teaching faculty—who are most likely to be theinstructors of gate-keeping courses and serve on department committeesthat govern the engineering curriculum—can positively or negativelyinfluence the diffusion of innovations in engineering education. Forexample, if the teaching faculty hold innovative beliefs about the role ofteachers and students, they are probably willing to experiment with andadopt new pedagogies or instructional methods. Conversely, if theteaching faculty hold traditional beliefs about curriculum and pedagogy,they are likely to see themselves as guardians of high academicstandards who must resist pressures to incorporate new instructionalpractices that they perceive as diluting the curriculum and loweringacademic standards.

Diffusion of InnovationEverett Rogers, in his widely cited book Diffusion of Innovations (2003),defines diffusion as the process by which an innovation is communi-cated through certain channels over time among the members of asocial system. How can the pattern of connections among individuals ina departmental or disciplinary network help facilitate the spread of newcurricular developments across that network? While the diffusion of aninnovation typically follows an S-shaped curve in which the rate ofadoption begins slowly, then accelerates as it spreads to a majority of thepopulation, and finally tapers off again as the point of saturation isapproached, this pattern has not occurred with relation to newengineering curricula and pedagogy.

The diffusion of an innovation often occurs through what can bedescribed as a two-stage process: first, the innovation must be acceptedby a sufficient number of “opinion leaders”; the opinion leaders thenencourage other members of the population to adopt the innovation.These individuals can be part of the following kinds of social systems:

1. All engineering faculty within an engineering discipline(department) within a single institution;

2. All engineering faculty within an engineering discipline across aset of universities;

3. All engineering faculty across a set of engineering disciplineswithin a single college or school of engineering;

4. All engineering faculty across a set of engineering disciplineswithin colleges or schools of engineering across a set of universities.

“The diffusion of an innovation often occursthrough what canbe described as atwo-stageproces…”


THE WORKSHOP

Whether opinion leaders accept an innovation is a function of theirreaction to the innovation and the degree to which they belong todiverse sets of networks. Reactions to an innovation largely depend onthe opinion leaders’ knowledge, attitudes, and beliefs. The particularchallenge here is that engineering faculty, in general, have receivedlittle preparation for scholarly teaching and lack rigorous knowledge ofteaching and learning scholarship. Further, because research onteaching and learning is fundamentally grounded in social sciencemethods for examining dynamic social systems, faculty who are mostfamiliar with quantitative research on relatively stable inanimateobjects and systems may be reluctant to accept its findings. Additionally,practical issues also may prevent the exploration of innovation. Forexample, a curricular innovation that is implemented via moduleswithin existing courses can diffuse more rapidly than one requiringwholesale curricular change.

The diffusion of educational innovations also depends on the networksin which opinion leaders participate. Actors’ decisions with respect toadopting or not adopting an innovation can be explained, at least inpart, by the pattern of interconnections among them. How closely actorsare related to one another and whether individuals or groups belong toa common, densely knit community affect the scope and rate ofdiffusion. Also important are the types of ties that bridge networks.Strong ties link any two members of a densely interconnectedcommunity. Weak ties link isolated clusters or communities.

Relatively dense communities of strong ties offer many pathways fordiffusion; however, such densely knit communities also exhibitresistance to change or innovation. Adoption of innovation by centralactors in the network can be crucial to overcoming this resistance, bothbecause of the large number of ties that they maintain and because ofthe prestige that typically accompanies their central role within theircommunity. Strategically located actors with ties to other communitiesalso play a pivotal role in the diffusion of innovation. Such actors arefrequently the first ones to become exposed to new ideas, and the factthat they bridge multiple communities makes them less bound by thetraditional norms and practices of any given community and moreprone to be early innovators.


THE WORKSHOP

In understanding the role of networks in diffusion, it is important tomove beyond a simple binary notion of adoption or non-adoption. Thefollowing range of behaviors is part of the diffusion process (see Plank’sorienting memo):

• Accommodation. Engaging the innovations in ways that represent afundamental revision of one’s pedagogical views and practices.

• Assimilation. Making an attempt to accept and implement thereforms, but only after transforming the tenets or interpretations ofthem to fit one’s pre-existing pedagogical beliefs and ways of doingthings.

• Parallel structures. Taking the reform seriously but adopting itspractices only some of the time or in some contexts, thereby leavingintact and unchanged other (possibly conflicting or antithetical)practices at other times and in other contexts.

• Decoupling/symbolic response. Giving lip service or making super-ficial changes to give the appearance of compliance, withoutchanging previous practices in any serious way.

• Rejection. Refusing outright to engage or adopt the reform; outrightdismissal of a mandate.

BREAKOUT GROUPSIn the remainder of the two days, members participated in the separatebreakout sessions to which they were assigned (on organizationalcontext, faculty rewards, or diffusion of innovation). The workinggroups were assigned the following tasks:

• Review what is already known by sociologists and engineers fromprevious research in the topic areas.

• Determine the gaps in the current knowledge and what engineersand sociologists want to know.

• Develop hypotheses related to the three topic areas.• Prioritize the hypotheses based on their importance and ability to be

effectively studied.• Develop a research agenda for each topic area, several hypotheses,

and identify methods for examining the research questions.

Engineering faculty and sociologists engaged constructively in theseworking groups. Participants implicitly understood what each groupbrought to the table. The sociologists brought the base of theory andresearch to bear on the problems faced by the engineering educators.


THE WORKSHOP

The sociologists also provided the expertise in research methods andexperimental design. The engineers provided the background, topog-raphy, politics, and operations of the engineering colleges that thesociologists lacked. The engineers had knowledge of state-of-the-artengineering education research, the history of failed and successfulinnovations in engineering education, and familiarity with the avenuesof dissemination, such as relevant journals and conferences. Engineersused the sociologists as consultants, and sociological concepts, theory,language, and methods framed much of the conversation.

All three working groups raised questions about the definition ofinnovation including:

• Have we defined innovation?• How will the innovations be measured?• How do we identify courses that are associated with innovation?• Is there a shared understanding of innovation?

Each group had free-wheeling, rich, and deep discussions as theydeveloped common understandings and worked toward developingstudy hypotheses and designs.

Organizational Context Group. This group grappled with defining andmeasuring innovation and with comparing the impact of organizationalcontext for the acceptance of innovation. First, they agreed on theimportance of defining the target unit of analysis for studyingpedagogical and curricular innovations. Some important units ofanalyses included the type of institution (e.g., public or private); thetype of department, including whether or not sub-disciplines are housedin separate departments/schools (e.g., chemical engineering, electricalengineering, mechanical engineering, civil engineering); and the rankof faculty members. This group agreed that accrediting data couldprovide information for comparing departments and institutions.

The group prioritized their questions and discussed methods of investi-gation. The highest-priority questions concerned variation in innovationand acceptance of innovation by location in the prestige hierarchy. Theprestige of schools and departments, the hierarchy and stratification ofprestige, and the place in the national rankings are key means ofjudging how innovations spread (i.e., from top to bottom, from bottom totop, or from middle to either top or bottom). Given their priority, theysuggested the following specific research questions about the effect oforganizational context and prestige on innovation:

The highest-priorityquestions concernedvariation ininnovation andacceptance ofinnovation by locationin the prestigehierarchy.


THE WORKSHOP

• What are the effects of institutional prestige on innovation; the agents of change in different types of institutions?

• Are the innovations of low status individuals or departmentsemulated?

• Do people in stronger or weaker systems adopt innovations? • Is the middle of the range the best place to innovate? • Are promising practices adopted across a range of institutions?• How do race, ethnicity, and gender fit into the relationship

between organizational context and acceptance of innovation?• What are the perceived costs and risks to adoption at different

levels in the hierarchy?

The group agreed that multi-method approaches including surveys andethnographies were the way to answer these questions.

The Organizational Context and Faculty Behavior Group developed astudy design to begin to answer the questions they had posed.

Proposed Study: Why is there resistance to change within departments,institutions, and disciplines such that new engineering curricula andthose who teach them are not valued? How can this situation change?

HYPOTHESIS: Prospects for innovation vary with the type of insti-tution, and characteristics of faculty and students within them.

a. The more prestige an institution has, the more it is likely tocreate rather than adopt innovation.

b. Non-PhD-granting institutions are more likely to adoptinnovation.

c. Women faculty members are more likely to adopt innovation.

MEASURING CONTEXT: Effects of context can be measured bycomparing differences among types of institutions or by selectingdifferent disciplines (e.g., biomedical engineering versus civilengineering) within different types of institutions or vice versa

Study Method:• Collect data (prestige, innovation, and control) on all institu-

tions. Data sources include documents, websites, ABET self-reports, and a survey.

• Collect faculty characteristics from websites and a survey.• Analyze data, e.g., contingency tables, multivariate modeling.


THE WORKSHOP

Outcomes: Describe and explain the variation in creation andadoption of innovation by prestige level and type of institution.

A final question posed was, how does this advance interesting questionsin sociology as well as engineering education?

Faculty Rewards Group. The members of this working group agreed thatjob rewards are complicated, contradictory, and change over time. Forexample, universities want faculty to produce more grants but are alsounder pressure to meet teaching outcomes by state legislatures andaccreditation bodies. The group asked, “What rewards will mitigate thecosts of innovative teaching in environments that place highest value onresearch productivity and acquisition of external funding?” Manyadministrators and engineers do not believe that the scholarship ofteaching and learning is rigorous. As a result, it does not receive theprestige that other kinds of research receive. Junior faculty may bemore willing to try different teaching innovations but do not have timeto innovate because they want to obtain tenure. Senior faculty memberswho adopt changes become marginalized.

Can rewards help? And if so, what kinds of rewards? Workshop partici-pants identified a series of rewards, some at the individual level andsome at the department or college level. These included:

1. Increased enrollments—Increased enrollment in engineeringprograms and increasing the number of U.S.-born engineeringstudents in graduate school.

2. Personal gratification—Some individuals may receive intrinsicrewards from being good teachers.

3. Altruistic reasons—Innovations are adopted for the larger good.4. Prestige—Promotions and perceived importance of place in a strat-

ification or ranking system may provide incentive for curricularinnovations.

5. Mentoring—Younger faculty may receive rewards, help, or attentionfrom more seasoned faculty. Does mentoring motivate theacceptance of innovation?

6. Monetary rewards—Salary increases and internal or external grantsprovide tangible rewards.

7. Time—Released time for developing or testing new curriculum canencourage innovation.

8. Collegiality—Collegiality makes faculty happy. Do faculty membershave people they can talk to about teaching, and from whom theyreceive help?

9. Other resources—Teaching assistants or graders can alleviate anincreased workload.

Junior faculty may be more willing to trydifferent teachinginnovation but do nothave time to innovatebecause they want toobtain tenure. Seniorfaculty members whoadopt changesbecome margin-alized.


THE WORKSHOP

Workshop members discussed many strategies for studying the effec-tiveness of different types of rewards in different contexts. Theysuggested the following kinds of research projects:

• Compare departments that have been funded by the NSF to pursueinnovative educational projects. Did funding result in change? Wererewards a driving motivation during this innovative change? Didrewards (like teaching development or more money) help with thechanges?

• Learn what people value so what they value can be offered. Why arefaculty members doing innovative teaching? Is it because theirresearch isn’t going well or because they receive positive rewards?Study faculty who go against the grain, and do case studies to findout what motivates them.

• Match institutions to find out if there are structural or culturalissues that motivate some departments to change. Compare themissions of institutions that want to change with similar institutionsthat do not.

• Survey faculty to determine what they actually do in their class-rooms, and compare the innovative institutions with the non-innovative institutions to determine whether there are differences.Look at the three gate-keeper classes—statics, thermodynamics, andcircuits.

The Faculty Rewards Group then narrowed their questions and began todevelop a study to examine the effect of teaching awards (i.e., teachinggrants and faculty rank) on innovation adoption in classrooms.

Proposed Study: Do different kinds of faculty rewards matter? Thisstudy will examine the effect of the following independent variables(rewards) on innovation adoption in three gatekeeper courses (statics,thermodynamics, and circuits):

• Release time for teaching development• Travel money• Screening of candidates• Regular seminars or teaching workshops• Teaching mentorship programs• Peer observation of teaching or teaching portfolios• Provision of teaching assistants


THE WORKSHOP

The method for this department-level study is to conduct a survey ofchairs, gather data, and conduct regression analysis of variables thatcorrelate significantly with faculty innovation, especially those havingto do with rewards, controlling for whether departments received anNSF grant for instructional innovation as well as other institutionalcharacteristics. If these factors are significant, then other departmentscan adopt those rewards that were related to innovation.

The major controls for this study are institutional variables includingdepartmental prestige, size, and type of institution. Individual controlvariables include rank of faculty member, gender race, ethnicity, andplace of degree

Diffusion of Innovation Group. The engineering faculty in this workshopagreed that two of the primary methods of dissemination—journalarticles and presentations at professional meetings—occurred in forumsnot seen by most engineering faculty (e.g., most engineering faculty arenot members of the American Society for Engineering Education). Theyconcurred that additional links between engineers were needed andasked how to increase the number of links so that there was greaterdiffusion of pedagogical and curricular innovations. They listed thefollowing innovations:

• Outcomes-based assessment• Developing student self-identity • Student teams and team training• Design decisions using computation• Active/cooperative learning• Capstone project courses• First-year design labs• Building learning communities among students• Technology

The group agreed that the diffusion of these innovations did not meanthat they were accepted, used, or had positive effects on studentlearning and retention (especially of women and minorities). In fact,the sociologists reiterated that diffusion is not binary (that is, a yes or nooutcome). Rather, as noted, diffusion includes accommodation, assimi-lation, parallel structures, or symbolic responses as well as acceptance orrejection. Nonetheless, diffusion is a necessary, if not sufficient, cause ofacceptance of innovation.

…additional linksbetween engineerswere needed andasked how toincrease the numberof links so that therewas greater diffusionof pedagogical and curricularinnovations.


THE WORKSHOP

Are disciplinary societies the best diffusion channel? Or is another alter-native, the coalition model, the best? In the coalition model several insti-tutions develop and implement a curriculum, which helps to jumpstartdiffusion. However, according to group members, curricula do notdiffuse well, but teaching methods and practices do.

The Diffusion of Innovation group then proposed to examine the effectsof networks on the dissemination of innovations. Group membersagreed, however, that in some cases the network is the independentvariable that predicts diffusion while in other cases networks could bethe dependent variable or desired outcome. They also discussed the studydesigns and measures that could be used to make the study operational.

Innovations to study: • Capstone or design lab courses because they are more binary and

more easily researchable

Networks to study:• Connections among education and technical research professors in

departments, centers, and schools • Students from one school who bring practices to the schools in

which they are teachers• Citation and co-authorship analyses• Departments of equivalent size and prestige

Study designs or measures:• Explore the boundaries of the social network (e.g., community of

practice) before beginning.• Examine diffusion of an innovation (such as first-year engineering)

among the ~300 engineering departments, noting which depart-ments adopted and when.

• Compare well-funded innovations with those with less funding.• Conduct three case studies: one successful, one not, and one in

progress.• Determine which departments or schools are early adopters, middle

adopters, and late adopters.• Look at people and departments who adopted and then abandoned

innovations.• Compare “S” curves of various innovations.• Examine PhD networks (i.e., “genealogical” lists of faculty and their

doctoral students) to show affiliations and isolation.

…diffusion includesaccommodation,assimilation, parallelstructures, orsymbolic responsesas well asacceptance orrejection.


THE WORKSHOP

Data sources: • Department self-studies for ABET• Second-hand sources: course catalogs, papers, and articles• Citation analysis• ASEE Profiles of Engineering and Engineering Technology Colleges• Surveys of department heads or undergraduate directors

Control factors: Control factors will include the size of the department, ranking,location, coalition membership, control (i.e., public, private, or forprofit), ABET accreditation, student demographics, and centralizedor decentralized structure.

The second phase of the research will include the creation of links(develop or rewire effective networks) based on what had been learnedas a result of the first phase.


THE WORKSHOP


Closing Plenary Session and Next Steps

At the closing plenary session, reporters for each of the workinggroups presented emerging research questions, hypotheses, andstudy designs that workshop participants had been inspired topursue after the workshop. Following these presentations, workshopparticipants discussed the next steps for the project. The project PIswere tasked with producing a final monograph (including partici-pants’ white papers) that would be disseminated to deans ofengineering and sociology sections such as the Section on Science,Knowledge and Technology, and relevant others. Follow-up surveys ofworkshop participants would determine whether they werecontinuing to converse with one another to refine and develop ideasand joint and individual proposals, replicate the workshop model, ormake presentations at professional meetings. The PIs also hoped toengage in these activities.

Project Outcomes

The PIs had been hopeful, but not particularly confident, that theworkshop would achieve its goal of bringing sociologists andengineering educators together to learn what is known and to developstudies for what we need to know to increase the acceptance ofdiffusion of new engineering curriculum and pedagogy. The PIs wereconcerned that the sociologists would not find the research questionspresented by engineers to be particularly compelling from their point ofview and that the engineers and engineering educators would notaccept sociological research as valid and useful. However, they werepleasantly surprised when the two groups came together and, aftersome adjustments for differences in language, found common groundand identified problems of mutual interest. In fact, engineers appearedparticularly interested in working with sociologists. In the words of oneengineering educator,

“Engineers do not know how to do pedagogical research, so thesuggestion is that they work with social scientists.… Conceptually, theinformation from social science is important. Rigorous evaluation isnecessary. Cross-disciplinary work is necessary.”

AREAS OF AGREEMENTThroughout the workshop sessions there were strong areas ofagreement among the participants.

• There are structural and cultural contexts, such as prestige hierar-chies, reward systems, and networks that are a part of all organiza-tional and individual decision processes in accepting innovations.These factors are frequently ignored in “best practice” studies.

• Measuring the scope and rate of acceptance of innovations requiresstudying a variety of units of analysis, including individuals, depart-ments, and schools of engineering. In addition, these units need tobe stratified by factors such as size, prestige, and level, in order tounderstand similarities and differences.

• In addition to quantitative analysis, case studies (as long as they arecomparative) and other methods such as content analysis need to beincluded. Multi-method studies provide the highest level of rigor,richness, and understanding.


Sociologists Engineers

• Engineering educators need to work with sociologists andother social scientists to conduct these studies.

• The more rigorous the study, the more likely the findingswill be accepted, if disseminated through mixednetworks.

Groups of engineers and sociologists are continuing to haveconversations. Figure 3 shows an increase (compared toFigure 2) in conversations among and between sociologistsand engineers. Almost all of the participants agreed that theywanted to continue to participate in the project, furtherexpand their research designs, and develop fundable grantproposals. Since the conference at least one researchproposal has been sent to NSF. Continued activity couldbenefit sociologists as well as engineering educators. Thebroader impact of this project could be an increased under-standing of ways in which sociological insights can bebeneficial for enhancing innovations to improve the qualityof the STEM workforce. Evidence from rigorous joint studiescould lead to the greater adoption of new engineeringcurricula and pedagogy that could help increase the numberof U.S. engineering undergraduate majors and improve theirproblem-solving abilities. The resulting study designs andsystematic evidence go beyond potentially biased, context-free best practices studies and can be applied to other STEMdisciplines facing problems bringing about curricular andpedagogical change.


PROJECT OUTCOMES

0.0

0.5

1.0

1.5

2.0

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Fig. 3 Post-workshopdiscussion rank scores forEngineers and Sociologists

MEAN DISCUSSION SCORE RANGES0–3: No discussion with

other participants3–6: At least one discussion with

other participants7–10: A few discussions with

other participants

References

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———. 1987. The National Action Agenda for Engineering Education. Washington, DC:American Society for Engineering Education.

———. 1994. Engineering Education for a Changing World: A Joint Report of theEngineering Deans Council and Corporate Roundtable. Washington, DC: AmericanSociety for Engineering Education.

Burris, Val. 2004. “The Academic Caste System: Prestige Hierarchies in Ph.D.Exchange Networks.” American Sociological Review 69:213-18.

Fox, Mary Frank. 1992. “Research Productivity and the Environmental Context.” Pp.103-11 in Research and Higher Education: The United Kingdom and the UnitedStates, edited by Thomas G. Whiston and Roger L. Geiger. Buckingham, England:Open University Press.

———. 2001. “Women, Men, and Engineering.” Pp. 249-57 in Gender Mosaics:Social Perspectives, edited by D. Vanoy. Los Angeles: Roxbury Press.

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REFERENCES


David BowenAssistant Professor ofEngineeringCalifornia State University, East [email protected]

Jennifer CroissantAssociate Professor of Women’sStudiesUniversity of [email protected]

Mary Frank FoxNSF Advance Professor ofSociologyGeorgia Institute of [email protected]

Sandra HansonProfessor of SociologyCatholic [email protected]

Cheryl Leggon Associate Professor of Public PolicyGeorgia Institute of [email protected]

Michael LouiProfessor of Electrical andComputer EngineeringUniversity of Illinois, [email protected]

Gerhard SonnertResearch Associate, Departmentof Physics, and Lecturer,Department of SociologyHarvard [email protected]

Willie Pearson Jr.Professor and Chair of the Schoolof History, Science, andTechnologyGeorgia Institute of [email protected]

Susan AmbroseAssociate Provost for EducationCarnegie Mellon [email protected]

Lori BreslowDirector, Teaching and LearningLaboratoryMassachusetts Institute [email protected]

Linda GrantProfessor of SociologyUniversity of [email protected]

Laura KramerDepartment of SociologyMontclair State [email protected]

Peter MeiksinsProfessor of SociologyCleveland State [email protected]

Edmund TsangAssistant Dean of Engineeringfor Undergraduate ProgramsWestern Michigan [email protected]

APPENDIX 1:

Workshop Participants & Attendees

ORGANIZATIONAL CONTEXT AND FACULTY BEHAVIOR

FACULTY REWARDS

Robin AdamsAssistant Professor ofEngineering EducationPurdue [email protected]

Maura BorregoAssistant Professor ofEngineering EducationVirginia Technical [email protected]

Val BurrisProfessor of SociologyUniversity of [email protected]

Jeffrey FroydProfessor of ElectricalEngineeringTexas A&M [email protected]

Erin LeaheyAssistant Professor of SociologyUniversity of [email protected]

Stephen PlankAssistant Professor of Sociology & Center for SocialOrganization of SchoolsJohns Hopkins [email protected]


APPENDIX 1: WORKSHOP PARTICIPANTS & ATTENDEES

DIFFUSION OF INNOVATION

Social Dynamics of Campus Change:Creating an Interdisciplinary Research AgendaMembers Room – National Academy of Sciences Building2101 Constitution Avenue, NWApril 26-27, 2006

AGENDA

Wednesday, April 26

8:00 Welcome and Charge – Members RoomNorman Fortenberry, CASEERoberta Spalter-Roth, ASA

8:30 Panel Presentations and Discussion – Members RoomFACULTY REWARDS:

Susan Ambrose, Carnegie Mellon University; Peter Meiksins, Cleveland State

ORGANIZATIONAL CONTEXT AND FACULTY BEHAVIOR:

Jennifer Croissant, University of Arizona; Mary Frank Fox, Georgia Tech

DIFFUSING INNOVATIONS:

Jeffrey Froyd, Texas A&M University; Val Burris, University of Oregon

9:45 Break

10:00 What Do Engineers Want to Know?FACULTY REWARDS: Rm. 148ORGANIZATIONAL CONTEXT & FACULTY BEHAVIOR: Rm. 180DIFFUSING INNOVATIONS: Rm. 250

12:00 Break

12:15 Working Lunch – Members Room

APPENDIX 2:

Workshop Agenda


1:30 What Do Sociologists Know?FACULTY REWARDS: Rm. 148ORGANIZATIONAL CONTEXT & FACULTY BEHAVIOR: Rm. 180DIFFUSING INNOVATIONS: Rm. 250

3:30 Break

3:45 What Do Engineers and Sociologists Want to Know?FACULTY REWARDS: Rm. 148ORGANIZATIONAL CONTEXT & FACULTY BEHAVIOR: Rm. 180DIFFUSING INNOVATIONS: Rm. 250

5:45 Adjourn to Member’s Room for Dinner (6:30 pm)

Thursday, April 27

8:00 Developing Research Questions and ModelsFACULTY REWARDS: Rm. 148ORGANIZATIONAL CONTEXT & FACULTY BEHAVIOR: Rm. 180DIFFUSING INNOVATIONS: Rm. 250

10:00 Break

10:15 Developing Research Agendas and Study MethodsFACULTY REWARDS: Rm. 148ORGANIZATIONAL CONTEXT & FACULTY BEHAVIOR: Rm. 180DIFFUSING INNOVATIONS: Rm. 250

12:15 Break

12:30 Working Lunch – Room TBDDiscussion of possible follow-on activities

1:45 Closing Plenary: Sharing Work and Identifying Next Steps

4:00 Adjourn

The Acceptance and Diffusion of Innovation: A Cross-Curricular Perspective 36

APPENDIX 2: WORKSHOP AGENDA

APPENDIX 3:

Papers Presented by Workshop Participants


The progression from engineeringeducation innovation to institution-alized best practice is a multi-stage

process typically involving multipleindividuals and institutions. In order toorganize and create an agenda forresearch efforts to better understand theprogression and influential contexts andbehaviors, it is beneficial to model thisprocess and then be specific as to whichstage of the process each researchquestion is addressing.

The main agents in this process areinnovators, disseminators, contemplaters,adopters, adapters and deserters. Othersmay influence them, but it is this set ofagents that are the direct actors in theprocess.

Innovators create the innovation.Disseminators (often the innovator, but ina role distinct from innovating) publicizethe innovation, make any supportingmaterials or information available and

otherwise promote the innovation. Thosewho become aware of the innovation,through the efforts of the disseminator orotherwise, can be considered contem-plators. Contemplators who decide toimplement the innovation as originatedare adopters. Contemplators or adopterswho create and implement a variation ofthe innovation are adapters. Innovators,contemplators, adopters or adapters whodecide to stop using the innovation aredeserters. Innovators, contemplators,adopters and adapters who integrate andimbed the innovation into curriculumand pedagogy are institutionalizers.Eventually, as new innovations replaceprior innovations, institutions desert anold innovation when institutionalizing anew innovation. These relationships aredepicted in Figure 1.

As with most complex systems, theinstitutionalization of EEI’s can stallmany places within the process. Gooddissemination plans built around wanting


APPENDIX 3: PAPERS PRESENTED BY WORKSHOP PARTICIPANTSORGANIZATIONAL CONTEXT AND FACULTY BEHAVIOR 1

Engineering Education Innovations: Modeling the Influence of OrganizationalContext and Faculty BehaviorDavid M. Bowen California State University, East Bay

ABSTRACT

What aspects of Universities’ organizational context play a role in facilitating or obstructing thecreation, dissemination, adoption and institutionalization of Engineering Education Innovations(EEI’s)? What faculty behaviors and organizational context combinations result in successful EEIimplementation? First, we introduce a multi-stage model as a conceptual framework for organizingresearch, and for identifying linkages between different processes as key transition points. Specificresearch areas are then identified and discussed in relation to the framework, with emphasis onthose of particular concern to small school programs.

innovations will ultimately fail in spite ofthose plans, though the plans might besuccessful in attracting many contem-plators and adopters. Once adopted, aresource intensive innovation may disap-point and languish when supportingresources are not maintained. Adoptersmay be isolated within their institutions,and consequently unable to influencedepartment or college curriculum andpedagogy to institutionalize aninnovation.

The many ways that the process canfail to result in institutionalization ofEEI’s requires that our research effortsaddress the entire process, to understandgermane barriers and catalysts in eachpart of the process. Only in this way canwe build a foundation sufficient tosupport policy and policy makers in morerapid and ubiquitous acceptance ofbeneficial EEI practices by academicinstitutions.

Particularly vulnerable to failure arethe transition points in the process whereone agent’s behaviors and actions arerequired to influence the next agent’saction and behaviors for continuation ofthe process towards institutionalization.An example is when a disseminator needsto capture the attention and imaginationof contemplators for the innovation togerminate in other settings, or whenadopters need to influence others beyondtheir classrooms to install innovationsinto the instructional fiber of the insti-tution. The sociological issues drivingbehaviors at these transition points needto be better understood.

Fortunately, these transition pointshave the potential to be influenced bypolicy. For example, the NSF requires

dissemination plans in engineeringeducation funding proposals, andencourages multi-investigator and multi-institutional collaborations. Thesepolicies serve to strengthen successfulnavigation of transition points by obliginginnovators to become active dissemi-nators, and by predefining a set ofcontemplators who are pre-disposed tobecoming adopters of innovations inmultiple institutions, each with uniqueorganizational contexts.

Small School PerspectiveInstitutions each have their own contextwhich may make certain transition pointsmore or less difficult to traverse thanothers. For example, while smallerschools are likely to face less institutionalinertia for adopters wanting to institu-tionalize an innovation, they are alsolikely to find identification and selectionof innovations to contemplate foradoption more difficult.

Associated with a ‘small’ context isthe circumstance that there is not a largegroup of faculty to parse the search forEngineering Education Innovations(EEI’s).

The task of identifying EEI’s anddetermining which to pursue can be timeconsuming to the point of abandoning theadoption effort altogether. Short of that,time consumed by identification andselection leave less time to spend onplanning and implementing the EEIchosen for adoption, resulting in areduced likelihood of success. Obviously,a coordinated effort at a large schoolwould consume a smaller proportion oftime for each involved faculty member.



Further, smaller engineering programstend to offer single sections of eachcourse once a year, and possibly lessfrequently for some electives. This meanstheir feedback loop on implementation ofEEI’s is long and the rate of learningabout the success or failure of anattempted EEI implementation is corre-spondingly slow. Likewise, the oppor-tunity to ‘tweak’ an innovation to modifyit to fit particular small school circum-stances or to improve it from the previousoffering, presents itself long after theinitial implementation.

Contemplaters at small schools wouldlike to have access to a list of the mostimportant recent innovations instead ofhaving to commit limited resources tocarefully consider each potentialinnovation to determine if it might beapplicable to their situation. If researchthat identified the ‘best’ recent innova-tions was readily available, that wouldprovide a significant service to those whowant to adopt innovative practices, butwho don’t know where to start. If theresearch could provide further infor-mation about the context(s) in whicheach innovation has been successful, aswell as the faculty behaviors necessary forsuccess, this would be a tremendousbenefit.

Information about past experienceswith contexts and behaviors contributingto failed implementation attempts wouldalso be very valuable information to smallschool faculty for deciding which innova-tions to pursue, which to avoid, andwhich to assign to a ‘wait and see’ status.Other information about specific innova-tions that would be useful for contem-platers includes:

• Necessary educational foundation(e.g., does utilizing the innovationrequire programming in a particularlanguage or other softwareknowledge?)

• Likely resource requirements (start-upand sustaining), such as time commit-ments, space requirements, materialsand technical support

• Degree to which the innovation isdiscipline specific or independent(e.g., discipline specific innovationsmight include lab equipment/activ-ities, content modules, demonstra-tions, while discipline independentinnovations might include use ofteams, games/competitions, designactivities, use of new technology,student centered learning approaches,‘game show’ environment, etc.)

• Measured benefits (positive outcomes)that previous adopters have experi-enced.

If such research based information couldbe made readily available it would greatlyfacilitate contemplaters from smallschool contexts. One promising venue forsuch information presentation would be atype of ‘Innovation Tracker’ website,serving as a clearing house for innovatorsto make their EEI ideas and materialsavailable. Adopters could use the site torate and comment on their ‘user experi-ences.’ Contemplaters could use thisinformation in their selection process—similar to e-shopping websites. If a sophis-ticated enough tool were produced,contemplaters could put in organizationalcontextual descriptors. Using this infor-mation, the website could recommendappropriate innovations based on a



contemplator’s organizational context.This could be a living research document.Periodically, the ‘top ten’ innovations or‘editors choices’ could be designated,based on review of user comments, onrate of adoption, or awards bestowed byprofessional societies.

Current State of ResearchWhile research describing specificinnovations abounds (e.g., the mostrecent ASEE conference had over 1,500papers presented!), research describingrelevant organizational contexts andfaculty behaviors are less welldocumented, appearing primarily asanecdotes and hints buried within reportson specific innovations.

There are some notable exceptions tothis, including work by the FoundationCoalition (http://www.foundation-coalition.org/), and SUCCEED(http://www.succeed.ufl.edu/default.asp),both NSF sponsored multi-universitycoalitions focused on engineeringeducation.

Other notable exceptions are reportsof the NSF sponsored 1997 EngineeringEducation Innovators’ Conference, specif-ically, in the summary of the sessions:

BUILDING EFFECTIVE DISSEMINATION PROCESSES(http://www.nsf.gov/pubs/1998/nsf9892/dissem.htm), and

INSTITUTIONALIZING ENGINEERINGEDUCATION INNOVATIONS(http://www.nsf.gov/pubs/1998/nsf9892/inst.htm)

In these workshops, attendeesidentified the following as a summary ofthe most important issues and recommen-dations for dissemination:

• Dissemination of innovations shouldbe planned up-front, as part of thedesign, taking into account theintended audience

• Innovations should be modular, sothat users can choose all or part of theinnovation

• Personal interactions between usersand developers (through workshops,conferences, etc.) are very importantfor dissemination

• There needs to be better documen-tation of the processes through whichinnovations are developed.

• Motivation for innovation dissemi-nation is weak at present but can beimproved by NSF insisting on solid,innovative dissemination plans foreducational developments

• Faculty could be rewarded foradopting or adapting innovationsmade elsewhere.

• There is a need to change the facultyculture and faculty reward system toincrease the recognition of the valueof innovation

Workshop, attendees similarlyidentified the following as a summary ofthe most important issues and recommen-dations for institutionalization:

• Understand, involve and motivatestakeholders at an early stageincluding identification of internaland external “champions”

• Develop a credible justification forchange, e.g., industry representatives



• Formulate a step-by-step plan forimplementing change, includingidentifying required resources andflexible strategies to deal withresistance

• Communicate through all possibleand reasonable means.

• Conduct independent, data-basedbenchmarking, assessment, and evalu-ation.

• Reward innovation and use strategicinitiatives for internal funding inorder to achieve “bottoms-up”innovation

• Make innovation an integral part ofthe institutional mission.

Embedded in the ‘best practices’ outlinedabove are many potentially relevantfaculty behaviors and organizationalcontext factors. Given the experience andstature of the 1997 workshop attendees,their observations provide a good startingpoint for forming testable hypotheses.Combining these observations with theinnovation model presented in Figure 1will help to build a research agenda andfocus research efforts through creation ofspecific, testable hypotheses.

For example, do innovations that hada dissemination plan ‘up-front’ getcontemplated by more faculty thaninnovations that did not have suchforethought applied toward dissemi-nation? Does this ‘wider net’ result inmore adopters/adapters, or just morecontemplators that eventually desert theinnovation?

If there are institutions that rewardfaculty for adopting and/or adaptinginnovations created elsewhere, how dothese rewards influence the rate of

adoption/adaptation compared to institu-tions lacking such reward mechanisms? Can industry representatives and externalchampions help adopters/adaptersachieve institutionalization? For example,can we report relative rates of institution-alization of innovations for organizationswith as compared to those withoutsupport for the innovation fromIndustrial Advisory Boards?

Data SourcesSupporting research on the process ofinstitutionalizing engineering educationinnovations requires compelling data. Twolargely untapped data resources for researchon EEI’s are; ABET self-reports andfeedback from professional conferences.

California State University, East Bayrecently acquired ABET accreditationunder the ABET’s EC 2000 criteria. Theprocess of designing and documentingthe continuous improvement processregarding teaching, required by ABET,was enlightening.

Collectively, similar documentationfrom other institutions has the potentialto provide an important source ofresearchable material regarding diffusionof engineering education innovations andorganizational context. These ABET selfreports regularly appear on departmentalwebsites. This is a largely untappedresource.

One of the primary disseminationvehicles for EEI’s is ‘presentation atprofessional conferences.’ This is astandard part of virtually all dissemi-nation plans, and often the number ofpapers and presentations are quoted as anindication of dissemination. However,



other than individual faculty exchangingindividual communication, there is asignificant lack of a formalized feedbackmechanism regarding outcomes from thisprocess.

Most conference sessions concludewith evaluation forms being provided,usually with questions regarding theusefulness of the presentations. However,at most conferences, presenters do notreceive feedback from those evaluations.Did audience members find the materialuseful? How likely are they to incorporatelessons learned into their own pedagogy,or to communicate ideas from the sessionto other faculty members not in atten-dance?

If executed appropriately, conferencesession evaluations could be a rich sourceof valuable information regarding thefirst impression the innovation had onpotential adopters. Furthermore, it couldbe used to identify individuals for a shortfollow-up survey to find out if they did infact end up incorporating any of theinnovations they were exposed to intotheir educational practices, or if theyserved as disseminators and passed theideas on to colleagues. This could providevaluable information in researching thetransition from contemplation toadoption.

ConclusionsImproving engineering education andcreating engineering graduates able tonavigate and thrive in evolving environ-ments is a critical task for maintainingand strengthening competitiveness.Improvements in engineering educationare driven by innovations that utilize new

technologies, better understanding ofstudent learning processes, and thatbetter model new work environments’levels of collaboration and communi-cation.

However, innovations may not diffuse.Inferior innovations may stifle superiorinnovations. Innovations developed atnon-Ph.D. granting institutions may notbe experienced by the next generation ofuniversity professors. Innovationssuccessful in the original organizationalcontext may fail in other contexts, andthese failings may not be understood ascontingent, but rather serve to taint theinnovation for all future contemplators.

Much research is needed to develop abetter understanding of the creation-to-institutionalization process. A first step isto validate the model and determine itsefficacy by identifying a number ofengineering education innovations andtreating them as case studies. This wouldinclude instances where innovationsdeveloped to the point of institutional-ization, as well as innovations thatstagnated at a stage prior to institutional-ization.

Can we identify the original creator ofan innovation and quantify the dissemi-nation efforts? Can we document whycertain contemplators decided to adoptwhile others deserted the innovation?How long does it take for innovations toprogress from one stage to another? Canwe investigate a particular innovationand identify specific organizationalcontexts and faculty behaviors that lead toultimate institutionalization at onelocation and desertion at a differentlocation?



Are there certain, ‘bellwether’individual innovators, publications orinstitutions that greatly influence agentsat particular stages of the process? Whatmotivates disseminators who are not theoriginal creators? What venues provideinformation to contemplators?Utilizing the model and case studymethods including in-depth interviewsallows exploration of these fundamentalrelationships.

In this paper, we propose a conceptualmodel for organizing research onengineering education innovations. Themodel allows researchers to address morefocused and sophisticated questions.Instead of broadly asking whether certainorganizational contexts and facultybehaviors positively or negativelyinfluence innovations, we can ask focusedquestions regarding critical transitionpoints in the process bridging thecreation, dissemination, adoption,adaptation, desertion and institutional-ization phases of the process.

By better understanding the complexinterrelationships between the stages inthe process and the influence of organiza-tional contexts and faculty behaviors, wecan provide policy makers, deans,department chairs and faculty with toolsto successfully promote engineeringeducation innovations.

AcknowledgementsI would like to acknowledge and thankthe National Science Foundation forsponsoring, and the AmericanSociological Association and the NationalAcademy of Engineering’s Center for theAdvancement of Scholarship onEngineering Education for hosting theSocial Dynamics of Campus Changeworkshop.

I would similarly like to express mygratitude to Jennifer Croissant, MaryFrank Fox, Cheryl Leggon, SandraHanson, Michael Loui and Willie Pearson,the members of the ‘OrganizationalContext and Faculty Behavior’ panel ofthe workshop, for their participation indiscussions allowing me to better artic-ulate some of the ideas presented in thepaper.

The author is solely responsible forthe material, views and opinionsexpressed in this paper. i



There are several sets of disciplinaryframeworks and hypotheses thatguide potential modeling of organi-

zational change with regards to the devel-opment and implementation of new ideasabout curriculum and pedagogy. Eachdiscipline or area of study brings a set ofassumptions and guiding images whichenable the study and application oftheories of organizational change, andalso inhibit examination of other coreissues. The primary resources discussedin this short paper include organizationaltheory, theories of technological change,theories of curricular change, and consid-eration of professional and occupationalchange.

Neo-institutionalism is an examinationof the ways that organizations respond totheir external environment (DiMaggioand Powell 1983). The premises of neo-institutionalism include an observation ofthe homogeneity of institutions, whichneeds to be explained, and presuming abounded rationality within institutions,which means that instrumental, social,and cultural logics all constitute thedecision processes of actors within organ-izations. There are three primary mecha-nisms of institutional isomorphism:normative, mimetic, and coercive.Coercive isomorphism is the impositionof characteristics due to legal orregulatory mechanisms, such as the

appearance of Federal regulationsgoverning human subjects protectionleading to the almost instantaneousproliferation of Institutional ReviewBoards and Human Subjects ProtectionPrograms at research universities.Normative isomorphism is the result ofthe exchange of ideas, primarily throughpersonnel, who bring with them a stockset of ideas generally imparted in theireducation or through professional associa-tions. Business fads (quality circles, forexample), are often propagated throughthis mechanism, as well as other kinds ofinstitutional arrangements and policies.Finally, mimetic isomorphism is thename for processes whereby personnel atinstitutions gain information about otherfirms perceived to be successful in thesame or closely related fields or sectorsand try to imitate them. For example,Technology Transfer Offices have prolif-erated across universities based on thesuccess of a very few institutions, on theperception that these are the best organi-zational mechanism for capturing rentsfrom university-generated intellectualproperty. These offices have generallystarted from the initiative of upperadministration, although the emergenceof a new set of professionals (evidence byan association such as AUTM, theAssociation of University TechnologyManagers) means that normative isomor-



Organizational Contexts and Faculty Behavior:Frameworks for Research on the SocialDynamics of Campus ChangeJennifer Croissant University of Arizona

phism is most likely in effect as theseoffices are implemented.

The mechanisms of neo-institution-alism are not often considered inconjunction with analyses of internaltechnological change, that is, the imple-mentation of new workplacetechnologies. Why this is important is notso much that the process of curricularchange necessarily requires technology,although that is little explored with thedevelopment of information technologyfor instruction, but that curricularchange might profitably be seen asanalogous to technological change, atleast along specific dimensions. What wedo know about technological change inorganizations is that it is subject tocomplex internal dynamics of socialpower (Vallas 1993; Thomas 1994). Forexample, one heuristic that emerges fromThomas is the idea that one should “lookto the middle” for the dynamics of intra-organizational technological change. Thatis, those in the middle of organizationalhierarchies will likely have the mostpractical power in implementingtechnology. Upper-level executives willlack specific technical knowledgealthough providing overarching visionsfor a firm, while the production workerswho will be using the technology as afeature of their work will likely face asimilar lack of both knowledge and alsolimited power to influence decisionprocesses. Taken as an analogy, then, itcould be expected that mid-level adminis-trators (Department Chairs and Deans) orperhaps emerging educational paraprofes-sionals in University Teaching Centerswould be taking the lead on imple-menting campus change. We should then

be exploring the various kinds of socialpower amongst groups constitutingcurricular change dynamics. From other disciplines, such as history orarchaeology, we may also find value intheories of technological change. Forexample, frameworks such as Schiffer’s(2005) implementation of a life-historyand performance matrix approach tomaterial artifacts may provide usefulheuristics for the study of organizationalchange processes. The life historyapproach traces an artifact’s specificbiography from the collection of rawmaterials to final discard, coupled with aperformance matrix which allows for theevaluation of various social groups’(Bijker 1995) perspectives as to whetheror not the technology meets various kindsof material, economic, and social orideological needs.

Curricular change itself is a field ofinquiry, although most scholarship hasbeen focused on pre-college curriculumand less on post-secondary education.Slaughter (1997) has provided perhapsthe most compelling qualitative model ofcurricular change. After focusing onstandard models of change (such asdemographic models and changeorienting in learned disciplines), thefocus is on the multiple external socialissues as well as internal and professionalissues which constitute curricular forma-tions. In this model there is attention toboth the concerns of external entities,such as corporations or funding agencies,as well as discipline-driven scholarlyconcern, and demands on universitiesarising from social movements concernedwith equity of access and representationin curricula.



Finally, in considering the social andorganizational dynamics of changingcurricula and pedagogy, ideas regardingprofessional and occupational changemay be relevant. For example, there hasbeen the proliferation of highly trainednon-faculty workers on many differentkinds of campuses. There are mediaspecialists, instructional specialists, manykinds of adjuncts, fixed term lecturers,and affiliated quasi-administrators andstaff positions that have occupationalagendas and professional goals. They alsoseek power and influence on curricula,and are of course often on the front linesof retention efforts, such as in studentservices or college- or school- leveladvising offices.

Role proliferation is co-occurring witha systematic unbundling (Rhoades 1998)of faculty work, as the research-service-teaching Humboldtian integrated modelis replaced by faculty as “contentproviders” for instructional specialists,experts who are queried by extensionprofessionals, and as faculty becomeincreasingly managers of research ratherthan working at the bench themselves.None of these are particularly new trends,but they do suggest that there arecomplex dynamics of faculty work whichwill change faculty interest in and powerto constitute curricular and pedagogicalchange. The changing nature of facultywork at universities, particularly“unbundling” (Rhoades 1998, Finkelstein2003) runs counter to the kind ofintegrative responsibility assumed in themost theories of curriculum andpedagogy.The study of the diffusion of curricularinnovations would clearly benefit from

consideration of a multidisciplinary set ofquestions about both organizationalchange and its interconnections withtechnological change. However, evenperfunctory study of these issues suggeststhat the final task that remains is acritique of diffusionist language. Likemetaphors of evolution applied to socialprocesses, diffusion is a term that bothprovides insight as well as obscuringcertain processes. For example, Rogers’s(2003/1962) seminal work establishes achange-oriented bias in the literature ondiffusion. Like the diffusion of gasses,taken to be inevitable even if varying overcertain properties, the diffusion ofinnovations is taken to be purely a matterof the transformation of ideas, andexpected to be inevitable even iftemporarily impeded. The language of“laggards” and examples that representnon-adopters as irrational or superstitiousconsistently impede the systematic under-standing of evaluations of technologiessuch as provided by Schiffer (2005).Further, Rogers’s model is weak in itsconceptualization of material constraints,such as relevant manufacturing or useinfrastructure, or the presence of estab-lished material practices for whichchange would be disruptive of powerdynamics, in his considerations of therejection of innovations. Rejection ofchange may be in any given actor’s self-interest, despite potential organizationalbenefits.

With this challenge in mind, there arefour broad areas for consideration indeveloping a research agenda for studyingcurricular and pedagogical change onuniversity campuses. More detailedresearch on mechanisms such as institu-



tional isomorphism, issues of power andwork practices in the adoption of innova-tions, intra- and extra-mural interestsshaping curricula, and changing occupa-tional demographics and university struc-tures and their effects on faculty work areall elements that require more detailedassessment across disciplines and kinds ofuniversity structures. Such a completeresearch program would then provideuseful heuristics for the productive imple-mentation of change projects.i

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Vallas, Steven P. (1993). Power in theWorkplace: The Politics of Production atAT&T. Albany: SUNY Press.



Scientific and engineering work takesplace within organizations that mayeither stimulate or inhibit the devel-

opment of ideas and positive outcomes ofparticipation and performance (Blau,1973; Long and McGinnis, 1981; Pelz andAndrews, 1976). The organizational andinstitutional context of work is importantacross academic fields, but it is particu-larly important in science andengineering. Scientific work is conductedwithin organizational policies and proce-dures. It relies upon the cooperation ofothers. It requires human and materialresources. Further, the scope andcomplexity of research and the use ofadvanced technology heighten relianceupon facilities, funds, apparatus, andteamwork. In this way, science andengineering are essentially more “social”and “organizational” than fields outsideof sciences. Compared to sciences, thehumanities, for example, are more likelyto be performed solo rather than asteamwork; to be carried out in theabsence of equipment and instrumen-tation; to require modest funding; and inshort, to be less interdependent enter-prises (Fox, 1991, 1992).

The participation and performance ofengineering faculty—in pedagogy,curricula, and other areas—then becomeinstitutional and organizational issues,subject to change. Just as an organizationis structured, so too it may be restruc-

tured in administrative priorities, rewardstructures, allocation of resources, andpatterns of collaboration, communi-cation, and exchange. The re-shaping ofan institution constitutes institutionaltransformation—and the potential fortransformation underlies programmaticinitiatives, including NSF ADVANCE forInstitutional Transformation and theASA/CASEE agenda for “Social Dynamicsof Campus Change.”

In this paper, my questions are: 1)What do we know about institutionalcontext—and in turn, the potential of insti-tutional transformation—for outcomesincluding innovative participation andperformance among academic scientistsand engineers? 2) What do we need toknow further about institutional transfor-mation—with implications for practicesand policies to support “new engineeringcurricula and pedagogy”?

Institutional Context, Transformation, and OutcomesMuch of what we know about institu-tional context for outcomes amongacademic scientists and engineers centersupon “performance,” particularly publi-cation productivity. This is because publi-cation is a central social process ofscience (Merton, 1973; Mullins, 1973),and is critical to academic reward struc-tures, across “institutional types andmissions”1 (Fairweather, 1993).



Institutional Transformation in AcademicScience and Engineering: What is at Issue Mary Frank Fox Georgia Institute of Technology

In understanding performance in scien-tific fields, personal/individual factors,such as motivation or creativity, play apart. But these individual characteristicsdo not exist in a social vacuum, and bythemselves, explain little in outcomes ofperformance (with implications foroutcomes in other ways, as well). Forexample, no direct relationship has beenfound between measured creative abilityor intelligence and outcomes of researchproductivity (Andrews, 1976; Cole andCole, 1973). Rather, organizational condi-tions in the workplace, such as a pool ofresources in excess of minimum needs(Damanpour, 1991), are important. Thepresence or absence of such organizationalconditions may enhance or block the trans-lation of people’s creative characteristicsinto productive or innovative “outputs.”

Fundamentally, it difficult to separatethe behavior and performance ofindividual scientists from features of theirsocial and organizational contexts (Allisonand Long, 1990; Blau, 1973; Fox, 1991,1992, 1998, 2001, 2003, 2006; Hagstrom,1967; Long and McGinnis, 1981; Reskin,1977). Behavior is tied to the organiza-tional signals, priorities, and flow ofhuman and material resources thatprovide the ways and means forperformance. The notion of scientists inspontaneous intellectual creation outsideof administrative or organizational frame-works is illusory.

What factors may then operate towardtransforming institutions for “innovative”outcomes in academic engineering? I will

focus here upon three factors: leadership,reward structures, and patterns of infor-mation, communication, and exchange.

First, leadership is critical to institu-tional transformation because leadersshape organizational visions, send institu-tional signals and messages, and havepower to implement change. In academicinstitutions, the support of central admin-istration is frequently indispensable forinstitutional transformation because high-level administrators can make decisions,set policy, and allocate resources in favorof transformation (for examples, seeAsmar, 2004; Lindman and Tahamont,2006).

This administrative role is particularlyinstrumental for transformation inacademic settings with more, compared toless, bureaucratic structures, and decision-making that operates relatively “top-down”(Damanpour, 1987). Prestigious researchuniversities (and other settings), however,tend to be driven strongly by the profes-sional and expert authority of the faculty;and this may reduce the impetus andimpact of upper-level, administrativedecision making (Birnbaum, 1988),including that keyed to institutional trans-formation.

Science and engineering faculty, inparticular, are strong “players” in researchuniversities, because the high costs oflaboratory staff and graduate training, aswell as scientific apparatus, are fundedsubstantially by research grants awardedto individual faculty. This decentralizesinfluence in academic institutions, so that



________________________

1Fairweather’s (1993) study of faculty and chairs at over 400 institutions, across institutional types(research, doctoral, comprehensive, and liberal arts) indicated that faculty who follow a research-orientedmodel are paid the most at each type of institution, including comprehensive and liberal arts institutions.

authority resides with principal investi-gators situated in their individual “labora-tories,” as well with those in higheradministrative ranks (Fox, 2000).Decentralization of authority and decisionmaking has organizational advantages: itenables flexible and rapid response toissues by individual groups, and it mayenhance responsibility. However, for insti-tutional transformation, decentralizationentails cost: decentralization tends toreduce capacity to forge a broad, unifyingorganizational strategy in education andother areas (Fox, 2000: 57-58; Harrison,1994: 102).

The capacity for innovative institu-tional transformation rests, in part, upon“finding ideas that fit needs” (Daft, 2004:427; Daft and Becker, 1978). In univer-sities, in which research values predom-inate, this may mean that the acceptanceof strategies for institutional transfor-mation is enhanced when the innovationhas a research-basis or strong researchcomponent (for example, see Allan andEstler, 2005: 230), or when the innovationis carried out in ways perceived to be“rigorous” and “theoretically sound”(Asmar, 2004).

Relatedly, institutional transformationis enhanced by positive “incentives” thatsupport innovative practices and behavior.Resistance to innovation tends to comefrom those who are invested in the statusquo. An institutional reward structure canenhance transformation by reducingindividuals’ risk and resistance, “aligning”individuals’ efforts toward transformationwith positive recognition and rewards(salary and advancement). Further, in order to reward faculty’sinvolvement in innovative institutional

transformation—such as “new curriculaand pedagogy”—an institution needsmeans to assess or measure suchinvolvement and to make clear the guide-lines to be used in evaluation (Branskampand Ory, 1994; Seldin, 1984). Institutionaltransformation is enhanced when desiredinnovations “count” for individuals as wellas the institution at large, and whencriteria for evaluation are clear (Fox,Colatrella, McDowell, and Realff, forth-coming; Whitman and Weiss, 1982).Networks of communication and exchangefor innovative, institutional transformationare important as well. Implementationand adoptions of institutional innovationsare supported by effective networks forinnovation through means including: 1)coalitions developed among persons atvarious ranks within the institution whocan help steer the process of change anddevelop commitment to change; 2) earlyand continuing information conveyedabout the need for change and the stepsneeded to ensure change, without adverseconsequences for faculty, students, andadministrators; and 3) training madeavailable for innovation (Daft, 2004: 426-428).

Further Understanding of Institutional TransformationIn order to understand, implement, andsustain institutional transformation, weneed to know more about the ways inwhich transformation relates to particularcharacteristics of organizations, and thestudents and faculty within them,including:1. Size of institution: are smaller,

compared to larger, institutions“amenable” to institutional transfor-



mation—and how may the process ofpositive transformation vary with sizeof institution?

2. Autonomy of faculty: how may style ofleadership for institutional transfor-mation operate effectively withinorganizational settings in whichfaculty freedom and autonomy arehigh, and in which faculty identifymore strongly with their broaderresearch communities compared tothe institutional locations in whichthey are appointed?

3. Level of institutional impetus forstatus-maintenance or mobility: giventhat perceptions about an institution’snational ranking often take prece-dence over other measurable factors ingoverning institutional decision-making (Alvesson, 1990; Gioia andThomas, 1996), how does an insti-tution’s level of “strive for standing”affect momentum for transformationin curricula or pedagogy?

4. Subfields of faculty: is institutionaltransformation—in curricula andpedagogy—facilitated/impeded by thecharacteristics of the engineeringsubfields in which faculty teach and doresearch; for example, subfields thatare relatively new (compared to old),experimental (compared totheoretical), or applied (compared tobasic)?

5. Faculty and student composition: doesdiversity in composition of the facultyand/or students, in gender andrace/ethnicity, interface with capac-ities for institutional transformation incurricula and pedagogy?

The aim then is to better understandcomplex considerations of the ways inwhich characteristics of institutions, andof the students and faculty within them,affect strategies and practices for imple-menting and sustaining positive institu-tional transformation. i

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Fox, Mary Frank. “Research Productivityand the Environmental Context.” InResearch and Higher Education: TheUnited Kingdom and the United States,edited by T. G. Whiston and R. L. Geiger.Buckingham, England: The Society forResearch into Higher Education & OpenUniversity Press, 1992.

Fox, Mary Frank. “Women in Science andEngineering: Theory, Practice, andPolicy in Programs.” Signs: Journal ofWomen in Culture and Society 24(Autumn 1998): 201-229.

Fox, Mary Frank. “OrganizationalEnvironments and Doctoral DegreesAwarded to Women in Science andEngineering Departments.” Women’sStudies Quarterly 28 (Spring/summer2000): 47-61.

Fox, Mary Frank. “Women, Science, andAcademia: Graduate Education andCareers.” Gender & Society 15 (October2001): 654-666.

Fox, Mary Frank. “Gender, Faculty, andDoctoral Education in Science andEngineering.” In Equal Rites, UnequalOutcomes: Women in AmericanResearch Universities, edited by L.Hornig. New York: KluwerAcademic/Plenum Publishers, 2003.

Fox, Mary Frank. “Women and AcademicScience: Gender, Status, and Careers.”In Dissolving Disparity, CatalyzingChange: Are Women Achieving Equityin Chemistry? Edited by C. Marzabadi,V. Kuck, S. Nolan, and J. Buckner.Oxford University Press, 2006.



Fox, Mary Frank; Colatrella, Carol;McDowell, David, and Reallf, MaryLynn. “Equity in Tenure andPromotion: An Integrated InstitutionalApproach” In Advancing Women inScience and Engineering: Lessons forInstitutional Transformation. Edited byA. Stewart, J. Malley, and D. LaVaque-Manty. Ann Arbor, Michigan: TheUniversity of Michigan Press, forth-coming.

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Long, J. Scott and McGinnis, Robert.“Organizational Contexts andScientific Productivity.” AmericanSociological Review 46 (1981): 422-442.

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Mullins, Nicolas C. Science: SomeSociological Perspectives. Indianapolis,Indiana: Bobbs-Merrill, 1973.

Pelz, Donald and Andrews, Frank.Scientists in Organizations: ProductiveClimates for Research andDevelopment. Ann Arbor, Michigan:Institute for Social Research, 1976.

Reskin, Barbara. “Scientific Productivityand the Reward Structure of Science.”American Sociological Review 42(1977): 491-504.

Seldin, Peter. Changing Practices inFaculty Evaluation. San Francisco:Jossey-Bass, 1984.

Whitman, Neal and Weiss, Elaine. FacultyEvaluation: The Use of ExplicitCriteria for Promotion, Retention, andTenure. AAHE-ERIC Higher EducationResearch Report. Washington, D. C.:American Association for HigherEducation, 1982.



As we enter a new millennium, girlsand women have earned apermanent place in the previously

male domains of science andengineering. However, barriers to accessstill exist. The President of one of themost elite universities in the U.S. BHarvard University B recently addressed agroup of economists. In his comments,Lawrence Summers referred to an innatedifference in science ability favoring boys.Those of us who have been studyinggender and science over the past fewdecades were chilled by these comments.A large body of research exits whichprovides overwhelming empiricalevidence that young boys and girls startout with very similar interests andabilities in science. School systems,occupational systems, and a culture offemininity in the U.S. and elsewherework to systematically encourage thisinterest and ability in boys anddiscourage it in girls. This Acooling out@process results in a scientific labor forcewhich is largely male and women remainunder-represented in science andengineering occupations B one of themost elite and influential sectors of theU.S. Labor Force (National ScienceFoundation, 2004). The shortage ofwomen is a reflection of the continuingbias and gender inequity in science andengineering.

The U.S. science and engineeringsystem is not just a male system. It is awhite male system. Non-Asian women ofcolor remain under-represented inscience and engineering. Research onwomen of color in science andengineering is limited. Historically,studies of science as well as feministtheory and research tended to have awhite, middle-class bias. Thus, women ofcolor are largely invisible in these litera-tures. The multi-cultural approach tounderstanding gender stresses the inter-section of race, class and gender. It is thiscombination of statuses that makes for aparticular group=s attitudes, position, andopportunities (Lorber, 2001).

In this paper I will provide somebackground on a multi-cultural feministapproach to studying the structure ofscience and engineering education andoccupations. Much of my work has beenon African American women in scienceeducation systems, hence that will be myprimary focus. In spite of the barrierswhich the white male science system setsup for women, minorities, and minoritywomen, my work has shown high levels ofinterest and involvement in science andengineering among young AfricanAmerican women. The multi-culturalgender approach recognizes the quandaryinvolving the high level of interest andinvolvement in science and engineeringamong young African American women



Lessons from Multi-Cultural Feminism forChanging Science and EngineeringSandra L. Hanson Catholic University of America

in the context of a science system whichdoes not recognize those who are notwhite and not male as candidates forcompetence and success in science.Implications of the multi-culturalfeminist approach for policy, practice,and needed research in the U.S. scienceand engineering system will be provided.

A MULTI-CULTURAL FEMINIST LENSIncreasingly, research on gender hasmoved away from seeing gender as alearned trait and a property of individualswho are socialized into a particulargender role. More recent theorizing aboutgender sees it as a macro structure thathierarchically stratifies society. Powerand conflict are taken into account inunderstanding gender and gender isviewed as something that is constructed(Connell, 1987; Grant et al, 1994; Lorber,1994; Thompson and Walker,1995; Ferree,1990; Osmond and Thorne, 1993). Genderis seen here as a principle of organizationrather than a characteristic ofindividuals. Thus the study of gender is astudy of the production and reproductionof power hierarchies and of the unequaldistribution of resources in families,schools, work organizations, and commu-nities that reflects and helps maintainthese hierarchies.

These macro-structural theories makea contribution by recognizing gender as astructure based on power relations.Feminist theory is similar to other macrogender theories in seeing unequal powerrelations (male hegemony) as a criticalcomponent of the creation of gender in asociety and a central feature of all socialorganizations. However, feminist theory

takes a distinctive approach to under-standing gender. The approach includesissues involving history, agency, andideology. Feminist perspectives avoid adeterministic notion of women as passivevictims by pointing out the fact thatgender is constantly being re-constructedand negotiated as well as constructed. Adialectic between structure and agency isposed in which structure conditions lifebut people change social conditions(Agger, 1998).

The combination of history andagency provided by feminist theories hasimplications for change. Sometimes struc-tures are questioned and historical andstructural conditions combine to create asense of agency for women leading tovictories that may or may not restructurethe gender hierarchy but do contest it(Connell, 1987). The combination ofindividual agency and historical consider-ation provide for elements of choice,resistance, and transformation ingendered science systems that are notinherent in either structure or biology.

Feminist theory also incorporatesideologies to a greater extent than struc-tural theories and shows how dominantgroups rule by consent not by force. Thusgender hierarchies are maintained inpart by ideologies or dominant beliefsystems that favor the dominant groupand justify the gender order as a naturalone (Sage, 1990). These ideologies are aform of cultural power that create a patri-archal definition of the situation for menand women (Connell, 1987). Ideologiesabout science as a white male domain arepresented to young girls and early on inthe educational system. Ideologies aboutwomen’s family and work positions are



also present in science education andoccupation systems. Feminist theory hasgone a long way in advancing our under-standing of gendered systems. However,early work on gender (including thatusing a feminist approach) suffered froma white, middle-class bias.

The multi-cultural feminist approachto understanding gender comes out ofrelatively recent work by women of colorwho have attempted to correct past biasesin the social sciences (Collins, 1990; Zinnand Dill, 1996). An awareness of biasespresent in much work on gender led to afocus on the intersection of race, class,and gender (Anderson and Collins, 1995;Glenn,1985; Rothenberg, 1992; West andFenstermaker, 1995). Here there is noargument that it is race, class, or genderas the primary basis of hierarchy. Instead,it is argued that it is the combination ofstatuses that make for a particulargroup’s attitudes, position, and opportu-nities (Lorber, 2001). Collins (1990) hasbeen particularly effective in describingthis approach. She suggests that no oneform of oppression is primary. Ratherthere are layers of race, class, and genderoppression, within individual, community,and institutional contexts. And she arguesthat each of these locations are potentialsites of resistance. The approach alsoemphasizes the unique subcultures ofminority women and the role of thesecultures in affecting both the structuresthat limit and direct women’s lives as wellas the agency which allows them to retainsome control and influence within thesestructures (Hanson and Kraus, 1998; 1999).The unique subculture of AfricanAmerican gender systems is importantbackground for understanding the science

and engineering experiences of youngAfrican American women.

Research has suggested that gendersystems in African American subculturesmight provide young women a unique setof resources B resources that might beimportant for generating interest andsuccess in science. The cultural context ofthe African American community is onewhich historically saw women workingand heading families (Anderson, 1997).African American women continue tohave high rates of labor force partici-pation. Work and family roles are notseen as conflicting (Collins, 1987). Instead of work being in opposition tomotherhood, here it is seen as animportant dimension of motherhood. Andit is the perceived incompatibilitybetween science careers and familypursuits that keeps many women fromentering and persisting in the pursuit ofscience degrees and occupations (Matyas,1986; Ware and Lee, 1988).

Historical analyses have suggestedthat the tradition of male dominance inwhite families in the U.S. has not beenreplicated in African American families.In part because of the legacy of slavery,the African American family has beentypified by greater equality in familydecision making and division of labor(Gutman, 1976; Hill, 1971; Kane,2000). Asa result of these arrangements it has beensuggested that gender roles are moreegalitarian here than in white families(Wade, 1993). Wilcox (1990) found thatAfrican American women are moredissatisfied than white women with theamount of power women have in societyand Dugger (1988) found that they werealso more aware of gender discrimi-



nation. These patterns contribute togreater self esteem, independence, andassertiveness as well as high educationaland occupational expectations amongyoung African American women (andtheir parents) relative to other women(Anderson, 1997; Hanson and Palmer-Johnson,2000; Hill and Sprague,1999;National Center for Education Statistics,2000a). All of these characteristics havebeen shown to be related to success inscience and engineering (Hanson, 1996).In my earlier examination of the highschool science achievement process(Hanson and Palmer-Johnson, 2000), mycolleague and I found that AfricanAmerican families compensate for disad-vantages on some resources (e.g., socioe-conomic status) by providing youngwomen with an excess of other resources(e.g., unique gender ideologies, workexpectations, and maternal expectations).And unlike white parents, they sometimesprovide more of these resources to theirdaughters than their sons. Along the sameline, Higginbotham and Weber (1992)found that African American families puta greater stress on education andoccupation as sources of mobility fortheir daughters (relative to whitefamilies) since marriage is not seen hereas a source of mobility (as it is in whitefamilies).

IMPLICATIONSAlthough research on women in sciencehas proliferated, the focus is often ondifferences between men and womenwith little attention to subgroups ofwomen. It is a mistake to think of womenas an undifferentiated group. Researchersincreasingly have come to the conclusion

that not all women have the same experi-ences in science and engineeringeducation and occupations (Hanson andPalmer-Johnson, 2000; Mau et al., 1995).In fact, preliminary research hassuggested that young African Americanwomen are particularly interested inscience (sometimes more so than theirwhite counterparts) (Hanson and Palmer-Johnson, 2000; Hanson,2004; NationalCenter for Education Statistics, 2000a).

In spite of this interest on the part ofAfrican American women, they find itdifficult to persist in science educationstructures (including engineering) thatare based on white, male models. Thesestructures are seen as hostile to womenand people of color in general. Bias existsat multiple levels. Asres (1999),Schiebinger (1999), and others argue thatscience is Adiversity stupid. Educationinstitutions have been designed to valueand transmit a certain culture: white andmale. They have not become this way byaccident. We need to change our mentalmodels of knowledge and include diversesources of knowledge from multiplecultures. Asres (1999) argues that weneed science and engineering educationthat reflects our society and the contribu-tions of all groups. Currently, the contri-butions of women and people of color arelargely hidden. Both material and intel-lectual resources are needed, Asresargues, in making these institutionalchanges. Without them, technical andeconomic advance will be more difficult.It is clear that we need to broaden ourtalent base in science. As Schiebinger(1999), Hanson(1996) and others argue,we are missing out on new scientificadvance by limiting the pool of science



talent to white males. Schiebinger (1999)shows how advances in science havedeveloped and progressed in areas such asmedicine once women became engagedin the profession. Thus these white maleinstitutions, by definition, are discour-aging minorities, women, and minoritywomen from participating. Consequently,the flow out of science programs andengineering programs is high (andgetting higher) amongst these groupswhile enrollment rates are low (andgetting lower) (George, 1999; Bonous-Hammarth, 2000; Asres, 1999). A focus onrecruiting women, minorities, andwomen of color is important. However, Ihave argued there is both talent andinterest there. The more importantchange needed is in the structure ofscience and engineering institutions.Changing our mental models of sciencetalent and of diversity is important. Whenthis happens, women, minorities, andwomen of color will feel morecomfortable in the science andengineering classroom . There will be aconsequential increase in the number ofwomen, minorities, and women of colorteaching in the classroom.2 Currently,these numbers are alarmingly low. Dataon chemistry classrooms and physicsclassrooms in the high school settingshow that women are becoming morevisible (e.g., 40% of high school physicsteachers are female) but AfricanAmericans are not (e.g., 1 % of highschool physics teachers are African

American) (Horizon Research, 2002). With regards to engineering and the

high drop-out and low enrollment ratesamong minorities, women, and women ofcolor in this area of science (Brainerd etal., 1999; George, 1999; Bonous-Hammarth, 2000). A recent tally of highschool graduates planning on a major inengineering revealed stability in genderrepresentation, but improvement inminority representation (Noeth andHarmston, 2003). Again, the problem isnot a lack of talent and interest amongstfemale students, minority students, andwomen of color. These groups are makingtremendous gains in science knowledge,interest, and education (NSF, 2004).Rather, a multi-cultural feministperspective argues that it is a problemwith institutions and a white male bias inengineering that needs to be addressed.Engineering is a creative activity. One’screativity is a product of one’s experi-ences. Without diversity we limitcreativity in engineering (Wulp, 1999). Arecent study of the climate in engineeringfound that women enter the engineeringclassroom with considerable confidenceand interest but that this dips by the endof the freshman year (Brainerd et al.,1999). When faculty, course-content,textbooks and mental models reflect avalue on cultures that are not white andare not male, classroom climates willshift and all individuals will feel welcomein engineering.3



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2 See Toro-Ramos (1999) for a description of the success of an engineering program designed for Hispanicwomen. Success here was a result of a cooperative learning environment, supportive peers, recognition ofand value on diversity, an outcomes-based curriculum (not just lecture oriented), use of research teams,women and minorities in leadership roles, a strong relation with industry, and small class sizes. 3 Individuals can contact Dr. Asres for a list of non-white inventors at [email protected]

Data on minority women in scienceand engineering are hard to come by. TheNational Science Foundation and theNational Science Board (National ScienceBoard, 2000; National Science Foundation,2004) have gone a long way in providingincreasing amounts of information ondiverse populations in science andengineering education and occupations.However, the data are often provided forrace groups and gender groups, but not forrace/gender subgroups. Even whensubgroups are mentioned, the informationprovided is often minimal. Organizations,agencies, and individual researchers needto collect and present data on science andengineering that go beyond white vs. blackand male vs. female contrasts. Acknow-ledgment of the variations within racegroups (by gender) and within gendergroups (by race) is needed.

Contemporary feminist frameworksthat use a multi-cultural lens do not seewomen and African Americans andAfrican American women as merelyvictims of racism and sexism. Sometimesstructures and status quos are questionedand challenged. Thus, it is not juststructure that is important for under-standing African American women’sexperiences in science but these youngwomen’s responses to race and genderstructures. Sometimes young people do notabsorb all the culture’s gender and racemessages and obediently comply. And it isoften unique cultures and histories thatare the sites of resistance. My research onthe high level of interest and involvementamong young African American womencan only be understood within the contextof the unique gender culture and historyof the African American family.

Recent data from the NationalScience Foundation suggest that experi-ences in science from post-secondaryschool through occupations are distinctlydifferent for women from differentrace/ethnic groups (National ScienceFoundation, 2004). The data suggest thatincreasingly, African American femalesare present in science. For example,African American women (but not Whitewomen) earned more than half of theBachelor’s degrees in science andengineering awarded to their race/ethnicgroup in 1997. During the same year,African Americans were the only racegroup where women earned over half ofthe Master’s degrees in science andengineering. African American womenearned 46% of the Ph.D.’s awarded toAfrican Americans in science and engi-neering in 1997 while White womenearned 38% of the degrees awarded toWhites. Finally, data from the NationalScience Foundation suggest that AfricanAmerican women make up a muchlarger portion of African American scien-tists (36%) than is the case for Whitewomen (22%).

Thus, we cannot assume that AfricanAmerican women will be morediscouraged in science than Whitewomen. That is not to say that theseyoung women avoid racism and sexism intheir pursuit of science. Existing studiesof African American women in sciencedocument considerable barriers thatthese women face (Kenschaft, 1991;Malcolm, 1976; Ovelton Sammons, 1990;Vining-Brown,1994). Researchers havefound that young African Americanwomen report a sense of feelingunwelcome in science and engineering



classrooms (Cobb, 1993; Vining-Brown,1994; Clewell and Anderson, 1991;Malcolm,1976; Hueftle et al., 1983). Thelimited literature on African Americanwomen in science suggests that they areaware of both race and gender barriersbut perceive race as a larger barrier thangender (Rayman and Brett, 1994;Hanson,2006).

Although young African Americanwomen show high levels of interest andinvolvement in science, most research onminority women’s science experienceshas not found them to show higherscience achievement than other youngwomen (as measured by grades andstandardized exam scores, especially inthe high school years) (Hanson, 1996;Catsambis, 1995; Clewell and Anderson,1997). These differences mirror racetrends in the broader achievement liter-ature and can be seen as coming out ofbiased practices that affect opportunitiesfor minority students. Testing biaseswhich favor middle-class white studentsare most likely part of the explanation fordifferences in standardized exam scores(Lomax et al., 1995). Ogbu (1978; 1991)has argued that African Americanstudents’ achievement may be lower thanthat of whites since they see fewerreturns, a biased system, and so pull backtheir efforts. Mickelson (1990) adds to thisby suggesting these young people believein education in general but not forthemselves, thus predicting poorerperformance. Ainsworth-Darnell andDowney (1998) have shown that it maynot be so much frustration and lack ofhope that work against African Americanstudents’ school achievement and forwhite middle-class students’ achievement,

but the lower resources for success(including poorer schools and higherrates of poverty) that work to keep theserace differences in place. Finally, racedifferences in achievement are no doubtkept in place by stratification withinschools involving tracking and differ-ential learning opportunities (Cooper,1996; Oakes and Lipton, 1992). All of thesefactors, along with evidence of racism inthe school and classroom (Persell, 1977;Anderson, 1988; Feagin et al., 1996) haveimplications for education and testingpolicies and ultimately for the opportu-nities that young African Americanwomen experience in science andengineering. It is important thatresearchers focus on these biased educa-tional structures and vehicles for changewithin them.

Findings from our recent survey ofyoung African Americans’ experience inthe science classroom suggest thatteachers play a large role in encouragingor discouraging interest in science(Hanson, 2006). In answers to open-endedquestions, when the young women talkabout teachers, it is often the case thatthey might like science but their teachersdid not make it fun. Many reported thatthey did not like the teacher and that theteacher did not teach them much. Repliesof this nature seem to go hand in handwith the ones that suggest science is toohard or not interesting. These repliesinvolving teachers often suggest apotential love for science but bad experi-ences with teachers that took away froma development of science interests. Theimplications of these responses for thescience classroom involve therelationship between perceived abilities



(and confidence) in science and one’spositive attitude toward science. Youngpeople’s feelings about abilities in asubject are often influenced by teachersand peers. These self-fulfilling propheciesin science often revolve around race andgender. When teachers walk into thescience classroom expecting talent fromevery student, it is likely that the self-fulfilling prophecy will be one of talentdevelopment rather than talent loss. Moreresearch on the dynamics of race andgender in the science and engineeringclassroom is needed.

The multi-cultural gender lens usedhere helps us understand young AfricanAmerican women’s interest andinvolvement in science and engineeringthrough an examination of the uniquehistory and norms about femininity in theAfrican American culture. Research usinga multi-cultural feminist lens helps usunderstand the strengths and abilitiesthat sometimes exist among groups ofyoung people who have statuses (ormultiple statuses) which we would expectto universally work against them. Doublejeopardy arguments assume that AfricanAmerican women will be doubly disad-vantaged in the White male sciencesystem. We may find double jeopardy interms of resistance within the scienceand engineering system, but that does notnecessarily convert to double jeopardy ininterest and involvement. Thus, thisapproach and the work coming out of it,is a work of hope and optimism in thecontext of continued racism and sexismin science.

SOME SUGGESTED QUESTIONS FOR FUTURE RESEARCH1. What are the institutional processes

(involving pedagogy, curriculum, peerenvironments, textbook content,instructor’s attitudes, instructionalstrategies, testing strategies, etc.) thatdeflate the interest and engagement ofwomen (and minority women) inengineering?

2. How can engineering content andclassroom environment be made lessAdversity stupid?

3. What are the characteristics ofengineering programs that have beensuccessful in recruiting and retainingwomen (and minority women)? Arethe factors that help recruit and retainWhite women the same as those thathelp recruit and retain minoritywomen?

4. How can high school science classesincorporate strategies (involvingteachers, climate, content, etc.) thatencourage all students, regardless ofrace or sex, in science andengineering?

5. What changes are needed in order tocreate standardized science exams(e.g., the SAT and ACT exams) that arenot race, class, and sex biased?

6. What are the new inventions, ideas,questions, and answers in engineeringthat might come out of a culturallydiverse talent pool?

7. What are the costs (in efficiency,knowledge creation, technologicaladvance, economic progress, etc.) thatare associated with a White, male biasin engineering?

8. Is the new engineering pedagogy lessbiased towards women and minorities,



and do schools and departments thatadopt it have a lower drop out rate forthese groups?

9. Are engineering faculties aware of thediversity in science/engineeringinterest across groups of women? Arethey aware of the relatively high levelof interest amongst African Americanwomen? If not, how can this awarenessbe cultivated? i

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Science and engineering are forcesdriving sustainable economic devel-opment worldwide. Consequently, to

enhance its position in the globaleconomy, it is more imperative than everthat the United States (US) develop itshuman talent in science, technology,engineering, and mathematics (STEM)fields. Traditionally, the pool from whichthe US drew STEM talent was largelycomprised of non-Hispanic white males,and any shortages in STEM talent weremet by importing talent from abroad.Within the past two decades, there hasbeen a decrease in US citizens’ partici-pation in STEM due in large part todeclining interest in STEM among non-Hispanic white males coupled with thedecreasing viability of importing STEMtalent and heightened security concernsresulting from the events of September11, 2001.

Despite the significant increases inthe proportions of the US populationcomprised by Asians, African Americansand Hispanics, the US STEM workforceremains overwhelmingly white (BEST2006; Ong and Park 2006). In 2003,African Americans, Hispanics, AmericanIndian/Alaska Natives comprised 40% ofthe 24 years of age and younger US

population, and are estimated to increaseto almost 50% by 2020 (Childstats.gov,2005). In urban centers, the majority of K-12 schools are comprised ofracial/ethnic minorities. These groupshave a tradition of underparticipationand underachievement in STEM duelargely to unequal access to high qualityeducation (Orfield and Yun 1999).Consequently, an increasingly largerproportion of the school age population atall levels is becoming more racially andethnically diverse, while the US STEMworkforce in general, and the professo-riate in particular, are not.

The timeline for some targetedprograms has been sufficiently long toenable identification of what does—anddoes not—work to increase racial/ethnicdiversity among the US STEM workforce.4

Characteristics of effective policies,programs and practices have beengleaned from program evaluations andvarious evaluation research literatures(e.g., sociology and education), as well asfrom program evaluation reports(Pearson et al 2004). Higher educationdesign principles are prominent in boththe BEST report (BEST 2004) and thefinal NSF workshop report on pathways toSTEM careers (Martin and Pearson 2005).



Participation by Minorities in STEM and theDifferences that Participation MakesCheryl B. Leggon Willie Pearson, Jr. Georgia Institute of Technology

________________________

4 Efforts targeted to increase racial/ethnic diversity in STEM are examples of the “canary-in-the-mine”phenomenon: that is, what works for one group tends to work for other groups (non-Hispanic whitewomen and men) as well.

In STEM fields, introductory courses—such as calculus, for example—function asgatekeepers insofar as they “weed out”students from the major. Researchfindings indicate that introductorycourses that are inter-disciplinary, cross-disciplinary, and/or multidisciplinary,and that place STEM in a larger societalcontext work to diversify STEM majors atthe undergraduate level (Margolis 2002;BEST 2004). This is consistent withresearch that indicates that oneimportant factor in selecting careers forracial/ethnic minorities (and women) isthe extent to which a field contributes tothe larger society and/or theirracial/ethnic community (Eccles 1994;Margolis and Fisher 2002). Therefore,curricula emphasizing problem-solvingapplications of STEM are effective in bothrecruiting and retaining students fromracial/ethnic groups under representedin STEM fields relative to their proportionof the general population (Leggon 2003).

Another characteristic of effectivecurricula and pedagogy is to providehands-on learning experiences in environ-ments that are encouraging andsupportive. Some research findingsindicate that for students from underrepresented groups in STEM, a lack ofencouragement has the same effects asdiscouragement insofar as it reinforcesstudents’ doubts about their own ability topursue a STEM career. Environmentsmust be supportive financially as well.Under represented students from under-graduate institutions lacking the infra-structure to provide research experiencesbenefit from summer research experi-ences, which should provide a stipend forthese students to compensate for foregone

income. Most students from racial/ethnicgroups under represented in STEM needto work to pay for tuition and livingexpenses because African Americans,Hispanics, and American Indians areoverrepresented in lower socio-economicstatus groups; there is insufficientresearch on the interactive effects ofrace/ethnicity and class. STEM academicenvironments must be intellectuallysupportive to recruit and retain studentsfrom racial/ethnic groups under repre-sented in STEM, as recommended in arecent report (Foundation 2005, p. 5):“Doctoral education and the various disci-plines may engage in habitual practices—from the nature of student orientationprograms to what is considered importantin an academic field—that serve as asubtle discouragement to interest forstudents of color. The image of thedoctorate, (STEM) discipline by disci-pline, must become less abstract andmore socially responsive in a non-reductive way.”

Racially and ethnically diverse STEMfaculty can not only enhance therecruitment and retention of studentsfrom under represented race/ethnicgroups in STEM fields (Foundation 2005),but also can enrich the pedagogy,curriculum and culture of STEM disci-plines. The enrichment infuses vitalityand creativity into the STEM enterprise,thus enhancing the US’s competitivenessin the global economy.

Just as it is crucial for a curriculum toprovide the research experience andanalytical skills for a STEM profession, itis also crucial for a curriculum to“socialize” students into a STEMprofession. Therefore, an integral part of



an effective STEM curriculum is to teachstudents the “unwritten” rules of theprofession: how to navigate the under-graduate, graduate, and postdoctoraleducation processes as well as the jobmarket. Pearson’s (2005) research onchemists indicates that African Americanstudents have a very different experiencefrom that of non-Hispanic white studentsin the same courses at the same insti-tution; for example, many AfricanAmerican graduate students do not knowthe importance of having pre-doctoralpublications. Nor do many students ofcolor know how to select the “right”postdoc (Lichter 2003). This is a socio-logical challenge because postdocs areselected by the PI, who may or may notpractice inclusivity. Socialization intoSTEM professions includes problemchoice and retention, how to presentpapers and posters at professionalmeetings and the appropriate behaviorfor professional social events (Pearson2004).

With few exceptions, such as theLeadership Alliance and MeyerhoffPrograms, programs designed largely tobroaden the participation of racial/ethnicminority groups underrepresented inSTEM, research findings concerningincreasing participation in STEM fieldshave neither been sufficiently dissemi-nated nor integrated into planning newprograms and evaluating exiting programs(BEST 2004; BEST 2006; Martin andPearson, 2005; Leggon 2006). Somemechanism at the national level to facil-itate and enhance the sharing of strategiesand information among programs,consortia and institutions would minimizeduplication of efforts and gaps. Many

effective practices come from data thatprovide “snapshots” of different groups atdifferent points in the career path. Whatis urgently needed are longitudinal datathat follow individual participants intargeted programs throughout under-graduate and graduate schools, postdoc(s),and careers in the full-time paid STEMworkforce. Many efforts to diversify theUS STEM workforce focus overwhelm-ingly on the inputs—i.e., enhancing theintellectual capital of individuals aspiringto a STEM career—and increasing output.These efforts often neglect the quality ofpreparation and experience, both ofwhich impact attainment of tenure inacademic environments and/or avoidingacademic careers in research universitiesaltogether. It is equally important to betterunderstand how STEM careers developand unfold and how ascriptive statusessuch as race/ethnicity and gender impactcareers and educational experiences;longitudinal data are crucial to enhancethis understanding. STEM curricula andpedagogy should be informed by the needsof present employers in the global STEMworkforce and should be sufficientlyflexible to prepare today’s students fortomorrow’s jobs in the global STEMworkplace, which is becoming increas-ingly diverse in terms of race andethnicity.

Advances in science and technologyare occurring at unprecedented rates,and make the value of human capitalgreater than ever. No nation can afford touse its human resources inefficiently andineffectively. To promote innovation andfoster creativity in STEM fields, it isabsolutely imperative for the US todevelop and nurture talent and



encourage all of its citizens to participatein STEM careers. Increasing the partici-pation of under represented racial andethnic groups the STEM workforceincreases the variety of perspectives inSTEM fields, thereby enriching thevitality, creativity and quality of thescience and technical enterprise. According to Science Indicators (NSB2006), most growth in STEM in the lastdecade has been among underrepre-sented minorities and women. Women ofcolor continue to comprise a higherproportion of STEM degree recipientswithin their racial/ethnic group than doEuropean American or white women ofwhites; however, there are striking fielddifferences. For example, in fields wherewomen tend to be extremely underrepre-sented—such as engineering, computerscience, and physics—women of color’srepresentation in these fields relative totheir male peers exceeds that of womenin general; why this is the case has notbeen the subject of much research.Perhaps, Hanson will speak to this.Gender plays a significant role in theparticipation of African Americans andLatinos in STEM. Unfortunately, moststudies of women do not disaggregategender data by race/ethnicity (Leggon2003, 2006). Yet, the literature revealsthat women of all racial backgroundsshare some common experiences withrespect to participation in STEMeducation and professions. All areseverely underrepresented amongtenured faculty at research universities(Nelson 2006; Pearson 2006). Some havegained tenure on a second attempt afterbeing denied earlier.

We continue to lack a full under-

standing of the impact of the underrepre-sentation of women and minority facultyon increased participation of theserapidly growing segments of thepopulation. Despite recent increases indegree recipients, the percentages ofunderrepresented racial/ethnic collegeand university faculties remainsrelatively unchanged at less than 3-4percent over the last few decades. Otherareas of further research include: institu-tional climate issues; effects of criticalmass on recruitment and retention ofunderrepresented racial/ethnic groups;career satisfaction; interactive effects ofrace/ethnicity and gender (Pearson 2005,2006; Leggon 2006). i

REFERENCES

BEST (2004). A bridge for all: highereducation design principles to broadenparticipation in science, technology,engineering and mathematics. SanDiego.

BEST. (2006). fromhttp://www.bestwork-force.org.

Eccles, J.S. (1994). “UnderstandingWomen’s Educational andOccupational Choices: Applying theEccles et al Model of Achievement-Related Choices.” Psychology of WomenQuarterly, 18, 4: 585-609.

Leggon, C. B. (2003). “Women of color inIT: degree trends and policy implica-tions.” IEEE 22(3): 36-42.



Leggon, C. B. (2006). “Women in science:racial and ethnic differences and thedifferences they make.” Journal ofTechnology Transfer 32: 321-329.

Lichter, R. (2003). The future of thepostdoc: proceedings of a workshop.Arlington, VA, National ScienceFoundation.

Margolis, J., and A. Fisher (2002).Unlocking the Clubhouse: Women inComputing. Cambridge, MA, MITPress.

Martin, C. D., and W. Pearson, Jr. (2005).Broadening participation through acomprehensive, integrated system: finalworkshop report. Arlington, VA, NSF.

National Science Board (2006). Scienceand Engineering Indicators. Arlington,VA: NSF

Ong, M., and C. Park (2006). “PromotingEducational Equity and Diversity inScience, Technology, Engineering, andMathematics: The Current Landscapeand Challenges.” Cambridge, MA:Project SEED, The Civil Rights Projectof Harvard University

Orfield, G. and J.T. Yun (1999).“Resegregation in American Schools.”Cambridge, MA: Civil Rights Project,Harvard University

Pearson, W., H. Etzkowitz, C. Leggon, J.Mullis, T. Russell and J. Brown (2004).Leadership Alliance: Report of theEvaluators.

Pearson, W. (2005). Beyond SmallNumbers: Voices of African AmericanPhD Chemists. Amsterdam: Elsevier.

Pearson, W. (2006). “The Status ofWomen on the Faculties of Top-RankedSTEM Departments: LimitedProgress.” Paper presented at theannual meeting of the AmericanAssociation for the Advancement ofScience. St. Louis, February 18, 2006.

Woodrow Wilson National FellowshipFoundation (2005). Diversity and thePh.D.: A review of efforts to broadenrace and ethnicity in US doctoraleducation, Woodrow Wilson NationalFellowship Foundation.



Q. How many professors does it take tochange a light bulb?

A. Change? What is change? — Old joke

INTRODUCTIONIn engineering, the area moment ofinertia measures the resistance of anobject to bending and deflection.Engineering education suffers from largemoments of inertia: engineeringinstructors resist the changes in pedagogyand curriculum recommended byrepeated efforts to reform engineeringeducation, including several large demon-stration projects supported by theNational Science Foundation (NSF). Infact, most engineering instructors areunaware of these recommendations, eventhough they have appeared in numerousoutlets. As Froyd (2005) observed, tradi-tional dissemination mechanisms such asjournal publications and conferencepresentations do not reach mostengineering instructors. Even ifengineering instructors knew about theserecommendations, however, it is not clearthat they would change their practices.

In this white paper, I identify aspectsof the organizational context of

engineering education and the typicalbehaviors of engineering instructors thatmay impede the adoption of enhancedpedagogical practices and innovativecurricular ideas. To dramatize the contextand behaviors, I describe two fictional buttypical engineering professors, a mid-career Professor M and a young ProfessorY, at a typical engineering school in afictional but typical research university.

Toward the end of this paper, I suggestquestions for sociological research thatcould provide insights into organizationalcontext and faculty behaviors. Forexample, social class and acculturationcould explain why engineeringinstructors valorize the technical anddisparage the nontechnical, and why theyresist change in educational practices.The application of the sociological imagi-nation may eventually identify the leversthat could overcome the moments ofinertia in engineering education.

MEET OUR CHARACTERSOur mid-career faculty member, ProfessorM, was born and raised in Asia. Uponcompleting his baccalaureate degree, hecame to the United States and enteredNortheast Institute of Technology, where



Moments of Inertia: Toward an Agenda for Sociological Research on Why EngineeringProfessors Resist Changes in Pedagogy and CurriculumMichael C. Loui University of Illinois at Urbana-Champaign

March 22, 2006; revised May 27, 2006

he earned the doctorate. During hisgraduate studies, he was supportedentirely by research assistantshipsprovided by his doctoral advisor’s grants,except for an industrial internship in onesummer. Although most doctoralgraduates from his department took jobsin industry, M decided he could bestcontinue his research at a university.When M joined Great Prairie Universityin 1993, there was no orientation for newfaculty members, and he walked into hisfirst classroom with no prior preparationor experience in college teaching.

Our young faculty member, ProfessorY, was born in the United States. Althoughshe came from a working class family, anuncle who was an electronics technicianencouraged her to study engineering. Sheentered Desert State University, whereshe earned the B.S. and continueddirectly to its Ph.D. program. During herdoctoral studies, she was supported by anNSF graduate research fellowship.Because she planned an academic career,she decided to spend one year as ateaching assistant; she led the laboratorysections of a sophomore-level course. TheTA training program at Desert Stateconsisted of a one-day workshop,primarily on grading assignments and oncampus policies, such as the policy onstudent cheating. The supervision of TAswas spotty; she saw the course coordi-nator only a few times during the year,and he never observed her lab sections.When she accepted a faculty position atGreat Prairie University in 2004, she hadwanted to attend the week-long Scienceand Engineering Education Scholarsprogram offered at Pennsylvania StateUniversity, which prepared twenty-nine

new faculty members from eight differentinstitutions for teaching responsibilities,but Great Prairie would not pay for theprogram. Great Prairie had offered itsown program on teaching for new facultyfor a few years, but with large budgetreductions university-wide, it had cut theprogram in 2003. When Y entered herfirst classroom at Great Prairie in the fallof 2004, her preparation for teaching hadconsisted of only the one-day workshop atDesert State.

ORGANIZATIONAL CONTEXTThe largest undergraduate engineeringprograms in the United States are locatedat large public research universities likeGreat Prairie University. Its engineeringschool is consistently ranked among thetop twenty in the nation for researchquality. The engineering dean has set agoal of reaching a rank in the top tenwithin five years. The dean and thedepartment chairs continually exhort thefaculty to write more research proposals.The steady erosion of the state budget hasplaced even more pressure on theengineering school to bring in externalfunding.

Professor Y received some start-upfunding from the engineering school, butshe is working on an NSF CAREERproposal. Senior professors have told herthat she needs to win a CAREER award toearn tenure. Consequently, they haveadvised her to focus on research for theCAREER proposal rather than on under-graduate teaching. They have urged hernot to make any changes in the under-graduate courses that she is teaching,because the time required for changes



would detract from her research. For theeducation part of the CAREER proposal, Y plans only to mention that she is devel-oping a new graduate course in herresearch specialty.

Professor M has been fortunate toobtain research grants during his entirecareer at Great Prairie. Because hiscurrent funding will end within tenmonths, however, he is working on newindividual grant proposals to support hissix graduate students. Since his researchspecialty is no longer fashionable, thefunds available for this specialty aredeclining. He is trying to find a way to fithis interests into an interdisciplinaryproposal that he and several colleaguesplan to submit to a special NSF initiative.

Research accomplishments andexternal funding are the most importantcriteria for promotion and tenuredecisions at Great Prairie University. Forpromotion cases, Great Prairie evaluatesteaching by using only the results ofstandard rating forms completed bystudents. Great Prairie does not use anypeer review of teaching for promotiondecisions or even for improvement ofteaching. A fortiori, because of its strongtradition of academic freedom and profes-sorial autonomy, instructors neverobserve each other teach.

Like traditional engineering curriculaat many institutions, the engineeringcurricula at Great Prairie Universityinclude many technical requirements.Among required 128 semester credits,students take only 16 semester credits ofhumanities and social science electives;there are no other nontechnical electives.The engineering curricula preparestudents for all parts of the Fundamentals

of Engineering Exam, the first steptoward professional licensure. Thecurricula include all of the knowledgeand skills that professors at Great Prairiebelieve students need to becomepracticing engineers. Engineeringinstructors assume that engineering B.S.graduates from Great Prairie willprimarily go to industry—software,electronics, automotive, chemical,manufacturing, aerospace, etc.; civil andenvironmental engineering graduateswill enter private practice. Theinstructors are not aware that as theundergraduate student body hasgradually become more diverse,increasing numbers of graduates choosecareers in financial services, government,consulting, patent law, and even teaching.

The required engineering courses atGreat Prairie—statics and dynamics,electric circuits, thermodynamics, etc.—are invariably taught in large audito-riums with fixed seats. In teaching thesecourses, instructors lecture in order tocover a large amount of materialefficiently. A few years ago, when thebudget was in better shape, the universityinvested in the latest educationaltechnologies: desktop computers, videoplayers, and LCD projectors wereinstalled in the large auditoriums, and aWeb-based course management systemwas chosen. With recent budget cuts,Great Prairie has struggled to maintainthese services, and network outages arecommon.

The engineering programs at GreatPrairie have been accredited by theAccreditation Board for Engineering andTechnology (ABET) for many years.Department chairs endeavor to meet the



ABET requirements with as little effort aspossible. While accreditation is necessary,engineering administrators feel that moreeffort should go into improving theresearch profile of the school.Engineering professors are generallydismissive of ABET: they don’t feel thatthey should listen to ABET visitors whocome from lower-ranked schools.

FACULTY BEHAVIORSProfessor M has never read a book ornewsletter on college teaching. ProfessorY has read only an earlier edition ofTeaching Tips by McKeachie, when shewas a graduate teaching assistant. Fromthe book, she knows that she could useactive and cooperative learning strategies,which promote student learning betterthan traditional lectures. But if she usedactive learning strategies, she wouldcover less material. Colleagues who teachmore advanced courses would criticizeher for not preparing students properly.

Professor Y has heard about theAmerican Society for EngineeringEducation (ASEE), but Professor M hasnot. In any case, Y and M do not knowhow they might benefit from membershipin ASEE. Neither Y nor M has ever seenan ASEE publication or an article from anASEE conference, and they are unawareof NSF-supported work in engineeringeducation. Professors Y and M feel theycan barely keep up with research publica-tions and conferences in their ownengineering societies.

Professor Y does not attend workshopson teaching offered by the university’sCenter for Teaching and Learningbecause she feels that she cannot spare

the time. She also worries that seniorcolleagues would think she spends toomuch time on teaching. Professor Mattended a workshop on using PowerPointin which the presenter cited results fromrecent educational research. He did notaccept the research results, however. Thequalitative part of the research study hadno numbers, and the quantitative part didnot resemble a properly conducted scien-tific experiment. In any case, theresearch study did not seem particularlyapplicable to engineering.

Professor M likes to use PowerPointbecause he believes that he should transferas much of his knowledge as possible fromhimself to the students. He thinks he couldearn higher teaching ratings by spendingmore time to polish his PowerPoint presen-tations, perhaps by incorporating videoclips. Because his ratings have been satis-factory, however, M feels that enhancinghis presentations would not be worth thelarge additional effort.

On the few occasions when ProfessorM talks with other engineeringinstructors about teaching, theyinvariably complain about the inability ofstudents to understand abstract conceptsand to solve unusual problems. ProfessorY listens to these conversations, but shehesitates to contradict the commonwisdom.

Professor M believes that engineeringinstructors are gatekeepers to theengineering profession, rather than devel-opers of student potential. Consequently,in required technical courses, he gradeson a competitive curve, with limitedpercentages of A and B grades. He expectsa sizable percentage of students to faileach required course.



DIRECTIONS FOR SOCIOLOGICAL RESEARCHI hope that my description of engineeringeducation at the imaginary Great PrairieUniversity is only a slight exaggeration ofthe reality at most institutions. (Ofcourse, the climate for innovativeeducation at some institutions could beeven worse!) I propose that changes inengineering education are impeded bythe organizational culture of theengineering school and the individualbehaviors of the engineering faculty:

Organizational impediments• Because few doctoral students in

engineering take academic positions,doctoral programs do not preparestudents for academic careers.

• The training of teaching assistants isoften limited to policies and proce-dures. The training programs do notintroduce the research literature oncollege teaching.

• Engineering schools at researchuniversities emphasize researchaccomplishments and grant fundingover teaching.

• Teaching is evaluated only by studentratings, not by peer review, unlike theevaluation of research.

• Budgetary constraints may limit theupkeep and use of educationaltechnologies.

• When courses are large, it is difficultto implement pedagogical innovations.

• When curricula have many requiredcourses, it is difficult to changecurricula.

Behavioral impediments• Professors are ignorant about the good

practices in college teaching

(McKeachie 2006). They do not readabout college teaching, and they rarelyattend workshops. Consequently, mostengineering instructors do not knowabout ASEE or about reforms inengineering education. They might notaccept the results of educationalresearch, which looks different fromscientific research.

• Professors equate teaching withlecturing to cover content. They aim totransfer knowledge to students, ratherthan to encourage students toconstruct their own understandings.Professors blame students for failing tolearn.

These organizational and behavioralimpediments arise from instructors’fundamental beliefs about teaching andlearning. For example, many engineeringinstructors believe that students are bestmotivated by competitive gradingschemes. Unfortunately, many beliefs ofengineering instructors are myths(Pendergrass et al. 2000).

Rather than try to change the beliefsof instructors directly, academic leadersmight instead introduce new ways ofthinking about engineering education.For example, Boyer (1990) proposed tothink of teaching as a scholarly activity:at their best, teaching practices aregrounded in the research literature,committed to the advancement ofknowledge, and evaluated through peerreview. Professors are sometimes shockedto learn that there is a large, reliableresearch base of knowledge about collegeteaching: models of student development,student motivation, classroommanagement, syllabus construction,



effective pedagogies, teaching evaluation,etc. It seems to me that engineeringinstructors should learn something aboutthis knowledge base before they contem-plate large-scale pedagogical andcurricular changes.

Instead of thinking of teaching as ascholarly activity, engineering instructorsmight accept a characterization of collegeteaching as a professional activity.Because engineering is a profession,engineering instructors would agree thatthey should model professional behavior.Ideally, like other professions, collegeteaching should require preparation tolearn the knowledge base that supportsgood practice. Like other professionals,college teachers retain professionalauthority when they choose instructionalobjectives and set academic standards;they exercise professional judgment whenthey grade students’ work. Just as doctorsserve the health needs of their patients,teachers promote the learning of theirstudents: college students are notcustomers, but clients who receive theservices of professionals.

Here are some questions for socio-logical research on organizational contextand faculty behavior, to explain whyengineering instructors resist change inpedagogy and curriculum:• Do the organizational impediments

listed above actually obstruct theadoption of new pedagogies andcurricula? Which impediments aremost important?

• Do the behavioral impediments listedabove actually account for theresistance of engineering faculty tonew pedagogies and curricula? Whichimpediments are most important?

• What changes would remove theseimpediments?

–Would engineering instructorschange their teaching if they co-taught courses with knowledgeablecolleagues?

–Would tangible recognitions forexcellent and innovative teachingchange faculty behavior? Or wouldsuch extrinsic motivations drive outthe intrinsic motivation to teachwell?

–Under what conditions wouldleadership from an engineeringdean produce significant changes?In 1989, the engineering dean atCarnegie Mellon Universityinitiated a review of all curricula inthe engineering school (Rutenbar1991). This review culminated innew curricula, such as theintegrated curriculum in electricaland computer engineering(Director et al. 1995).

• Do the beliefs of engineeringinstructors about teaching andlearning account for their failure tochange their pedagogies andcurricula? Do demographics andcharacteristics of engineering facultyaccount for different beliefs?

• In general, would women born in theU.S. like Professor Y be more receptiveto educational change than men fromoverseas like Professor M?

• Do engineering faculty who have readbooks or attended workshops oncollege teaching—as doctoral studentsor in their first year—believe thatpedagogies and curricula shouldchange?



ACKNOWLEDGMENTSI am grateful for the perceptivecomments of Jonathan Kimball, AnthonyMarchese, and Craig Zilles on a draft ofthis paper. Kimball observed that whereasprofessors aspire to excellence inresearch, as judged by external peers,they strive only for adequacy in teaching,as measured relative to local colleagues:teaching is good enough if the studentratings are close enough to the depart-mental median. Marchese surmised thatthe new engineering programs at OlinCollege, Rowan University, and SmithCollege could be more innovative in bothcurriculum and pedagogy than olderprograms because they started fromscratch. Zilles reminded me that mostinstructors think that to improveteaching, they need to expend moreeffort, rather than to change pedagogies.He also suggested co-teaching as apossible route toward improvinginstruction. Finally, he noted thatinstructors’ misconceptions aboutteaching are as difficult to dislodge asstudents’ misconceptions aboutmechanics (Hestenes et al. 1992). i

REFERENCES

Boyer, E. (1990). ScholarshipReconsidered: Priorities of theProfessoriate, Princeton, N.J.: CarnegieFoundation for the Advancement ofTeaching.

Director, S. W., Khosla, P. K., Rohrer, R. A.,& Rutenbar, R. A. (1995). Reengineeringthe curriculum: design and analysis ofa new undergraduate electrical andcomputer engineering degree atCarnegie Mellon University.Proceedings of the IEEE, 83 (9)1246–1269.

Froyd, J. (2005). The EngineeringEducation Coalitions Program. InEducating the Engineer of 2020:Adapting Engineering Education to theNew Century (pp. 82–97). Washington,D.C.: National Academies Press.

Hestenes, D., Wells, M., & Swackhamer, G.(1992). Force concept inventory. ThePhysics Teacher, 30 (3) 141–158.

McKeachie, W. (2006). Teaching Tips, 12thed. Boston: Houghton Mifflin.

Pendergrass, N. A., Laoulache, R. N., &Fortier, P. J. (2000). Mainstreaming aninnovative 31-credit curriculum forfirst-year engineering majors.Proceedings of the 30th ASEE/IEEEFrontiers in Education Conference (pp.S2G-12 to S2G-17), October 18–21, 2000,Kansas City, Mo. Retrieved March 18,2006, from <http://fie.engrng.pitt.edu/fie2000/papers/ 1347.pdf>

Rutenbar, R. A., ed. (1991). A new ECEcurriculum for Carnegie Mellon.Pittsburgh, Pa.: Department ofElectrical and Computer Engineering,Carnegie Mellon University.



The guiding questions for thisworkshop aim at identifying facili-tators and impediments for the

diffusion of new engineering curriculaand pedagogical practices. As is properlypointed out in the workshop description,the relevant factors cover different levelsof individual, departmental, institutionaland disciplinary change, as well as thepolitical, structural, and culturaldomains.

This contribution draws attention to acultural factor—the fundamental beliefsheld by engineers about what is goodengineering, and what makes a goodengineer. It cuts across the three divisionsof this workshop—Organizational Change,Faculty Rewards, and DiffusingInnovations. With few exceptions,5 littlesystematic research exists on this topic;the beliefs about good engineering heldwithin the engineering community haveremained something of a “black box.” Itmay be time to take a look into that blackbox, because these beliefs may be centralin several respects, not the least by beinga framework variable for the success andspeed of the diffusion of new engineeringcurricula and pedagogical practices. Inshort, it is my hypothesis that thenormative ideas that faculty members

hold about what is good engineering havea potentially critical impact on thesuccess or failure of innovation efforts. Normative beliefs and innovationI expect the largest part of this workshopto be devoted to questions of how to set upincentives and disincentives for faculty toadopt and propagate new curricula andteaching methods of engineering, andhow to build institutional structures thatsupport the process. These appear to bethe most obvious targets—variables thatcan be most directly influenced--if onewants to change faculty behavior. Thesimple underlying model is that incen-tives (rewards), disincentives (punish-ments) for certain behaviors will affectthe occurrence and frequency of suchbehaviors.

However, going back to Max Weber’sclassical analysis of power in humansociety, sociologists commonly distinguishbetween legitimate power (authority) andillegitimate power (coercion). Thisdistinction alerts us to the fact that onecan make faculty members do all sorts ofthings by applying appropriate sets ofincentives and disincentives--but thedifference is what happens when nobodylooks. If behavior is caused by externalmechanisms of reward and punishment,it is not maintained in the absence ofthese mechanisms. There is a whole



Criteria for Good Engineering and theirImportance for Pedagogic InnovationsGerhard Sonnert Harvard University

________________________

5 E.g., Lovitts 2005.

arsenal of passive resistance to changethat can be employed if the institutedchanges do not coincide with what theinvolved individuals consider their corevalues about good engineering: fromsabotaging, ridiculing and makingdisparaging remarks, to getting by withthe absolute minimum of effort.

It therefore appears advisable tosurvey the normative landscape of theengineering community so as to be betteraware of potential roadblocks as well asadvantages for pedagogic innovations.More specifically, the legitimacy andacceptance of new curricula inengineering will be heavily influenced byits match or mismatch with facultymembers’ beliefs about good engineering.If there is a mismatch—i.e., if these newcurricula are considered not helpful oreven harmful to what is considered goodengineering—it will be more difficult todisseminate these new curricula, and thiswill require certain adjustments inapproach and strategy.

The engineering career as a process of socializationValues reside in the general culture of thediscipline and they are imparted toindividual practitioners throughprocesses of socialization. These processesare shaped by structural factors of theinstitutional environment. At a very basiclevel, an engineering career can beunderstood as a process of occupationalsocialization, with mentors, departments,and the engineering community at largeas agents of socialization.

The explicit mission of engineeringeducation is to teach studentsengineering knowledge and skills. In the

process of education, they will alsodevelop ideas about what kind ofengineering is excellent, good, solid, orflawed. These values about engineeringare rarely taught formally or separatelyfrom the engineering content in coursesand projects, yet they pervade the wholeeducational experience. The ideas ofbeginning engineers in this respect aretypically influenced by observingengineering faculty, their work, and theirbehavior. From a socializationperspective, one would expect that, overthe course of their studies, students’views on the topic of good engineeringshould become more similar to thefaculty’s views—especially their mentors'and role models'--on average. Conversely,dropping out from engineering programsmay in part be related to a normativemismatch (in addition to performancevariables).

One would expect—and this could beinvestigated empirically--that social-ization processes still continue amongengineering faculty members and profes-sional engineers, albeit at a slower paceand within a more limited range, onaverage. This is potentially important forthe diffusion of innovations as well as forthe likelihood and trajectory of change offaculty choice.

Robert K. Merton (1968, 1973) spokeof an “ethos of science” guiding the socialsystem of science. It encapsulates thefundamental beliefs of scientists that,according to Merton, contain four majorelements: communism (later calledcommunalism to avoid an obviousconfusion), universalism, disinterest-edness, and organized skepticism. At anabstract level, the engineering



community as a whole probably alsosubscribes to these four elements, but itseems useful to take a closer empiricallook at the normative ideas held withinthe engineering community.Distinguishing between good and verybad engineering should not be difficult,and a consensus should be reached easily.Some engineering products, projects, orapproaches turn out to be so flawed thatthey immediately and patently fail. Bycontrast, it is a much more interestingquestion—in terms of the underlyingvalues about engineering—how to distin-guish between excellent and very goodengineering, or engineers.

Questions of the latter type are centralfor stratification processes in engineering(e.g., for the decisions about who gets thetop jobs, who gets the marginal jobs, andwho has to drop out of engineeringaltogether). Peer reviews of the quality ofan engineer’s work affect hirings andpromotions, as well as the publication ofarticles, and the award of research grants.

Previous findingsBecause underlying norms of goodengineering, as mentioned, drive stratifi-cation processes in engineering, there areadditional potential benefits of knowingthe normative landscape of engineeringand of monitoring the changes occurringin it. Normative divergence or lack ofnormative assimilation to reigningmodels of good engineering might be onecause of the difficulties experienced bymembers of traditionally underrepre-

sented groups in engineering (e.g.,women, ethnic minorities). In respect togender, our own previous research onscientists already identified a number ofgender differences in their open-endedresponses when we asked them what theyconsidered good science (and what theyconsidered bad science) (Sonnert &Holton 1995a, b, Sonnert 1995)—whichmay well have parallels in engineering. We became aware of important genderdifferences in scientific styles thatexpressed themselves in differences inresearch strategies and problem choice,as well as in differences in the rates ofpublications and career outcomes. To citejust three of many findings, 36% of thewomen interviewees, but only 20% of themen, mentioned that good scientific workhad to be thorough or comprehensive.Women also talked about integrity as anaspect of good work more often than mendid (14% vs. 5%). By contrast, men weremore likely than women to refer tocreativity or originality as characteristicsof good scientific work (43% vs. 30%). Wealso conducted a small study amongHarvard science students about theirideas of science, and again foundnormative differences between thegenders, with women being more inter-ested in the applications of science(Branscomb et al. 2001).6 Similar genderdifferences may also exist in engineering.

Moreover, the more fuzzy and implicitthe criteria are, the more easily biasenters judgments. Mary Frank Fox's workhas shown that progress and success of



________________________

6 We did this study in the context of a larger effort to revise the common dichotomy of basic vs., appliedscience by adding a third category, which we called “Jeffersonian science”—basic science in a well-definedsocial interest (Sonnert & Holton 2000, also see Stokes’s ( 1997) similar concept of Pasteur’s quadrant.

women in science are helped by thepresence, and hindered by the absence, ofclear normative criteria—and this findingshould have obvious implications for thefield of engineering also (Fox 1991, 2000,2001, Fox & Colatrella 2006).

Research suggestionsIn sum, it may be helpful to identify thenormative styles and presuppositionswhich, often unspoken and perhaps notfully realized, lie behind engineers'choices of research and work problems,selection of co-workers, job decisions, andthe rhetoric in published results, becausethese normative presuppositions are alsolikely to have an impact on the efforts toinstitute new engineering curricula. Thisleads to research questions, such as: Howdo American engineers define goodengineering, and how are differences inthat definition distributed across theengineering community? What ideasabout good engineering do incomingstudents have? To what extent and how dothese ideas change in the process ofeducation?

Finally, let me make two more concretesuggestions: a smaller and a larger projectthat ideally would form a sequence.

I. SELECTION CRITERIA FOR MEMBERSHIP IN THE NAEThe smaller project would investigate thereal-life criteria of engineering excellencethat that underlie the selection of anengineer to become a member of theNational Academy of Engineering. Theengineering community is highly strat-ified, and membership in the NAEsignifies the pinnacle of achievement, the

highest honor bestowed on an engineerby his or her peers. To my knowledge, theselection criteria have not yet beenstudied in depth, although the criteriaimplicit in the selection process are themost powerful real-life definitions of goodengineering and good engineers, as thenational engineering community under-stands the selection into the NAE to bethe ultimate seal of engineering excel-lence and thus tends to orient itself alongthese definitions.

This project could be done by an appro-priate number of confidential semi-struc-tured interviews with members of the NEAabout their ideas of engineering excellenceand about the ways in which they decidewhether someone is worthy to become anew member or not.

II. A QUANTITATIVE INSTRUMENT TOMEASURE VIEWS OF GOOD ENGINEERINGWhen first looking into a hitherto under-researched topic, the use of qualitativedata—such as in Project (I) above--is appro-priate, but there comes a point when amore quantitative approach becomes morebeneficial (e.g., for the ease and speed ofcollecting data). Using standard psycho-metric methods, I envisage constructing aquantitative instrument that determines,by means of a simple questionnaire,various normative types that exist amongengineers.

The findings of Project (I) would forman important starting point for theconstruction of the instrument. Inaddition, to tie in the new engineeringpedagogy to views of good engineering andgood engineers, one would need to analyzethe fundamental ideas about goodengineering behind the new engineering



pedagogy (through examination ofprogrammatic texts, pedagogical practices,interviews with proponents, etc.). Thiscould result in one or more of the provi-sional normative types that will become aninput into the second main part: theprocess of developing and testing a quanti-tative instrument.

The following is a hypothesized list ofpossible syndromes that might emergefrom this research, based on our researchon the issue of “good science.” Withoutdoubt, actual research on this topic wouldlead to a modified list, perhaps modifiedin major ways. Again, I think it would beimportant to know how these types aredistributed in the engineeringcommunity and whether this distributionvaries systematically (e.g., by seniority,work place, gender, race, and othercharacteristics, such as supporting newengineering curricula). Not the least, thesuccess of disseminating new engineeringcurricula may hinge on the distributionof these normative types in theengineering community.

Type A: Emphasizes arduousness andovercoming hardship in achieving results.

Type B: Focuses on elements of compe-tition between rival engineers or researchgroups to find the best solution for anengineering problem

Type C: Centers on challenging aprevailing engineering model orexemplar.

Type D: Centers on the hope to reachfoundational/principle-oriented findings.

Type E: Emphasizes the eventual applica-bility of engineering efforts to technicaland social problems.

Type F: Focuses on immediate economicbenefits.

Type G: Focuses on the potential for widedissemination, recognition, and rewardsubsequent to the publication of findings.

Type H: Rejects “androcentric” or“western” engineering and seeks alternatives to it. i

SELECTED BIBLIOGRAPHY

Belenky, M. F., Clinchy, B. M., Goldberger,N. R., & Tarule, J. M. (1986). Women’sWays of Knowing. The Development ofSelf, Voice, and Mind. New York: BasicBooks.

Ben-David, J. (1991). Scientific Growth.Essays on the Social Organization andEthos of Science. Berkeley: Universityof California Press.

Branscomb, L. et al. (2001). Report on theInterfaculty/Student Discussion Groupon Diversity in Science and Technology2000-2001. Workshops Funded by theOffice of the Provost, HarvardUniversity.

Chase, J. M. (1970). Normative criteria forscientific publication. AmericanSociologist, 25, 262-265.



Committee on Facilitating Interdisci-plinary Research, Committee onScience, Engineering, and PublicPolicy of the National Academies(2005). Facilitating InterdisciplinaryResearch. Washington, DC: NationalAcademies Press.

Crombie, A. C. (1994). Styles of ScientificThinking in the European Tradition:The History of Argument andExplanation Especially in theMathematical and Biomedical Sciencesand Arts. 3 vols. London: Duckworth.

Fox, M. F. (1991). Gender, EnvironmentalMilieu, and Productivity in Science. InH. Zuckerman, J. Cole, and J. Bruer(eds.), The Outer Circle: Women in theScientific Community, New York: W.W.Norton, 188-204.

Fox, M. F. (2000). OrganizationalEnvironments and Doctoral DegreesAwarded to Women in Science andEngineering Departments. Women’sStudies Quarterly, 28 (spring/summer),47-61.

Fox, M. F. (2001). Women, Science, andAcademia: Graduate Education andCareers. Gender & Science, 15, 654-666.

Fox, M. F., & Colatrella, C. (2006).Participation, Performance, andAdvancement of Women in AcademicScience and Engineering: What is atIssue and Why. Journal of TechnologyTransfer, 31, 377-386.

Galison, P. (1987). How Experiments End.Chicago: University of Chicago Press.

Gardner, H. (1982). Art, Mind, and Brain.A Cognitive Approach to Creativity.New York: Basic Books.

Goodman, N. (1978). Ways ofWorldmaking. Indianapolis, IN:Hackett.

Hughes, T. P. (2004). Human-Built World:How to Think about Technology andCulture. Chicago: University ofChicago Press.

Keller, E. F. (1985). Reflections on Genderand Science. New Haven: YaleUniversity Press.

Kitcher, P. (1993). The Advancement ofScience. Science without Legend,Objectivity without Illusions. New York:Oxford University Press.

Kuhn, T. S. (1977). The Essential Tension.Selected Studies in Scientific Traditionand Change. Chicago: University ofChicago Press.

Latour, B., & Woolgar, S. (1979).Laboratory Life. The SocialConstruction of Scientific Facts. BeverlyHills, CA: Sage.

Lovitts, B. E. (2005). How to grade a dissertation. Academe (Nov.-Dec.), 18-23.

Merton, R. K. (1968). Social Theory andSocial Structure. New York: Free Press.



Merton, R. K. (1973). The Sociology ofScience. Theoretical and EmpiricalInvestigations. Chicago: University ofChicago Press.

Shadish, W. R. (1989). The perception andevaluation of quality in science. InGholson, B., Shadish, W. R., Neimeyer,R. A., & Houts, A. C. (Eds.), ThePsychology of Science. Contributions toMetascience, Cambridge, UK:Cambridge University Press, 383-426.

Simonton, D. K. (1988). Scientific Genius.A Psychology of Science. Cambridge,UK: Cambridge University Press.

Sonnert, G., with the assistance of G.Holton, (1995a). Gender Differences inScience Careers: The Project AccessStudy. Rose Monograph Series of theAmerican Sociological Association.New Brunswick, NJ: Rutgers UniversityPress

Sonnert, G., with the assistance of G.Holton, (1995b). Who Succeeds inScience? Women’s and Men’s ScientificCareers. New Brunswick, NJ: RutgersUniversity Press.

Sonnert, G. (1995). What makes a goodscientist? Determinants of peer evalu-ation among biologists. Social Studiesof Science, 25, 35-55.

Stokes, D. E. (1997). Pasteur’s Quadrant:Basic Science and TechnologicalInnovation. Washington, DC: BrookingsInstitution.

Sulloway, F. J. (1989). Orthodoxy andinnovation in science: A multivariatesynthesis.

Talk given at the Harvard Department ofthe History of Science on February 3,1989.

Tobias, S. (1990). They’re not Dumb,they’re Different. Stalking the SecondTier. Tuscon, AR: ResearchCorporation.

Traweek, S. (1988). Beamtimes andLifetimes. The World of High EnergyPhysics. Cambridge, MA: HarvardUniversity Press.



INTRODUCTIONRewards energize, direct and sustainbehavior (Guzzo, 1979). Empiricalevidence indicates that rewards arepowerful determiners of faculty behavior(Fairweather, 1996). In fact, institutionalreward structures “provide the blueprintfor how faculty spend their time(Zahorski & Cognard, 1999).” I doubt thatanyone would disagree with these state-ments, but do we understand the complexnature of this reality as it relates toinnovation in engineering education?One of the questions for discussion in theupcoming workshop is whether thecurrent reward system at researchuniversities promotes or impedes thediffusion of innovation in engineeringeducation. Exploration of this issuerequires an interdisciplinary approach ifwe believe that the current systemimpedes educational innovation and areto convince faculty and administratorsthat the benefits to be derived fromchanging the system are worth theinvestment of time and energy. In orderto explore the question of the impact ofthe current reward system on thediffusion of innovation and change inengineering education, we need to betterunderstand

(a) what shapes our current perceptionsof the academy, including presentdefinitions of scholarship and itscontingent rewards (e.g., which leadsus to explore the history of highereducation and the resulting culture),

(b) why institutions persist in thetradition that defines faculty rewardspredominantly as they relate toresearch despite the other roles andresponsibilities faculty have (e.g.,which leads us to literatures on thepsychology of human behavior andorganizational development),

(c) what current forces in highereducation are pressuring institutionsto change (e.g., which leads us to thepolitical and economic realities ofthe times), and

(d) who potential agents of change areand what they need to effectivelyfacilitate change (e.g., which leads usto the change literature).

Given limited space, I will introduce inbrief the threads we need to weavetogether in the hope that they might serveas fodder for discussion and explorationinto the issue.


APPENDIX 3: PAPERS PRESENTED BY WORKSHOP PARTICIPANTSFACULTY REWARDS1

Exploring the Role of the Reward System in the Diffusion of Innovation in Engineering EducationSusan Ambrose Carnegie Mellon University

“Trying to change an educational system is like trying to move a cemetery:there’s not a lot of internal support for it.” (Hearn, 1996)

A. THE HISTORICAL CONTEXT THAT CREATED THE CURRENT CULTURE

HISTORY Rewards are shaped by culture, andculture is both created by and embeddedwithin an historical context. A very brieflook at how the history of highereducation in the U.S. evolved may shedsome light on a few of the deeply heldbeliefs in research universities (Boyer,1990; Glassick, Huber & Maeroff, 1997;Goodchild & Wechsler, 1997; Scott, 2006;Ward, 2003).

Colonial colleges, the first collegescreated in the colonies between 1636 and1789 with strong British roots, weredevoted to the improvement of youngmen’s minds rather than the promotionof career aspirations or social status.These men were being groomed forservice as clergymen and public servants.The liberal arts dominated education asfaculty worked to pass on the wisdom ofthe classics, and faculty were expected toengage with students in all aspects of lifeat the college. Students were the reasonthe colleges existed, and teaching wasviewed as a vocation, “a sacred calling —an act of dedication honored as fully asthe ministry (Boyer, 1990, p. 7).” Thepresident was all-powerful, and facultymembers were viewed and treated ashired underlings. These colleges, in otherwords, were characterized by theirteaching mission.

In the next phase of evolution,roughly 1790 to 1869, higher educationresponded to the needs of the emergentnation and began “practical” training inareas such as law, medicine, commerce,navigation and, as infrastructure became

more important, engineering. During thisera the Morill Act (1862) created landgrant colleges to link higher education toAmerica’s rapidly expanding agriculturaland technological growth. Historians ofhigher education often note that it isduring this era that “service” in buildinga new nation is added to the mission ofcolleges and universities, as is what wetoday call “applied research.” The termservice, in this era, did not mean internalservice (e.g., committee work, advisingstudents), but rather public servicedefined as “service to the public of thenational state (Scott, 2006, p. 24).” Therole of education during this time was, ingeneral, to be useful to society. Besidesexpansion of the mission and goals ofhigher education, expansion of types ofcolleges occurred as women’s collegesand historically black colleges and univer-sities were founded.

Beginning in the 1870s, we see yetanother type of institution emerges basedon the German university, what today wecall the modern research university. Theoverarching goal of these institutionsfocused on pushing a different frontier –the frontier of human knowledge. Theemphasis at these institutions was on thediscipline and work of the facultymember rather than the education ofyoung people; undergraduates became anuisance and graduate education waspushed to the forefront. According toScott (2006), by 1910 the research missiondominated U.S. universities and “therebegan a decline in the teaching missionduring the 20th century (p. 23).”

Institutions were no longer dominatedby presidents but by willful andautonomous faculty. It is also during this



era that we see the rise of national disci-plinary associations, consortial groupssuch as the Association of AmericanUniversities, and faculty professional-ization through the creation of suchentities as the American Association ofUniversity Professors (Goodchild andWechslar, 1997).

From the 1940s on universitiescontinued to evolve as they responded yetagain to the society around them, e.g.,wars, Sputnik, the Cold War. These eventsbrought about “a revolutionary shift infunding, as the federal governmentbecomes the dominant patron of themajor research universities (Scott, 2006,p. 28).” In essence, the public servicemission of universities in using appliedresearch to further national interests wasreinvigorated. It is during this time aswell that faculty members’ commitmentbroadens to include their profession/discipline as well as the institution wherethey work, and that prestige is awarded tothose individuals and institutions whoseresearch brings in outside funding.Throughout all of these phases, however,faculty members continued to educateundergraduates, and yet as early as 1958observers note that “while young facultywere hired as teachers, they wereevaluated primarily as researchers(Boyer, 1990, p. 11).” As in previous eras,access to higher education was broadenedas the government pushed for a masssystem of higher education.

What do we learn from thesesnapshots in time that is relevant to thediffusion of innovation in engineeringeducation? Glassick, Huber and Maeroff(1997) sum it up best:

The priorities of American higher educationhave been significantly realigned sinceWorld War II. The emphasis on graduateeducation and research has cast a longshadow over undergraduate education . . .The prime focus at these institutions movedfrom student to professor, from the generalto the specialized, and from loyalty tocampus to fealty to profession . . . As theresearch model came to prevail, facultymembers were too seldom recognized fortheir expertise in teaching or in applyingknowledge in the service of society (p. 8).

Colbeck (1992) calls this the “outputcreep” in higher education — the shiftfrom teaching to research that hasoccurred since World War II. As Boyer(1990) noted, “Almost all colleges pay lipservice to the trilogy of teaching, researchand service, but when it comes to makingjudgments about professionalperformance, the three rarely areassigned equal merit (p. 15).” So, whileAmerican higher education prides itselfon the diversity of colleges and univer-sities with different missions, “the profes-soriate tends to value research as thehighest goal, even in institutions withouta research mission (Altbach &Finkelstein, 1997, p. viii).” In other words,most institutions “follow the norms, andthe fads, of the prestigious research-oriented universities (p. 33).”

This history explains in large part theculture of academe that exists today, anda fuller reading of this history could verywell explain the depth of commitment tocertain aspects of academic culture thatremain intact despite calls for change.For critics who claim that change is hardfor those in higher education, this historyalso validates that, under the rightcircumstances, change can occur. And



yet, as we will see later in this paper,attempts over the past twenty years toreview and revise faculty roles, responsi-bilities and rewards have had littleimpact on research universities.

CULTUREHow has this history shaped culture?Using the definition of Edgar Schein(1992), one of the founders of organiza-tional psychology, culture is “a pattern ofshared basic assumptions that the grouplearned as it solved its problems ofexternal adaptation and internalintegration, that has worked well enoughto be considered valid and, therefore, tobe taught to new members as the correctway to perceive, think, and feel inrelation to those problems (p. 12).” Inother words, we socialize new genera-tions of academics into a culture that isbased on how current members of thegroup perceive, think and feel aboutthings. But where do those perceptions,thoughts and feelings come from?According to Schein (1992), culturesbasically spring from three sources:

(1) the beliefs, values, and assumptions offounders of organizations; (2) the learningexperiences of group members as theirorganization evolves; and (3) new beliefs,values, and assumptions brought in by newmembers and leaders. Though each of thesemechanisms play a crucial role, by far themost important for cultural beginnings isthe impact of founders (p. 211).”

Instead of focusing on particular“founders” of the early colleges anduniversities, the historical overview inthe previous section should serve as abasis for beginning to think about which

values and traditions have becomeembedded in academic culture, whichhave not, and why. Altback andFinkelstein (1997) remind us of severalaspects of that culture: “Generally,prestige is defined by how close an insti-tution, or an individual professor’sworking life, comes to the norm of publi-cation and research, of participation inthe ‘cosmopolitan’ orientation to thediscipline and the national profession,rather than to the ‘local’ teaching andinstitutionally focused norms (p. 9).” These are some of the traditional culturalvalues of academe that we continue topropagate by socializing graduatestudents to believe that, despite the broadarray of faculty responsibilities, the mostimportant -- given that it is rewarded -- isresearch (Braxton, Luckey & Helland,2002). As a result, we shouldn’t besurprised that current studies indicatethat young faculty today, across genderand institutional type, want to shift timefrom teaching to research (Finkelstein,Seal & Schuster, 1998).

What makes any attempt to under-stand academic culture both intriguingand challenging is that we are not talkingabout one culture, but rather severalcultures interacting with each other toinfluence attitudes and beliefs and hencedrive behavior: the culture of thedepartment (Walvoord et.al., 2000), of theinstitution (O’Meara, 2005), of the disci-pline (Bowen & Schuster, 1986), and ofhigher education (Berquest, 1992; Kuh &Whitt, 1988). For example, while “a disci-pline is the first mark of identity aprofessor receives (Kuh & Whitt, 1988, p.18),” disciplinary cultures are affected bytheir institutional context. Because



culture dictates values, traditions, organi-zational structures, expectations andbehaviors, we need a deeper under-standing of the various cultures if we areto change certain aspects of thesecultures.

B. PERSISTENCE VERSUS CHANGE INACADEMIC ORGANIZATIONSWhy does the academy hold so steadfastlyto the narrow definition of scholarshipdefined a century ago if, indeed, thedefinition does not represent the fullrange of faculty roles and responsibilities?Is it, as the cliché so often says, “humannature” — e.g., the fear of change, thesecurity in familiarity? And if so, what dowe know about organizations and changethat might potentially help universities tomove forward and implement change, inthis case giving educational endeavorsand innovations the prestige they deservein the reward structure? As Marchese(1992) has noted, “The problems thatexist are far less those of individualbehaviors than they are of the system. Itis the system that dictates what facultydo…it’s a system of contradictorymessages … (p. 4).”

While universities do need to examinethe plethora of research on organizationsand change (March & Simon, 1958;Martin, 2002; Schein, 1992), we also needto remember that the uniqueness ofacademic culture requires the devel-opment of a distinctive approach tochange. For example, Berquist (1992)analyzes the four cultures of the academy(in a book of the same name) in anattempt to “offer a preliminaryframework that can guide and inspirenew courses of action within those

complicated and often closed organiza-tions (p. xii).” Kezar (2001) takes heranalysis to a more concrete level as shepresents a synthesis of research literatureon the organizational change process,identifies key features of highereducation institutions, and offers atypology of change theories and adiscussion of the efficacy of thesemodels/theories for change in academicinstitutions. For example, one of the keyfeatures of universities is the interde-pendent nature of the organization:universities do not operate independentlyof disciplinary societies, the federalgovernment, accreditation agencies,unions, private foundations, and nationalassociations (e.g., AAU). As a result,faculty receive “multiple and perhapsmixed messages related to change,”making change less likely to occur unlessseveral of these forces overlap (Kezar,2001, p. 62).

Over the past decade, there has been“an orientation toward more appliedresearch, closer links between industryand the university, and more service tothe private sector (Altback & Finkelstein,1997, p. 11).” How do these developmentsimpact faculty behavior in light of thecurrent reward structure? The mixedmessages continue! Organizationsindicate what they value by what they payattention to and measure, and how theyallocate rewards and status — these aresome of the “culture-embedding mecha-nisms” that communicate the beliefs,values and assumptions of the organi-zation (Schein, 1992). In the case ofuniversities, there appears to be amisalignment of expectations andrewards that calls for a reassessment of



institutional missions and realignment ofthe reward system with the mission andpriorities institutions (Braxton, Luckey &Helland, 2002; Diamond, 1999).

As Locke (1995) points out, successfulchange “will be possible only throughreduction of the resistance produced bylegitimate concerns about altering thepresent system…if you wish to helpsomeone stop smoking, it generally isbetter to find ways of weakening or elimi-nating the purposes served by the habitthan it is to increase the intensity ofarguments for quitting (p. 507, 514).” Partof the change process, then, is identifyingthose legitimate concerns of the facultyand research universities (that are deeplyrooted in the culture) and reducingfactors of resistance. For example, ifoutstanding research brings fame, whichresults in visibility, which then attractsendowment, which is equated withprestige (Altbach & Finkelstein, 1997;Tang & Chamberlain, 1997), what are thealternatives for achieving visibility,money and prestige if we alter the rewardstructure to value educational innovationmore? If we believe in the power of theevidence presented and process used tojudge research, what equally valid andreliable evidence and process can we useto judge educational innovation? Theseare only a few of the questions we need toaddress if we are to bring about change.

C. CURRENT FORCES INFLUENCING HIGHER EDUCATIONThere are several new forces/trends inhigher education that affect faculty workand will interact with history and cultureas universities continue to evolve, revisittheir missions, and potentially redefine

what scholarship is and how to reward it(Altbach & Finkelstein, 1997; Finkelstein,Seal & Schuster, 1998; Gappa, Austin &Trice, 2005; Graubard, 2001; Locke, 1995;Rice, Sorcinelli & Austin, 2000; Tierney,1998; Walvoord 2000): • the proliferation of non-tenure track

faculty and faculty who are dispersedin terms of location and/or time ofday, e.g., proportion of faculty who arepart-time has almost doubled from 22percent in 1970 to 43 percent in 1999(Gappa, Austin & Trice, 2005);

• the advent of technologies that allowteaching to transcend time and space,but that also change the ways in whichfaculty members work;

• changing patterns of studentdemographics and expectations as thestudent body continues to diversify interms of age, race, ethnicity, andeducational background;

• the rise of competing educationalproviders;

• the emergence of new areas of special-ization that are challenging thestructure of traditional disciplines;

• fiscal constraints; • public scrutiny and accountability

demands; and • an increasingly diverse young faculty,

some of whom are expressing concernover the social relevance of their workand others who are expressing concernover the lack of a comprehensivetenure system that fully reflects therange of faculty roles and responsibil-ities.

On top of these factors that directlyinfluence higher education, add theflattening of the world a la Thomas



Friedman (2005), and it’s not surprisingthat faculty report being pulled inmultiple directions (Gappa, Austin &Trice, 2005). As the incomplete list aboveindicates, many of these pressures havethe potential to influence how facultyredesign curriculum, create new andrevise old courses, expand their pedagogy,interact with the increasing number ofpart-time colleagues to assure coherencyin the curriculum, etc.; moreover, theywill be forced to do these things asefficiently as possible (just meeting thebar) if the current reward structure ismaintained. What will this mean forfaculty workloads and for the quality ofthe educational experience we provide? In the midst of these developing trendsover the past two decades came ErnestBoyer’s now infamous book ScholarshipReconsidered (1990), which recom-mended a reconceptualization andexpansion of the definition of scholarshipto include other forms of intellectualactivity. Put succinctly, Boyer suggestedfour types of scholarship that reflectfaculty work: the scholarship of discovery,the scholarship of integration, the schol-arship of application, and the scholarshipof teaching. A few years later Glassick,Huber and Maeroff (1997) publishedScholarship Assessed to address the needfor the academy to create clear standardsfor evaluating the wider range of schol-arship proposed by Boyer, noting that weneed to uphold scholarly rigor for alltypes of scholarship. The academycomfortably relies on peer evaluation ofresearch as the primary basis for rewards,research that is communicated in publi-cations for scholarly recognition(Braxton, Luckey & Helland, 2002). On

the other hand, the academy has not yetdefined rigorous standards for the evalu-ation of teaching; teaching is not typicallypeer-reviewed, and it does not present a“product” as easily as research does.

D. POTENTIAL AGENTS OF CHANGEBecause of the current forces we can’tignore, and in spite of the currentacademic culture and concomitantresistance to change, we need to moveinto action and think about how to enablethe agents of change to evolve ourcurrent reward system to assure thateducational innovation is rewarded.There are both external and internalagents of change. Over the past fifteenyears, foundations, professional organiza-tions, accreditation agencies andgovernment agencies have not only triedto stimulate educational reform but,concomitantly, have called into questioncurrent practice around the rewardstructure. These entities have thepotential to help educational reform gainlegitimacy within the faculty rewardstructure. Given limited space, I won’tdiscuss how these entities have tried tostimulate educational reform (e.g., NSFfunded coalitions, centers for scholarshipin engineering education), but rather Iwill provide a few examples of how theyhave supported the academy to rethinkfaculty roles, responsibilities and thereward structure.

EXTERNAL AGENTS OF CHANGE With funding from the Lilly Endowmentand the Fund for the Improvement ofPostsecondary Education (FIPSE), disci-plinary associations and accreditationagencies began in the early 1990s to recon-



sider definitions of scholarly, creative andintellectual contributions within theirdomains (Adam & Roberts, 1993), in part asa response to Boyer’s call for broadeningour current definitions of scholarship.Twenty-two scholarly societies, learnedassociations and accreditation agenciesagreed to participate in articulating howfaculty in the respective fields definedscholarship. This is a first step.

Simultaneously, the AmericanAssociation of Higher Education created a“Forum on Faculty Roles and Rewards” toaddress three specific, interrelatedproblems: (1) the dominance of a single,powerful definition of what roles andactivities were worthy of faculty time andenergy, (2) how to develop mechanismsfor assuring quality in all areas of facultywork (e.g., no peer review for teachingand service), and (3) how to deal withaccountability imposed from outsideacademe (Rice, 1995). The PewCharitable Trust followed suit a few yearslater and funded the Council ofIndependent Colleges (CIC) and theConsortium for the Advancement ofPrivate Higher Education (CAPHE) tocreate a grant program titled “FacultyRoles, Faculty Rewards, and InstitutionalPriorities” with the overarching goal tofoster institutional transformation tosupport learning in order to bringcongruence between institutional missionand reward structures (Zahorski &Cognard, 1999).

Given this recent history, we are backto the questions of why the reward systemhasn’t changed to better reflect facultyroles, responsibilities and rewards, andwhether the current system impacts thediffusion of educational innovations. To

date, these efforts have not permeated theacademic culture and the reward systemthat reinforces current norms(Fairweather, 1996). Perhaps that isbecause changing the culture requires acoherent effort from internal as well asexternal agents of change.

INTERNAL AGENTS OF CHANGEInternally, we must rely on engineeringdepartment heads/chairs and deans tolead the change effort to gain legitimacyfor educational reform within the facultyreward structure (Serow, Brawner &Demery, 1998). In theory this should notbe difficult because, in recent years,studies have indicated that faculty, chairs,deans and administrators at researchuniversities view the balance betweenresearch and teaching in the rewardstructure as inappropriate and in need ofmodification, giving more weight to“vigorously evaluated teaching” (Gray,Froh & Diamond, 1992; Survey ResearchLaboratory, 1991). So those most closelyconnected to the reward structureadvocate, at least in theory, change.Consequently, we should examine theliterature that identifies effectiveleadership characteristics and, as Hoppeand Speck (2003) suggest, provide experi-ences for academic leaders to develop theskills they need as they move fromacademia to administration. Minimally,we need to equip these individuals withthe tools they need to • change the mental models of faculty

about what scholarship is, i.e., the wayof thinking about roles, responsibilitiesand rewards that is firmly embeddedin an academic culture that values thescholarship of discovery above all else;



o revamp graduate education to equipgraduate students with more than disci-plinary expertise (Rice, Sorcinelli andAustin, 2000) and inculcate them to valueteaching and public service as well asresearch; • examine their departmental, disci-

plinary and institutional cultures (e.g.,artifacts, espoused values, behaviors,underlying assumptions) because thenature and structure of departmentsand institutions, the style of adminis-tration, and the differences amongdisciplines all play roles in deter-mining how the process of changeshould be approached and imple-mented (Diamond and Adam, 1993).This is vital because effective changeefforts must be integrated successfullyinto the existing culture and climate(Hearn 1996); and

• understand the nature of organizationsand organizational change, aboutwhich much is written, and yet thisliterature has remained invisible to themajority of engineering deans andheads/chairs (Hearn, 1996; Kuh &Whitt, 1988; Martin, 2002; Schein,1992; Zahorski & Cognard, 1999).However, we should heed Kezar’s(2001) counsel for the development ofa distinctive approach to changewithin higher education given theuniqueness of academic culture,because “using concepts foreign to thevalues of the academy will most likelyfail to engage the very people whomust bring about change (p. vi).” Infact, the American Council onEducation (Eckel, Hill & Green, 1998,2001; Eckel, Hill, Green & Mallon,1999) and the Higher Education

Research Institute at UCLA (Astin &Astin, 2001) have published reports oninstitutional change/transformation inhigher higher education that could behelpful to engineering change agents.

We also need to acknowledge facultyas change agents in a variety of differentways. For example, money and status --but not necessarily job satisfaction -- comewith research productivity (Leslie, 2002).In fact, evidence suggests that faculty jobsatisfaction is tied more to collegiality,quality of life, and personal fulfillmentthan it is to prestige, security andauthority, a shift (since circa 1988) notedby researchers who study why facultymembers stay at or leave an institution(Barnes, Agago & Coombs, 1998; Johnsrud& Rosser, 2002; Manger & Eikeland, 1990).How might this trend influence change inthe academy?

CONCLUSION AND FUTURE RESEARCHI do believe the reward structure atresearch universities impedes thediffusion of innovation in engineeringeducation. The important questions inaddressing the issue are why that is trueand how we can influence change. Thethreads I have identified in this paperhopefully indicate that real change mayhave a better chance of occurring whenwe more deeply understand (1) thehistorical roots of academic culture thatare responsible for current values, tradi-tions and beliefs; (2) educational institu-tions as organizations with uniquefeatures that will impact the changeprocess; (3) current forces that challengeacademic culture; and (4) how to betterequip engineering leadership with the



knowledge, skills and tools they need toact as agents of change. i

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The purpose of this short whitepaper is to suggest an area forresearch that may be undertaken

by the Faculty Rewards group. The idea—to explore both the intrinsic rewards thatmotivate faculty to embrace pedagogicalchange as well as the fears and anxietiesthat keep them from doing so—comesfrom two sources. The first is a prelim-inary review of the literature whichyielded more studies on the impact of theexternal reward system on facultybehavior than on internal factors. Thesecond source is my own experience atMIT, an institution that has recently beenthrough a six-year period of intenseinnovation in pedagogy. Both sources aredescribed in more detail below.

PEDAGOGICAL INNOVATION AT MITIn 1999, MIT received two generousgrants that allowed it to embark on awide-scale series of innovations in under-graduate education. The first was fromthen-chairman of the MIT Corporation,Alex d’Arbeloff, and his wife, Britd’Arbeloff; they created the d’ArbeloffFund for Excellence in Education. Thed’Arbeloff Fund underwrites projects“designed to enhance and potentiallytransform the academic and residentialexperience of MIT’s undergraduatestudents” (http://web.mit.edu/darbeloff).

The second grant, from the MicrosoftCorporation, funded iCampus, a five-year,$25 million research alliance that was“aimed at achieving a broad, substantial,and sustainable impact on highereducation through informationtechnology” (http://web.mit.edu/icampus).

In total, there have been close to onehundred projects underwritten by eitheriCampus or d’Arbeloff funding. (Whilethe iCampus initiative has ended, thed’Arbeloff Fund continues to makeawards. The latest set of funded projectswill develop a set of freshmen project-based courses.) Together, these effortsrepresent a rich array of educationalexperiments. They include transformingMIT’s required two-course sequence inphysics from the standard lecture/recitation model to a studio physicsformat; developing a freshmen coursethat requires students to work in teams totackle a large-scale, open-ended problem;and experimenting with different ways tobring small-group tutorials into upperlevel courses. It could be argued that thejewel in MIT’s educational crown duringthis period of innovation has beenOpenCourseWare, the Institute’s effort topublish all its courses on the Web(http://ocw.mit.edu/ index.html).According to an article written by



Social Dynamics of Campus Change: Suggestions for a Research Agenda Faculty Rewards Working GroupLori Breslow Massachusetts Institute of Technology

journalist Sally Atwood for the May 2002alumni edition of Technology Review, “Ithas been 30 years since MIT last saw sucha groundswell of educational innovation,and it’s beginning to transform theclassroom experience.”

HAVE WE “TRANSFORMED THE CLASSROOM EXPERIENCE”?

Is Atwood’s enthusiasm justified?Although in no way do I wish to belittlethe enormous effort that many MITfaculty have invested in these educationalinitiatives, I believe a case can be madethat six years, 100 projects, and millionsof dollars later, MIT undergraduateeducation has not been essentiallychanged. Large lectures still predominatethe freshmen year, as well as a number ofupper level courses. Recitations continueto mimic the lecture experience in thatrecitation leaders (both faculty andgraduate students) stand in front of thechalkboard and work problems. In myexperience, it is rare to find facultymembers who think beyond the topics ontheir syllabus when asked what theirlearning objectives are. I believe mostMIT faculty approach teaching andlearning in the same way Derek Bok(2005) describes the vast majority of theprofessoriate do. “Few faculties,” hewrites:

engage in a continuing effort to assess howmuch their students are learning, identifydeficiencies, develop and test possibleremedies, and ultimately adopt thoseapproaches that proves most successful. (p.B12)

Why is this the case? The most commonexplanation places the blame on thefaculty reward system. As the argument

goes, if tenure were not based on publica-tions and if success in the classroom wereequally important, faculty would “followthe money.” Educational researchersWilliam Massey and Andrea Wilger(2000) sought to determine whether ornot this belief could be substantiated bydata. They interviewed 378 faculty at 19colleges and universities representing allcategories of the (former) Carnegie classi-fication system. Their research supportsthe prevailing thinking: Tenure andpromotion tops the list of incentivesrespondents considered to be the mostimportant regardless of institutional type.And, they write, “overall the importanceof research-based activities overshadowsfactors related to teaching” (p. # notaccessible).

Yet other major studies undertakensince as early as the 1970s report thatfaculty find teaching important. Smithand Geis (1996) cite several that reachedthat conclusion (Blackburn et al, 1980;Carnegie, 1989), as well as others thatfound teaching is a “major source of satis-faction” for faculty (Wilson & Gaff, 1971;The Chronicle of Higher Education, 1990).A 1992 survey of 23,000 faculty chairs,deans, and administrators at researchuniversities concluded that “a majority offaculty would like to reverse the pushtowards research and restore the balanceby increasing the emphasis on under-graduate teaching” (Gray, Froh &Diamond, 1992, as quoted in Smith &Geis, 1996). An ERIC search on “facultyrewards in higher education” since 1996found over 150 articles, many of whichconcerned the difficulty of trying tobalance research and teaching withinincentive systems that continue to favor



the former. It is a situation that isunlikely to change in the foreseeablefuture.

WHAT’S A RESEARCHINSTITUTION TO DO?

Assuming that the research-basedtenure system remains the 800 poundgorilla in the room, does that mean thereis nothing that can be done to “convince[the] individual and collective will offaculty members, chairs of departments,and deans of schools that there arebenefits to be derived from change thatare worth the investment of [their] timeand effort”? (Workshop Call forApplications, September 28, 2005). Myexperience at MIT tells me this iscertainly not the case as evidenced by thehundred plus faculty who did devoteconsiderable amount of time and effort totheir d’Arbeloff and iCampus projects.

But why did they do so given theimportance placed on research at MIT? Ican imagine a host of answers: the simpleenjoyment of teaching well, frustrationwith low attendance at lectures, concernover failure rates in some courses, a senseof responsibility to undergraduates,curiosity about how to improve learning(Breslow, et al., 2004). The facultymember who reformed freshmen physicsdid so in part because his son had beenprofoundly influenced by one of hisprofessors at Trinity University; the MITprofessor came to see firsthand the effecta teacher can have on his student. Iffaculty are de-incentivized by the tenuresystem to focus on teaching and learning,then there must be intrinsic rewards theyderive from spending significant amountsof time and energy on educational

innovation. What are they and can theybe used to motivate more change?

A related question that comes frommy work with MIT faculty is why some ofthem are so reluctant to change. Recently,the department head from one of thelarge enrollment undergraduate depart-ments came to talk about initiating aneffort in the department that would focuson pedagogy. The impetus for him doingso was the reluctance on the part ofseveral of his faculty to allow a controlledtrial of a new pedagogical method in oneof the department’s introductory courses.He was aghast that engineers would turndown the opportunity to collect data thatcould lead to better understanding!

But that situation wasn’t as surprisingto me. Linda C. Hodges (2005) writes thatone disincentive for educationalinnovation is fear. “Underlying this fear[of changing pedagogical approaches],”she writes, “may be the fear of loss, fearof embarrassment, or fear of failure” (p.121). Bonwell and Sutherland (1996) listfive reasons faculty resist using active orexperiential pedagogies: fear they won’tcover all the necessary material,increased amount of time to prepare,difficulty understanding how toimplement these pedagogies in largeclasses, lack of resources, and risk. (Onepiece of folk wisdom in academia is thatexperimenting with new teachingmethods is sure to result in lowerteaching evaluations.) I have had facultytell me that the problem with educationat MIT is that we’re letting in the wrongkind of students; that “they” (whoeverthey are) want to turn “us” into Harvard;and that “young people” just aren’t goodwith their hands any more. My sense is



that all of those explanations are fueledby deeper biases, concerns, and perhapseven longings.

While a fair amount of research hasexamined the role of extrinsic rewards inthe academy as motivators for facultybehavior, there seems to be less thatexplores both the intrinsic rewards thatcontribute to, as well as the anxieties thatkeep faculty from, changing. I would liketo suggest that an exploration of bothcould be one of the focuses of the FacultyRewards group. i

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Research and Undergraduate Teaching.Syracuse, N. Y.: Center forInstructional Development, SyracuseUniversity.

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Wilson, R. & Graff, J. G. (1971). “FacultyValues and Improving Teaching.”In G. K. Smith, ed., New Teaching, NewLearning. San Francisco: Jossey-Bass.

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Austin, A. E. (1996). “Institutional andDepartmental Cultures: TheRelationship Between Teaching andResearch.” New Directions forInstitutional Research, 90, 57-66.

Boyer, E. (1990). Scholarship Reconsidered:Priorities of the Professoriate.Princeton, N. J.: Carnegie Foundationfor the Advancement of Teaching.

Boyer, E. (1996). “From ScholarshipReconsidered to Scholarship Assessed.”Quest, 48(2), 129-139.

Brawer, C. E., et al. (2002). “A Survey ofFaculty Teaching Practices andInvolvement in Faculty DevelopmentActivities.” Journal of EngineeringEducation, 91(4), 393-396.

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Diamond, R. M. (2002). “DefiningScholarship for the Twenty-FirstCentury.” New Directions for Teachingand Learning, 90, 73-79.

Frayer, D. A. (1999). “Creating a CampusCulture to Support a Teaching andLearning Revolution.” CAUSE/EFFECT, 22(2), 10-17.

Frost, S. H. & Teoodorescu, D. (2001).“Teaching Excellence: How FacultyGuided Change at a ResearchUniversity.” Review of HigherEducation, 24(4), 397-415.

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Kreber, C. (2002). “Teaching Excellence,Teaching Expertise, and theScholarship of Teaching.” InnovativeHigher Education, 27(1), 5-23.



Leslie, D. W. (2002). “Resolving theDispute: Teaching Is Academe’s CoreValue.” Journal of Higher Education,73(1), 49-73.

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Mentoring is critical to thepersonal and professional devel-opment of students in science

and engineering, and mentoring is a keyavenue by which formal and informalknowledge about a discipline and itspractices are passed from one profes-sional generation to the next.Nevertheless, the social dynamics ofmentoring in academic contexts are notoften analyzed or are studied only insuperficial ways.

Research on mentoring in academicsettings typically has focused on thegraduate level and on processes by whichresearch skills and socialization forresearch-oriented careers are trans-mitted. Pedagogical skills and content ofcurriculum also are transmitted viamentoring, although the degree to whichpedagogy is formally addressed variesconsiderably in mentoring relationships.The potential of mentoring for curricularand organizational transformationlikewise has rarely been explored. Socialscientific research on mentoring hasexplored access to mentoring by aspirantsof diverse backgrounds (see e.g, Spalter-Roth and Lee 2000), quality and effec-tiveness of mentoring relationships(Sandler 1993), the impact of mentoringon career growth and development (Long1990) and problematic aspect ofmentoring relationships in academia andorganizations (Eby et al. 2004).

Although researchers have conceptu-alized mentoring in various ways inresearch, the most typical focus is on one-to-one personal mentoring, usuallyexplored from the perspective of theprotege. Some research has examinedother forms of mentoring, such as peermentoring or telementoring (Marasco2005). The most prominent, oftenimplicit, model of mentoring has typicallybeen one of a senior, knowledgeable andcharitable guide who shares his or herwisdom with one or more new recruits.Viewed in this way, mentoring has a“conservative” function of preserving andtransmitting values and practices reveredwithin a discipline or a profession to newrecruits who are selected, at least in part,based on their similarity to those alreadyat the helm. But it is possible to envisionmentoring in a more complex mannerand as a context that provides opportu-nities for organizational transformationand not simply imitation and replication.Mentoring has the potential to supportprotégés who bring to the discipline newand distinctive insights, learning styles,and approaches to pedagogy (Herzig2004).

In this memorandum, I first explorethe context of mentoring in sciences andengineering, drawing contrasts betweenscience and engineering disciplines andsocial science disciplines. I then addressthe role of mentoring as it affects diverse



Mentoring as a Context for Transformation inScience and EngineeringLinda Grant University of Georgia

(Revised, June 2006)

groups of aspirants to scientific andengineering careers, considering theimpact of mentoring both at theindividual and the organization level.Finally, I suggest ways in whichmentoring practices at the individual andorganizational level might be used aspositive forces for transformation,including transformation of pedagogy andcurriculum.

MENTORING AS A CONSERVATIVE FORCE IN SCIENCE AND ENGINEERING? Within the context of sciences, mentoringfrequently takes on a conservative castwherein successful mentors andmentoring organizations pass on codifiedknowledge and social capital to newgenerations of recruits. The goal often isreplication, or socializing aspirants toadopt values, outlooks, and professionalpractices that closely approximate thoseof the more-senior mentors. The fact thatmentoring usually is linked tosponsorship and financial support inscience and engineering educationincreases the likelihood that mentoringencourages replication rather than trans-formation (Grant and Ward 2004; Rosser2000; 2004).

Recruits to scientific and engineeringprofessions are carefully screened prior toentry so that they share common experi-ences and perspectives even before theystart their formal disciplinary education.Entry into engineering programs at theundergraduate is highly competitive. Incomparison to applicants entering manyother fields, engineering students areexpected to have pres-existing skills beforethey are considered viable candidates forentry. In graduate programs in science

and engineering, aspirants may beadmitted only if a senior faculty memberagrees to take them on, and frequentlyalso fund them, through their education.Since admission decisions are madebefore aspirants have opportunities todemonstrate their ability, these decisionscan be influenced by network ties (e.g.,recommendations of well-known personsin the field) and social capital (e.g.,reputations of schools that recruits previ-ously attended). Engineering curriculaand programs tend to segregate studentsfrom same-age peers in a distinctive set ofcoursework. High selectivity, immersionin specialized curricula, and separationfrom age-peers in other fields tends tointensify the influence of the organi-zation (in this case the university collegeor department) on not only the profes-sional socialization, but also the identity,of the aspirant (Wheeler 1966).

Social scientists have argued thatnormative structures of scientific andengineering training programs and earlyselection points lead, albeit often uninten-tionally, to homophily, or selection biastoward those whose biographies lookmuch like one’s own (Epstein 1970). Suchpractices may accentuate the importanceof early, but in the long-run inconse-quential, differences among aspirants ofdifferent status characteristics in theprofession. For example, example, boys’greater pre-college experience withcomputers and greater familiarity with“hacker culture” may provide advantagesto them as entrants into engineeringschool, even though such activities arenot very consequential for success(Margolis, Fisher and Miller 2000).Similarly, immersion in the “tinkering



culture” of engineering, more commonamong men than women, may advantagemen over women early in theirengineering careers, even though theseactivities are not directly relevant to mostcontemporary engineering jobs (McIlweeand Robinson 1992). Students who “fit themold” may have an easier time findingmentors as undergraduates and gainingrecognition for their accomplishments.They may be recommended for toppostgraduate positions, or urged morestrongly than others to go on to graduateschool.

At the level of graduate education, oneforms a relationship with a mentor earlyon and remains dependent on him or herfor sponsorship and financial support, therelationship may be biased toward repli-cation, rather than innovation. Personswho don’t fit well with the the disciplineare apt to be screened out initially. Thosewho deviate later may either be chastisedand brought back into line, or they maylose sponsorship and drop out of the fieldaltogether. These social dynamics may beparticularly disadvantageous for groupsthat have not been traditional recruits toscience and engineering, such as womenand minorities. They may be deviants intwo respects: they don’t fit the traditionalimage of an engineer in their persona,and/or they may hold different, noncom-formist perspectives about what the goalsof engineering and science should be(Grant and Ward 2004; Rosser 1990;2004). Once admitted to programs,students are immersed in “little societies”where certain types of norms andpractices are reinforced, while noncon-forming practices are discouraged ormarginalized (Fox 2000).

At the graduate level, dissertations inscience and engineering usually areclosely tied to the interests of the mentorand may even be assigned by mentors..The protégés’ need for financial supportand access to resources, such asspecialized lab equipment, reinforcesdependency on the mentor. Science andengineering research requires interde-pendence (Fox and Colatrella 2006), andlarge, usually hierarchically organized,research teams. Even when senior facultyare open to innovation and inclusiveness,day-to-day supervision of work may bedone by postdoctoral associates or more-senior graduate students who have littletraining for teaching or for under-standing the needs and learning styles ofa diverse set of more junior scholars.Therefore, protégés may not be exposed tomodels of effective teaching, nor havetheir distinctive orientations appreciated.Furthermore, teaching and mentoringtypically are under-recognized and under-rewarded in academic institutions,providing little incentive for scholars toimprove their abilities in these domains.

This model of mentoring andsponsorship has substantial overlap withmentoring in social science disciplines,but there are important differences.Social sciences less often require explicitsponsorship or funding linked to aparticular project. Instead, most graduateprograms encourage students to take abroad range of courses and work withseveral faculty members before choosinga mentor. If a mentoring relationshipproves unsatisfactory, students generallyhave options to seek other mentors (Grantand Ward 2004). Students also areexpected to propose their own disser-



tation topics, rather than to work on aproject assigned by a supervisor. Undersuch a system, aspirants are lessdependent on mentors and replication ofthe mentor’s interests and practices isless important. Mentors may be fair orunfair, effective or ineffective undereither system, but the consequences for aprotege may be greater in the sciencesand engineering model becausedependency on the mentor is greater andintellectual autonomy is diminished.Furthermore, exposure to multiplefaculty maximize opportunities forstudents to learn professional practicesfrom various people, including some whoare strong in pedagogy and curriculumdevelopment. Some graduate programs insocial sciences even have built-inteaching mentorships (for example, theSociology graduate program at NorthCarolina State University), and manydepartments and professional organiza-tions such as the American SociologicalAssociation sponsor active programs todevelop pedagogical skills and to shareinnovative curricula.

MENTORING, DIVERSITY, ANDORGANIZATIONAL CLIMATES Although all aspirants to scientific andengineering careers benefit fromeffective mentoring, substantial researchsuggests that mentoring is stratified, withsome groups of aspirants likely to receivemuch more effective mentoring thanothers (Grant et al. 2004; Spalter-Roth andLee 2000). Women and persons of colorusually are disadvantaged in mentoringrelationships, a factor that has beenlinked to their disproportionate attritionfrom the science and engineering

pipeline (Jordan 2006). These disadvan-tages rarely result from overt bias, butreflect more subtle ones, such as amentor’s inability to see a nontraditionalaspirant as a successor to himself,concerns that family commitments maylimit the potential of women aspirants, orworries that mentoring relationshipsmight be viewed as inappropriateromantic liaisons (Epstein 1970; Long1990). Although there is evidence thatwomen and persons of color may benefitfrom mentoring by persons of their“type” in fields where they are scarce(Eby et al. 2004), such expectations notonly overburden the relatively fewwomen and minority faculty but alsoabsolve other faculty from responsibilityfor mentoring under-represented groups.Since women and persons of color stillare scarce in most fields of science andengineering, the reality is that white menwill continue to do much of thementoring of nontraditional students(Etzkovitz, Kremelor and Uzzi 2000;Sandler 1991). Research has suggestedthat women find effective men mentorsin STEM disciplines, often by identifyingspecific men who are open to workingwith women (Bix 2006).

When mentoring is conceived of as acollective, rather than purely anindividual, activity, mentoring potentiallycan be transformative of organizationalclimates in academia and workplaces thatcan enhance diversity and positivechange. A classic study by McIlwee andRobinson (1992) demonstrated howeveryday experiences in engineeringschools and workplaces reinforcewomen’s marginality in the profession.More recently, Fox (2000) has



documented how everyday practices inscience and engineering graduateprograms can impede progress of women.Her work also identifies characteristics ofprograms with more favorablecompletion rates for women. Among theirbeneficial practices are effective responseto harassment faced by women, writtenguidelines for assessing performance, andsensitivity to the distinctive needs ofwomen protégés. Other work hassuggested that modifications in pedagogyand curriculum, along with effective andinclusive mentoring, can also help tostem greater attrition of under-repre-sented groups (Etzkovitz et al. 2000).Although individual mentors can beuseful in assisting aspirants in dealingwith less hospitable environments byhelping protégés devise coping strategies,tranforming such environments requirescollective mentoring. Some institutions,most notably MIT, have undertaken insti-tution-wide reforms not only to improveequity for individual women scientistsand engineers but also to enhancelearning environments to support thedevelopment of diverse students andfaculty (Hopkins 1999).

Mentoring also has a broader focusthan the learning of substantive contentand technical skills. Proteges also learnstyles of pegagody and what constitutesan appropriate curriculum throughmentoring. Thus, mentoring provides acontext for the replication of traditionalpractices, including the often-alienating“chalk and talk” style of deliveringengineering and science education that iscredited with driving both women andmen from these majors (Dingel 2006).But transformative mentoring could

provide a context in which creative andinnovation in pedagogy and curriculumare encouraged instead.

COLLECTIVE MENTORING FORTRANSFORMATIONAlthough mentoring often works toreplicate practices within organizations,it can when seen as a collective responsi-bility be transformative. Working collabo-ratively, mentors can function as icebreakers in organizations, making themmore receptive to nontraditional recruitsand to new ideas (Sandler 1993). Suchcollective mentoring can engage allorganizational members in dialogs aboutsocial dynamics of programs and conse-quences for learners. It can lead tobroader approaches to curriculum andclimate change, identification andremediation of subtle barriers affectingsome proteges, and broad disseminationof good mentoring practices throughoutthe organization. With such collabo-ration, a scholarship of mentoring, paral-leling the scholarship of teaching, candevelop, and effective mentors can berecognized and rewarded for thisimportant work. Institutions can be pro-active in enhancing the effectiveness ofmentoring, especially the mentoring ofstudents from under-represented groups.I offer a few possibilities for startingpoints for further discussion.

1. Mentoring should be a component ofprofessional development for faculty. Itonce was assumed that anyone with aPh.D. could teach. Although somefaculty may have a natural talent forteaching, it is now widely recognizedthat teaching skills of most can be



developed through systematiceducation, feedback, evaluation, andreward. The same is true formentoring. In research my colleaguesand I have conducted, scientists oftentalk about learning how to mentor orlearning to become more effectivementors for students of differing socialbackgrounds from themselves.Sometimes these are scientists whohad negative experiences as protégésin past mentoring relationships. Theywant to do better for their students,but they lack information about how todo so. Organizations might identifyscientists and engineers who exhibitstrong mentoring skills and use themas a resource in disseminating goodmentoring practices. Effectiveprograms developed at other institu-tions should be emulated.

2. Mentoring should be built into theevaluation and reward systems forindividual faculty. Too often goodmentoring is taken for granted andappropriated by an organization, withinsufficient recognition of thementor’s expertise and with little or noreward for good mentoring. Thoseinvesting heavily in mentoring mightbe penalized indirectly by having toolittle time to devote to those activitiesthat are rewarded by academic institu-tions and workplaces, such as researchand grant-writing. Or anotherundesirable outcome might be undueexploitation of students withoutallocating fair credit for their work. Yetsome programs have recognized andrewarded mentoring as real work (e.g.,the MIT program). Rewarding

mentoring in formal ways also sends amessage that it is an activity thatorganizations take seriously.Assessing effective mentoring willpresent significant challenges, as ourresearch has suggested that it is easierto recognize good mentoring from aprotégé than a mentor’s perspective.Often mentors do not know, in anysystematic way, what type of impactthey are having on aspirants. And asSandler (1993) has pointed out, manyscholars under-rate their efforts asmentors, or even shun taking onmentoring responsibilities altogether,because they fear that they cannot doeverything for every student who asks.Assessment tools needs to be cognizantof the fact that students likely benefitfrom contacts with multiple mentors,each of whom has something differentto offer. Envisioning mentoring as acollective responsibility of an organi-zation is a first step toward resolvingsome of these dilemmas.

3. Organizations should provide someoversight of adviser-advisee relation-ships and should be prepared tointervene when relationships are notsuccessful. As Fox (2000) has pointedout, this relationship is critical tosuccess in scientific and engineeringcareers, yet it is highly privatized. Inthe strong dependency relationshipscharacteristic of engineering andscience that I have described above,this may leave proteges vulnerable ifthey encounter difficulties inmentoring relationships. There aredecided advantages to maintainingflexibility in mentoring relationships,



but such relationships neverthelessmight be improved by collective state-ments of what represents “goodpractices” as well as sufficientmonitoring and supervision to protectproteges in very negative situations.Some oversight by departmental andcollege administrators is needed, sothat students have options if theyencounter serious problems in situa-tions of unequal power relationships.

4. Organizations should make commit-ments to the systematic analysis ofclimates of programs in engineeringand in science. Such a commitmentwould involve both ongoing,systematic assessment of organiza-tional climates from the perspective ofall members and identification anddissemination of good mentoringpractices. Organizations should be pro-active in fostering positive climateswhere productive mentoring relation-ships can flourish, rather than simplyreactive when serious problems occur.Systematic assessment of organiza-tional climates can address problemsas they arise, and relieve the relativelypowerless from the responsibility ofbringing problems to the attention ofsupervisors and administrators.Interdisciplinary collaborationsbetween engineers and social scientistswho have studied educational andworkplace climates can be valuablemeans to achieve this goal. i

REFERENCES

Bix, Amy Sue. 2006. “From Engineeress toGirl Engineers to Good Engineers: AHistory of Women’s U.S. EngineeringEducation.” Pp. 46-68 in RemovingBarriers: Women in Academic Science,Technology, Engineering, andMathematics, ed. Jill M. Bystydzienskiand Sharon R. Bird. Bloomington:Indiana University Press.

Dingel, Molly J. 2006. “GenderedExperiences in the ScienceClassroom,” Pp. 161-76 in RemovingBarrier: Women in Academic Science,Technology, Engineering, andMathematics, ed. Jill M. Bystydzienskiand Sharon R. Bird. Bloomington:Indiana University Press.

Eby, Lillian, Marcus Butts, AngieLockwood, and Shana A. Simon. 2004.“Proteges’ Negative MentoringExperiences.” Personnel Psychology 57(4): 456-479.

Epstein, Cynthia F. 1970. Woman’s Place:Options and Limits on ProfessionalCareers. Berkeley: University ofCalifornia Press.

Etzkowitz, Henry, Carol Kemelgor, andBrian Uzzi. 2000. Athena Unbound: TheAdvancement of Women in Science andTechnology. New York: CambridgeUniversity Press.



Fox, Mary Frank. 2000. “OrganizationalEnvironments and Doctoral DegreesAwarded to Women in Science and Engi-neering Departments.” Women’s StudiesInternational Quarterly 23: 47-61.

Fox, Mary Frank and Carol Colatrella.2006. “Participation, Performance, andAdvancement of Women in AcademicScience and Engineering: What is atIssue and Why?” Journal of TechnologyTransfer 31: 337-86.

Herzig, Abbe. 2006. “How Can Womenand Students of Color Come to Belongin Graduate Mathematics?” Pp. 254-70in Removing Barriers: Women inAcademic Science, Tehcnology,Engineering and Mathematics, ed. JullM. Bystydzienski and Sharon R. Bird.Bloomington: Indiana University Press.

Grant, Linda and Kathryn B. Ward. 2004.The Structure of Academic ScienceCareers and the Progress of Women.Paper presented at the 2004 AmericanSociological Association meeting, SanFrancisco.

Hopkins, Nancy. 1999. “MIT and Gender Bias:Following Up on a Victory.” Chronicle ofHigher Education, June 11, B4.

Jordan, Diann. 2006. “Introduction,” Pp.1-26 in her Sisters in Science:Conversations with Black WomenScientists about Race, Gender, and theirPassion for Science. W. Lafayette, IN.:Purdue University Press.

Long, J. Scott. 1990. “The Origins of SexDifferences in Science.” Social Forces68: 1297-1316.

Marasco, Corine A. 2005. “MentornetSupports Women in Science.”Chemical and Engineering News, 83(20): 55-61.

McIlwee, Judith S. and J. GreggRobinson. 1992. Women in Engineering:Gender, Power, and Workplace Culture.Albany: State University of New YorkPress.

Margolis, Jane; Allan Fisher and FayeMiller. 2000. “The Anatomy of Interest:Women in Undergraduate ComputerScience.” Women’s StudiesInternational Forum 23: 104-127.

Rayman, Paula and Belle Brett. 1993.Pathways for Women in the Sciences.Wellesley, Ma.: Wellesley CollegeCenter for Research on Women.

Rosser, Sue V. 1990. Female-friendlyScience: Applying Women’s StudiesMethods and Theories to AttractStudents. New York: Pergamon.

Rosser, Sue V. 2004. The Science GlassCeiling: Academic Women Scientists andthe Struggle to Succeed. New York:Routledge.

Sandler, Bernice R. 1991. “Mentoring:Myths and Realities, Dangers andResponsibilities.” MentorNET,http://www.mentornet.net/community/resources/readings/aspx.



Sandler, Bernice R. 1993. “Women asMentors: Myths and Commandments.”Chronicle of Higher Education, issue27, March 10, 1993, p. B3.

Spalter-Roth, Roberta and Sunhwa Lee.2002. “Gender in the Early Stages ofthe Sociological Career.” AmericanSociological AssociationResearch Brief,v. 1, # 2.

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It is no surprise to an academicaudience that universities areperceived by their faculties as “greedy

institutions.” This term, from the work ofLewis Coser, describes organizations that“seek exclusive and undivided loyalty,and … attempt to reduce the claims ofcompeting roles and status positions theywish to encompass within their bound-aries” (1974, p. 4). As Wright et al. argued(2004) the pressure on faculty hasincreased in recent years, stemming fromchanging expectations about teaching andresearch across all institutional types.Higher education has become increas-ingly greedy. In this review, I identifyseveral ideas connected to the “greedyinstitution” perspective, and from whichto draw future research questions.

INSTITUTIONS VARYAs obvious as this statement may be, thereis too often a “one size fits all” tone todiscourse about academic life; further,the realities of non-”Research I” institu-tions are often invisible to national disci-plinary leaders. It is essential to decon-struct “higher education,” attending tovariations along dimensions of institu-tional culture, mission, type, and size.Wright and her colleagues used research,comprehensive, and teaching as basictypes, but suggested that these be seen ascontinua, with many institutions strivingto be seen as moving along one or more at

any time. Local campaigns for changeshould be tailored with local conditionsin mind (Hurtado, 1994).

Institutional greed also varies histori-cally; its focus and extent are dynamic.The ways in which faculty are expected tobe productive change with external andorganizational trends. In research andcomprehensive institutions, there is aresurgence of interest in teaching. Thisstems from marketing pressures (and inpublic institutions, from legislativeinterest). Particularly with increasedtuition costs, criticisms of the studentexperience have led to a growingrhetorical emphasis on teaching.Competent undergraduate teaching isnow required, but excellence in teachingdoes not substitute for a strong researchrecord in personnel decisions. In a multi-disciplinary study, Milem et al. (2000)found that while faculty time spent inteaching related activities had risen, timespent on scholarship had not declined tocompensate for this increased work load.

ENGINEERING SCHOOLS AND DEPARTMENTS:PARTICULAR FORCES AT WORKA serious decrease in U.S. students’interest in engineering has been exacer-bated by the post 9/11 decrease in foreignstudent enrollments. Thus, considerationof curricular innovation is made in acontext of competition for students amongother institutions and other fields of study.



Greedy Institutions and Faculty Involvement in RetentionLaura Kramer Montclair State University

Changing ABET requirements (e.g.,ethics; writing requirements) haveconstrained program planning. Althoughmany faculty believe that formalizing afive year bachelor’s program would bestserve their students, the marketing conse-quences are seen as dire. Furthermore,for engineering faculty curricularchanges is a particularly delicate matter,because many engineering courses arepart of a tightly-coupled sequence, andchanges in coverage will likely becomepublic knowledge and may have practicalconsequences for those teaching othercourses in the sequence.

Universities have long relied onengineering programs to produce incomefrom corporate and governmentalsources. Diverting faculty attention fromactivities that bring in funds to workingon curricular and pedagogical initiatives(and then carrying them out) mightendanger the production of income todepartments, colleges, and universities(Hackett, 1990; Hearn, 1992). Deans willhave to fight to make these changes withtheir superiors, in light of the long-standing reliance on engineeringprograms to produce income fromcorporate and governmental sources.

Despite their business-suited, clean-shaven, “pocket protector” caricature,engineering professors are not somonolithic nor so different from non-engineering faculty. At the same time,there are important differences amongengineering disciplines, often unfamiliarto social scientists. For example, manymechanical or civil engineers dismissindustrial engineering’s greater successin recruiting women by saying that it isnot really engineering. Even among

engineering programs at institutions thatare of a similar size and sector, signif-icant differences in experience may berelated to region.

RESEARCH IS PARAMOUNTAcademic personnel decisions are widelyperceived (within and outsideengineering) to rest more on scholarlythan on teaching achievements. Facultyperceptions of institutional demands andrewards were a major focus of my owninterview study of engineering faculty atfive campuses, conducted in the 1990s(Kramer 2005). While junior facultyvoiced a strong commitment to teaching,they saw time devoted to teaching asundermining chances for career success;middle level faculty also saw scholarshipand research achievements continuing toovershadow teaching in personneldecisions. This view was shared by moresenior colleagues and those in servicedepartments. Further, people perceivedthat articles, grants, or awards related toteaching and/or advising meant little ornothing for career success. Whetherresearch or teaching expectations wereseen as on the rise, there was noperception that other expectations werebeing lowered.

Many were skeptical about adminis-trative sincerity in the absence ofmaterial support for curricular andpedagogical change, and in the face ofunchanging expectations of high researchproductivity. Even if the local norms wereperceived to be supportive of teachingand service, some faculty were concernedabout the views of future administrators,or institutions to which they might wantto move. Of course, outside offers may be



seen as necessary in order to improveone’s compensation.

Departments – the crucial site ofprofessional life

Except in very small institutions,departments are the key unit of organiza-tional culture and practice; this isgenerally true throughout academia(Clark 2004; Lee 2004). The departmentis at the intersection of the discipline andthe institution – and departments vary inthe relative influence of each upon them(Lee forthcoming). Departmentalcultures often differ in the same insti-tution. Indeed, in larger institutionsfaculty are sometimes unaware of theextent to which they differ from theircolleagues. There is often a formally orinformally established faculty division oflabor, with a small proportion of thefaculty viewed as knowledgeable aboutand involved in pedagogical, curricular,and retention issues. All others need notbe concerned with these areas (Stassen1995).

Departments influence the perceptionof the kinds of greed of the larger insti-tution (as well as adding their own); theyare critical filters of information. Thus, arelatively innovative member of a tradi-tional department might be unaware ofinnovations that were commonplace inanother department. In some depart-ments, faculty who devoted time to under-graduate teaching or curricularinnovation were suspected of inadequateinvolvement in research. This view oftendiscouraged public discussion aboutteaching by junior faculty. Some seniorfaculty did not realize the seriousnesswith which junior colleagues heard suchcomments (Kramer 2005).

Organizational and departmental loyalties(for example, concern about resourcereallocation) also influence facultyperspectives on curricular change.Serious competition for chunks of thestudent’s academic program, and inter-departmental competition for studentmajors may discourage a departmentfrom making changes, or encourage it todo so. Generally, a broad review ofcurricula is a serious concern for thosewith an existing piece of requirementswho may fear losing that source ofstudents in certain courses. Fear of losingwhen the “can of worms” is opened maymotivate faculty in at-risk departments toavoid curricular review.

Greed, faculty from nontraditionalgroups, and service demandsFaculty from underrepresented groupshave exceptional duties in undervaluedareas (committee work, recruiting,advising or mentoring of nontraditionalstudents (Olson, Maple and Stage 1995;Turner and Myers 2000). The depart-mental demography is obviously signif-icant: the fewer nontraditional faculty,the greater the burden on each individualfor committee service. Where there arefew nontraditional faculty but a higherproportion of students from those nontra-ditional groups, the burden is alsoheavier.

In addition to greater servicedemands on underrepresented faculty,equivalent performance may be differen-tially evaluated depending on expecta-tions held about members of differentgroups. For example, a woman professormay be expected to serve as the emotionalexpert when there are tensions betweenthe support staff and faculty — failure to



do so may be interpreted as lack ofservice, while a man is not judged asuncooperative for standing back fromintradepartmental conflict. Performanceof other role demands (research andteaching) may also be differently noticedand evaluated because of membership inan underrepresented group. Many of theinitiatives taken by various NSF-ADVANCEinstitutions aim to reduce or guardagainst such inequitable demands andevaluation.

Sociologists of work have found thatinequity in task assignments is reducedwhen they are systematized, with theassignments and processes made trans-parent (e.g., Britton 2003). Similarly,when performance evaluation is system-atized, there is some reduction ininequity. Transparency is important, buttraditionally lacking in faculty personneldecision making processes.

HELPING NEW FACULTY COPE WITH GREEDY INSTITUTIONSIt is essential to study the variety ofresources available to faculty at all stagesof the career, and the ways in whichallocation is tied to performance ofteaching, advising and curricular respon-sibilities. As part of such research, Ipropose the exploration of the newfaculty activities offered at a sample ofuniversities and to investigate the ways inwhich the engineering faculty inparticular are involved in these activities. Many institutions now offer some form ofnew faculty activities, ranging from oneday orientations in which people arebombarded with information abouthealth coverage and pension plans, andlibrary tours to semester-long weekly or

biweekly meetings in which faculty areable to get to know members of theircohort from across the campus whilebeing introduced (in more absorbable-sized and spaced experiences) to variousaspect of campus life, including infra-structure supporting research activityand pedagogical development, as well asinformation about the personnelpractices and processes of the institution. While new faculty on a campus(especially those who are not new tofaculty life) may experience the timeinvolved as another dimension of institu-tional greed, programs for faculty devel-opment (especially those aimed at peoplein their first year) are promising routes tocoming to terms with the realities that Ihave summarized in the previous pages:

• Institutions vary – and people comingfrom elsewhere (whether fromgraduate study, a postgraduatefellowship, or a teaching position) canlearn more effectively the character-istics of their new academic home insomething more organized thaninformal dependence on colleagueswithin the department. Inengineering, faculty may come fromindustry, and so variations over timeare also pertinent to their orientationto their new setting.

• Engineering is different, but…As siteswhere people socialize across disci-plinary lines, new faculty programsprovide exposure to colleagues withwhom one might otherwise assumeone had nothing in common. Thismeans new faculty programs mightspeed the diffusion of pedagogicalinformation, might increase respect



for other disciplines’ insights, andcould, for engineers, provide someinformal relationships that would facil-itate learning about the social scien-tific materials we are hoping willbecome more integral to the under-graduate engineering curriculum. Onenew faculty member may tell anotherabout a senior colleague whoseinterests intersect — networking fromthe bottom up.

• Research is paramount and new facultyprograms may save faculty memberstime by providing more informationmore efficiently than otherwise. Forexample, web sites may not beupdated; departmental colleagues maynot be familiar with some develop-ments on campus that are presented tothe new cohort; information that iscommon knowledge in onedepartment, but not another, can beshared interdepartmentally bymembers of the cohort. What organiza-tional arrangements are most effectivefor reducing or eliminating inequity?These should be investigated withinstitutional variation in mind.

• Departments are the crucial site ofprofessional life: with a high degree ofdepartmental cultural variability, newfaculty are likely to find a campuswide program a useful source ofinsight into alternatives that theirimmediate (departmental) colleaguesdo not offer. Again, new faculty maygain from sharing the informationthey bring from their home depart-ments as much as they gain frompresenters at sessions.

• Faculty from underrepresented groups,like all new faculty, may be the only

new members of their home department;in addition, they may be the solemember of their demographic group.By meeting one another acrosscampus, new faculty learn aboutdepartmental practices of protectingjunior faculty from undue servicedemands, and provide a sense of thenormal service expectations whichmay give some strength to those whoare being disproportionately called onfor service.

Generally, participants in a new facultyprogram may share information abouthandling greed: which demands can beacknowledged but not quite fulfilled,which meetings can be skipped, whichcommittees are particularly difficult,and which more senior colleagues,across campus, may be good sources ofadvice and support. In addition to thislateral communication, new facultyprograms can help improve communica-tions both upward and downward in theacademic organization. Based oninformal relationships and conversa-tions, the individuals who organize andrun the programs may becomeimportant sources of information for theadministration about faculty concernsthat might not otherwise be heard.

Data could be collected, for example,with telephone interviews of adminis-trators charged with faculty developmentactivities for new faculty (in the Provost’sand/or the Engineering Dean’s offices).This would produce a description of thevarieties of activities being used at asample of universities with largeengineering programs. If this focus isincorporated into a broader study of



engineering institutions, the descriptivedata about new faculty programs couldprofitably be correlated with data aboutinstitutions, departments, and/orindividual faculty members. For example,we could explore the relationshipbetween the kinds of programs found onthese campuses with usage patterns offaculty resources (such as centers ofteaching and learning), and with data onfaculty turnover patterns in the pre-tenure years. i

REFERENCES

Britton, Dana M. 2003. At Work in the IronCage. New York: New York UniversityPress.

Clark, Burton R. 2004. “TheOrganizational Foundations ofUniversity Capability.” Pp. 168-187 inSelf, Social Structure and Beliefs, editedby Jeffrey C. Alexander, Gary T. Marx,and Christine L. Williams. Berkeley:University of California Press.

Coser, Lewis A. 1974. Greedy Institutions:Patterns of Undivided Commitment.New York: The Free Press.

Hackett, E.J., “Science as a Vocation inthe 1990s,” Journal of HigherEducation, vol. 61, 1990, pp. 241-279.

Hearn, J.C., “The Teaching Role ofContemporary American HigherEducation,” in W.E. Becker and D.R.Lewis, The Economics of AmericanHigher Education, Boston: Kluwer,1992

Hurtado, Sylvia. 1994. “The InstitutionalClimate for Talented Latino Students,”Research in Higher Education, vol. 35,1994, pp. 21-42.

Kramer, Laura. 2005. “Explaining FacultyInvolvement in Women’s Retention.”Proceedings of the 2005 AmericanSociety for Engineering EducationAnnual Conference & Exposition.Washington, D.C.: American Societyfor Engineering Education.

Lee, Jenny J. 2004. “ComparingInstitutional Relationships withAcademic Departments: A Study ofFive Academic Fields.” Research inHigher Education 45: 603-624.

_____. Forthcoming. “The Shaping of theDepartmental Culture: Measuring theRelative Influences of the Institutionand Discipline.” Journal of HigherEducation Policy and Management.

Milem, J.F., Berger, J.B. and E.L. Dey,“Faculty Time Allocation: a Study ofChange Over Twenty Years,” Journal ofHigher Education, vol. 71, 2000, pp.454-475.

Olsen, D., S. A. Maple, and F.K. Stage.,“Women and Minority Faculty JobSatisfaction,” Journal of HigherEducation vol. 66, 1995, pp. 267-293.

Seldin, Peter. 1990. “AcademicEnvironments and TeachingEffectiveness.” Pp. 3-22 in HowAdministrators Can Improve Teaching,edited by Peter Seldin. San Francisco:Jossey-Bass.



Stassen, M. L.A., “White Faculty Membersand Racial Diversity: a Theory and ItsImplications,” Review of HigherEducation, vol. 18, 1995, pp. 361-391.

Umbach, P.D. and S.R. Porter. 2002. “HowDo Academic Departments ImpactStudent Satisfaction? Understandingthe Contextual Effects ofDepartments,” Research in HigherEducation 43: 209-234.

Turner, Caroline Sotello Viernes, andSamuel L. Myers, Jr. 2000. Faculty ofColor in Academe. Boston: Allyn &Bacon.

Wright, Mary C., Nandini Assar, EdwardL. Kain, Laura Kramer, Carla B.Howery, Kathleen McKinney, BeckyGlass, and Maxine Atkinson. 2004.“Greedy Institutions: The Importanceof Institutional Context for Teaching inHigher Education.” Teaching Sociology32: 144-159.

ACKNOWLEDGMENTSI had useful comments from Lisa Frehill,Laurel Smith-Doerr, and Mary Wright as Ithought about this paper.



For at least the past 50 years,engineering education has empha-sized scientific fundamentals and

mathematical analysis. More recently,however, responding to new technologicaldevelopments and to the demands ofemployers, proposals have emerged for a“New Engineering Curriculum” whichmoves beyond the traditional emphasis onbasic science and math. These proposalscriticize existing engineering curriculafor encouraging narrowness and special-ization and for producing engineers whoare ill-equipped to solve practicalproblems, to work in multi-disciplinarygroups and to communicate. The newcurriculum is designed, among otherthings, to encourage the development ofmore broadly trained engineers with asense of the overall process, good commu-nication skills, the ability to solveproblems and the ability to work ingroups (Prados 1998).

While the new curriculum has gainedsupport from a wide variety ofconstituencies (from employers togranting agencies to major professionalorganizations), there are also significantsociological barriers to its implemen-tation. Curricula are not simply lessonplans; they are the core of the ways inwhich academic activities are organized.Modifying curricula, thus, has significantconsequences for the work lives ofengineering educators. Encouraging the

implementation of the new engineeringcurriculum in university engineeringprograms requires consideration of thesociological context for such a shift.One way to approach this question is tomake use of the conventional distinctionin the sociology of work between workvalues and job rewards. Sociologists ofwork note that people enter theworkplace with a set of values, thingsthey believe are important characteristicsof the work they would like. These workvalues guide people in choosing jobs andin thinking about the jobs they actuallyhave.

Jobs, meanwhile, offer particularrewards — these serve as motivators(whether by design or not) to getemployees to do the jobs in which theyfind themselves. In theory, one wouldexpect that the ideal situation is one inwhich there is a close fit betweenemployees’ work values and the rewardsthey find available to them on the job.However, Kalleberg (1977) notes that therelationship between the two is actuallydynamic — it’s important to know whatemployees’ values are, if for no otherreason than that you need to understandpeople’s orientation to work. But, mostpeople have multiple job values, and mostpeople are able to live happily in asituation in which only some of theirvalues are being “rewarded.” Moreover, itmay be that people modify their values in



Work Values, Job Rewards and the New Engineering CurriculumPeter Meiksins Cleveland State University

response to the rewards that areavailable, adjusting to the situation ratherthan rejecting it.

This paper considers the prospects forthe new engineering curriculum in thecontext of the work values of engineersand of the rewards available to academicengineers in the contemporary university.It analyzes, first, the extent to which theprofessional values of engineers arecompatible (or incompatible) with thenew curriculum. It then considers thequestion of whether changes in thecontemporary university are creatingopportunities for the development ofrewards promoting the new approach toengineering education or obstacles to itsimplementation.

ENGINEERS’ WORK VALUESSince an emphasis on the scientificcharacter of engineering, and on thecentrality of scientific research, hasbecome a core value of the engineeringprofession, it is worthwhile asking howthat came to be the case. Historians(Noble 1977; Calvert 1971; Layton 1986)teach us that, in fact, science has notalways occupied so dominant a placewithin the engineering “sense of self.”

For example, late 19th and early 20thcentury mechanical engineers disagreed,often quite strongly, over the place ofscientific training in engineeringeducation and over how engineers werebest prepared for work in industry. Onone side, there were the advocates ofwhat was sometimes called the “shopculture,” who believed that the best wayto train engineers was through a kind ofpractical apprenticeship in actual

machine shops. In this view, scientificprinciples and research-based knowledgetook a back seat to the acquisition ofexperience in practical settings and tolearning by doing. They feared thatneglecting this practical side of anengineers’ training would undercut thhe“art” of engineering and produceengineers whose ability to function inreal industrial settings would beimpaired.

They were opposed by a growing“school culture,” which argued thatengineers, as professionals, requiredtraining not alongside mechanics, but inthe growing universities of late 19th-century America. Apprenticing inmachine shops, from their point of view,harked back to the early days of the indus-trial revolution when technologicalprogress was led by mechanics and otheruntrained pioneers. Now, however,technological progress depended onacquiring scientific knowledge; engineersneeded to be more than sophisticatedmechanics. They required formaluniversity training to acquire the scien-tific knowledge that was essential tofuture technological development. In theend, it was the school culture’s vision thatshaped American approaches to theproduction of engineers in the 20thcentury.

Edwin T. Layton (1971), in a classicarticle, argues that at least three differentviews of what engineering knowledgeis/was competed in 19th and 20th-centuryAmerica. One view held that techno-logical progress was driven by science –basic scientific research was thefoundation on which “practical”advances in technology rested. Many of



the activities in which engineers engagedwere reduced, by this view, to “appliedscience,” a kind of parasitic growth onthe more prestigious activity of engagingin basic research.

A second, minority view held that thecore of engineering was “design,” thepurposive adaption of means to reach apreconceived end. This interpretation ofwhat engineers did weakened theconnection between engineering andscience — advances in design do notautomatically require advances in basicscientific knowledge. Scientific researchcould obviously aid design, but it was notwhat drove it. In some ways, this view ofengineering subordinated science to the“art” of design.

Finally, a third view held thatengineering was neither applied sciencenor something distinct from science. Itwas, instead “engineering science,” adiscipline within science that differedfrom disciplines oriented to basicresearch (such as physics) but that hadthe characteristics of science nevertheless(scientific methods, professional scientificorganizations, the accumulation ofknowledge based on empirical research,etc.) While this view produced consid-erable confusion among outsiders,according to Layton, it arguably becamethe dominant view within theengineering profession itself.

That it did should come as no realsurprise. Accepting the view that theywere merely “applied scientists” involvedaccepting a subordinate status mostengineers were unwilling to embrace.Defining themselves as “designers,”however, involved an occupationalidentity rooted in something almost

impossible to define and largely lackinglegitimacy among the broader public.Layton notes that 19th and 20th-centuryAmerican engineers were engaged in aneffort to achieve professional status, todefine a particular expertise that theyalone possessed and to translate thisclaim into enhanced social status (Layton1986; Larson 1977; Abbott 1988).Embracing an identity as a kind ofscience proved to be the most effectiveway to do this. In the end, science offeredthe strongest basis for the professionalgoals of engineers. They acquired theprestige and public approval accorded toscience in general in the 20th century.Extensive funding for scientific researchin the post-World War II period providedthe material underpinnings for thegrowth of professional knowledge. And,science’s emphasis on research and thepursuit of new knowledge fit well withthe broader research orientation of thepost-war American university in whichacademic engineers were located. Thecurricular implication of engineers’scientific identity are fairly obvious. Anemphasis on courses in scientific funda-mentals and mathematics followslogically, as does an emphasis on trainingresearchers and a tendency for research,rather than teaching, to become the mosthighly valued aspect of the academicengineers’ job.

Thus, the emphasis on engineering asscience, and on the importance of scien-tific research to academic engineering,has roots in American engineers’ effortsto achieve professional status. As such,efforts to shift engineering educationaway from the scientific model mustconsider the links between this model



and the status concerns of theengineering profession.It should be emphasized, however, thatengineers’ version of professionalideology is not the “pure” ivory towermodel sometimes seen as inherent inprofessionalism per se. Some sociologicalanalyses of professionalism (e.g., Raelin1991) argue, erroneously, that profession-alism as an ideology and a form ofoccupational organization involves anassertion of the occupation’s right toautonomy and self-regulation and a decla-ration of practitioners’ independencefrom commercial or other “non-profes-sional” concerns. Doctors’ altruisticstance and rejection of external controlsand/or academics’ emphasis on objec-tivity, peer review, and the “uncorrupted”pursuit of knowledge are what profession-alism is all about, from this perspective.While it may be that some occupationshave developed professional ideologiesalong these lines (and even for cases likemedicine, there are obvious questions onecan ask about the strength of altruistic oranti-commercial values), it is apparentthat many professions, includingengineering, develop professionalideologies that incorporate a ratherdifferent set of values. Thus, the allegedprofessional emphasis on autonomy andindependence of political or commercialinterests has not been an importantaspect of engineerings’ professional senseof self. Engineers have, in various ways,asserted their right to a place at the tableand insisted on the importance anddistinctiveness of their potential contri-bution to the solution of both technicaland social problems (Layton 1986). Theyalso have embraced science, its methods

and forms of professional organization.But, engineers have not gone beyond thisto an assertion of their exclusive right todeal with social or technical problems(Meiksins 2002). Theirs is not a techno-cratic ideology of engineering power; it is,rather, what has been called an “organi-zational professionalism (Larson 1977),compatible with working inside largeorganizations in collaboration with, andoften in subordination to, various kinds ofnon-engineers.Significantly, sociological research onengineers’ work values indicates thatengineers don’t object to working onprojects NOT of their own choosing;professional autonomy, in this sense atleast, is not at the core of their identity.Rather, engineers value most doing inter-esting, cutting-edge work – work thatmakes use of their abilities and talentsand allows them to grow. If this meansworking within corporations orgovernment agencies on projectsdeveloped and or directed by someoneelse (even non-engineers), so be it(Meiksins and Watson 1989; Watson andMeiksins 1991).

Engineering professional valuesrepresent a “practical” professionalismthat lives in the real world, not the ivorytower. While engineers embrace a scien-tific identity, this co-exists with apractical orientation to solving problemsin the real world. Thus, one can arguethat engineering’s values also containelements that encourage a focus on less“pure” scientific research and on thekinds of activities the new engineeringcurriculum seeks to encourage.

Engineering values are complex, notmonolithic, containing elements both



hostile to and consistent with the newengineering curriculum. Moreover, itshould be added that engineering facultyare not homogeneous, and have graduallybecome more diverse. Values are likely tovary across different social groups withinengineering. For example, it has beenargued that women in technical fields aremore likely to grow dissatisfied with theextreme specialization imposed byresearch-oriented careers (Preston 2004).All in all, an analysis of engineers’ workvalues suggests that things could go eitherway for the new engineering curriculum.It thus becomes crucial to consider whatrewards are being offered to academicengineers and whether changes in thereward structure create openings for orobstacles to curricular change.

JOB REWARDS AND ACADEMIC ENGINEERSAmong the more significant realities inthe contemporary context of academicengineering is the relative decline ofgovernment funding of basic scientificresearch (Slaughter and Rhoades 2004).There has been a well-documented end tothe post-war, government-funded researchboom that fuelled the expansion ofacademic science and engineering andunderwrote the scientific curricularmodels that are now being called intoquestion. Researchers can no longercount on the availability of largegovernment grants, or on the easyrenewal of grants they have obtained. Inprinciple, at least, one could predict thatthe limiting of basic research dollarsmight erode the basis for the scientificidentity of engineers. As access to fundingfor this kind of research became moredifficult, one might expect engineers (and

others) to redefine themselves and to beattracted to other kinds of activities forwhich more plentiful rewards wereavailable.

However, the increased difficulty ofobtaining research funding doesn’tautomatically result in shift away fromscientific research as traditionallydefined. Rather, what some observersargue has resulted is an increasinglyintense battle for the research dollars thatare available (Preston 2004). In otherwords, some academic scientists andengineers have responded to the scarcityof grants by working even harder, andbecoming increasingly focused andspecialized in narrow areas of expertise toenhance their chances of obtaining thegrants that are available. In effect, theseresearchers have deepened theircommitment to the traditional researchorientation of the “old curriculum.”The fact that academic engineers andscientists persist in an increasinglydifficult “game” should not surprise usgiven the reality that, at most majoruniversities, career structures are stillbased on traditional assumptions aboutresearch productivity. Tenure, promotion,access to good students and good jobs allare linked to access to research funding.Moreover, cash-strapped universities(especially the big public universitieswhere some of the strongest engineeringprograms are located) have becomeincreasingly anxious to attract grantincome (Slaughter and Rhoades 2004).Attractive start-up funding is offered topotential researchers on the under-standing that they will “earn back” thesefunds in the form of external grants. Theclear message is that funded research is



important to universities and thatsuccessful researchers will be rewarded.The result is the increasingly grimcontest for research dollars, federalgrants, and traditional publications.

Not all university researcherscontinue to pursue traditional forms ofresearch funding, however. It has alsobeen well-documented that researchers inmany scientific and technical fields haveshifted their attention to private sectorfunding, which underwrites a growingproportion of university research(Slaughter and Rhoades 2004; Etzkowitz,Webster and Healey 1998; Brint 2002).These alternative sources of fundingoften encourage different kinds ofresearch from that traditionallysupported by NSF and other governmentagencies, research more focused onmarketable products and practicaloutcomes.

In principle, this would seem to leadin the direction of the new engineeringcurriculum, with its emphasis onpreparing engineers for work in thepractical business of solving real-worldtechnical problems and working in real-world organizations.However, university reward systemsframe this kind of research activity quitedifferently, encouraging a differentemphasis among those pursuing funding.

For example, universities have begunfocusing their attention on the use ofresearch as a source of income. Whilethey continue to encourage the pursuit oftraditional kinds of external funding, theyhave realized, as well, that the more“practical” kinds of research that can bedone in university laboratories (often thekind supported by private donors) can

lead to patentable products and otheroutcomes that might generate income forthe university. Universities have,therefore, become more and more inter-ested in encouraging their faculty tomake the development of commercial,patentable outcomes a priority and haveorganized themselves to take financialadvantage of this kind of research(Slaughter and Rhoades 2004; Owen-Smith and Powell 2004). Engineering isone of the fields most likely to be involvedin this kind of activity (Brint 2002a).

Access to private funding and theemphasis on patenting discoveries hastended to erode traditional scientificnorms (such as the sharing of scientificresearch results and norms aboutconflicts of interest). It also hasencouraged a kind of academic entrepre-neurialism among researchers, for whomthe opportunity to earn significantincomes, transform research into start-upcompanies and the like become attractivealternatives to traditional academic activ-ities.

As a result of these changes, engineersdissatisfied with the difficult battle forNSF funding don’t have to shift theirfocus away from research to trainingstudents in the ways recommended by thenew engineering curriculum. They canleave the academy for the private sectorOR become university-based entrepre-neurs focused on the development ofcommercial, patentable products. Giventhe traditional practical orientation ofengineers, this is less of a stretch than forthose in the sciences, for whomcommercial activity can be a new thing(Owen Smith and Powell 2004). At anyrate, the interaction of aspects of



engineers’ work values with these new“rewards” available to universityresearchers does not lead automaticallytowards focusing engineers on practicaltraining for undergraduate engineeringstudents in group work, communication,and practical problem-solving!

A final, important aspect of thecontemporary academic reward systeminvolves the new emphasis on under-graduate education and on makingeducators accountable for their students’achievement of measurable learningoutcomes. In all disciplines, there hasbeen a noticeable rhetorical shift towardsprioritizing teaching, and growingcriticism of some academics’ exclusivefocus on traditional publishable research.Many disciplines have launched efforts toencourage new and multiple pedagogiesand calls to increase the amount ofcontact between full-time, Ph.D. facultyand students are regularly heard. Inaddition, academics find themselvesunder considerable pressure to demon-strate their effectiveness as teachers. Thisgoes well beyond the now familiar studentevaluation of teaching and involvesefforts to define specific learningoutcomes and to develop methods forassessing the degree to which thoselearning outcomes are being achieved.

This new emphasis on teaching andaccountability has multiple origins, illus-trating well Sheila Slaughter’s (2002)point that curricular change is the resultof multiple influences, form both withinand outside the university. In part, itrepresents a response to work sponsoredby major national educational founda-tions, most notably the so-called “BoyerReport,” which argues against academics’

exclusive emphasis on basic research andsought to legitimate a greater focus onpedagogy by emphasizing the value ofwhat it called the “scholarship ofteaching and learning Boyer 1997).”Following Boyer’s lead, major nationalaccrediting bodies have created institu-tional pressures towards accountabilityand a new focus on teaching. They havebeen joined in this by state legislatureswho, often for political (mistrust of“liberal” academics) and budgetary(concern over the cost effectiveness ofpublicly supported university education)reasons regularly call for more concreteevidence that a university educationresults in measurable learning and “addsvalue” to the students it produces.

For somewhat different reasons,universities themselves have come to seeas desirable a focus on teaching and theability to demonstrate that studentsactually benefit from a universityeducation. A primary reason for this isuniversities’ understandable desire toattract and retain students in an era oftight resources. Competition for tuitionpaying students is intense; presenting“hard” evidence of educational quality isone way to sell a school to an applicant.Universities also find themselves underincreasing pressure from studentsthemselves. As tuition rises, and theopportunity costs of pursuing a universityeducation increase, students are moreand more likely to think of themselves asconsumers and to demand that the“product” they purchase be to their satis-faction. It becomes all the moreimportant, therefore, for universities tobe able to demonstrate that students’classroom experience is worthwhile and



will lead to the acquisition of knowledgeand skills relevant to their occupationalgoals (Slaughter and Rhoades 2004). These new emphases in education haveproduced changes in the rewards offeredto university teachers. Outlets forpublishing the scholarship of teachingand learning have proliferated, majorfoundations (such as Sloan and Carnegie)have made resources available forresearch and other activities in theseareas, efforts have been made withinuniversities to reward good teaching(teaching awards, small grants forinnovative teaching, etc.) and there haveeven been universities which havemodified their tenure and promotioncriteria to recognize academic achieve-ments outside the realm of traditionalpublished research. The new engineeringcurriculum undoubtedly is a product ofthis new academic context. It is possiblethat the new rewards for innovativepedagogy and the achievement ofmeasurable learning outcomes will proveattractive to at least some engineeringeducators, especially since the newcurriculum appeals to the valueengineers place on practical problem-solving activities.

However, one must also acknowledgethat the calls for improved pedagogy andaccountability are not the only thingsgoing on in undergraduate education. Infact, many universities can be said to beengaged in a contradictory project:emphasizing teaching/assessment/accountability/retention on the one handand increasing the pressure to cut costson the other. Instructional costs, whichrepresent a very large portion of mostuniversities’ budgets, are generally a key

target. Faculty are asked simultaneouslyto care more about teaching and to spendless on it — it is hard to see how this canlead to the desired outcome of newpedagogical approaches.

Many observers have also commentedon the growing emphasis in highereducation on commercializing the educa-tional product (Slaughter and Rhoades;Noble 2001). Part of this involves thefocus on patents already discussed.However, it goes beyond this to the ideathat pedagogy itself can be a source ofincome. Universities have become moreand more interested in on-line learningopportunities and the development ofcourseware and other commercial educa-tional products. Faculty are offeredrewards offered for developing theseproducts, rewards often as great as orgreater than those provided for the devel-opment of effective new pedagogies. Ifengineers shift their attention from basicresearch to the development of on-linecourses and “do-it-yourself” courseware,the goals of the new engineeringcurriculum (communication skills, groupwork, etc.) are unlikely to be realized.

CONCLUSIONEngineering values are not monolithic,and include both an emphasis on theimportance of a scientific identity and astrong emphasis on service and practicalproblem-solving. The new engineeringcurriculum could sound a responsivechord among academic engineers, giventhe right set of circumstances, specificallya rewards system that sent clear signals toengineers about what activities weremost important.



Unfortunately, at least from the point ofview of the proponents of the newengineering curriculum, the academicreward system has shifted away from anexclusive emphasis on basic research, butin such a way as to send multiple, mixedmessages to university faculty, includingengineers. The various trends discussedabove have created some rewards thatappeal to the traditional engineer’s orien-tation to scientific research, others thatappeal to the more practical, problem-solving side of engineering values (and,thus, to the advocate of the newengineering curriculum); they have alsocreated other rewards which appeal toother values altogether.Under the circumstances, it shouldn’tsurprise us that efforts to encourage newcurricula have encountered a complex,mixed professorial reaction. Robert Serowand his associates (1999; 2000), forexample find that engineers reacted thenew engineering curriculum in a varietyof ways, depending on their situation:a. some, especially those on the margins,

embraced it as a way to bring inincome, build a career.

b. others embraced it out of altruism –they retained a research orientationbut sought to combine it with a focuson the new curriculum (inevitablylikely to be a minority position)

c. still others were able effectively toignore or reject the initiative becauseof the availability of alternativerewards: grant income, patents, etc.which are encouraged AT LEAST ASMUCH IF NOT MORE by theiruniversity employers.

The new engineering curriculum, thus,confronts a complex sociologicalcontext. It is clear that employers aresupportive of many of its innovations(Lynn and Salzman 2002); it is hard toimagine that students would notwelcome a stronger orientation to theskills demanded by engineeringemployers. Yet, as has been arguedhere, the engineering profession andthe reward structure of universitiesare much more ambiguous and pointto the possibility of many differentoutcomes for this effort to modify thecurriculum.

This analysis points to a number ofquestions7 important to an assessment ofthe prospects for the new engineeringcurriculum and to an estimation of theeffectiveness of possible interventions inthe process of curricular change. Theseinclude:• With which sets of professional

engineering values are the newcurricular ideas compatible and howwould we find out?

• Has the new engineering curriculumsupport found more support withparticular sub-groups withinengineering (gender, specialization,etc.) or within particular types of insti-tution (public-private, differentCarnegie classifications)?

• Has the shortage of traditionalresearch funding eroded support fortraditional curricula in any way? Hasfunding supportive of the newengineering curriculum increasedsupport? What happens to the new



________________________

7 Thanks to Roberta Spalter-Roth for some of the ideas in this section.

curriculum when the funding runsout?

• Have the professional engineeringassociations (including SWE) playedany role in fostering the newcurriculum, as Slaughter’s (2002)argument predicts? What kinds ofeffects have their efforts had ondifferent groups of engineers?

• What effect does a shift to the newengineering curriculum have onengineers’ research activities? Are thetwo compatible or incompatible? If thelatter, in what ways?

• Some (e.g., Feldman 2001) havesuggested that the creation of a “post-university,” involving the eliminationof traditional disciplinary boundariesand barriers between the universityand the community, would help tofoster positive change in bothengineering curriculum and research.Is there evidence to support this claim?

• Do engineers trained in the newcurriculum make different careerchoices than those trained in the tradi-tional curriculum, with type of schoolheld constant? What effort has therebeen to demonstrate the effectivenessof the new engineering curriculum?Has evidence like this increased (ordecreased) support for the curriculumamong various constituencies(students, employers, engineers, etc.)Could it? i

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Boyer, Ernest L. 1997. ScholarshipReconsidered: Priorities of theProfessoriate. Princeton: CarnegieFoundation for the Advancement ofTeaching.

Brint, Steven. 2002. The Future of the Cityof Intellect: The Changing AmericanUniversity. Stanford, CA: StanfordUniversity Press.

________. 2002a. “The Rise of the‘Practical Arts.’” Pp. 231-59 in Idem,The Future of the City of Intellect: TheChanging American University.Stanford, CA: Stanford UniversityPress.

Calvert, Monte. 1967. The MechanicalEngineer in America, 1830-1910:Professional Cultures in Conflict.Baltimore: The Johns HopkinsUniversity Press.

Etzkowitz, Henry, Andrew Webster andPeter Healey. 1998. Capitalizing Know-ledge: New Intersections of Industry andAcademia. Albany: SUNY Press

Feldman, Jonathan. 2001. “Towards thePost-University: Centres of HigherLearning and Creative Spaces asEconomic Development and SocialChange Agents.” Economic andIndustrial Democracy 22:99-142.



Kalleberg, Arne. 1977. “Work Values andJob Rewards: A Theory of JobSatisfaction.” American SociologicalReview 42:124-43.

Larson, Magali Sarfatti. 1977. The Rise ofProfessionalism: A SociologicalAnalysis. Berkeley: University ofCalifornia Press.

Layton, Edwin T. 1971. “Mirror-ImageTwins: The Communities of Scienceand Technology in 19th-CenturyAmerica,” Technology and Culture12:562-80

________. 1986. The Revolt of theEngineers: Social Responsibility and theAmerican Engineering Profession.Baltimore: the Johns HopkinsUniversity Press.

Lynn, Leonard H. and Hal Salzman. 2002.“What Makes a Good Engineer?: U.S.,German and Japanese Perspectives.”Paper presented to European AppliedBusiness Research Conference.

Meiksins, Peter, 2002. “The Myth ofTechnocracy: The Social Philosophy ofAmerican Engineers in the 1930s.”History of Political Thought 21(3):501-23.

Meiksins, Peter and James M. Watson.1989. “Professional Autonomy andOrganizational Constraint: The Case ofEngineers.” Sociological Quarterly30:561-85

Noble, David. 1977 America by Design:Science, Technology and the Rise ofCorporate Capitalism. NY: OxfordUniversity Press.

________. 2001. Digital Diploma Mills: theAutomation of Higher Education. NY:Monthly Review Press.

Owen-Smith, Jason and Walter W. Powell.2004. “Careers and Contradictions:Faculty Responses to the Transfor-mation of Knowledge and its Uses inthe Life Sciences.” Sociologie DuTravail 46:347-77.

Preston, Anne E. 2004. Leaving Science:Occupational Exit From ScientificCareers.” NY: Russell Sage Foundation.

Prados, John W. 1998. “EngineeringEducation in the United States: Past, Present and Future.”http://www.ineer.org/Events/ICEE1998/ICEE/papers/255.pdf

Raelin, Joseph. 1991. The Clash ofCultures: Managers ManagingProfessionals. Boston: Harvard BusinessSchool Press.

Serow, Robert C. 2000. “Research andTeaching at a Research University.”Higher Education 40:449-463.

Serow, Robert C., Catherine Brawner, andJames Demerey. 1999. “InstructionalReform at Research Universities:Studying Faculty Motivation.” TheReview of Higher Education 22(4):411-423



Slaughter, Sheila. 2002. “The PoliticalEconomy of Curriculum-Making inAmerican Universities.” Pp. 260-89 inBrint, Steven. The Future of the City ofIntellect: The Changing AmericanUniversity. Stanford, CA: StanfordUniversity Press.

Slaughter, Sheila and Gary Rhoades. 2004.Academic Capitalism and the NewEconomy. Baltimore: The JohnsHopkins University Press.

Watson, James M. and Peter Meiksins.1991. “What Do Engineers Want?Work, Values, Job Rewards and JobSatisfaction.” Science, Technology andHuman Values 16(2):140-72



INTRODUCTIONIn institutions that favor funded research,many academic departments haveadopted a faculty workload model thatmay support but often ends up under-mining the diffusion of innovation inengineering education. The practice ofworkload assignment, which I find quitecommon in many regional state univer-sities that aspire to become a researchinstitution, is to give a lighter teachingand service load to those faculty memberswho are usually newly hired and whosetenure and promotion depends heavily onexternally-funded research, and to assigna heavier teaching and service load toother faculty members who either haveexplicitly expressed no desire to beengaged in research or are currently notinvolved in research.

This division of labor frees up those inthe department who are interested inresearch to generate proposals, fundedresearch, and scholarly publications -- allindicators of productivity that aremeasured by central administration.Under this arrangement, there is animplicit but unspoken agreement that theteaching faculty would be left alonebecause they are responsible for gener-ating the largest percent of student credit-hours in the department, anotherquantifiable indicator that central admin-istration uses to scrutinize an academicunit’s productivity. Depending upon thebelief system of the teaching faculty, whomost likely are the instructors of gate-

keeper courses and serve on departmentcommittee that governs the engineeringcurriculum, there can be positive ornegative influences on the diffusion ofinnovation in engineering education.

POSITIVE INFLUENCE ON THE DIFFUSION OF EDUCATION INNOVATIONIf the teaching faculty members aresearchers in the true sense of the word,i.e., they are open to new ideas and seethemselves as co-learners in theclassroom with a new generation ofincoming students, they can have a directpositive influence on the diffusion ofinnovation in engineering education byadopting the new pedagogy or instruc-tional method in the classes they teach.Indirectly, these teaching facultymembers would allow a more flexibleengineering curriculum as members ofthe curriculum committee, and wouldaccept the scholarship of teaching andlearning as legitimate research asmembers of the tenure and promotioncommittee. A department is lucky whensuch a workload arrangement leads to anopportunity for diffusion of innovation inengineering education.

The change agents may pull in one ortwo additional colleagues through theshear force of their enthusiasm, andevidence of student learning and satis-faction may be sufficient to win overanother one or two more colleagues.However, to have a wider and lastingimpact, these change agents will need to



Social Dynamics of Campus ChangeEdmund Tsang, Ph.D. Western Michigan University

have the ability to identify the hiddenforces that are often at work in academicdepartments that affect the facultyindividually and the department collectively.

From personal experience, before Iwas ready to embrace a new approach toteaching and learning, I underwentchanges in my attitude about studentsand motivation. What facilitated myinvolvement in integrating the service-learning pedagogy into engineering weremy dissatisfaction with studentperformance and how the engineeringcurriculum is meeting the need of today’sstudents. After I was sufficientlymotivated, I was ready for change; liter-ature on engineering education reformthen widened my perspective on teachingand learning.

So when I suggest to colleagues toconsider a new instructional method or toreview multimedia resource materialsthat could improve their courses and theywould answer “That seems a lot of workfor what it’s worth” or “I am not reallyinterested,” these comments tell me theremight be other underlying factors fortheir resistance because facultyinstructors are in general a hard workinggroup and they are intellectually curious(or at least they once were, otherwisethey would not have made it throughtenure and promotion).

From a change agent’s perspective,how can he/she identify the underlyingpersonal and social forces at work thatmay facilitate or hinder a faculty toaccept change? Is there a researchprotocol to assist the change agent (withstep-by-step instructions) to inventory theattitude, motivation, and personality of a

faculty which place that faculty in thecontinuum of the stages of readiness forchange? What are the kinds of questionsto ask or the traits/characteristics toobserve that would allow the changeagent to place the faculty in thiscontinuum? Finally, is there a thresholdon the continuum that serves as thetipping point, showing the faculty is nowamenable to change, i.e., ready to accepta new approach to teaching and learningand ready to embrace innovation inengineering education? Such an investi-gation will increase the effectiveness ofchange agents to have a wider and lastingimpact.

NEGATIVE INFLUENCE ON THE DIFFUSION OF EDUCATION INNOVATIONMore often than not though, thedepartment workload arrangementdescribed on page one is a Faustianbargain because most teaching facultyhas a belief system regarding the roles ofteachers and students in learning thatprevents them from searching for instruc-tional strategies to match the diversebackground and learning needs of today’sstudents. In fact, they do not see the needto change the instructional strategiesbecause they were successful in the wayhow they were taught. They are nottroubled by the high withdrawn or failurerates in their classes; in fact, theyconsider it a badge of honor because theysee their role in the department is toweed out the weak students, with tacitapproval from the department colleagues.These teaching faculty members haveappointed themselves as the guardian of“high” academic standard, and they standin the way of the diffusion of instruc-



tional strategies that have been provensuccessful elsewhere by labeling anyinclusion of new approaches toinstruction as diluting the content of thedisciplines or lowering the academicstandard. A further irony is that while theteaching faculty is not involved inresearch (in fact, many of them were notinvolved in research when they werehired by the institutions because researchwas not then a primary mission of theinstitutions nor was their researchrecords the reasons for their hiring), theyhave decided that disciplinary research isthe only legitimate scholarly work anddiscount the scholarship of teaching andlearning in tenure and promotiondecisions.

From personal experiences, the abovescenario describes a situation that is morecommon that we would like toacknowledge in engineering schools,including my own. There are gatekeepercourses which are subscribed by severalundergraduate programs but have a highattrition rate. The mindset of thiscategory of the teaching faculty isexemplified by one instructor that I knowwho proudly states that he is the mosteffective recruiter for the businesscollege. While he is known as being a fairand effective lecturer, this instructor hasnevertheless turned down an invitation toreview an award-winning coursewarethat could help students in his class toassimilate visually the materials covered

in his lecture. All that this instructorneeded to do to assist his students wholearn visually was to request that the freesoftware be placed on the collegecomputer network and to inform hisstudents of its availability, even if hispersonal learning style is didactic. I wantto add that this faculty is not averse tocomputer simulation; when I visited himin his office one time, I found himplaying the game of Solitaire on hisdesktop computer.

As associate dean of undergraduatestudies, I want to understand the socialand political forces at work in thedepartment that contributes to theemergence of this view of teaching andlearning with quiet acquiescence fromthe rest of the faculty, and I want toidentify the factors that influence aplurality of faculty to allow this view tobecome dominant in the facultydiscussion on teaching and learning.

I also want to understand the social-ization factors of faculty alliances so thata new alliance can be created to support achange agent and counter the existingview. I want to know what strategies areeffective in shifting the current alliancefrom opposing to embracing innovation.Can we draw a parallel from historicalexamples of shifting social alliances anduse that as a model to create facultyalliance in an academic department andinstitution to support the diffusion ofinnovation in engineering education? i



CHALLENGES FACED IN MY LOCAL ENVIRONMENTBefore launching into the kinds ofchallenges faced in my local environment,it would be useful to characterize my localenvironment. First and foremost it isimportant to understand that although Ihave been involved in engineeringeducation for over 15 years, I am new tomy current university (less than one year).Below I focus on the following importantattributes of my institution:

Launching a Department of EngineeringEducation in a College of Engineering (2005-06). The focus of the department isresearch, although the department wasbuilt from a service-oriented program onthe freshman engineering year. We willbe launching a K-12 initiative thissummer that involves research onengineering thinking at the K-12 level,with an ultimate goal of understandingand promoting pathways into engineering(a teaching certificate program is alsoscheduled). We anticipate launching amaster’s level certificate program to meetthe needs of current engineeringeducators (e.g., engineering faculty orprofessional staff who seek an acceleratedprogram to develop capabilities in

conducting engineering educationresearch).

Similarities and differences of institutionaland college culture. In many ways our insti-tutional culture is representative of mostresearch intensive universities with astrong engineering program (Purdue is alarge school that is ranked highly bothnationally and internationally). Althougheducational innovations are now includedin tenure and promotion reviews, thegold standard is still on research — facultyin the Department of EngineeringEducation will be reviewed on theirresearch contributions to this field.Aspects of our institutional culture whichfacilitated the launch (top down andbottom up strategies): 1) alignmentbetween the President’s, Dean’s andChair’s long term goals that created apathway for creating the new department,2) investment into research (in particu-larly interdisciplinary research) thatcreated considerable resources for hiringfaculty, 3) a freshman year program inwhich existing faculty had been buildingpartnerships across campus aroundengineering education scholarship, and 4)existence of science and math educationcommunities.


APPENDIX 3: PAPERS PRESENTED BY WORKSHOP PARTICIPANTSDIFFUSION OF INNOVATION 1

CASEE / ASA “Social Dynamics of CampusChange: Creating an Interdisciplinary Research Agenda” Workshop: Diffusion of Innovation ThemeRobin S. Adams Purdue University

Key challenges that speak to under-standing change pathways, howengineering education scholarship isperceived and encouraged, implementingand sustaining educational innovations,and how engineering education relates tothe other engineering professions:

Narrow perceptions of engineering education.Although we have considerable support,the perceptions of what engineeringeducation scholarship means to facultyand our new graduate students appear tobe relatively narrow (a focus on thepractice, not the research). Illustratingthe breadth (and complexity) ofengineering education scholarshipappears to be an important hurdle, andan important aspect for creating multiplepathways to investigate, develop, anddiffuse engineering education innova-tions. Similarly, little expertise exists forevaluating engineering educationresearch contributions.

A continual perception of engineeringeducation as service to the university. Mostfaculty who approach the department areseeking expertise in pedagogy orassessment, although there are somefaculty who are interested in researchcollaborations. This can make collabora-tions challenging in that it supports aprocess of engaging engineeringeducation faculty late in the collaborativeprocess (sending a message that they arean “add-on” rather than having a centralrole).

Unclear conceptions on the nature ofresearch (and perhaps the academicenterprise). My sense is that the nature of

research in engineering is not a part ofeveryday thinking — that researchphilosophies in the more traditional fieldsof engineering (e.g., mechanicalengineering, civil engineering, and evenbiomedical engineering) are oftenimplicit, out of visible range, and not afrequent nor integral part of theengineering culture (in education thereare courses on research methods, inengineering there are not). This makes itdifficult to understand or imagine othermodes of research, and may impact thebreadth and depth of conceptions ofresearch (e.g., a “silo” view of rigor inengineering education research as quanti-tative, a “scientized” view of research asfollowing the scientific method).

Defining impact. The “impact” of ourdepartment is being seen (by many) as ameasure of the impact of our departmenton the education system locally atPurdue. This is a large and somewhatunwieldy goal for any department (e.g.,this kind of impact goes far beyondresearch to include the arts of negoti-ation, organizational leadership, andpolicy development). If impact isanalyzed only locally, and not nationally— this may contribute to narrowingconceptions of engineering education(e.g., research contributions areevaluated by national colleagues).

Living the interdisciplinary challenge. Whileinterdisciplinarity research is beingpromoted broadly at Purdue andprocesses exist to evaluate andacknowledge interdisciplinary work, theformation and functioning of interdisci-plinary collaborations is a continual



challenge. For those processes that dosupport interdisciplinary work, they arerelatively new and are likely to requirecontinual review and iteration.

There are also many opportunities (andtestbeds) on this campus for under-standing 1) the diffusion of innovationand how engineering education emergesas a area of scholarship, 2) how interdisci-plinary research and partnerships growand evolve, 3) pathways into engineeringeducation, and 4) how a local modeldiffuses nationally and potentially inter-nationally.

MY RELATED WORK AND EXPERIENCEOver the past 5 years I’ve been workingon projects that seek to engage people inengineering education research as onekind of diffusion pathway. Assumptionsunderlying these activities include abelief that 1) research should informeducation practice, 2) the distancebetween research on learning and thepractice of teaching should be minimized(to encourage stronger connections andinterdisciplinary discourse), 3) efforts topromote diffusion should be based on auser-centered philosophy (e.g., under-standing the needs of faculty and howthey approach their academic work), 4)educators are crucial for sustainable andcontinual education improvement (as keydecision makers within the academicsystem), and 5) community and socialnetworks play a crucial role inconstructing, diffusing, and enablingknowledge.

I have three projects around the question“how to build capacity in engineeringeducation research”:

(1) The Institute for Scholarship onEngineering Education - Center for theAdvancement of EngineeringEducation (see Adams et al). This is a year long program designed tobuild and sustain communities ofengineering education scholars whocan investigate student learning issuesand transform findings into actionableimprovements. Key attributes of theISEE model include: 1) investigatingengineering learning environments aseducation research laboratories(promoting reflective practice byencouraging the idea that all learningenvironments are laboratories forunderstanding learners and thelearning process), 2) promoting a“scholarship of impact” by askingparticipants to develop impact studies(studies that included explicit plans forbuilding impact networks and effec-tively communicating researchfindings) around potential zones ofimpact (classes, programs, etc.), 3)facilitating community building(locally and nationally) through awaterfall recruitment strategy and ahigh level of interactive community-centered and assessment-centeredactivities (e.g., interactive studyposters, work-in-progress sessions, andsharing stories), and 4) adopting auser-centered approach on under-standing the challenges of being anISEE Scholar (via various evaluationactivities).



Findings from evaluating our model (thatappear useful for a discussion on thediffusion of innovation) are highlightedbelow.• Openness and curiosity about quali-

tative research — Many Scholars werenot familiar with more qualitativeapproaches to inquiry yet were veryinterested in learning more aboutthem. Similarly, although Scholars feelcomfortable with quantitativemethods, they lack confidence and areconfused about how to use them ineducational research contexts.

• Engineering education fit their passionand their personal career goals butmay not fit the values of theirdepartment - Many Scholars did notperceive their departments orUniversities as particularly supportiveof education or research on education,yet all the Scholars felt that theirparticipation in ISEE fit their careergoals (which make for an interestingquestion about how they saw theircareer goals aligning with the valuesof their respective campuses). Scholarsspoke of a “passion” for engineeringeducation and appear to choose toengage in activities that may be seenas flying in the face of adversity (andperhaps helps explains a driving needto find communities of like-mindedfolks).

• Learning and implementationchallenges - 1) formulating research orimpact questions (one Scholar notedthat formulating a research questionwas its own form of research), 2)knowing when, why, and how toemploy various research methods (andhow to analyze the data once you get

it), 3) navigating the human subjectsprocess and recruiting participants, 4)learning how to narrow down thescope of a project so it is feasible, 5)navigating and finding (and under-standing) relevant research (new disci-plinary language), and 6) a preferencefor quantitative data yet a lack ofknowledge around analyzing this kindof data.

• Organizational / structural challenges -1) continuing community building(locally and distributed), 2) findingtime and managing conflicting obliga-tions, and 3) setting project milestones.

• Strategies Scholars employed tonavigate challenges - 1) encouragingconnections between existingengineering research knowledge andexperience to help transition to a newform of research, 2) sharing work-in-progress, and 3) building communityof like-minded colleagues and socialnetworks.

(2) Exploratory research characterizingthe process of becoming an interdisci-plinary engineering educationresearcher (Allendoerfer and Adams,unpublished).Five engineering educationresearchers (not ISEE scholars) atvarious stages in their interdisci-plinary pathway were interviewed toilluminate stories around what itmeans to be an interdisciplinaryresearcher, pathways for entering andnavigating interdisciplinary workspaces, and the construction of inter-disciplinary identities.



• Pathways into and within engineeringeducation research — Participantswere driven by nagging questionsabout their current teaching andlearning contexts, disenchanted withmainstream views and step acrossdisciplinary boundaries to seek outalternative views, proactive and inten-tional about creating opportunities forincreasing their participation in a newcommunity of practice, and driven bypersonal motivation and persistence.Interestingly enough, pathways fordoing this kind of work resembled theprocess by which participants did theirdisciplinary work (identify a problem,seek information from sources bothwithin and outside of their currentdiscipline, synthesize ideas frommultiple fields, and apply what theyfind to the problem at hand).

• Constructing identities as interdisci-plinary researchers — Each participanthad different ways of describing theiridentity, yet all spoke to an interdisci-plinary identity (e.g., a bridge, trans-lator, and liaison). A sense of not quitefitting into traditional disciplines wasapparent (“I’m this weird hybrid,” “Iwas already considered ‘fuzzy outthere’ by the engineering school”).The scholars’ ways of talking aboutthemselves acknowledged that thecommunity which they have entered isinherently an “in between” space.

• Challenges consistently encounteredand strategies employed — The partici-pants spoke to the risks involved instepping away beyond the boundariesof their home disciplines, and alsoabout the difficulty of entering a newcommunity of practice (“a culture

shock”). Some lacked collegial support,and some were faced with questions ofwhere to publish their work and howto make this work “count”. New termi-nology had to be learned, new liter-ature had to be navigated, and newapproaches to research methods and“what counts” as evidence had to belearned and accepted. In discussingstrategies for overcoming thesechallenges, the scholars repeatedlyemphasized the importance of findinga supportive community (mentors,supportive colleagues or supervisors,and broader communities of like-minded people).

(3) Descriptive research characterizingcareer trajectories in engineeringeducation (see Adams and Cummings-Bond).This was a study to create a landscapeview of engineering education careertrajectories across three groups: 1)CAREER grant recipients (representedthe least risky trajectory in that itmore closely aligned with a traditionalresearch focus), 2) recipients of“potential to impact engineeringeducation” AWARDS, and 3) PHDdissertations in an engineeringeducation topic (the most riskytrajectory). Publicly available data wascollected on current academic positionand data on the nature of the insti-tution (e.g., Carnegie class, geographicregion, and the presence of a centerfor teaching or learning or anengineering education center).Findings illustrate that engineeringeducators (even those in the most riskygroups) were finding careers in



academia — particularly at researchintensive institutions. A key findingwas the influence of having a centerfor teaching and learning or anengineering education program (e.g.,NSF funded Coalition) on career paths.In particular, a high percentage ofPHD recipients and AWARD recipientswere associated with schools that hadthese kinds of communities ornetworks. This suggests that theculture of these institutions may bedifferent and that there may be wererelatively thriving communities ofengineering education scholars whocould be mentors, provide professionaldevelopment and research opportu-nities, and possibly strong socialnetworks.

The other area of activity are 2 projectsaround the question “how to bridgeresearch and practice” (see Turns et al).(1) Research-to-Practice Workshops.

The focus of these workshops was toengage engineering faculty in researchthat could have implications foraddressing their individual teachingchallenges. Many effective teachingworkshops exist, and are successful.However, workshops attendees oftenexperience failures (short and longterm) with bringing these teachingpractices into their classrooms.Although there may be many struc-tural barriers, one barrier may be thatsuch workshops often do the “work” oftranslating research implications —potentially limiting a deep under-standing of the research (and anability to relate research to specificeducational settings). Research-to-

Practice workshops were designed toreduce the distance between theresearch and the educator by engagingeducators in actual research, and thendiscussing ways to use the research fora set of “teaching challenge”scenarios. We found that the leapinvolved in reading research was not alarge barrier and that when givenactual research, participants couldidentify a variety of implications fordifferent situations. These beyondseeking an answer (e.g., follow aguideline) to experimenting with theways research can be brought intoclassrooms (e.g., having students readthe studies, having students solve thestudy task and compare themselves tothe participants in the study,presenting some of the research aspart of the lecture).

(2) A design expertise continuum.A goal of the design expertisecontinuum is to support educators invisualizing learners’ growth towardacquiring engineering designexpertise. Through iterative proto-typing and building on adoption/adaptation research in K-12, someattributes of a successful continuumwere identified. Attributes include 1)identifying appropriate courses ofaction (by illuminating learningtargets and pathways), 2) promotingsynthesis by linking to other research(including pedagogical contentknowledge), 3) making learners’behaviors, thinking, and attitudesvisible as well as using language that isrecognizable and salient with users,and 4) organizing and representing



information to illustrate thecomplexity of learner trajectories(rather than watering down results).

MY VIEW ON FUTURE RESEARCH NEEDSThere are many opportunities forresearch (e.g., many unansweredquestions on how to apply K-12 findings to engineering education). I can imagineresearch on individuals/collections ofindividuals, pathways/mediators, andcultures/contexts around the followingtopics:

Framing engineering education — What’s theproblem, what drives change, who getsinvolved, and for what purpose?• How do different actors within

engineering education frame “theproblem” (and how do these framingsrelate — is there a common ground)?What are their intentions and motiva-tions? How does this relate to what areperceived as effective approaches toaddressing these problems? Whatcatalyzes people to get involved evenwhen they know they are taking risks?

• What are the dominant theories (oroperating theories) that guideengineering education (e.g., is design auseful analogy that helps make visibleintention and judgment)? What areconceptions of innovation inengineering education (andadaptation, diffusion, action), and howdo these guide approaches toengineering education?

• What examples exist of confrontingconceptions of engineering educationand what can we learn from them? Is

there a conceptual change model ofengineering education (and if so whatis the role of internal motivations)?Where does change get “boggeddown”? What conceptions are beingconfronted (or need to be confronted)?

Interdisciplinarity and engineering educationdiscourse — What are pathways/networksand what is the role of engineering educationwithin engineering colleges?• What are pathways into engineering

education discourse? Who enters thosepathways and why (and who needs tobe there)? In what ways mightgraduate students be pathways forchange (liaisons)? In what ways doesan interdisciplinary approach supportcollaborations beyond “service”? Whatis the role of social networks in thechange process? What are invisiblenetworks?

• Where and how does engineeringeducation fit within the broadengineering profession? Central?Fringe? Unconnected? How do concep-tions of engineering educationcompare among engineering practi-tioners and engineering faculty /administrators? In what ways wouldbringing engineering educationtowards the “center” of theengineering profession (rather thansome liminal space) influenceapproaches to improving engineeringeducation?

• In what ways is engineering educationsimilar / different from otherengineering fields (e.g., design as acentral theme, user-centered design asa philosophy)? In what ways mightapproaches to research be a thread



that links the various engineeringprofessions? What are conceptions ofresearch and how do they compare(are we more alike than we think)?Where are points of commonality?

A focus on educators — What do they findchallenging / motivating, and how do they seethemselves as actors within engineeringeducation?• What is the role of faculty in the

engineering culture? In the changeprocess? What are points of common-ality for engaging faculty in change(e.g., reflective practice)? What arefaculty epistemologies of teaching andlearning? Adminstrators?

• What are assumptions about faculty(efficiency and time issues, motiva-tions, etc.)? How and in what ways doengineering faculty construct theiridentities? How does this relate to theiridentity as engineering educators?How do engineering educators seethemselves as decision makers? Inwhat ways might their conceptions ofpower and agency influence theirmotivations and abilities to engage incurricular reform?

• What kinds of information aboutteaching and learning make it intoclassrooms? Into decisions aboutengineering education? How is infor-mation presented, translated (re-designed?) or brought to attention?

A focus on engineering educationresearchers— What is the role of research inengineering education and why should it beengineers (versus someone else) do it?• How and why do people get involved in

engineering education researchprograms? What makes them stayinvolved? How does work from theseexperiences diffuse into the localcommunity? What challenges do theyexperience and in what ways are theseindicative of those involved inengineering who do not engage inengineering education research?

• In what ways might these programs beintermediate steps towards diffusingeducation innovations? What could wedo better? How could we speed up orscale up the rate of diffusion?

• How does new knowledge gain legit-imacy and spread? What are welearning from our participants’ experi-ences when they return to their localcommunity (e.g., impediments, facili-tators, resources and networks theyuse or create)?

• If faculty engage in classroom researchor broader research, do they becomemore accepting of / proactive inseeking out research relevant to theirsituation? What contributes to seekingout other information (or not)? i



REFERENCES

Adams, R., Allendoerfer, C., Bell, P., Chen,H., Fleming, L., Leifer, L., Maring, B. &Williams, D. (2006). “A model forbuilding and sustaining communitiesof engineering education researchscholars.” Proceedings of the AnnualAmerican Society of EngineeringEducation Conference, Chicago, June(accepted).

Adams, R. & T. Cummings-Bond (2004).“Career Trajectories in EngineeringEducation.” Proceedings of the AnnualAmerican Society of EngineeringEducation Conference, Salt Lake City,June.

Turns, J., R. S. Adams, J. Martin, M.Cardella, S. Mosborg & C. J. Atman (toappear 2006). “Tackling the Research-to-Practice Challenge in EngineeringDesign Education: Insights from aUser-Centered Design Perspective,”International Journal of EngineeringEducation (invited paper).

The following is provided as an “fyi” — forthose who are curious about what ideashave guided my work and thinking:

Academic culture and organizational theory• Faculty issues and the academic

culture — e.g., reward systems andvarious barriers including thechallenges of balancing external andinternal motivations such as research/ teaching and career / personal life

(Huber & Morreale), challengesunique to interdisciplinary issues (e.g.,Gidjunis, Stokols et al)

• Organizational theory, social networks,and design organizations (e.g, Van deVen & Hargrave, Barley)

• Diffusion of innovations, tradingzones, social life of information (e.g.,Rogers, Galison, Brown & Duguid)

Theories of learning• Communities of practice (and

knowledge building communities ofpractice) — disciplinary and interdisci-plinary communities (e.g., Lave &Wenger, Barretti, Cook et al)

• Adaptive expertise and cognitive flexi-bility

• Reflective practice, the scholarship ofteaching and learning, andpedagogical content knowledge

• Identity development (as a driver forhuman behavior and social interac-tions)

Interdisciplinarity• Interdisciplinary thinking (e.g., Kline,

Klein, Lattuca, Weingart & Stehr),interdisciplinary environments (e.g.,Newstetter), and interdisciplinarydiscourse (e.g., Frost & Jean, Spanner)

Design theory• Use-inspired research (Pasteur’s

quadrant) and user-centered design(includes systems theory.)



BIBIOGRAPHY

Barley, S.J. (2001). Puddle jumping as acareer strategy. Unpublisheddocument. Stanford University: Centerfor Work, Technology andOrganization, Department ofManagement Science andEngineering.

Barretti, M. (2004). What do we knowabout the professional socialization ofour students? Journal of Social WorkEducation, 40(2), 255-283.

Brown, J. S. and Duguid, P. (2001). Thesocial life of information. Cambridge,MA: Harvard Business School Press.

Cook, T.H., Gilmer, M.J., & Bess, C. (2003).Beginning students’ definitions ofnursing: An inductive framework ofprofessional identity. Journal ofNursing Education, 42(7), 311-317.

Frost, S.H. & Jean, P.M. (2003). Bridgingthe disciplines: Interdisciplinarydiscourse and faculty scholarship. TheJournal of Higher Education, 74(2),119-149.

Galison, P. (1997). Image and Logic. TheUniversity of Chicago Press: Chicago.

Gidjunis, J. (2004). Interdisciplinaryresearch urged. The Chronicle ofHigher Education, 51(15), A25.

Huber, M.T. & S. Morreale (2002).Disciplinary Styles in the Scholarship ofTeaching and Learning: ExploringCommon Ground. Washington DC:

American Association for HigherEducation and The CarnegieFoundation for the Advancement ofTeaching.

Klein, J.T. (1990). Interdisciplinarity:History, theory, and practice. Detroit:Wayne State University.

Klein, J.T. (1996). Crossing boundaries:Knowledge, disciplinarities, and inter-disciplinarities. Charlottesville, VA:University Press of Virginia.

Lattuca, L. (2001). Creating interdiscipli-narity: Interdisciplinary research andteaching among college and universityfaculty. Nashville: VanderbiltUniversity Press.

Lauzon, A.C. (1999). Situating cognitionand crossing borders: Resisting thehegemony of mediated education.British Journal of EducationalTechnology, 30(3), 261-276.

Lave, J. & Wenger, E. (1991). Situatedlearning: Legitimate peripheral partici-pation. New York: CambridgeUniversity Press.

Newstetter, W.C., Kurz-Milcke, E. &Nersessian, N.J. (2004). AgentiveLearning in Engineering ResearchLabs. Proceedings of the AnnualFrontiers in Education Conference,Savannah.

Riordan, T. & J. Roth (Eds) (2005).Disciplines as Frameworks for StudentLearning: Teaching the Practice of theDisciplines. Sterling, VA: Stylus.



Rogers, Everett M. (1962). Diffusion ofInnovations. The Free Press. New York.

Schwartz, DL, Bransford, JD, and Sears, D(2005). Efficiency and Innovation intransfer. In Jose Mestre, Transfer OfLearning: Research And Perspectives.Informaiton Age Publishing Inc.

Spanner, D. (2001). Border crossings:Understanding the cultural and infor-mational dilemmas of interdisci-plinary scholars. The Journal ofAcademic Librarianship, 27(5), 352-360.

Van de Ven, A.H. & Hargrave, T.J. (2002).“Social, technical, and institutionalchange: A literature review andsynthesis.” In M.S. Poole and A. H. Vande Ven (Eds.), Handbook ofOrganizational Change. New York:Oxford University Press.

Weingart, P. & Stehr, N. (Eds) (2005).Practising Interdisciplinarity. Toronto:University of Toronto Press.

Wenger, E., McDermott, R., and Snyder,W., Cultivating communities of practice,Cambridge, Harvard Business SchoolPress, 2002.



In participating in this workshop,there are two engineering facultyaudiences for diffusion of engineering

pedagogies that I want to serve. First arethe classroom teachers—engineeringfaculty—the ultimate consumers of thepedagogies. Second are the innovatorsand opinion leaders who have the powerto help disseminate pedagogies asinformed insiders. Before we can discussthe research needed, there are important“cultural” aspects for reaching theseaudiences that need to be considered.

First, the roles of researcher andteacher are conflated for engineeringfaculty. The type of engineeringeducation publications characteristic ofthe past several decades (written byengineering faculty) indicate a highlypractical focus on the details of newpedagogies, often at the expense ofevidence supporting their efficacy.However, as logical technical researchers,engineering faculty consider themselvesanalytical thinkers who are convinced byrigorous, quantitative evidence. Thus,there is a disconnect between theinnovators, who focus on the mechanicsof their new pedagogies, and theconsumers, who desire rigorous evidence.It should be noted that these two groupsare essentially the same; they simply actdifferently based on their roles withrespect to a specific innovation. Recently,this has led to calls for increased rigor in

engineering education work (Gabriele,2005; Shulman, 2005).

But convincing teaching faculty is notas simple as appealing to their purporteddisciplinary modes of thinking. Aspotential users of new engineeringpedagogies, engineering faculty arelargely unconvinced by the same quanti-tative evidence they demand. Labareeargues that due to application andimmediacy considerations, teachers valuecontext-specific experience and useexperience-based but subjective reasoningin making classroom decisions. As aresult, they tend to devalue generaliza-tions about what works in the classroomand reinvent the wheel, working alone,with little overall sense of what has beenshown to work in most situations. Theyreject theory and generalizations asinconsistent with their copious teachingexperience (2003).

Leaders within the engineeringeducation coalitions have gone throughthe long and difficult process of discov-ering for themselves that compellingresults alone do not convince engineeringfaculty (Clark, Froyd, Merton, &Richardson, 2004). Throughout the 1990s,the National Science Foundation investedmillions of dollars in engineeringeducation reform at over 40 institutions. Irecently interviewed coalition faculty andwas amazed by their stories of the painfuluphill battles in trying to get their



Challenges to Diffusing Engineering Education InnovationsMaura Borrego Virginia Tech

reforms institutionalized. (I imagine JeffFroyd will elaborate on this in hiswhitepaper.) Thus, not only is there acomplex set of misconceptions to overcomein convincing faculty consumers of thevalue of a new pedagogy, but there are alsomisconceptions among innovators andopinion leaders as to diffusion processesand their proper applications, specificallythe relevance of theory.

Among this group of engineeringeducation innovators and opinion leaders,there is the same reliance on practicalexperience and disregard for theory. Thisleads to misconceptions about specificaspects of diffusion of innovations theory.As in teaching, these faculty rely on theirpersonal experiences, and a singleexperience can trump widely-testedtheory. An example is identification ofleaders, both leading institutions thatshould be emulated and the individualopinion leaders within an organization.Top-ranked ivy league schools are mostcommonly cited as the ones to emulate,but research has shown these schools areoften less innovative than those seeking tomove into the top of the rankings (Blau,1994). In the case of individuals, there aremany types of higher education opinionleaders, depending on the arena:research, administration, or teaching.Prior work in identifying teaching leadersa perceived by faculty within depart-ments of chemistry, math and physicssuggests that there are opinion leaderswithin the faculty ranks and that otherthan department chairs, administratorshave little influence on faculty teachingdecisions (Dearing, Casey, Larson,Singhal, & Rao). Likewise, much of theliterature on institutional change

advocates leadership from both adminis-trative and faculty levels (Kuh & Whitt,1988).

Take for example but one illustrationof the way engineering faculty haveundertaken diffusion studies. Publishedas a conference paper, a group ofengineering education coalition membersstudied the diffusion of three softwaretools. The methods section states that theyconsidered many different types ofinnovations, many less concrete, but inthe end argued that the formal distri-bution systems for these three softwaretools overwhelmingly indicated that thesewere the most successful innovations(Serow & Zorowski, 1999). I think thisstudy reflects the approach of engineersin two ways. First, the software tool isportable and requires no alteration to use.Engineers can be extremely utilitarian inthis sense, not valuing a theory orphilosophy but rather a studentworksheet or software tool that can beapplied with little thought. Second, theresearchers chose something that waseasily quantified. In each case, thesoftware was distributed through apartner such as a textbook company or anational lab, which had tracked distri-bution of the software. Though EverettRogers’ book (1995, 2003)was cited, itmerely provided context for a studyfocused on “the numbers,” rather thanguiding data collection or analysis.

Though these aspects of engineeringfaculty behavior are explained better bythe applied aspects of engineering (withrespect to other disciplines), engineerstend to identify much more with the“hard science” categorization ofengineering (Biglan, 1973). Unfortu-



nately, this leads to issues of status thatcan cause engineering faculty to furtherdevalue the contributions of educationresearch. The “hard” characterizationarises from well-developed, agreed-uponand enforced standards of rigor inphysical science and engineering disci-plines. These articulated standards makeit easier to evaluate and demonstratesuccess, which can draw the focus ofinstitutional administrators and fundingagencies to award a disproportionateamount of resources to these disciplines.Whether this is a cause or an effect isunclear, but it is definitely a roadblock.The National Academies reportFacilitating Interdisciplinary Researchcites numerous instances of resistance tocollaboration within science andengineering research teams becausecertain members feel their disciplinarycontribution is more important than theothers (in these cases physics over othertechnology disciplines). The reportactually cites this behavior as the singlebiggest roadblock to interdisciplinarycollaboration, as perceived by leaders inindustry, academia, and governmentresearch labs (Committee on FacilitatingInterdisciplinary Research, 2005). Whilecollaboration is not necessarily the goal indiffusing engineering pedagogies (butapplying knowledge from social scienceresearch is), it is a more observablebehavior arising from the same under-lying attitude toward knowledge fromother disciplines that prevents directapplication by engineers.

The core problem is this: There is ahuge body of research in education (how toteach) and sociology (how to disseminate)that engineering faculty are not using.

I believe involving informed engineeringfaculty in sociology-grounded studies isthe right next step. Two distinct types ofresearch are needed to advance diffusionof engineering education innovations:

A. Ethnographic or socio-culturalresearch into specific higher educationcontexts for engineering educationreform. This largely qualitative workwould answer the questions: How doengineering faculty think they learnabout and decide to use teachinginnovations? What effects do workenvironment and culture have on thediffusion process for engineeringteaching innovations, particularlysince faculty often control their ownclassrooms?

B. Quantitative and/or applied workundertaken to widely educateengineering education innovators andopinion leaders. With engineeringfaculty as the audience, their ownnorms must be addressed: substantive,quantitative, and prescriptive researchconducted with the participation of an“insider” engineering educationleader. Examples include:

1. Research, mapping what is knownabout diffusion of innovations,particularly opinion leaders, toengineering education through aseries of illustrative studies that canbe used to educate engineeringfaculty. Case studies are a particu-larly effective means of reachingthis audience, so they can serve asexamples demonstrating the basicprinciples guiding an effective



dissemination plan and how toapply them.

2. Workshops focusing on dissemi-nation plan design, conducted incooperation with “insider”engineering education leaders withestablished audience credibility.

It is my understanding that too muchdiffusion of innovations (DoI) research ishistorical rather than experimental, or atleast not forward-looking enough (Rogers,1995, 2003). In a more proactive experi-mental approach, group A would receivea DoI-informed intervention and becompared to group B, a control groupreceiving the standard disseminationtreatment. Though I am more interestedin in-depth qualitative research, I fearthat too much emphasis in this method-ology might go the direction of educationresearch, that is, to be largely ignored byengineering faculty because theirinterests as an audience are not directlyaddressed. The challenge is to advancethe sociological knowledge base whileproviding research in the correct setting(engineering) that includes someresearch design characteristics thatappeal to engineers. i

REFERENCES

Biglan, A. (1973). The Characteristics ofSubject Matter in Different AcademicAreas. Journal of Applied Psychology,57(3), 195-203.

Blau, P. M. (1994). The Organization ofAcademic Work (2nd ed.). NewBrunswick: Transaction Publishers.

Clark, M. C., Froyd, J., Merton, P., &Richardson, J. (2004). The Evolution ofCurricular Change Models Within theFoundation Coalition. Journal ofEngineering Education, 93(1), 37-47.

Committee on FacilitatingInterdisciplinary Research. (2005).Facilitating Interdisciplinary Research.Washington, D.C.: National AcademiesPress.

Dearing, J., Casey, M., Larson, R. S.,Singhal, A., & Rao, N. Work recentlyfunded by National ScienceFoundation grant #REC-0335593.

Gabriele, G. (2005). AdvancingEngineering Education in a FlattenedWorld. Journal of EngineeringEducation, 94(3), 285-286.

Kuh, G. D., & Whitt, E. J. (1988). TheInvisible Tapestry: Culture in AmericanColleges and Universities (Vol. 17(1)).Washington, DC: The GeorgeWashington University, GraduateSchool of Education and HumanDevelopment.



Labaree, D. F. (2003). The PeculiarProblems of Preparing EducationalResearchers. Educational Researcher,32(4), 13-22.

Rogers, E. M. (1995, 2003). Diffusion ofInnovations. New York: The Free Press.

Serow, R. C., & Zorowski, C. F. (1999).Diffusion of Instructional Innovationsin Engineering Education. Paperpresented at the American Society forEngineering Education AnnualConference, Charlotte, NC.

Shulman, L. S. (2005). If Not Now, When?The Timeliness of Scholarship of theEducation of Engineers. Journal ofEngineering Education, 94(1), 11-12.



There is an extensive sociologicalliterature on the diffusion ofinnovation that dates back many

years. Among the classic works in thefield are Ryan and Gross’s (1943) study ofthe diffusion of hybrid corn seed in anIowan farming community and Colemanet al.’s (1966) study of the diffusion of anew drug among physicians in fourMidwestern cities. No attempt will bemade here to survey this sprawling liter-ature (see Rogers 1995). However, twogeneral findings of this literature bearmentioning at the outset. First is the factthat the diffusion of an innovationtypically follows an S-shaped curve inwhich the rate of adoption begins slowly,then accelerates as it spreads to amajority of the population, and finallytapers off again as the point of saturationis approached. Second is the fact that thediffusion of an innovation often occursthrough what can be described as a two-stage process in which the innovationfirst must be accepted by a sufficientnumber of “opinion leaders,” whoseexample then encourages adoption byother members of the population. Neitherof these patterns should be construed asuniversal. Nevertheless, together theyprovide an orienting framework for muchof the research on the success, failure, ortiming of the diffusion of innovations.

The Network PerspectiveThe focus of this essay will be on thecontribution of network theory andmethods to the study of innovationdiffusion. Network analysis is distin-guished by the attention that it gives tothe links or relations among social actors,the structure or pattern of such linkages,and the implications of those patterns forsocial behavior. In contrast to researchthat takes individuals and their attributesas the focus of study, network analysisfocuses on the manner in whichindividuals interact with one another andhow those interactions constitute astructure that can be studied andanalyzed in its own right. As applied tothe study of innovation diffusion, thenetwork perspective argues that actors’decisions with respect to adopting or notadopting an innovation can be explained,at least in part, by the pattern of intercon-nections among them.

This thesis is by no means new to thestudy of innovation diffusion. Indeed,insofar as the diffusion of innovation iscommonly conceived as a process ofcontagion or imitation, one could arguethat actors’ exposure to the influence orexample of one another has always been,at least implicitly, a preoccupation ofresearchers in this field. Nevertheless,what network analysis brings that mostprior research lacks is: (1) an explicitconcern with collecting systematic data



Network Perspectives on the Diffusion of InnovationVal Burris University of Oregon

on the social relations among actors; (2)an extensive repertoire of mathematicallytractable measures and models fordescribing the patterns revealed in suchdata; and (3) a body of theory for makingsense of the social and behavioral conse-quences of different network topologies.

Proximity: Cohesion and EquivalenceAt the heart of much network theorizingabout innovation diffusion is the conceptof proximity (Marsden and Friedkin 1994;Valente 1995). Stated in the broadestterms, the network perspective arguesthat “something about the social struc-tural circumstances of ego and altermakes them proximate such that ego’sevaluation of the innovation is sensitiveto alter’s adoption” (Burt 1987, p. 1288).Generally speaking, there are two mainideas about what that “something” is. Thefirst is based on the concept of structuralcohesion. The second is based on theconcept of structural equivalence.

Structural cohesion is a measure ofwhether, how closely, or how strongly twoactors are linked to one another. Themost restrictive definition of structuralcohesion is “adjacency” — the existenceof a direct link between ego and alter.The concept can also be expanded beyondthe dyad to encompass various measuresof the extent to which ego and alter areconnected, not only directly butindirectly, via chains of relatively shortdistance or within communities ofrelatively dense and strong ties. Structural equivalence defines proximityin terms of the similarity shown by egoand alter in the overall profile of theirlinks to the larger network, whether ornot they are directly or closely linked to

one another or belong to a common,dense-knit community. Sometimes equiva-lence is measured not strictly in terms ofthe similarity of links to identical sets ofthird parties but to third parties who,themselves, occupy structurally analogouspositions within the network. In thenomenclature of network analysis, this isreferred to as “regular” equivalence. There is evidence to suggest that bothcohesion and equivalence can play animportant role in the diffusion ofinnovation. In the field of reproductivehealth, several studies have shown thatthe likelihood of adopting a new contra-ceptive technology by women in devel-oping countries was enhanced bycohesive ties to women already using thetechnology (Entwisle et al., 1996; Valenteet al., 1997). In the field of organizationstudies, Davis (1991) showed thatadoption of the “poison pill” takeoverdefense by corporate managements wasstrongly influenced by the existence ofdirect board interlocks with previousadopters. The strongest evidence of theimportance of structural equivalence forthe diffusion of innovation comes fromBurt (1987), who, in a reanalysis ofColeman et al.’s (1966) data on theadoption of a new drug by physicians,showed that similarity in the timing ofadoption was better explained by struc-tural equivalence than by direct interper-sonal ties.

These two notions of proximitysuggest different mechanisms by whichego’s likelihood of adopting an innovationmay be influenced by alter’s prioradoption. In the case of structuralcohesion, it is reasonable to invoke inter-personal mechanisms of trust, deference,



persuasion, and conformity withcommunity norms. In cases of structuralequivalence that are not accompanied bydirect interpersonal ties, we canreasonably exclude such mechanisms.Instead, any similarities in the timing orlikelihood of innovation adoption must beattributed to more indirect processes ofthe enforcement of similar role expecta-tions by disjoint sets of actors or tocompetitive efforts on the part ofsimilarly situated actors to keep pace withothers of their kind. Such causal mecha-nisms are not mutually exclusive.Moreover, it should be noted that,although cohesion and equivalence areconceptually distinct, empirically theyare often aligned. Hence, it is possiblethat these two forms of proximity overlapin terms of their causal effects.

Structural Properties of Social NetworksAnother important distinction in thenetworks literature on innovationdiffusion is that between strong ties andweak ties. Here the terms “strong” and“weak” do not refer to the intensity of anindividual relation (something that isencompassed within the concept ofcohesion), but to the articulation betweenthe multiple relations maintained by anindividual actor or the structural positionof a particular link within the network asa whole. Strong ties are links that aresituated in dense clusters within anetwork, where any direct link betweenego and alter is likely to reinforced bymultiple indirect ties among members ofa densely interconnected community.Weak ties are links that bridge betweenrelatively segregated clusters or commu-nities within a network and are seldom

duplicated by alternative paths of compa-rably short length.

Most naturally occurring socialnetworks display a distinctive mixture ofstrong and weak ties. Such networkstypically exhibit a much higher thanrandom degree of local clustering (strongties) together with a sufficient number ofbridging actors to create links betweenotherwise separate clusters (weak ties).The combination of these two propertiesyields what is known as the “small world”phenomenon in which path lengthsbetween any two randomly chosenmembers of the network are surprisinglyshort — typically on the order of five orsix links (Watts 2003).

An associated property of most socialnetworks is a highly skewed distributionin the number of links maintained byactors. Actors who contribute most to theconnectivity of the network, by either thelarge quantity or the strategic nature oftheir links, are called “central” actors.Some central actors have ties thatconnect them to a large proportion of theother members of their community, inwhich case they may be described as localhubs or stars of the network. Others havea substantial number of (weak) ties toclusters outside their own, in which casethey may be said to function as bridges,brokers, or gatekeepers to the largernetwork. Some display a combination ofthese properties.

Figure 1 (adapted from Burt [2004])illustrates the topology that is typical ofmany naturally occurring, small worldnetworks. Note the pattern of relativelydense-knit clusters linked to one anotherby occasional bridges that span from onecluster to the next. In the insert at the



lower left, the actor labeled “James” canbe described as a local hub of cluster Binsofar as he is linked directly to a highproportion of the other members of thatcluster. James’s links are mainly strongties in the sense that many of those towhom he is linked are also linked to oneanother. Actor “Robert” has fewer links toother members of cluster B; however, heoccupies a highly strategic position as thesole bridge between clusters B and C andone of only several bridges betweenclusters B and A. Robert is thus distin-guished mainly by his weak ties that linkhim to actors or clusters that are nototherwise connected among themselves.Each in his own way, both James andRobert can be described as relativelycentral actors within the network.

Together they illustrate the mix of hubsand bridges (strong and weak ties) thatare crucial for the overall connectivity ofsmall world networks.

These structural properties of socialnetworks have important implications forthe diffusion of innovation. In principle,local clusters provide ample pathways fordiffusion to occur. However, so long asadoption is contained within a smallnumber of relatively isolated clusters,diffusion across the larger network willbe limited. Only when adoption begins tospread across the weak ties betweenclusters are we likely to witness a sharpincrease in the rate of diffusion.Eventually, as most of the interconnectedclusters become saturated, we are leftwith only a small number of relatively



Figure 1. A Small World Network

isolated actors or clusters whose adoptionwill be delayed or blocked. Networkdynamics of this sort offer one expla-nation for the classic S-shaped curve thatis observed in many studies of innovationdiffusion.

Parallels can also be drawn betweenthe concept of “opinion leaders” and thedifferent types of central actors within anetwork. As noted earlier, relatively densecommunities of strong ties offer amplepathways for diffusion; however suchdense-knit communities also tend toexhibit resistance to change orinnovation. The adoption of aninnovation by locally central actors canbe crucial to overcoming this resistance,both because of the large number of tiesthat they maintain and because of theprestige that typically accompanies theircentral role within their community.Strategically located actors with ties toother communities also play a pivotal rolein the diffusion of innovation. Such actorsare frequently the first ones to becomeexposed to new ideas (Burt 2004), and thefact that they bridge between multiplecommunities makes them less bound bythe traditional norms and practices of anygiven community and more prone to beearly innovators.

Variations in Network Topology: Examples from EpidemiologyAlthough most naturally occurring socialnetworks exhibit certain structuraluniformities, it is important not to ignorevariations in network topology that existbetween different social spheres.Instructive parallels can be made here tothe field of epidemiology, which, likeresearch on the diffusion of innovation, is

concerned with modeling processes ofcontagion. In the research on sexuallytransmitted diseases (STDs) and the socialnetworks through which those diseasesare spread, important differences havebeen identified in patterns of clustering,the extent of bridging between clusters,and the degree of skewness in thenumber of sexual contacts. All of thesehave implications for the spread ofdisease and for public health efforts tolimit that spread.

For example, within sexual networksthat have highly skewed distributions inthe number of sexual contacts (typical ofthe spread of HIV/AIDS among adultpopulations), or where the there exists adistinct category of structurally equiv-alent actors who function as bridgesbetween clusters (e.g., long-distance truckdrivers who regularly have sex withcommercial sex workers), the most effica-cious use of public health resources willbe a targeted strategy aimed at influ-encing the behavior of those with themost sexual contacts or who function asvectors of transmission betweenotherwise isolated communities (Gopfertand Robert 2001). Less amenable to inter-vention, but also crucial to the success orfailure of public health efforts is theextent of cyclic (loop-like) clustering,which tends to provide a fertileenvironment for the gestation of disease.

Figure 2 illustrates a disease trans-mission network with some of theseproperties, drawn from a community-wide study of HIV/AIDS contact tracingrecords in Colorado Springs (Potterat etal., 2002). In this network it is easy toidentify certain actors who are likely tohave disproportionate impact on the



spread of disease, either because of thehigh number of their sexual (or needle-sharing) contacts or because of theirstrategic position as bridges betweenotherwise isolated segments of thenetwork. The topology of this network ispredominantly dendritic (branch-like),although it also exhibits a moderate levelof cyclic clustering and can thus becharacterized as intermediate in itssusceptibility to the spread of disease.

Other STD transmission networks lackeither a distinctive core of high-activityactors or an identifiable category ofbridging actors. Figure 3 illustrates anetwork of this kind, drawn from a studyof sexual relations among students in an

American high school (Bearman, Moody,and Stovel 2004). Compared with thenetwork shown in Figure 2, this networkexhibits a much flatter distribution in thenumber of sexual contacts, and there isalso a much wider and more equal distri-bution of weak, bridging ties. The extentof cyclic clustering is also lesspronounced, which ceteris paribus makesfor a less fertile environment for diseasetransmission. In a network of this kind,there is little basis upon which to design atargeted disease containment strategy,and a more broadly directed educationalcampaign will likely be most effective inslowing the spread of disease.



Figure 2. HIV/AIDS Disease Transmission Network

Of course, where the diffusion ofinnovation is concerned, we are morelikely to be interested in how contagionmight be encouraged rather thanprevented, but the same variations innetwork topology are relevant in eithercase. Knowledge of network topology isessential to designing an effective strategyto promote the diffusion of innovation.Where they exist, actors with dispropor-tionately high numbers of social tiesand/or actors who create bridges betweenotherwise separate segments of thenetwork must be identified if resourcesare to be targeted in an effective manner.In more homogeneous networks, the mosteffective diffusion strategy may be onethat eschews targeting and relies mainlyon broadly focused efforts at communi-cation and persuasion.

Networks and Innovation Diffusion in Higher EducationThe network perspective has madeimportant contributions to the study ofinnovation diffusion. Nevertheless,further advances in this area confrontsignificant challenges. Perhaps the mostpressing is the relative paucity of data onsocial networks of sufficient completenessand detail to allow us to model thediffusion of different types of innovationsacross different populations. As a conse-quence, the data from a small number ofclassic studies (such as those mentionedat the beginning of this essay) continue tobe used and reused by researchers in thefield. This is a serious limitation insofaras different types of innovations willlikely encounter different obstacles toadoption, and different target populations



Figure 3. High-School Sexual Network

may exhibit variations in networktopology that have important conse-quences for the dynamics of diffusion.

With regard to the field highereducation, there are (to my knowledge)no systematic network studies of thediffusion of innovations, either inteaching or in research. There is somerelated research on the structure andfunctioning of academic networks fromwhich one might hazard some hypothesesabout processes of innovation diffusionwithin higher education. Two specificareas that have been studied arecoauthorship networks (Newman 2001;Moody 2004) and networks created

through the exchange of Ph.D.’s(Hanneman 2001; Han 2003; Burris2004). The image that emerges fromthese studies is one of academic disci-plines as “small worlds” in whichrandomly chosen pairs of scholars,departments, or universities are typicallyseparated by only a short path of interme-diate ties. Figure 4, which is drawn froma study of coauthorship links amongphysicists (Newman and Girvan 2004),illustrates this pattern. Note here thecommonly observed topology of relativelydistinct and moderately dense clusterslinked by occasional bridges betweenclusters.



Figure 4. Coauthorship Network among Physicists

Judging from the available research,academic disciplines also tend to behighly stratified networks with relativelyskewed distributions of social connectionsand strong correlations between thenumber of social ties maintained byactors (either as individuals or as depart-ments) and subjective perceptions of theirprestige. This pattern is illustrated inFigure 5, which is based on a study of theexchange of graduates among Ph.D.-granting departments in sociology (Burris2004). The density of this network ismuch higher than the coauthorshipnetwork shown in Figure 4, andclustering is much weaker. Average pathdistances between departments are quitesmall, but this is mainly because of theextraordinarily high number of links

maintained by the most central actorswithin the network. The five departmentsshown in blue are among the historicallymost prestigious in the discipline. Thesehave an average of roughly 120 linksapiece to other Ph.D.-granting depart-ments created through the placement orhiring of graduates. Arrayed around themin red are fifteen moderately prestigiousdepartments that have an average ofroughly 50 links apiece to other depart-ments in the network. The remaining 75Ph.D.-granting departments (in yellow)have an average of roughly 18 linksapiece. With respect to proximity, themost central departments in the networktend to be both cohesive amongthemselves (i.e., they recruit Ph.D.’sheavily from one another) and struc-



Figure 5. PhD Exchange Network in Sociology

turally equivalent with respect to thelarger network (i.e., they frequently placePh.D.’s in less prestigious departmentsbut rarely hire from those same depart-ments).

In each of these respects, academicdisciplines would appear to be structuredin such a way that the acceptance of aninnovation is likely to depend crucially onits adoption by the most central actors ofthe network, but which poses few barriersto diffusion (at least from a networkstandpoint) once the innovation has beenaccepted by a critical number of theseopinion leaders. Of course, these are onlyhypotheses based on analogies with theflow of other types of influence,resources, and information withinacademic networks. It should also benoted that, even among the relativelylimited number of disciplines studied innetwork research, moderate differencesin the patterns enumerated above areevident. Only by collecting the additionaldata needed to study the role of socialnetworks in the diffusion of specificinnovations within specific disciplinescan we hope to ascertain the validity ofthese hypotheses and their consistency(or variability) across academic fields.

Collecting the data necessary for anetwork study of innovation diffusionwithin higher education would requireconsiderable effort and ingenuity, but isnot beyond the realm of feasibility. As thefocus of such a study, it would beimportant to select an educationalinnovation that is relatively discrete andfor which adoption or non-adoption canbe unambiguously ascertained atmultiple points in time. The most likelycandidates would be innovations in

curriculum or degree requirements thatare formally instituted at either thedepartment or college/university level.Presumably, data on the adoption or non-adoption of such innovations could beacquired through a combination ofarchival and survey methods. Pedagogicalinnovations of a more informal nature orthat are made at the discretion ofindividual faculty are likely to be impos-sible to track with sufficient accuracyunless one is dealing with a very smallsample.

The bigger challenge is posed by theneed to collect data from which one canconstruct a model of the network of socialconnections within the relevantpopulation of actors that is reasonablyaccurate, even if it is inevitably less thancomprehensive. The most feasiblestrategy here is to seek out archival data,ideally of a sort that is available inmachine-readable format. As noted above,previous researchers have mined data oncoauthorship ties to model networks ofassociation, influence, and informationexchange among individual scholars and,indirectly, among the departments anduniversities of which they are members.Others have drawn upon archival data onthe flow of Ph.D.’s among departmentsand universities as another way ofmodeling such networks. Data on thejoint participation of departments oruniversities in common organizations,initiatives, or events might be used for asimilar purpose. Where direct data onsocial ties among actors are lacking,rough proxies for network positions orrelations might be employed. Judgingfrom the existing research on academicnetworks, prestige rankings of academic



departments or universities are likely toprovide reasonable proxies for centralitywithin inter-organizational networks.Similarity of prestige rankings might alsobe used as a rough proxy for structuralequivalence within networks. Geographicproximity is likely to be moderately corre-lated with cohesion within networks.Exclusive reliance on proxies of this sortis not recommended, but they might beuseful as supplements to more directlymeasured indicators of network positionsand relations.

Despite the formidable challengesposed by data collection, there is poten-tially much to be gained from applyingthe tools and methods of network analysisto innovation diffusion within highereducation. Previous research demon-strates the importance of networktopology for the process of innovationdiffusion. Knowledge of the underlyingstructure of social networks can beinvaluable in the design of strategies topromote the diffusion of innovation. Moredetailed information on the structure ofsocial networks within particularacademic disciplines and the role thatthose networks play in the diffusion ofinnovation would be of great use to thoseinterested in educational reform. i

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IntroductionMany different perspectives might beadopted to explore questions of howinnovative practices that enhance instruc-tional effectiveness and student learningwithin undergraduate engineeringeducation start from an individual orsmall group that originally creates aninnovative practice and are adapted by alarge number of faculty members acrossthe engineering education community.Some perspectives focus on explanationsof how diffusion of innovative practicesoccurs. Other perspectives emphasizefactors than enhance or hinder diffusionof innovative practices. Drawing uponwork in decision-based design (Chen,Lewis, & Schmidt, 2000; Hazelrigg, 1998),the current paper chooses the perspectiveof a small team that has created andtested an innovative practice, foundevidence of effectiveness, and seeks topromote wider adaptation. The team

proposes to create a plan of action topromote adaptation and wants to base itsdecisions in creating the plan upon solidintellectual foundations. Other perspec-tives might be extremely valuable indeveloping the necessary intellectualfoundations, but in this paper, the focuswill be on the nature of the intellectualfoundations that support decisions relatedto development of the adaptation plan.

Several considerations motivateselection of the perspective. First, notethat the team has not chosen to focus oncreating awareness of its work; instead, ithas chosen to focus its energy onadaptation of its practice. Greaterawareness of their project might promoteadaptation, but awareness would be ameans, not an end. Broader adaptation isemphasized by funding agencies thatsupport development of innovativepractices and the team seeks to addresspriorities established by the program that



Creating a Plan to Promote Broader Adaptation of Education InnovationsJeffrey E. Froyd, Research Professor Texas A&M University

ABSTRACT

Creating plans that promote broader adaptation of innovative educational practices is an essentialpart of efforts to improve engineering education. However, the intellectual foundations for developingan adaptation plan are spread across many different research areas and may not have reached thestage where they can adequately guide decisions required in creating such plans. The present paperexplores how expanded knowledge bases might support development of action plans to promotebroader adaptation of innovative educational practices. Specifically, questions that address devel-opment of (1) plans to contact potential adapters, (2) plans to initiate, sustain, and grow supportnetworks, and (3) plans to produce materials that will enhance broad adaptation, are offered.

helped support initial development of theinnovative practice. Second, the team hasnot chosen to focus its energy on satis-fying promotion and/or tenure (P&T)criteria for individual members of theteam at their home institutions. MeetingP&T criteria might be a very high priorityfor individual members and might beanother goal that the team has formu-lated. However, the selected goal isbroader adaptation, not tenure and/orpromotion. Third, the nature of theinnovative practice is fixed. Considerableresearch has investigated characteristicsof an innovation, such as relativeadvantage, compatibility, complexity,trialability, and observability (Rodgers,2003). However, the team has created aninnovation, so it cannot alter the charac-teristics of the innovation. Other perspec-tives might explore selection ofinnovative practices for student learningand institutional effectiveness that resultin practices that will diffuse more easily.With the chosen perspective, explorationsof the intellectual foundations thatsupport decisions in development of anadaptation action plan might begin.

To aid in reflection on the nature ofintellectual foundations, a list of potentialdecisions that the team might consider indeveloping its adaptation action plan willbe offered first. The decisions might bebroadly grouped in the followingcategories, but not necessarily in thefollowing order.

• Contact Plan• Sustaining Network Plan• Supporting Materials Plan

Contact PlanIn a contact plan, the team must decidebetween passively promoting broaderawareness of its innovative practice andwaiting for individuals and/or institu-tions to contact the team for further inter-action and actively contacting selectedindividuals with details about theinnovative practice and seeing ifindividuals desire further information.The team may also select a combinationof the two strategies. The followingquestions would need to be addressed inthe formation of a contact plan:

• What factors should be consideredwhen deciding whether to initiatecontact, wait to be contact, or combinethe two activities? How might the intel-lectual foundations supportconstruction of a contact plan and aidanalysis of investment of resources insuch as plan?

• If the team elects to actively initiatecontact, what factors should beconsidered in deciding whom tocontact and how to make contact?What types of individuals might bemore prepared to accept theinnovative practice? What types ofindividuals might be more effective inpromoting the innovative practice attheir institution? This raises questionsof differences between innovators andearly adopters in Rodgers’ model(Rodgers, 2003). Innovators may bemore willing to consider adaptation ofan innovative practice, but they alsotend to be individuals who have lessinfluence in promoting adaptation byother individuals in their organizationor community. Early adopters are



more influential in promotion innova-tions, but initiatives to encourageparticipation by early adopters mayhave to be fashioned differently.Additional research in identifying andreaching early adopters would behelpful to construction of the team’sstrategies. In considering whom tocontact, should characteristics of insti-tutions be considered? What types ofinstitutions might be more supportiveof the innovation? How would thesetypes of institutions be characterized?This element might consider socio-cultural structures of “receiving” insti-tutions that facilitate or impede theacceptance and institutionalization ofan innovation, For example, studieshave shown relationships betweenculture and educational change (Kezar& Eckel, 2002; Merton et al, 2004).However, multiple frameworks havebeen offered for culture (Schein, 1992;Tierney, 1988; Bergquist, 1992), andfurther work is necessary to developculture audits that might identifyspecific factors that may aid selectionof institutions, in terms of theirreadiness for change.

• If the team elects to begin a plan inwhich they wait to be contacted,different questions need to beconsidered. What types of materialsmight promote/enhance likelihood ofcontacts in a passive contact plan? Theimportance of developing materialsand events for a range of individualswho are willing to invest differentamounts of time and energy in findingout more about a particular innovationhas been suggested elsewhere (Froyd,2001). Different materials and events

may be needed to address differentaudiences, but less work is available toguide choices of events and materialswith respect to audiences.

• Does specialized, contextualknowledge play a much larger role indevelopment of a contact plan thangeneral research?

Sustaining Network PlanThe value of networks of relationships insustaining learning and innovativepractice have been demonstrated througha variety of theoretical perspectivesincluding social capital (Maskell, 2000;Etcheverry, Clifton, & Roberts, 2001; TheWorld Ban, 2002; Kilpatrick, Bell, & Falk,1999), communities of practice (Lave &Wenger, 1991; Wenger, 1998; Wenger,McDermott, & Snyder, 2002), emphasis oncommunity-centered learning (Bransford,Brown, & Cocking, 1999), and facultylearning communities (Cox, 2001; Layneet al., 2002). Although the preceding workemphasizes the value of supportivenetworks, the team that developed theinnovative practice might find little onwhich to base decisions about how toinitiate, nurture, grow, and sustaincommunities that provide scaffolding formembers who choose to study theinnovative practice more deeply andpossibly adapt it.

There are various types of groups that theteam might decide to foster: local usergroups, regional networks, and nationalnetworks. Each might require differentefforts to create and support. The teammight seek answers to the followingquestions:



• What is the knowledge base thatimproves understanding of how facultymembers decide whether to partic-ipate in supportive learning commu-nities, user groups, regional networks,or national networks? What factorspromote participation? What factorshinder participation? Work exploringfaculty participation in learningcommunities intended to promotegreater understanding of the perva-siveness and complexity of genderinequity provides small clues(Covington & Froyd, 2004), but moreresearch is needed.

• What roles might prior knowledge playin decisions regarding participation?Cognitive learning theories havedemonstrated the enormous impor-tance of prior knowledge in learning(Bransford, Brown, & Cocking, 1999),and prior knowledge would beexpected to be an important factorparticipation decisions. However, moreknowledge of the roles of priorknowledge would be helpful. Further,it would be helpful it the team knewhow it might capture informationabout prior knowledge with minimalexpenditures of resources. Sosa,Eppinger, and Rowles (2004) haveshown how organizations mightbenefit from aligning interrelation-ships among their teams with theinterrelationships of the interfaceswithin complex designs. Similar align-ments between structural interrela-tionships of support networks andinterrelationships among priorknowledge of potential network partic-ipants may be possible.

• What roles might workshops play ininitiating, growing, and sustainingsupportive learning networks?Workshops frequently appear in plansto promote broader adaptation ofinnovative educational practices.Pimmel (2003) has evaluated effec-tiveness of workshops in promotinginnovative educational practices.However, there has been little researchon how workshops might supportdevelopment of networks. Moregenerally, what roles might expertiseon innovative educational practicesplay in the growth of networks?

• What might be learned from theexperiences of POGIL (The POGILProject; Hanson & Wolfskill, 2000),EPICS (EPICS at Purdue University;Oakes & Spencer, 2004) and SENCER(SENCER) in forming and sustainingnetworks that support innovativeeducational practices?

• What can be learned from the study ofnetworks through random graphtheory (Strogatz, 2001; Barabási &Albert, 1999)? Research in randomgraph theory has shown the impor-tance of hubs; however, can factorsthat might characterize a hub early inthe evolution of a network beidentified?

• What roles might communicationstechnology play in promoting devel-opment of networks around innovativeeducational practices (Sherer, Shea, &Kristensen, 2003; Courter, Freitag, &McEniry, 2004)?



Supporting Materials PlanMaterials including textbooks, assessmentand evaluation data, modules, projects,laboratory experiment manuals, andproblem sets can reduce the amount oftime and energy that potential users mustinvest in order to adapt an innovativepractice. Given that the team mightdevelop a large array of differentmaterials, the team is interested in whattypes of materials promote adaptationeffectively in relation to the effortexpended in developing the resources.Hutchinson and Huberman (1994) stressthe importance of building relationshipsbetween developers and users of theinstructional materials. From theirsynthesis of the literature, they offeredthe following factors that will affectpotential adaptation: accessibility, avail-ability, and adaptability; relevance andcompatibility; quality, redundancy ofmessages; linkages among users;engagement; and sustained interactivity(Hutchinson and Huberman, 1994). Forthe last factor, they noted thatOverall, the best single predictor ofknowledge use and gain is intensity ofcontact(s) between disseminators andreceivers. “The process that succeedsbest…involves frequent contact, someface-to-face interaction, and an exchangebetween dissemination specialists andparticipants that lasts more than a fewmonths over time (Louis, Dentler, Kell,1984, p. 17).” Sustained interactivity wasalso illustrated by the RDU [Research,Development, and Utilization]experience. The reviewers of thatexperience found that the amount of timethat field agents spent with staff at thelocal schools, both before and after initial

implementation, was one of the mostimportant predictors of success in theeffort (Louis, et al., 1981). (Hutchinsonand Huberman, 1994)Emphasis on sustained interactivity isconsistent with strategies intended todevelop sustained support networks.

Development of materials that arefocused on an audience that willimplement an innovative practice, e.g.,textbooks, ignores audiences that areunwilling, at their present level ofinterest, to invest sufficient time andenergy to engage an extended body ofmaterial. Consideration might also begiven to audiences that will invest limitedtime and energy to explore an innovativepractice (Froyd, 2001). The usefulness ofa staged model of adaptation (Froyd,2001); pre-awareness, awareness, interest,search, decision, and action could bestudied. Given the importance of thepotential audience in development ofmaterials, the team might also want toconsider collaborative development ofmaterials from the beginning. Involvingusers in materials development issupported by collaborative technologies(Cunningham; Hendricks, 2001).Collaborative materials developmentraises at least two questions:

• How might the team locate potentialusers who are willing to invest energyand time in contributing to devel-opment of materials?

• How does collaborative materialsdevelopment interact with strategiesfor development of sustained supportnetworks?



ConclusionsAlthough the existing knowledge base onpromoting broader adaptation needs to beexpanded to address types of questionsraised by the hypothetical project team, atleast one message echoes throughout:focus on increasing the capabilities ofpotential adapters to use the innovativepractice rather than developing materialsthat explain and justify the innovativepractice. The latter perspective shouldnot be ignored, but sole emphasis onexplaining and justifying is unlikely,given the present research, to promotebroader adaptation. Broader adaptationdepends more significantly on the qualityof relationships and the interconnect-edness of the participants in supportnetworks than the quality of a single setof materials. More research thataddresses effective strategies for initi-ating, sustaining, and growing supportivenetworks is needed to allow thehypothetical team to select strategies thatwill promote broader adaptation whilerequiring quantities of resources that arelikely to be available to the team. i

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INTRODUCTIONThis paper concerns the network founda-tions for the diffusion of innovations. Ourprimary concern is howthe pattern ofconnections among individuals in adepartmental or disciplinary network canhelp facilitate the spread of newcurricular developments across thatnetwork. We approach this question atmultiple levels. We ask how the pattern ofties in a network affects diffusion. Wethen move on to discuss the types ofpositions in networks that facilitatediffusion at the individual level andhighlight factors that might encourageinfluence, or inter-personal agreement,between dyads that are connected withina network. The success of an innovationdepends both on individuals who areaware of the innovation accepting it, andthe process of spreading this acceptanceto others. Importantly, research on socialnetworks cannot help determine whichindividual (or department) is most likelyto adopt first, but it can assess howdiffusion might be affected depending onthe network position of this initialadopter, thereby informing decisionsabout where to begin efforts to introducea new curriculum. We take up such issuestoward the end of the paper.

HISTORY Those interested in the history of socialnetwork approaches to diffusion have a

rich tradition to draw on. For a generaloverview, see Valente 1995. Classic workson innovation diffusion include:Coleman, Katz, and Menzel 1966; Crane1972; Katz, Levin, and Hamilton 1963;Kerkchoff and Back 1968; Morris 1993;Myers 2000; Rogers 1962. Early workfocused on describing the diffusioncurves and relating network position toadoption. While most work (includingours below) focuses on the diffusion ofgoods through direct contact, a strongargument can be made for adoption basedon similar interests represented byposition in network (Burt 1987; Friedkin1984; Mizruchi 1993). Unlike contagionmodels, this argument suggests that thosewith similar patterns of ties have similarinterests, similar levels of access to thenew practice (e.g., curriculum), andsimilar motivations for adopting it. Howindividuals become exposed to theinnovation is overlooked; to understandthis, we turn to the literature on diffusionthrough direct contact with others.

GLOBAL NETWORK DIFFUSION PROCESSESPaths through networks: a simple example. Consider figure 1, which is a graphicalrepresentation of school social network,with points (nodes) representing studentsand lines (ties) representing friendshiprelations. For now we consider the ties tobe symmetrical. Imagine that a newinnovation in, say, free music



Network Foundations for the Diffusions of InnovationsJames Moody and Erin Leahey

downloading was discovered by one of thestudents. We would expect the student toshare this information with his or herfriends, who would likely pass it on totheir friends and so forth. This is thebasic diffusion process we are trying todescribe with network models: given thata small number of initial adopters

communicate an innovation to theircontacts, how does the pattern of contactsaffect the ultimate diffusion of theinnovation?

To answer this question, we assumethat everyone has an equal probability ofadopting given each opportunity forexposure.1 If we randomly selected anindividual to be the initial adopter andexamined the resulting pattern ofdiffusion, and repeated this process manytimes, we could capture the overallpattern of diffusion with a set of diffusioncurves, where the horizontal axismeasures time and the vertical axis theproportion of the population who adopt.Figure 2 shows a summary of just such asimulation applied to the networkdepicted in Figure 1. The inset shows themean curve relative to a network withrandomly distributed ties (but identicalvolume). Why is the rate of diffusionslower in the observed network than inthe randomone? Why is the curve lower?There are four features of network thataffect diffusion rates throughout anetwork, relative to other networks with asimilar contact volume (i.e., number ofties): network distance, clustering,multiple paths, and timing.

Network DistanceThe primary requirement for a contagiondiffusion model is that non-adopters comeinto contact with adopters, this meansthat the network must be connected. Infigure 1, the small number of isolated



________________________

1 That is, at each point in time, every “adopter to non-adopter” contact represents an opportunity for thenon-adopter to adopt. If a non-adopter has many adopting friends, then he or she will have many moreopportunities to adopt. This is a simplifying assumption that removes much of the internal decisionmaking process in order to highlight the effect of network structure.

nodes in the middle-right of the plotcannot be exposed (through the network)to adopters, and thus no network-contagion effects are possible (thoughthey might adopt through some othersource). Given simple connectivity, theprobability that i will pass the innovationto j is typically less than 1. As such, theprobability of passing an innovation down any single long chain of contacts (igjg kg l) decreases as a power function ofthe length of the path. Thus, all elseequal, innovations diffuse over a networkin inverse relation to the average distancebetween nodes.

Network ClusteringIf diffusion were certain, then the mostefficient network would be one whereevery linkfrom an adopter leads to a non-adopter, which would be represented by aspanning tree. In real world networks,relations tend to clump aroundsubstantive features (Feld 1981),especially geographic bounds (Leahey&Cabrera 2006). These clusters tend tomake diffusion patterns “lumpy,” asdiffusion will be facilitated withinclusters, but then get stuck there waitingto move out through a local bridge: aperson or node that serves to connect twoclusters. If we focus solely on therecursion feature of clustering (the extentto which ties coming fromnode kreconnect to the people k received theinnovation from), then both theory andsimulation suggest that as networkclustering increases, diffusion ratesdecrease (Pool and Kochen 1978; Skvoretz1985; Watts and Strogatz 1998).

Multiple PathsWhile path length decreases thelikelihood of transmission, multiple pathsprovide alternative routes and thusincrease the probability of transmission.Thus while each step in a chain repre-sents an “and” condition for the proba-bility, each alternative route provides an“or” condition. Figure 3 illustrates thisrelation with the diffusion of a low-proba-bility good over long distances. Thus, allelse equal, the more paths connecting twonodes, the higher the likelihood ofdiffusion transmission.

TimingFinally, the results from above all assumea network where the edges are constantand diffusion occurs across an otherwisestable network. In reality, any real-worldsocial network will be continuallychanging, and changes in the edges affectthe likelihood that an innovation can passfrom one person to another. In essence, ifnode j leaves the network before mode iadopts the innovation, then j cannot learnof the innovation from i. Such timingissues can have a dramatic effect on



opportunities for contact and thusinfluence (Moody 2000). The lowestpossible diffusion occurs when relationsare of short duration and nodes have onlyone relation at a time, creating shorttime-ordered paths through the network.In contrast, when relations overlap signif-icantly in time (they are temporallyconcurrent), information can potentiallytravel through all edges, magnifying thelikelihood of diffusion (Morris andKretzschmar 1997).

POSITIONS IN NETWORKS: STARS & STRUCTURAL HOLESThe features described above relate todifferences across networks. A glance atfigure 2, however, reveals significantvariation within networks. If we were tocontrast the fastest diffusion curve (thetop of the box-plot tails) with the slowest(the bottomtails), time to reaching 50%ofthe population is nearly double in theslowest diffusions. This internal variationrests on the position of the initialadopters. When initial adopters arecentral players in star-shaped networksand/or “brokers who carry informationacross the social boundaries betweengroups” (Burt 1999, p37) they are likelyto be opinion leaders whose ideas spreadeasily, quickly, and widely.

If the initial adopter is a central nodein a star-shaped network, then theircontacts’ contacts’ reach the entirenetwork quickly and widespread diffusionis likely. This is the individual levelcorrelate of the distance featuredescribed above. Since central nodes arecloser to everyone, the average pathdistance between adopters and non-adopters is comparatively low. All else

equal, diffusion will be faster when initialadopters are central to the network(Friedkin and Johnsen 1997; Valente andDavis 1999).

If the initial adopter serves as a bridgethat spans clusters within the network,then the rate of diffusion will alsoincrease. This individual positioncorrelate of clustering rests on Burt’snotion of structural holes (Burt 1992; Burt1999; Burt 2005): essentially the gapsbetween clusters. A node bridges a struc-tural hole if their contacts are not incontact with each other. If ideas run arisk of becoming sequestered in clusters,then promoting diffusion rests heavily onindividuals who connect otherwisedisconnected people. When clustering isevident, individuals serving as bridges arecritical to spreading an innovationbetween clusters.

FACILITATING NETWORK DIFFUSIONThe above summary of work on the struc-tural features of diffusion in networkssuggests a few broad features. Diffusionwill be most rapid and complete whenshort average distances, relatively lowclustering, multiple paths and compa-rably stable relations characterize thenetworks. Within such networks,diffusion will proceed most rapidly ifcentral actors can be convinced toinitially adopt and if cluster-brokers canbe convinced to share the informationwith others. How do we build suchnetworks?

The simplest heuristic for buildingrapid diffusion networks is randommixing, particularly given the compar-ative search costs for attempting toengineer such networks. That is, recent



work (Watts and Strogatz 1998) showsthat a relatively small number of randomconnections between otherwise discon-nected clusters in networks can rapidlydecrease average distance and increasethe number of unique paths (Moody andWhite 2003). As such, perhaps a verysimple yet structurally effectivecurricular development model will focusattention on creating new links betweenindividuals or groups that are likely toadopt and the wider body of engineeringinstructors. While random mixing has theadvantage of making the most with theleast effort, any mixing strategy that doesnot simply reinforce current clusteringpatterns will rewire a network to facil-itate diffusion.

Some consideration might also begiven to the type of network tie that couldbest foster discussion and adoption of aninnovative curriculum. One couldimagine, for example, some sort ofworkshop or short-term exchangeprogram that brings innovators intocontact with other faculty members. Butwould this be enough? Investigations intothe kinds of relationships that best fosterdiscussion of pedagogy and related imple-mentation strategies would be useful inthis regard. Previous research suggeststhat weak, advice-seeking relationshipstend to foster attitudinal agreement,whereas longer, stronger, close workingrelationships tend to foster behavioralagreement, or shared practices, betweenindividuals (Leahey and Cabrera 2006).

We note that a variety of otherfeatures can influence diffusion of aninnovation, regardless of networkstructure and relationship type, and weassume such considerations will be taken

up by other panels in this workshop. Forexample, previous researchers have foundthat the rationality (Strang and Soule1998), compatability, and portability(Abbott 1999) of an innovative practiceare positively related to diffusion, andcomplexity (Rogers 1962) is negativelyrelated to diffusion. How an innovation ispresented, and the means by which it isencouraged may also affect diffusionrates. Institutional theorists DiMaggioand Powell (1983) stress the importanceof both mimetic processes (when peoplesimply copy others) and coerciveisomorphic pressures,which can emanatefrom either formal policies or informalpressures or invitations to adopt theinnovation.

Edge Effects of Status DistinctionsFinally, we want to make one clear pointof caution. The summary above works onthe assumption of bi-directional flow inthe networks across edges. However,academic disciplines and departments areformally structured around status, withlower-prestige departments and assistantprofessors in a subordinate positionrelative to prestigious departments andfull professors. As such, it is likely thatinnovation flows more readily fromtopdepartments to less prestigious depart-ments (Leahey 2005) and from seniorfaculty to junior faculty or faculty-intraining (Leahey, in press). That is, anotherwise simple “contact” tie between afull professor and a new assistantprofessor is really likely to be a directedtie fromthe full to the assistant, anddiffusion is likely to follow in thatdirection. This suggests that there may begreater diffusion returns by convincing



senior faculty (especially those in topdepartments) to adopt the innovationinitially. There is, of course, an obvioustrade-off here. On the one hand, seniorfaculty have well-developed courses andlittle incentive to change them. On theother hand, they may have the least toloose if the implementation of a newcurriculum represents time lost to otheractivity favored by promotion and tenurecommittees. Our goal here is to highlightthis asymmetry in network relationsbetween individuals and departments andsuggest how it informs efforts toimplement a new curriculum widelywithin a discipline. Exactly howimportant players (central nodes, bridges,prestigious departments, and fullprofessors) are convinced to adopt thenew curriculum is an important subjectideally addressed by other panels in thisworkshop. i

REFERENCES

Abbott, Andrew. 1999. Department andDiscipline. Chicago: University ofChicago Press.

Burt, Ronald S. 1987. “Social Contagionand Innovation: Cohesion VersusStructural Equivalence.” AmericanJournal of Sociology 92:1287-335.

———. 1992. Structural Holes: The SocialStructure of Competition. Cambridge,Massachusetts: Harvard University Press.

———. 1999. “The Social Capital ofOpionion Leaders.” Annals of theAmerican Academy of Political andSocial Science 566:37-54.

———. 2005. Brokerage and Closure: AnIntroduction to Social Capital. Oxford:Oxford University Press.

Coleman, James S., E. Katz, and H.Menzel. 1966. “The Diffusion of anInnovation Among Physicians.”Sociometry 20:253-70.

Crane, Diana. 1972. Invisible Colleges:Diffusion of Knowledge in ScientificCommunities. Chicago: University ofChicago Press.

DiMaggio, Paul J., and WalterW. Powell.1983. “The Iron Cage Revisited:Institutional Isomorphism andCollective Rationality inOrganizational Fields.” AmericanSociological Review 48:147-160.

Feld, Scott L. 1981. “The FocusedOrganization of Social Ties.” AmericanJournal of Sociology 86:1015-35.

Friedkin, Noah E. 1984. “StructuralCohesion and EquivalenceExplanations of Social Homogeneity.”Sociological Methods and Research12:235-61.

Friedkin, Noah E. and Eugene C. Johnsen.1997. “Social Positions in InfluenceNetworks.” Social Networks 19:209-22.

Katz, Elihu, Martin L. Levin, and HervertHamilton. 1963. “Traditions ofResearch on the Diffusion ofInnovation.” American SociologicalReview 28:237-52.



Kerkchoff, A. C. and K. W. Back. 1968. The June Bug: A Study of HystericalContagion. New York: Appleton-Century Crofts.

Leahey, Erin (2005). “Alphas andAsterisks: The Development ofStatistical Significance TestingStandards in Sociology.” Social Forces.Vol. 81, No. 1, pp. 1-24.

Leahey, Erin (in press). “TransmittingTricks of the Trade:Mentors and theDevelopment of Research Knowledge.”Teaching Sociology.

Leahey, Erin, and Joseph F. Cabrera(2006). “Interaction and Consensus inResearch.” Working paper.

Mizruchi, Mark S. 1993. “Cohesion,Equivalence and Similarity ofBehavior: a Theoretical and EmpiricalAssessment.” Social Networks 15:275-307.

Moody, James. 2000. “The Importance ofRelationship Timing for Diffusion.”Social Forces 81:25-56.

Moody, James and Douglas R.White. 2003.“Social Cohesion and Embeddedness:A Hierarchical Conception of SocialGroups.” American Sociological Review68:103-27.

Morris, Martina. 1993. “Epidemiology andSocial Networks: Modeling StructuredDiffusion.” Sociological Methods andResearch 22:99-126.

Morris, Martina and M. Kretzschmar.1997. “Concurrent Partnerships andthe Spread of HIV.” AIDS 11:641-48.

Myers, Daniel J. 2000. “The Diffusion ofCollective Violence: Infectionsness,Susceptibility, and Mass MediaNetworks.” American Journal ofSociology 106:173-208.

Pool, Ithiel d. S. and Manfred Kochen.1978. “Contacts and Influence.” SocialNetworks 1:5-51.

Rogers, E. M. 1962. Diffusion ofInnovations. New York: Free Press.

Skvoretz, John. 1985. “Random andBiased Networks: Simulations andApproximations.” Social Networks7:225-61.

Strang, David, and Sarah Soule. 1998.“Diffusion in Organizations and SocialMovements: From Hybrid Corn toPoison Pills.” Annual Review ofSociology 24:265-290.

Valente, ThomasW. 1995. Network Modelsof the Diffusion of Innovations.Cresskill, NJ: Hampton Press.

Valente, ThomasW. and Rebecca L. Davis.1999. “Accelerating the Diffusion ofInnovations Using Opinion Leaders.” The Annals of the American Academy ofPolitical and Social Science 566:55-67.

Watts, Duncan J. and Steven H. Strogatz.1998. “Collective Dynamics of ‘Small-World’ Networks.” Nature 393:440-442.



INTRODUCTIONEverett Rogers, in his widely cited book,defines diffusion as the process by whichan innovation is communicated throughcertain channels over time among themembers of a social system (Rogers,2003). Several aspects of this definitionare noteworthy and can productivelyguide research into whether and howdecision-makers within schools ofengineering will be receptive topedagogical reforms. First, the definitiondoes not assume adoption (implemen-tation) by even one member of the socialsystem in question. As soon as infor-mation about the innovation has reachedjust one person (or decision-makingentity), a diffusion process can be said tohave begun. Information may eventuallyreach all members of the social system, oronly some. The spreading of that infor-mation — plus other accompanyingforces, such as perceived social pressureand the consensus or harmony within thebroader institutional environment(Frank, Zhao, & Borman, 2004; Rowan,1982; Wejnert, 2002) — may lead all,some, or none of the system’s actors toadopt the innovation.

Secondly, the words process and timeare key to Rogers’s conceptualization (andto the most satisfying and informativeempirical research on the topic ofinnovation and organizational change).

The often-observed S-shaped cumulativeadoption curve can only be discerned ifone studies actions over time (Mahajan &Peterson, 1985; Rogers, 2003). At least asimportantly, an understanding of howvarious potential influences (e.g., types ofinformation, types of change agents,patterns of communication amongcolleagues) have differential effects astime elapses will only be gained ifdiffusion is recognized and analyzed as amultifaceted, unfolding process.

Consistent with Rogers’s conceptual-ization, Strang and Soule identifydiffusion at the most general level as “thespread of something new within a socialsystem” (Strang and Soule, 1998, p.266).Those authors describe the flow ormovement of a behavior, strategy, belief,technology, or structure from a source toa potential adopter (or adopters), typicallyvia communication and influence. Strangand Soule make a strong case for notsimply equating diffusion with increasedincidence; they urge us not to treatdiffusion as simply an observed outcomeof “use versus non-use.”

In order to acknowledge diffusion ofinnovation as a process (as opposed tosimply an outcome observed if and whenadoption of a practice occurs), I organizemy discussion around nine distinctaspects of the spread of something newwithin a social system. For the purposes



Pedagogical Change in Schools as Diffusion of Innovation: Some Sociological Guidelines for ResearchStephen B. Plank Johns Hopkins University

of this paper, the something new shouldbe assumed to be a set of pedagogicalpractices and accompanying philosophiesthat some change agents would like to seeimplemented within post-secondaryschools of engineering. Depending on thecontext or particular example underdiscussion, the members of a socialsystem might best be imagined as one oranother of the following:• All faculty and instructors employed by

one school of engineering,• All faculty and instructors nested

within a set of schools of engineering,• All departments nested within one or

more schools of engineering, or• A set of schools of engineering.

Listing these four different opera-tionalizations of the members of a socialsystem serves to remind us that innova-tions are marketed to, and sometimesimplemented by, actors and decision-making bodies at various levels of aggre-gation.

As I briefly discuss each of the nineaspects of diffusion, I give particularattention to (1) findings and perspectivescoming from the sociology of educationsubfield, (2) observations or principles Idraw from my involvement with middleschool and high school educationalreform, and (3) sociological studies ofdiffusion processes more generally.

NINE OUTCOMES OF INTERESTTable 1 presents the nine areas ofattention, including suggestions aboutappropriate units of analysis (which maychange depending on the area underconsideration) and interesting orimportant questions that may be asked.

1. Timing of awareness.If one is going to stay true to the concep-tualizations of Rogers (2003) and Strangand Soule (1998) — authors who are verythoughtful and thorough in theirtreatment of diffusion of innovation —then one will begin by studying thespread of awareness or informationbefore studying changes in individuals’behaviors (e.g., adoption or rejection). Itis illuminating to document, describe,and/or formally model:• The proportion of actors within the

social system who have a workingunderstanding of the proposed reformby Time t.

• The paths by which information comesto Person i (e.g., directly from anexternal change agent, from aninternal colleague to whom i is onlyweakly connected, or from an internalcolleague with whom i is closelyconnected).

• Various factors that may predictwhether Person i will have a workingunderstanding of the reform by Time t(examples listed in Table 1).

2. Adoption rates and interrelationships ofmultiple innovations.In conjunction with studying thediffusion of information and the spread ofawareness, clearly we will be interestedin studying actual behavioral change —the adoption of an innovation. As wepursue this task, it is important toconsider the existence of multiple innova-tions, and the interrelationships that mayexist among them.

Researchers who study the successesand failures of educational reform initia-tives quickly realize that they do not have



the luxury of comparing interventionsites with pure control sites that arereceiving “the absence of any inter-vention or treatment.” Furthermore,educational organizations are constantlysubject to (or generative of) multipleproposed reforms and paths toimprovement. In light of these facts, thediffusion of, or resistance to, any oneinitiative should be analyzed and under-stood within the context of other newideas or reforms that are circulatingthrough the social system simultaneously.

Rogers (2003) directs our attention totechnology clusters, which consist ofmultiple distinguishable elements of atechnology (or innovation, moregenerally) that are perceived as beingclosely interrelated. It may be the casethat change agents will be mostsuccessful in promoting a giveninnovation if it is “bundled” or“packaged” with other closely relatedinnovations. For example, one canimagine that engineering professors whoteach freshmen would be most enthusi-astic about expanding the opportunitiesfor group projects and hands-on experi-mentation if they knew that seniorcapstone projects featuring these sameskills and activity structures were beingpromoted within their schools.

Rogers advises that we avoid thedubious assumption that the trajectory ofa given innovation (i.e., its spread orstalling) is independent from otherinnovations. Furthermore, as we thinkabout the interdependencies of multipleinnovations, it becomes clear thatmutually reinforcing (complementary)bundles of innovations are not the onlyinterrelationships that we need to

consider. Mahajan and Peterson (1978,1985) identify four categories ofinnovation interrelationships that canaffect the adoption rate as well as thecumulative number of adoptions of aninnovation. These four are:• Independence, whereby innovations are

independent of each other in afunctional sense, but adoption of onemay enhance adoption of others;

• Complementarity, whereby increasedadoptions of one innovation result inincreased adoptions of other innova-tions (e.g., washing machines anddryers);

• Contingency, whereby adoption of oneinnovation (e.g., computer software) isconditional on adoption of otherinnovations (e.g., computer hardware);and

• Substitution, whereby increasedadoption of one innovation results indecreased adoption of other innova-tions (e.g., Coke and Pepsi).

Extensions and refinements of thestandard (quantitative, parameterized)diffusion models have been developed toincorporate these possible interrelation-ships. Similarly, extensions of thestandard models have been made torecognize that outcomes of the diffusionprocesses for individual actors (i.e., one’sreceipt of information or one’s decisionabout adoption or resistance) might notbe simply binary.

Cynthia Coburn is one sociologist ofeducation who has been thoughtful aboutmoving beyond a binary conceptual-ization. Her study of teachers’ reactionsto competing ideas about readinginstruction in California schools between



1983 and 1999 is informative (Coburn,2004). This study is mostly qualitative,but the typology Coburn develops forteachers’ possible responses to institu-tional pressures to alter their instruc-tional styles could certainly be incorpo-rated into quantitative modeling ofdiffusion. Coburn moves beyond a simpledichotomy of acceptance versus rejection.She develops the following range ofreactions a teacher might have toproposed reforms:• Accommodation. Engaging the reforms

in ways that represent fundamentalrevision of one’s pedagogical viewsand practices.

• Assimilation. Making an attempt toaccept and implement the reforms, butonly after transforming the tenets orinterpretations of them to fit one’s pre-existing pedagogical beliefs and waysof doing things. When assimilationoccurs, an actor will often come tounderstand the reform, its goals, andpractices, in ways that differ substan-tially from what was intended by itsdevelopers or the change agents.

• Parallel structures. Taking the reformseriously and adopting its practices butonly some of the time or in somecontexts, thereby leaving intact andunchanged other (possibly conflictingor antithetical) practices at othertimes and in other contexts.

• Decoupling/symbolic response. Givinglip service, or making superficialchanges to give the appearance ofcompliance, without changingprevious practices in any serious way.

• Rejection. Outright refusal to engage oradopt the reform; outright dismissal ofa mandate.

3. Innovativeness of individuals or organizations.We are likely to gain a more completeunderstanding of individuals’ or organiza-tions’ receptivity to a particularinnovation if we consider the actors’ pastexperiences with other innovations andgeneralized openness to change. Table 1offers questions to be asked along theselines, and a guiding principle derivedfrom prior research.

4. Opinion leadership.Early innovators and opinion leaders areoften (perhaps usually) two distinctsubgroups within a social system. Morethan forty years ago, Carlson (1965)studied the diffusion of modern math inthe public schools of Allegheny County,Pennsylvania, as an educationalinnovation. He focused on the decisionsand sociometric positions of thirty-eightschool superintendents. These superin-tendents were key decision-makers whoseawareness and enthusiasm werenecessary if principles of modern math(e.g., newly conceptualized textbooksfeaturing set theory, Venn diagrams, andprobability; audiovisual aides; andsummer institutes to retrain schoolteachers) were to become established inthe schools they oversaw.

Carlson demonstrated how theearliest innovator among these thirty-eight was a sociometric isolate with nointerpersonal network connections withany of the other superintendents. The factthat this early innovator embraced themodern math approach in 1958 appar-ently did little or nothing to encourageother superintendents and school districtsto do the same. Only when the members



of a clique of centrally connected andinfluential opinion leaders embracedmodern math a year or two later (afterapparently being prompted by otherchannels of communication and encour-agement) did a rapid sequence ofadoptions occur — culminating in allthirty-eight superintendents embracingmodern math by 1963.

One general lesson suggested byAllegheny County’s somewhat-distant pastis that change agents would be wise toidentify key opinion leaders in a socialsystem and to focus dissemination ofinformation and initial training opportu-nities on these individuals. This may beeasier said than done, however, as keyopinion leaders generally hold that desig-nation precisely because they are verymuch in conformity with the norms andstandard operating procedures of theirorganizations. If the change agents whoare urging reforms in the pedagogy ofengineering schools are perceived as (A)outsiders to the social system and (B)advocates of something that isantithetical to the norms and priorities ofthe social system then overtures to keyopinion leaders within a school might fallonto unreceptive ears. The mostproductive research (and, for that matter,strategizing by change agents) will directattention to how successfully externalchange agents can minimize the extent towhich they are perceived as (A) and (B),above.

5. Structure and content of diffusionnetworks.See Table 1 for summarized points.

6. Adoption rates contingent on organizational traits.Strang and Soule (1998) state that the

most common design in diffusionresearch has examined variability in thetiming of adoption of a single practiceacross a single community (a relationallyand culturally connected population, touse their phrasing). They assert that ourunderstanding of diffusion processes —and the factors accelerating them orstalling them — will grow more quicklyand comprehensively if we consider (a)the spread of a given practice withinmultiple communities or (b) the spread ofdifferent practices within a singlecommunity. Clearly the most ambitiouscomparative effort would entail multiplepractices studied within multiple commu-nities. As one considers pedagogicalchange in post-secondary engineeringeducation, it is important to considerwhat defines a fairly closed community ofpractice. If one were to study ten schoolsof engineering as they encountered aproposed pedagogical change, could wejustify calling these ten distinct commu-nities? Or is the reality that collegialrelations, channels of communicationand influence, and cultural under-standings of “how we do business” spanmultiple institutions such that no oneinstitution should be called a (relatively)bounded community of practice?

If we can agree on where the true(empirically existing) boundaries ofcommunity or social system lie, we canthen engage in the exciting exercise ofconceptualizing analyses of individualsnested within communities of practice asthey receive or fail to receive infor-mation, and subsequently make decisions



about embracing or rejecting pedagogicalreforms.

7. Use of communication channels.Information about an innovation willgenerally reach different potentialadopters from different sources or viadifferent network paths. Furthermore,the most active and effective dissemi-nators of information will often changeover time (e.g., from external changeagents to internal converts or, alterna-tively, obstructionists). See Table 1 foradditional summarized points.

8. Consequences of an innovation.In addition to studying the spread ofinformation and the actual adoption orrejection of an innovation, we willprobably want to study potential effectson various outcomes or subsequentprocesses. Table 1 lists some outcomes ofinterest for the particular case ofpedagogical change in post-secondaryschools.

9. Desistence rates.Desistence rates and processes are anarea of inquiry that can be consideredsecondary — most likely a topic for laterconsideration. The idea is that we shouldnot be satisfied by understanding just thetiming of, and processes driving, (1)actors’ initial receipt of information (ifany is received) and (2) actors’ initialadoption an innovation (if this occurs). If learning about an innovation, andadopting that innovation, can be conceptualized as two potential “birth”processes, then one might want to studydesistence of the innovation as a “death”process.

Researchers likely will develop event-history models of the likelihood ofadopting the innovation by time t forthose who had not adopted it by time t-1.It would then be informative to alsodevelop models of the likelihood ofceasing to use the innovation by time t’among those who had begun using it atsome earlier time and had not yetabandoned it by t-1’. It seems likely thatthe rate of abandonment among previousadopters will affect the rate of newadoption among potential adopters. It alsoseems likely that abandonment processeswould be characterized by points of rapidacceleration, probably triggered by theactions of an organization’s key opinionleaders, just as adoption processes aregenerally characterized by the S-shapedcurve with a point of rapid accelerationas the so-called early adopters inspire theso-called early majority.

CONCLUDING COMMENTSI am writing this final section afterhaving attended the ASA/CASEEworkshop. The workshop was a valuableopportunity for engineers and sociologiststo talk about the research interests andperspectives each group brings to thestudy of innovation diffusion, and also totalk about the sorts of curriculum andinstruction that can be imagined for post-secondary education.

The workshop confirmed for me theimportance of heeding the caution voicedby Rogers (2003) and others to guardagainst the pro-innovation bias. Icertainly do not object to educational andprofessional leaders conceptualizingpedagogical reforms that might benefit



post-secondary students and the profes-sions into which they are entering. And Ido not object to change agents activelystrategizing about how to increase thespeed and/or fidelity with whichproposed reforms are, first, communi-cated and, secondly, adopted. I do notobject to using past and future researchas a resource for advising change agents.

We should, though, strive to giveattention to the false-starts and failedattempts at widespread diffusion just asenergetically as we study the processesthat take off and get sustained. And, if weare to be good sociologists, as we attemptto study diffusion processes we will needto try to get inside the hearts and mindsof the potential adopters. We will need tounderstand the pressures, incentives,perceptions of mission, and perceptionsof professional identity that guide theiractions. We cannot let ourselves jump tothe conclusion that individuals who arenot receptive to a reform — even in theface of detailed information about it — arenaive or misguided. When we findindividuals who are not receptive to areform, I think we will generally find allor some of the following accompanyingthat situation:• Change agents who have not invested

enough time in, or developed effectivestrategies for, the interpretative workand cultural framing of the reform(i.e., convincing the potential adopters— one hopes in a very genuine way —that the reform is not inconsistentwith their goals and worldviews).

• Potential adopters who simply have notreceived organized or thorough infor-mation about the reforms.

• Potential adopters who have

considered the reform but haveconcluded that it is not consistent withtheir goals or worldview.

At the ASA/CASEE workshop, we madevaluable first steps toward definingparticular innovations that have beendeveloped (or are currently beingdeveloped) in engineering education. Theworking group of three engineers andthree sociologists of which I was amember discussed various innovationsthat have been attempted (or might beattempted) in the realm of teaching andlearning (particular task and reward struc-tures, or methods of instruction). We alsodiscussed innovations that have occurredin the curricular or organizational realms(the sorts of courses offered and/orrequired; new types of departments orinterdisciplinary centers that have beenestablished). Our working group began toconceptualize research designs to inves-tigate innovation diffusion either fromarchival sources or via prospective studiesinvolving original data collection. Wediscussed formal quantitative models (andthe data structures needed to estimatethem) that can be utilized in studyingdiffusion of innovation. Some of thesemodeling strategies are used at theaggregate level (considering proportions ofpotential adopters who have tried aninnovation by a given time). Additionally,to study the actions or situations ofindividual potential adopters, there areavailable a variety of event-history orhazards formulations (Strang & Tuma,1993; Strang, 1995) and models that incor-porate sociometric measures and nesteddata structures (e.g., Frank, Zhao, &Borman, 2004). i



REFERENCES

Carlson, R.O. (1965). Adoption ofEducational Innovations. Center forthe Advanced Study of EducationalAdministration, University of Oregon,Eugene.

Coburn, C. (2004). Beyond decoupling:Rethinking the relationship betweenthe institutional environment and theclassroom. Sociology of Education 77:211-244.

Frank, K.A., Y. Zhao, & K. Borman.(2004). Social capital and the diffusionof innovations within organizations:The case of computer technology inschools. Sociology of Education 77: 148-171.

Mahajan, V., & R.A. Peterson. (1978).Innovation diffusion in a dynamicpotential adopter population.Management Science 24:1589-1597.

Mahajan, V., & R.A. Peterson. (1985).Models for Innovation Diffusion. SageUniversity Papers Series, QuantitativeApplications in the Social Sciences, No.07-048. Beverly Hills, CA: SagePublications.

Rogers, E. (2003). Diffusion ofInnovations (Fifth Edition). New York:Free Press.

Rowan, B. (1982). Organizationalstructure and the institutionalstructure: The case of public schools.Administrative Science Quarterly 27:259-279.

Strang, D. (1995). mhdiff: SAS-IML proce-dures for estimating diffusion models.Technical Report 95-3a. Department ofSociology, Cornell University.

Strang, D., & S.A. Soule. (1998). Diffusionin organizations and socialmovements: From hybrid corn topoison pills. Annual Review ofSociology 24: 265-290.

Strang, D., & N.B. Tuma. (1993). Spatialand temporal heterogeneity indiffusion. American Journal ofSociology 99: 614-639.

Wejnert, B. (2002). Integrating models ofdiffusion of innovations: A conceptualframework. Annual Review ofSociology 28: 297-326.



1. Timing of knowingabout an innovation by members of a social system

2. Rate of adoption ofdifferent innovationsin a social system

Members of a socialsystem (usually individualpeople)

Innovations

• What do we learn if we keep awareness of thepractice analytically distinct from use of thepractice? (Short answer: Potentially verymuch.)

• What predicts Person i having a working under-standing of the practice by Time t? – Type of institution of employment (by

Carnegie classification or other criteria)?– Type of institution attended for graduate

school?– Years since degree completion?– Number and type of professional confer-

ences attended in past five years?– Frequency and content of communication

with others inside and beyond one’s currentinstitution?

• As we track multiple innovations within onesocial system, do we see evidence that pairs orbundles of these innovations are independent,complementary, contingent, or substitutes?

• How much information about each innovationhas reached the potential adopters by Time t?

• Has any such information come directly fromchange agents external to a potential adopter’sinstitution, or has it been communicatedby/through others within a potential adopter’sinstitution?

• How much normative or regulative pressureexists to consider each innovation?

• How complex do potential adopters perceiveeach innovation to be?

• How congruent with, or divergent from, preex-isting worldviews and practices do potentialadopters consider each innovation to be?

• What sort of cultural framing, interpretativework, or “sales work” has been done by changeagents or key opinion leaders around eachinnovation?

• What do we learn if we expand our conceptual-ization from a binary “adoption or not” to (1) ascale measuring intensity of use or (2) atypology such as (a) rejection, (b) decoupling/symbolic response, (c) parallel structures, (d)assimilation, and (e) accommodation? (Shortanswer: Potentially very much.)



Table 1. Nine Areas of Attention for Diffusion Research: Guided by Rogers’s Table 2.2 (Rogers, 2003, pp.96-98).

DEPENDENT VARIABLE

APPROPRIATE UNITS OF ANALYSIS

INTERESTING/IMPORTANT QUESTIONS TO BE ASKED



3. Innovativeness ofmembers of a socialsystem

4. Strength of opinionleadership

5. Diffusion networks

6. Rate of adoption ofinnovations indifferent institutionsor organizations

Members of a socialsystem (may be individualpeople or departmentswithin a university oruniversities within apopulation of universities)

Members of a socialsystem (usually individualpeople)

Dyadic network tiesconnecting pairs ofindividuals (or organiza-tions) in a system, butalso structural locations(as related to structuralequivalence)

Ideally, a nestedstructure of individualswithin institutions ororganizations

• Do past histories of experimentation andopenness to change suggest that some peopleor institutions within the population have a high“baseline” willingness to innovate while othershave a low “baseline” willingness to innovate?

• Past research suggests that the most innovativemembers of a system are very often perceived asdeviant from the social system and are accordeda status of low credibility by the averagemembers of the system. Is this generalizationupheld in the case of pedagogical innovativenesswithin and among engineering schools?

• Who are the opinion leaders in a given insti-tution? (Research and theory tell us thatopinion leadership is an informal position that isearned or established through technical compe-tence, social accessibility, and conformity to thesystem’s norms. It does not necessarily reside– perhaps resides only very infrequently – withthose who are official or formal leaders.)

• When it is discovered which individuals functionas opinion leaders, it is then interesting to askwhether they have been recruited by externalchange agents or institutional administrators(formal leaders) as internal change agents.There are reasons to expect the success of suchrecruitment/co-opting to be variable or condi-tional.

• Within the social system(s) under study, what isthe network structure of affiliations, connec-tions, and/or structural equivalence?

• See papers by Burris, and by Moody and Leahey,in this volume for further discussion.

• Each school of engineering, or departmentwithin a school, is likely to have a uniquecombination of professional norms, reward andincentive systems, concentration of opinionleadership, relationships with change agents,and network attributes (structure and content offlows of information and influence). How dothese attributes affect the rate of adoption of aninnovation?

Table 1. Nine Areas of Attention for Diffusion Research: Guided by Rogers’s Table 2.2 (Rogers, 2003, pp.96-98) (continued)

DEPENDENT VARIABLE

APPROPRIATE UNITS OF ANALYSIS

INTERESTING/IMPORTANT QUESTIONS TO BE ASKED

what sociologists know about the acceptance and diffusion ... · diffusion on innovation within...

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