scripting social learning: investigating students

Scripting Social Learning:

Investigating Students’ Perceptions of

Social Constructivist Learning in

Minerva’s Online Learning Environment

AN HONORS THESIS

SUBMITTED TO THE GRADUATE SCHOOL OF EDUCATION

OF STANFORD UNIVERSITY

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR HONORS IN EDUCATION

Jacob Wolf

May 29, 2019

APPROVAL

HONORS ADVISOR: Roy Pea

HONORS PROGRAM DIRECTOR: Professor John Willinsky

Scripting Social Learning Wolf ii

Abstract

This thesis investigates the tension between promoting accessibility and utilizing social

constructivist pedagogies to produce insights about effectively scripting collaboration in a

computer-supported collaborative learning environment. Through the lens of student perceptions,

I explore the educational model of Minerva Schools at KGI, a new blended higher education

institution which uses a proprietary platform to deliver small discussion-based classes while

requiring students to live together in residential cohorts. Minerva’s classes employ “active

learning” pedagogies which use tracked and graded collaborative participation to actively engage

students in class sessions. I assessed 67 students’ perceptions of the presence of social

constructivist pedagogy in Minerva’s platform using the Constructivist On-Line Learning

Environment Survey. Further, critical incident interviews offered insight into the experiences of

20 students learning on the platform with particular attention paid to productive and

unproductive social learning incidents. Findings indicate that though students perceived a strong

learning community overall, they perceived social distance and artificiality in the online learning

environment specifically. Strict collaborative scripting offers partial explanation for this

disconnect. However, students also reported that scripting improved the classroom discourse at

times by creating opportunities to participate in class for students who otherwise would not

participate. Results can inform the direction of future research in the evaluation of CSCL

environments as well as the design of collaborative scripts which both scale and accommodate

underrepresented students.

Scripting Social Learning Wolf iii

Acknowledgements

First and foremost, I must acknowledge a group of people without whom this project

would not have been possible: the Minerva students who offered their time and insights in

support of my work. I am also thankful for Stanford Undergraduate Advising and Research who

offered me the grant to compensate these students for their time. The students’ experiences are

the basis of this project, and I am grateful that they allowed me to take a peek into their lives at

Minerva.

Relatedly, I will be forever indebted (for this and many other reasons) to Anna

Pauxberger, my in into the Minerva community but more importantly my friend and constant

supporter.

Additionally, this project would be significantly different (and significantly worse)

without the guidance and support of Dr. Roy Pea, an advisor with a spring of knowledge deeper

than I could have ever reached on my own. If the construction of knowledge occurs by standing

upon the shoulders of giants, Roy was one of my most important giants in this project.

Speaking of giants, I cannot imagine what this thesis would have been without the

consistent support of those guiding the Education Honors Program: Dr. John Willinsky, Monique

Harrison, Cindy K. Lam, and Caroline Stasulat. These people offered me many forms of support,

but most importantly they believed in me, always pushing me to achieve more clarity, more

rigor, and more cohesion.

I have one final giant to recognize, Dr. Jennifer Wolf, whose dedication to the

development of conscientiousness, education-minded individuals was the spark that ignited this

project and whose encouragement was the fuel that kept the flame ablaze.

Scripting Social Learning Wolf iv

I am also fortunate to have been surrounded by an ever-inspiring group of peers in the

Education honors thesis cohort. Writing about social learning has nothing on actually

experiencing it with such a supportive, thoughtful, and interesting group of peers.

This project was also heavily supported by my friend and academic soulmate, Jenny Han,

who seems to have an endless capacity to discuss learning, how it happens, and who gets the

opportunities to do it.

Lastly, I must give thanks to the community which has supported me throughout the

process of writing this thesis: to Ashwin Agarwal, David Gonzalez, Sienna White, Vivan

Malkani, and Isaiah Smith who tolerated my requests for our room to be a productive space and

whose love reminded me that there was more to life than productivity; to Chester and Emily who

have been there for me from the beginning, reminding me to sleep and not to take myself too

seriously; and to the whole of the Synergy community which challenged me, laughed with me,

questioned me, listened to me, and kept me grounded throughout the year I worked on this

project.

Scripting Social Learning Wolf v

Table of Contents

Abstract ............................................................................................................................... ii

Acknowledgements ............................................................................................................ iii

Table of Contents ................................................................................................................ v

List of Tables ................................................................................................................... viii

List of Figures, Illustrations, etc. ..................................................................................... ix

Chapter 1 – Introduction ................................................................................................... 1

Distance Learning ......................................................................................................... 2

Minerva .......................................................................................................................... 4

Research Questions ....................................................................................................... 8

Chapter 2 – Literature Review ......................................................................................... 10

Constructivism and Online Learning Environments .............................................. 10

Individual cognitive constructivism and social constructivism .............................. 16

The significance of the online social environment ................................................... 18

But where is the social constructivism? .................................................................... 21

Social constructivist learning in practice .................................................................. 23

Chapter 3 – Methodology ................................................................................................. 28

Overall design .............................................................................................................. 28

Participants .................................................................................................................. 29

Scripting Social Learning Wolf vi

Instruments .................................................................................................................. 32

Data Analysis ............................................................................................................... 34

Chapter 4 – Results .......................................................................................................... 38

The Social Dimensions of the Minerva Learning Environment ............................. 38

Minerva-Directed Learning ....................................................................................... 45

Full-class discussions. ............................................................................................... 46

Reflection polls. ........................................................................................................ 50

Student-Directed Learning ........................................................................................ 53

Breakout groups. ....................................................................................................... 54

Chats. ........................................................................................................................ 58

Learning Community ................................................................................................. 60

Online and IRL community. ..................................................................................... 61

Community formation. .............................................................................................. 63

Chapter 5 – Discussion .................................................................................................... 65

Scripting Collaborative Learning .............................................................................. 65

Risks of overscripting. .............................................................................................. 67

Benefits of scripting. ................................................................................................. 74

Balancing overscripting and underscripting. ............................................................ 79

Chapter 6 – Conclusions .................................................................................................. 81

Implications ................................................................................................................. 82

Limitations ................................................................................................................... 86

Scripting Social Learning Wolf vii

Future Work ................................................................................................................ 89

On the tension between quality and accessibility ..................................................... 90

References ........................................................................................................................ 93

Appendix A – Constructivist On-Line Learning Environment Survey (COLLES) ..... 104

Appendix B – Semi-Structured Interview Protocol ...................................................... 107

Appendix C – Facebook Participant Recruitment Post ................................................ 110

Appendix D – Theory Mapping During Qualitative Data Analysis ............................. 111

Scripting Social Learning Wolf viii

List of Tables

Table 2.1: Definitions of Rovai’s (2002a) Elements of a Sense of Community. ......................... 20

Table 3.1: Class distribution of all participants in study (N = 67). ............................................... 31

Table 3.2: Class, gender, race/ethnicity, parent college status, family income status, continent of

the response to the question “Where are you from?”, and US vs international distributions of the

participants interviewed in the study (n = 20). ............................................................................. 31

Scripting Social Learning Wolf ix

List of Figures, Illustrations, etc.

Fig 3.1: Geographic breakdown of Minerva’s student population ............................................... 30

Fig 4.1: Online and IRL (In Real Life) elements of the Minerva learning environment on a

spectrum of student-directed vs Minerva-directed ....................................................................... 45

Fig 4.2: Frequency of social constructivist learning elements in Minerva learning environment as

assessed by the Constructivist On-Line Learning Environment Survey ...................................... 52

Fig 4.3: Frequency of social constructivist learning categories in Minerva learning environment

as assessed by the Constructivist On-Line Learning Environment Survey .................................. 53

Scripting Social Learning Wolf 1

Chapter 1 – Introduction

John Sperling, founder of the University of Phoenix, was the first in his family to attend

college. Though he came from a low-income family of farmers and initially struggled to compete

with his more educationally privileged peers, Sperling achieving many traditional measures of

academic success: he was awarded an academic fellowship to study at King’s College

Cambridge, a PhD, and ultimately a professorship at San Jose State University. Still, Sperling

was consistently frustrated by the elitist nature of his peers and academic surroundings.

Eventually, Sperling left his position as a professor to found the Institute for Professional

Development which would eventually transform into the University of Phoenix. By focusing on

offering degree programs catered to the schedules and needs of working adults, the University of

Phoenix was founded with the goal of making higher education more accessible.

Extending this goal of accessible education, the University of Phoenix began offering

online classes in 1989 to increase the number of students it could reach. While not the first

university to offer online classes, the University of Phoenix was an early adopter of the internet

as a medium for education and would go on to become one of the names most commonly

associated with online learning (Hanford, n.d.). While today the University of Phoenix is steeped

in controversy over the effects and effectiveness of for-profit education, at its founding the

University of Phoenix aimed to serve a population which was (and still is) often overlooked by

traditional universities: working adults who want to return to school (Halperin, 2014). These

individuals, however, represent only a subset of all those who have been disenfranchised and

excluded from educational opportunity. With scalability as an essential feature of digital

technologies, online education has the potential to extend educational opportunities to even more

people. However, in order for this scaling of educational opportunity to be effective, we must


first ensure that the experience we are attempting to scale is meaningful, inclusive, and

productive. This thesis makes a contribution to the work of extending educational opportunities

through online learning by examining a specific element of online education: its capacity to

produce or enhance social learning. In an age of individualized learning, a focus on social

learning is necessary for the development of online educational experiences which are as rich as

(or perhaps even richer than) their In-Real-Life (IRL) counterparts.

Distance Learning

The goal of making education more accessible is one that is deeply rooted in the history

of online education. In a historical review of online higher education, Lee (2017) describes how

online education has consistently maintained – at least in rhetoric – a goal of making education

more accessible for non-traditional students who could not attend IRL classes. This historical

review relates online education to distance education by defining distance education with the

following features: “the separation of teacher and learner, and concomitant use of technological

media to unite teacher and learner” (p. 5). As a subset of distance education, online education is

distance education which is mediated by web technologies. The Open University in the United

Kingdom offers a good example for this concentric relationship: founded 50 years ago as an

mail, television, and radio-based distance university, today the Open University delivers

education almost entirely online (“Exhibition: The OU Story: The first 10 years 1969-1979,”

n.d.). As such, the claims about the increased accessibility of online education compared to IRL

education can be traced to accessibility claims made about distance education dating back at least

as early as 1871 when Anna Eliot Ticknor founded the Society to Encourage Studies at Home

with the goal of providing women with a liberal education (Larreamendy-Joerns & Leinhardt,

2006).


However, the purpose of Lee’s review is ultimately to problematize the accessibility

claims of online education. This problematization is apparent in the gap between the claims of

accessibility for non-traditional students and what Levin (2007) describes as “authentic

accommodation.” This accommodation goes beyond students simply being able to access

educational experiences and pushes colleges to “provide programs, services, and an institutional

climate to students who are disadvantaged” (p. 14). For example, traditionally underserved

students should have the resources they need to succeed in a college environment, like healthcare

and remedial programs, and after graduation students should benefit from their education in the

job market. These accommodations acknowledge and address barriers to students’ access that are

not directly related to the college but which the college must take responsibility for if it is to be

truly accessible.

Instead, Lee argues, much of online education uses technology to create new learning

opportunities for students who already have authentic accommodation in high-quality

educational experiences. Online education of this type deploys innovative technologies in its

curricula and assumes that technological advancement implies accessibility, relying on the

historical claims that distance education was built upon a foundation of accessibility. However,

Lee claims that these institutions confuse quality for accessibility. Instead, at times increases in

pedagogical quality come with decreases in authentic accommodation (Brooks & Kanuka, 2010).

For example, Asunka (2008) found that when students taking an online course from a Ghanaian

university were asked to participate in group project-based learning, the students reported that

the course was more time demanding and more difficult to fit into their schedules. While the

collaborative learning assignments had the potential to make the learning more relevant and

engaging to the learner, the increased time required by collaboration made the course more


difficult to complete and negatively influenced students’ motivation to participate in the course.

In contexts where students have particularly limited time to dedicate to learning (for example,

non-traditional learners who are balancing learning with other responsibilities), collaborative

learning could make online learning less accessible.

Minerva

Twenty years after the University of Phoenix started offering online classes, the tension

between authentic accommodation and quality is as present as ever. Platforms like edX and

Coursera offer free Massive Open Online Courses (MOOCs) which large numbers of people can

take at their individual pace. Here, openness implies increased accessibility. Meanwhile, these

companies are also increasingly shifting towards paid credential-based content models with more

project-based learning (Young, 2017b). One specific example of the tension is that of The

Minerva Schools at KGI. Minerva is the result of a collaboration between Keck Graduate

Institute and the for-profit education technology company The Minerva Project which builds the

platform Minerva schools is hosted on.

This complex for-profit/non-for-profit mixture places Minerva somewhere between for-

profit institutions like the University of Phoenix and not-for-profit institutions like The Open

University. Minerva is further unique in that it utilizes a blended model of online learning in

which some of its classes take place online and, simultaneously, students are required to live

together as a cohort in a residence hall. There are residences in seven different cities around the

world, and each cohort of students moves to a different city every semester after their initial year

at Minerva (Wang & Goldberg, 2017). To accommodate the dispersed nature of the student

population, Minerva has developed proprietary classroom management and facilitation software

where students participate simultaneously in small seminar-style classes based around


discussion, breakout groups, peer instruction, quizzes, polls, and team presentations (Katzman,

Regan, & Bader-Natal, 2017).

Minerva’s design contrasts with the massive and open nature of MOOCs, instead

favoring a class model where all students must be virtually co-present with an instructor. This

co-presence feature reduces the size of classes and the timeframe in which students can

participate in the course. All of the seminars are recorded and students are assessed based on the

specific contributions they make to the class (either in writing or verbally), as evaluated by

instructors employed by Minerva (Levitt, Bader-Natal, & Chandler, 2017). Using this software,

students can take classes from any location with an internet connection, and are encouraged to

use each “city as a campus” by utilizing spaces like public libraries, theatres, coffee shops, and

local organizations in place of school “libraries, centers, classrooms, and auditoriums” (Wang &

Goldberg, 2017, p. 310).

Minerva’s novel infrastructure (or lack thereof) is paired with an intentionally designed

pedagogical approach which Minerva dubs active learning. This pedagogical approach is built

upon the idea that students should be engaged at least seventy-five percent of the time while in

class. Minerva engages students to this high degree by crafting lessons around participation and

requiring that participation specifically respond to previous contributions in the class (which

requires students to be constantly participating or preparing to participate) (Fost, Levitt, &

Kosslyn, 2017). According to Minerva, this approach helps students to acquire practical

knowledge by focusing on learning and applying four core competencies: thinking critically,

thinking creatively, communicating effectively, and interacting effectively (Kosslyn, 2017).

With its model of education, Minerva aims to use technology not to make higher

education more accessible but to disrupt the standard model of higher education (Asimov, 2015).


In contrast to the open nature of a MOOC, Minerva is highly selective. In the 2016 round of

admissions, Minerva was more selective than both Harvard and Stanford, and Minerva intends to

stay that way: founder Ben Nelson claims, “the Minerva school is trying to... become the most

selective university in the western world” (“College admits 2,037,” 2016; “Stanford offers

admission to 2,063 students from around the world,” 2016; Young, 2017a). As with other elite

colleges, this selectivity stems from Minerva’s belief that scarce educational resources must be

distributed to the students with the most potential. Other elite institutions implicitly exercise this

belief by hoarding large endowments which directly benefit a relatively small portion of the

world’s student population. However, Minerva demonstrates this belief about the distribution of

limited resources explicitly through claims on its website like “Supporting the World’s Best

Minds” and “Our students are all extremely bright, driven, and curious — innate qualities that

are geographically distributed and found at every socioeconomic level. We believe these

qualities, in addition to their outstanding academic achievement, deserve to be nurtured.” This

focus on the extremes among students contrasts with the idea that all students have the potential

to learn and the right to a quality education. In line with Brooks and Kanuka’s (2010) description

of the tension between progressive pedagogy and authentic accommodation, the technological

innovations Minerva implements, like proprietary software, as well as the pedagogical practices

Minerva uses, like small class sizes, contribute to the scarcity of educational resources.

Ultimately, this scarcity (and the demand for the resource) is what makes Minerva’s educational

experience elite and what challenges Minerva’s ability to authentically accommodate all

students.

Despite the reduction in authentic accommodation, the emphasis on developing tools and

methods for higher quality learning – and in particular social learning – in online environments


does respond to a growing body of research that shows that students expect more of their

interactions in online learning environments and that the community of an online learning

environment influences learning outcomes (Dougiamas & Taylor, 2003; Paechter, Maier, &

Macher, 2010). For example, in a study of their process of creating and evaluating an online

Masters course which was built upon social learning theory, Taylor and Maor (2000) found that

students were most dissatisfied with the extent to which the course allowed for and promoted

communicative interactivity between students and between students and tutors. Digging deeper

into the class data, they found that the opportunities students had to interact were highly

monological rather than dialogical – when interacting, students primarily posted their own ideas

rather than engaging back and forth with the ideas of others. This dialogic interactivity is

important to understanding when and how social learning occurs and how online learning

environments will contribute to an understanding of social learning in an online context

(Scardamalia & Bereiter, 2014).

However, there are problems in the field of researching online learning which must be

addressed if research is to make a meaningful contribution in this area. Lee (2017) argues that

focusing on the positive outcomes of online education ignores the need to develop authentic

accommodation and limits the development of the field of online education. Instead, she

proposes “careful observation of general pedagogical practices in real-life online [higher

education]” (p. 7). Further, Davies, Howell, and Petrie (2010) have found that most graduate

student research (research done for dissertations and theses) in the area of online education is

overly descriptive rather than using rigorous research methodologies and overly evaluative

without developing a solid theory base. These findings call for research that is both rigorous and

generalizable enough to contribute to a cumulative base of knowledge.


As an intentionally elite higher educational experience, Minerva’s relationship to

authentic accommodation is complicated at best. However, Minerva does present a “real-life”

online learning environment which has a particular focus on promoting social learning. Further,

Minerva’s approach to learning offers insights into the opportunities which online learning

presents to increase accommodation as well as the challenges which must still be addressed. By

studying students’ experiences in Minerva’s learning environment as well as the nature of the

environment itself, we can garner insight into the social factors of an online learning

environment and how those factors impact learning modalities. As we will find, in some cases,

Minerva’s environment will promote productive social learning in an online space. From these

instances, we will gain examples of effective elements of learning environment design. At the

same time, we will also find that some elements of Minerva’s online learning environment are

not productive for or even impede students’ learning. In these cases, we will use students’

experiences to determine the points at which students are deterred from participating in effective

social learning and the environmental factors which are consequential in this limitation.

Ultimately, the increased knowledge of social online learning produced by this thesis,

both practical and theoretical, presents an initial vision for online learning platforms which

translate the knowledge of social online learning developed by Minerva to models of education

which allow for greater access. As such, this thesis offers one brick of the many that will build

the bridge between quality and accessibility in online education.

Research Questions

With this timely opportunity, I turn to the following research questions to guide this

research with a particular focus on social learning processes in online learning environments:


● How does the learning community which arises in an elite college online learning

environment affect students’ perception of the way they learn?

a. What elements do students identify to be the social dimensions of the Minerva

online learning environment?

b. How do students perceive these elements contributing to or impeding the social

and individual components of the learning process?


Chapter 2 – Literature Review

To begin with the inquiry outlined in Chapter 1, we must first define a phrase which is

pivotal to this research: social learning. To generate this definition, the following chapter will

invoke the history of constructivism, a psychological and sociocultural theory about the ways in

which people construct knowledge individually and collaboratively. This history is intertwined

with the development of online learning and offers further insights into the tension between

progressive pedagogies and accessibility online. With this foundational knowledge of

constructivism and its relationship to online learning, we can begin to investigate the ways that

social learning manifests through learning communities and discourse in learning environments

as well as the ways in which social learning is promoted in online learning environments.

Constructivism and Online Learning Environments

As a Swiss biologist, psychologist, and epistemologist, Jean Piaget’s life work was

largely devoted to formulating a theory of development and learning which challenged the

dominant idea in the field of psychology at that time: the stimulus-response schema proposed by

the theory of behaviorism. In this schema, learning is the association drawn between an

environmental stimulus and an organism’s response to that stimulus. Behaviors form as these

stimuli-response associations emerge and this development of behavior is the development of the

organism at large (Skinner, 1953). In other words, learning leads to development. Piaget (1964)

contends, however, that this simply doesn’t make sense; for a stimulus to trigger a response,

there must be some kind of structure which integrates the stimulus while provoking a behavioral

response.


Piaget describes this integration as a pair of connected cognitive processes: assimilation

and accommodation. Unlike the passive associations which occur in the stimuli-response

schema, assimilation and accommodation are active processes which the learner engages with by

understanding how objects are modified. Objects can be both concrete (like dogs) and abstract

(like numbers) and can be modified through actions like sorting or joining groups of objects into

classifications. This operationalization of objects and the process of transforming them is strictly

relational in that operations can only be meaningful in context. What does it mean to sort a set of

objects if there is no way to unsort the set? What does it mean to sort a set of objects if there is

no way to modify them to be distinct? Further, precisely because these operations are strictly

relational, they create cognitive structures which house a learner’s understanding of an object

and all its mutable properties. According to Piaget’s theory, assimilation occurs when simple

cognitive structures are generalized to construct more complicated structures. In other words,

humans attempt to generalize their knowledge of known objects to construct knowledge about

unknown objects. In the areas where this generalization cannot accurately describe a new object,

accommodation occurs to refine existing cognitive structure in order to relieve the disequilibrium

of structures caused by the generalization (Fosnot, 2005).

Piaget’s theory of learning through assimilation and accommodation mirrors the theory of

development through equilibration. The parallel theory of development arises from Piaget’s

biological work studying the way organisms have evolved through biological equilibration

driven by natural selection. Organismal development occurs in the totality of the system that is

the organism: body, nervous system, and cognition. While physical structures develop through

equilibration on a scale of millions of years, cognitive development of structures can happen

over the course of a lifetime. As humans move through stages of cognitive development, their


movement is motivated by the equilibration of somatic maturation, experiences in the world, and

social transmission between individuals. To Piaget, the parallels between equilibration in

development and assimilation and accommodation in learning are no coincidence, they are an

indication that the mechanism driving development is the same as the mechanism driving

learning: equilibration through assimilation and accommodation. Unlike theories of behaviorism

and maturationism which see development resulting from learning, Piaget’s theory claims that

learning is development and vice versa (Fosnot, 2005; Piaget, 1964).

This shift in thinking would go on to become the basis for the theory of constructivism, a

theory which offered new insights into how people learn and has had particular significance in its

application to the development of technologies for learners. Coordinating Piaget’s writing with

developments in AI technology, Seymour Papert developed a theory which would later come to

be known as ‘constructionism’ that utilizes tools to situate learners in a “microworld” where they

can directly engage with the laws and ideas in particular fields in an embodied way using

“objects to think with” (Harel & Papert, 1991). In tandem with this theory came Papert’s

proprietary programming language, LOGO, an early object-based programming language used to

engage students with computers and programming. LOGO is used to communicate with a digital

Turtle object to give it directions to move around in a digital geometric plane. The Turtle

computing experience is an example of a microworld in which children can engage with

powerful mathematical ideas. By directing the Turtle FORWARD 100, the learner can imagine

themselves as the Turtle, moving through space to understand the concepts of reference points,

distance, direction, and number.

The computer was essential for Papert in developing constructionism. Two ideas

foundational to his seminal work, Mindstorms, were that “it is possible to design computers so


that learning to communicate with them can be a natural process” and “learning to communicate

with a computer may change the way other learning takes place” (Papert, 1980, p. 6). As such,

LOGO was one of the first digital learning environments of its kind: a tool not for teaching but a

tool for learning. Rooted in Piaget’s constructivism, constructionism is deeply learner-centered

with the learner acting as the primary agent in constructing knowledge. This deep interweaving

of technology with constructivist learning theory helped set the foundation for the creation of

learning environments with digital tools.

Andy Clark (1998) expanded on Papert’s work in his book, Being There: Putting Brain,

Body, and World Together Again. Here, Clark situates Papert’s “objects to think with” in a

networked interaction between the learner’s mind, brain, and environment. Clark initially

described language as the primary network connecting these elements, but the rise of the internet

offered a new network and new possibilities for connection (Kop & Hill, 2008). This evolution

of embodied, networked constructivism into the realm of online learning has been written about

extensively as the theory of connectivism and acts as the basis for the earliest Massive Open

Online Courses (MOOCs) (Kop & Hill, 2008; Rodriguez, 2012; Siemens, 2014).

The definition of a MOOC varies significantly depending on who is offering the MOOC

and why they are offering it. Some MOOCs are offered by non-profit institutions which aim to

spread the reach of their institutional brand or instructional materials, some are offered by for-

profit companies which aim to capitalize on real or perceived skills gaps in the workforce.

MOOCs can, in principle, increase access to traditionally exclusive content, offer credentials to

non-traditional students to help them transition careers, or promote the brand of academic

institutions. They can be paid or open access and can be synchronous (students take classes at the

same time) or asynchronous (students take classes on their own schedules). In general, however,


MOOCs create online learning environments, that is, virtual, non-physical places which are

made up of digital learning aids like lecture videos, forums, email chains, and chat groups and

are populated by large numbers of students who could potentially be located anywhere.

Despite the wide variety of formats which MOOCs can take, MOOCs are notably

homogeneous in regard to who participates in them. A study on access patterns of sixty-eight

MOOCs offered by Harvard and MIT found that participants from the US tended to live in more

affluent and better-educated neighborhoods than the average US resident. Further, students with

greater socioeconomic resources like income, prior educational experience, access to technology,

and parents with high levels of educational attainment were more likely to earn a certificate from

a MOOC than those with fewer socioeconomic resources (Hansen & Reich, 2015). The

difference in access and achievement in MOOCs highlighted by this study mirrors a larger issue

in education technology known as ‘the digital divide.’

This digital divide exists in two ways: access to technology and use of technology

(Attewell, 2001). First, minority students and students of low socioeconomic status are less likely

to own computers and have access to the internet than their White and high socioeconomic status

peers (Horrigan, 2015). Second, there exists a gap in the ways students use digital tools based on

the digital use skills, sometimes called digital literacy, which students have developed

(Hadziristic, 2017). This second-level divide exists in schools where minority students are more

likely to be exposed to digital content which requires passive consumption rather than active

engagement (Attewell, 2001; Purcell, Heaps, Buchanan, & Friedrich, 2013). For example, a

student tasked with watching a video of a physics lecture is passively engaged while a student

designing circuits in a simulated lab environment is actively engaged. The second-level divide

also exists outside of school where White students and students of higher socioeconomic status


have more time with the internet and with digital devices they can use to build digital skills and

networks independently and with their more digitally connected parents (Attewell, 2001;

Robinson et al., 2015).

This dual-layer digital divide complicates the notion of authentic accommodation

discussed in Chapter 1. In some ways, actively engaging students in an online learning

environment increases the authentic accommodation of the experience because it attempts to

better accommodate underserved and minority students who have not traditionally been served

by technology. However, actively engaging students in an online learning environment can also

decrease the authentic accommodation of the experience when active engagement requires a

greater commitment from the students through active engagement strategies like student-to-

student collaboration or real-time engagement with an instructor (practices which could

overburden non-traditional students). While the technological advancements to the experience

can improve the quality of the experience, they can also reduce the authentic accommodation of

the experience (Lee, 2017). As such, achieving authentic accommodation and bridging the digital

divide cannot be completely conflated.

Instead, attention must be paid to the pedagogical practices these online learning

environments employ and the environment factors required to facilitate them. In regard to

MOOCs, two distinct types of MOOCs have arisen as online learning environments have

developed: cMOOCs (connectivist MOOCs) and xMOOCs (extended MOOCs) (Clarà &

Barberà, 2013; Rodriguez, 2012). Whereas cMOOCs are heavily grounded in the theory of

connectivism and prioritize collectively constructing knowledge as an emergent property of the

network of learners, xMOOCs extend more traditional university content to engage large

numbers of learners in a massive but individualized online learning environment. This distinction


between learning collaboratively in an online learning environment and learning as an individual

in an online learning environment echoes a branching of constructivism, present from the earliest

days of the theory: individual cognitive constructivism and social constructivism.

Individual cognitive constructivism and social constructivism

While Piaget’s foundations as a biologist allowed him to formulate a cognitive model of

learning based on his understanding of organismal development, Lev Vygotsky’s situation in

Soviet Russia influenced him to approach constructivism from a more socio-historical

perspective. This is not to say that Vygotsky was not concerned with the psychological basis of

learning and development. Like Piaget, Vygotsky was deeply interested in the implications of

experiments on the developmental processes of humans and animals, even if his experimental

practice differed significantly from the practices of American psychology (Vygotsky & Cole,

1978). However, to incorporate a Marxist theory of society, Vygotsky was interested in

developing a psychological theory which incorporated the influence of historical changes in

society on human consciousness and behavior.

To integrate these historical changes, Vygotsky posed the question of how humans

related to their physical and social environment. Specifically, Vygotsky was interested in “the

relationship between the use of tools and the development of speech” (Vygotsky & Cole, 1978,

p. 19). While earlier theorists of child development had largely separated the practical use of

tools from the use of language (which Vygotsky introduces as “signs”), Vygotsky claims that

“the dialectical unity of [practical intelligence and sign use] in the human adult is the very

essence of complex human behavior” (Vygotsky & Cole, 1978, p. 24). The idea that language

and practical intelligence were deeply related led Vygotsky into explorations of the role artifacts

play in the development of the mind. Here, artifacts are physical or abstract entities which


connect current humans to previous human practices by the particular form of matter the artifacts

incorporate. All human artifacts from pottery to hammers to language are socially situated and

take on the collective knowledge that is realized in the process of their creation. Thus, artifacts

are representations of the collaborative development of knowledge (Cole & Wertsch, 1996; Pea,

1997). With this understanding of artifacts in mind, knowledge must be seen as not just

individually constructed by the learner but also as socially situated in the cultural context of the

learner. To learn, the learner must be in conversation with other learners, past and present. This

branch of constructivism is commonly known as social constructivism.

Though some argue that the distinction between the constructivism of Piaget and

Vygotsky on the grounds of the social situation of the theory is overblown, Piaget’s contribution

to constructivism does have a larger focus on the learner as an individual cognizing the world

through the creation of schemata which exist solely in the mind of the learner (Cole & Wertsch,

1996; Grendler, 2009). The focus on the individual in the construction of knowledge is often

referred to as individual, personal, or cognitive constructivism. Similarly, Papert’s (1980)

constructionism focuses so extensively on an individual learner’s interactions with microworlds

and objects to think with that there is little discussion of the learner’s situation in a wider social

context. Alternatively, Clark’s (1998) contributions of networked learning refocus constructivist

learning as a process situated in and dependent upon the learner’s environment and more closely

align with Vygotsky’s social constructivism.

As previously discussed, these two branches of constructivism still exist in the

development of online learning environments. The social and individual cognitive branches of

constructivism have significant overlap in the agency they give to the learner to construct

knowledge through the development of theories about the world. However, the role of the social


and cultural context of the learning environment still divides the two branches. This branching

between individual cognitive and social constructivism will be used in this thesis in order to help

make sense of the social and individual components of students’ learning processes. Further, the

social constructivist concept that learning and development are embedded in a cultural context

will be the basis for understanding how the social environment of Minerva contributes to the

development of the online learning environment at Minerva.

The significance of the online social environment

To define the “social environment” of an online learning environment, we will consider

both the ways a collaborative online environment is socially constructed and the way a

community forms around the users of an online environment. Before considering the community

of an online environment, we must first understand what an online environment consists of and

how one is constructed. To develop this understanding of online environments, this thesis will

utilize Harrison and Dourish’s (1996) distinction between “space” and “place” in collaborative

environments as well as Dourish’s (2006) later expansion on social influence in the construction

of both space and place.

With the rise of computer supported cooperative work (CSCW) in the 1990s, the

architects of the environments which support CSCW began to design the environments around

notions of spatial organization to facilitate interaction. In response to this focus, Harrison and

Dourish (1996) introduced the idea that focusing solely on spatial organization and spatial

metaphors was insufficient in structuring interactive behavior in CSCW environments. Instead,

they introduced a distinction between the spatial organization of an environment and the sense of

place which users felt in the environment. While a “space” includes features like relational

orientation and reciprocity, proximity and action, partitioning, and presence and awareness, a


“place” is a space in which users act based on their individual and collective understanding of

behavioral appropriateness and expectations. In other word, “space is the opportunity; place is

the understood reality” (Harrison & Dourish, 1996, p. 3). Further, because a sense of place must

be generated by the users of a space (it cannot be imposed by the designers), place-ness of a

space is a cultural phenomenon rooted in human social action.

Dourish’s (2006) later reflections on the space/place distinction have led to a different

approach to the distinction which is still compatible with the original distinction but which

allows for an understanding of the socio-historical factors that influence the development of

space. In the revamped theory, space, in addition to place, involves a social element which

influences the ways users interact with and make meaning in a digital locale. This social space

extension is driven by French cultural theorist Michel de Certeau’s (1984) discussion of the

relationship between practice and spatiality. According to de Certeau, spatial practices should be

distinguished as two types: strategic spatial practices and tactical spatial practices. Strategic

spatial practices are practices influenced by the designers of a space based on the way users must

interact with a space in response to its design. For example, making certain student’s video feeds

larger than other students is a way the designers of the Minerva platform exert their control over

the students’ practices in the space. Alternatively, tactical spatial practices are practices which

arise in a space as people create individual and collective meaning for a space by moving

through it and putting it to use. These practices arise from historical relations that influence

movement, culturally-based approaches to representing space for navigation, and ritual

associations with a space. For example, students at Minerva not using a particular type of

headphones because of their poor noise-cancelation quality arises from a historical relation in

which students critique the use of these headphones during class.


With an understanding of online environments based on the elements of space and place,

we can now consider the learning community which forms in an online environment. In practice,

the construction of a learning community in online environments can be seen in the sense of

community which the users of the environment feel. In analyzing online environments

specifically created for learning, Rovai (2002a) identified the most important elements of a sense

of community to be (1) feelings of connectedness, (2) cohesion, (3) spirit, (4) trust, and (5)

interdependence among members. These terms are largely left undefined, and so I have pulled

from other scholars to create definitions of these elements (Table 2.1). By surveying students

about their perceptions of these elements in an online course, Rovai’s studies found that students

who felt a stronger sense of community in their classes perceived their learning to be greater than

students who felt less of a sense of community in their classes (Rovai, 2002b).

Connectedness “The feeling of belonging and acceptance and the creation of bonding

relationships” (Rovai, 2002b, p. 322).

Cohesion “The resultant of all the forces acting on all the members to remain in the

group” (Festinger, 1950, p. 274).

Spirit “[Willingness] to invest emotional commitment in other people and other

things” (Bellah, Madsen, Sullivan, Swidler, & Tipton, 2007, p. 194).

Trust Trust is best represented by psychological safety, “a shared belief held by

members of a team that the team is safe for interpersonal risk taking”

(Edmondson, 1999, p. 350).

Mutual

interdependence

among members

There are two types of mutual interdependence: task interdependence and

outcome interdependence. “Task interdependence refers to the

interconnections among sub-tasks such that the performance of one sub-

task depends on the completion of other sub-tasks... Outcome

interdependence is defined as the extent to which team members’

personal benefits and costs depend on successful goal attainment by

other team members” (Miyake & Kirschner, 2014, p. 423).

Table 2.1: Definitions of Rovai’s (2002a) Elements of a Sense of Community.


Additionally, Drouin (2008) found that an increased sense of community showed a

positive correlation with student satisfaction with an online course and online course retention.

Kizilcec and Halawa (2015) showed a correlation between learners’ sense of social belonging

and the self-reported success of learners in online learning environments. Social belonging refers

to a learner feeling comfortable and connected socially and academically in the learning

environment. Kizilcec, Davis, and Cohen (2017), as well as Kizilcec, Saltarelli, Reich, and

Cohen (2017), went on to show that social belonging could be undermined by social identity

threat, the fear of being seen as less capable because of one’s social group, but that social

belonging and affirmation interventions could increase student persistence in online learning

environments.

With this framing of a sense of community and social belonging, the social environment

of an online environment can be understood in two ways. First, the social environment is defined

by the social elements which make up the space and place of an online learning environment.

These elements include the collective cultural and historical meaning that users create as they

navigate a digital space as well as the cultural expectations and behavioral appropriateness which

give a digital space a sense of place. Second, the sense of community students feel in an

environment can act as a metric for students’ experiences of social environments.

But where is the social constructivism?

Based on the definition of the social environment of an online learning experience

proposed above, we can begin to investigate the influence that the social environment has on

students’ learning. The studies cited above examine associations between social environmental

factors like sense of community, social belonging, and social identity threat and course success


metrics like course satisfaction, perceived amount of learning, engagement with the course, and

course attrition. These findings show that the learning community of an online learning

environment does have a significant effect on the success of the course as judged by these post-

evaluative metrics. However, few researchers have explored the impact of the social environment

on the way that learning happens in the online learning environment, the central question of this

thesis.

The current trend toward big data analytics has influenced research on online education

as online courses present a large amount of data about how students interact with and in an

online learning environment. Thille, Schneider, Kizilcec, Piech, Halawa, and Greene (2014) have

written extensively about the application of data analytics to the field of education, discussing

the use of data analytics to predict motivational variables, to deliver feedback and just in time

hints to learners, and to provide feedback to teachers on their teaching strategies. Typically,

online learning platforms collect clickstream data about users which includes data points like the

percentage of course videos a student watches, the number of posts a student makes to a

classroom forum, and the pace at which a learner consumes course content. However, Thille et

al. (2014) argue that this clickstream data does not explain how learning is happening. It does not

include the necessary context to understand a student’s process of learning. Instead, they propose

the development of a skill model which organizes data into learning objectives which are made

up of skills learners should be able to complete after they achieve the objective. These skills

include things like “Application of random variables,” “Binomial parameters,” and “Central

Limit Theorem.”

However, even this skill model approach produces an understanding of the learning

process that is intentionally limited in order to achieve the goal of providing guidance to


instructors and course improvement teams (Bier, Lip, Strader, Thille, & Zimmaro, 2014). While

the skill model approach to understanding learning online helps describe when and in what order

students acquire skills to achieve learning goals, it does not offer insight into how learners

acquire skills. An insight into skill (and more broadly concept) acquisition is essential to

understanding the ways students learn and, relevant to this thesis, if those ways match social

constructivist learning theory.

Social constructivist learning in practice

For the purposes of this thesis, I will identify social constructivist learning in practice

using the lens of knowledge building theory and discourse theory. Knowledge building

originates from an epistemology which holds that, like constructivism, knowledge is created

rather than discovered. Knowledge building has been extended, however, into a pedagogy which

contributes to developing the goals and processes of applying the constructivist view of

knowledge to learning environments (Scardamalia & Bereiter, 2014). The knowledge building

approach is particularly useful in determining student actions that represent social learning

because it primarily values a student’s contribution for what it adds to the community. By

building upon group process, knowledge building holds that individuals can learn and acquire

skills on a group level. Further, knowledge creation is a contribution to a community and thus

knowledge creation is more accessible than only creating a novel idea. Instead, interpreting

knowledge, clarifying problems, and offering different perspectives on issues are all a part of

knowledge building and contribute to the state of knowledge (van Aalst, 2009). With this view of

collective knowledge building, the collective knowledge of a field cannot be attributed to a

specific person but instead is defined by the collective community of knowledge builders in that

field.


Because knowledge building results from a collective effort, it occurs individually

through a process called idea improvement. This process holds ideas at the center of learning and

makes all members of the knowledge community responsible for the maintenance and

development of these ideas. Ideas are defined as real, ever-improvable concepts which are

generated by learners. Ideas are interconnected with other ideas and must be situated in the

context of the learner’s experience as well as the authoritative knowledge of the field in which

the idea falls. Ideas are constantly being developed in an iterative fashion by all members of the

community (Smith & Bereiter, 2002). As Minerva’s active learning pedagogies almost always

require students to engage with each other through learning interactions, we should see evidence

of idea improvement in students’ experiences in the learning environment.

Practically, idea improvement occurs through discourse between and among learners.

One highly influential basis for interpreting educational discourse arises from the theory

proposed by Mikhail Bakhtin which many scholars see as an expansion on Vygotsky’s

“sociocultural situatedness of mediated action” (Wertsch, 1991, p. 48). In his expansion, Bakhtin

develops a theory of dialogicality in discourse: “the ways in which one speaker’s utterances

come into contact with, or ‘interanimate,’ the utterances of another” (Wertsch, 1991, p. 1991).

Dialogical discourse occurs in spoken interactions, but the crux of Bakhtin’s contributions

explore the way dialogical discourse occurs over the course of human development through

written textual discourse as well as internal discourse where an individual’s thoughts and ideas

build upon the thoughts and ideas of others through a “shift in accent” (as cited in Wertsch,

1991, p. 55). This dialogical approach to discourse aims to promote innovative and creative

thought where ideas are constantly being developed. Alternative to this aim of innovation, Yuri

Lotman (1988) proposed a type of discourse which instead aims to transmit knowledge without


the intention of developing it. This type of discourse has come to be known as monologic

discourse (Wells, 2006). The distinction between monologic and dialogic discourse has become

an important concept in practically developing interactions which support learning and has often

been applied to individual pedagogical activities: whereas lectures are seen to be monological (as

non-negotiable transfers of knowledge), teacher-student exchanges are seen to be dialogical (as

co-constructions of meaning).

However, O’Connor and Michaels (2007) complicate this strict association of monologic

and dialogic activities through the investigation of the use of activities which have traditionally

been seen as monologic in larger dialogic interactions. For example, in isolation, known-answer

questions where the teacher asks students to answer questions they already know the answer to

could be an exercise in recitation rather than constructive knowledge building. However, when

these known-answer questions initiate an activity structure where students investigate their

reasoning behind a known answer, the overall interaction can be meaningfully dialogic despite

the known-answer question portion being monologic. In reality, all such pedagogical activities

are part of a larger context of a learning environment. In order to determine the monologicality

or dialogicality of a learning space, we cannot focus solely on individual activities. Instead, we

must consider “structural dialogicality at the level of utterances and utterance sequences, viewed

within the larger context of norms and practices in the classroom” (O’Connor & Michaels, 2007,

p. 279). By investigating students’ perceptions of the social dimensions of the Minerva learning

environment, we can construct an understanding of the larger context of Minerva’s learning

environment, essential to understanding Minerva’s relationship to monologic and dialogic

discourse.


The importance of context in understanding dialogicality requires an understanding of

how norms and practices develop in discourse. Approaching conversational norms and practices

through the lens of interactional sequence allows us to understand the way actions orient the

conversation based on rules in some conversational context (Goodwin & Heritage, 1990). In

interactional sequence, actions are contributions to a conversation and rules are the options for

actions that each participant has during each turn of the conversation. For interactional sequence

to be maintained, actions must demonstrate an understanding of past actions and of projections

of future actions. To accomplish this exchange at the conversational level, participant hold each

other accountable to avoid or fix actions which “break” the interactional sequence. This

collective accountability of the participates create the rules of the conversational context which

influences participants’ actions. However, actions also influence the rules of a conversational

context because sentences are mutable as the interaction progresses and the same event can be

categorized in different ways based on where the interactional sequence has led the conversation.

Naturally, the interactional sequence is highly dynamic but also subject to manipulation

based on power imbalances present in the conversational context. For example, “turn sharks”

may repeatedly take the turn to speak and limit the contribution of others (Erickson, 2008, p. 37).

To combat these barriers to effective dialogical discourse, collaborative environments will often

structure interactions to promote productive interactions. In Computer Supported Collaborative

Learning (CSCL) contexts, these structures are called “scripts,” “sets of instructions regarding

how the group members should interact, how they should collaborate, and how they should solve

problems” (Dillenbourg, 2002, p. 1). However, with the potential benefits of scripting also come

the risks of overscripting collaborative interactions. These risks include (1) disturbing natural

interactions by manipulating the interactional sequence, (2) disturbing natural problem solving


sequences, (3) increasing cognitive load by requiring students to think about the script as well as

the problem, (4) didactising collaboration by standardizing interactions, and (5) goalless

interactions which are reliant on the script to generate purpose for the interaction.

Over the course of this chapter, we have followed the threads of constructivism from its

philosophical foundations to is practical implementation through scripted collaborative

discourse. Along the way, the influence of constructivist ideas and practices on the development

of online learning environments has been clear. At the same time, it is also clear that there still

exists a gap in fully understanding the potential of online spaces as opportunities for social

learning. As an online learning environment deeply embedded in the fabric of online social

constructivism (and one which pushes its boundaries), knowledge gained from Minerva’s model

of education has the potential to help fill the gap.


Chapter 3 – Methodology

Minerva is an institution that has dedicated significant amounts of time, money, and

thought to developing an online experience which builds upon many elements of social

constructivist learning. The result is an online platform for learning with deeply integrated

opportunities for sustained interactions between students and instructors. These interactions and

the digital space they occur in are a prime context to investigate how social learning happens

online, with special focus on the influence of the features and norms comprising the platform. By

studying Minerva’s model and students’ experiences, this thesis seeks to generate insights into

the development of the social dimensions of online learning environments as well as the ways in

which these elements affect the processes of learning.

Overall design

Assessing the learning process (and particularly the constructivist learning process)

presents certain challenges. Because the learning process has many constitutive aspects

unfolding over time and is not necessarily exclusively linked to a specific outcome,

understanding learning processes cannot be solely based on understanding outcomes.

Furthermore, because social constructivist learning is simultaneously personal and social, the

process is subjective while the ultimate knowledge construct is informed by others’

constructions. Thus, to understand the process, we must acknowledge a learner’s own

perceptions of the ways that they learn (Jonassen, 1991). The importance of process in

understanding learning points to the need for qualitative research about learning which includes

the perspective of the learner to examine the learning process. Such qualitative research allows

for richer and social-contextually embedded descriptions of learning that attempt to reveal its


complexity. In particular, open-ended interviews allow learners to speak about their learning

experiences in their own words and from their own perspectives, helping address the subjective

nature of constructivist learning (Patrick & Middleton, 2002).

Qualitative insights into online constructivist learning can be further strengthen by

utilizing a mixed-methods research methodology which combines the different insights

generated by qualitative and quantitative data research approaches. A mixed methods approach

will allow for better interpretation of the quantitative data through students’ qualitative

experiences and better coordination of specific students’ qualitative experiences with the more

general feelings of a larger subset of the student body. As such, my research methodology will

both survey participants to quantify their social constructivist experiences at Minerva and

interviews students in order to garner a qualitative insight into participants’ perceptions of their

learning experiences at Minerva.

Participants

A group of sixty-seven students were surveyed for this study. Of these sixty-seven

students, five students from each class were selected for interviews, totaling twenty interviews.

Students were recruited using an online form which was posted in a Facebook group for Minerva

students on November 26th, 2018 and which closed on December 26th, 2018. To accomplish this

posting, I utilized a personal contact with a Minerva student who agreed to post my recruitment

information and form in Minerva’s student Facebook group (post included in Appendix C).

Participants were informed that they would be compensated for their participation in the study.

Minerva students are largely traditional college students, aged 18-23 who are seeking

four-year bachelor's degrees. Like other colleges, some students are transfer students and have

studied at other higher education institutions prior to studying at Minerva. Overall, Minerva’s


student body is comprised of a particularly high percentage of students from outside the United

States, 78% according to statistics published on Minerva’s website in May 2019 (Fig, 3.1).

However, further statistics for Minerva’s entire student population could not be found for

commonly-reported demographics of the student population like race/ethnicity, gender, or socio-

economic status. This study collected limited demographic information from participants

students including gender, race/ethnicity, parents’ levels of education, socioeconomic status, and

geographic origins (Table 3.1 and Table 3.2). The more specific demographic questions were

asked of the students interviewed in the study and all of the questions were framed as self-

identifications of the identity characteristics.

Fig 3.1: Geographic breakdown of Minerva’s student population. Figure posted on Minerva’s website

(minerva.kgi.edu) as of May 2019. Student from Asia make up the largest percentage (29%) while students

from Latin America make up the smallest percentage (10%). Student from North America make up 23% of

population, students from Europe make up 22% of the population, and students from Africa and the Middle

East make up 16% of the population.


Class Year

First Year Second Year Third Year Fourth Year

26.87% 28.36% 32.84% 11.94%

Table 3.1: Class distribution of all participants in study (N = 67).

Class Year First Year Second Year Third Year Fourth Year

25.00% 25.00% 25.00% 25.00%

Gender Female Male Prefer Not to

Respond

70.00% 25.00% 5.00%

Race/Ethnicity Asian Asian/Caucasian Black/African Latino White/Caucasian

25.00% 5.00% 5.00% 20.00% 45.00%

Parents College Status Graduated

College

Did Not

Graduate College

75.00% 25.00%

Family Identifies as “Low Income” Yes No Uncertain

10.00% 80.00% 10.00%

“Where are you from?” Continent Africa Asian Europe North

America

South America

5.00% 25.00% 25.00% 30.00% 15.00%

US vs. International From The US Not from the US

20.00% 80.00%

Table 3.2: Class, gender, race/ethnicity, parent college status, family income status, continent of the response

to the question “Where are you from?”, and US vs international distributions of the participants interviewed in

the study (n = 20).


Instruments

With a desire to ground my research with a quantitative analysis of students’ experiences

at Minerva and an understanding of the need for qualitative research on constructivist learning, I

will answer my research questions using two different research instruments. First, I will use the

Constructivist On-Line Learning Environment Survey (COLLES) developed by Taylor and Maor

(2000) and included in Appendix A. This instrument was designed to measure the perceived

social constructivist nature of an online learning environment based on the following

characteristics:

● Professional Relevance: How relevant is online learning to students' professional

practices?

● Reflection: Does online learning stimulate students' critical reflective thinking?

● Interactivity: To what extent do students engage online in rich educative dialogue?

● Tutor Support: How well do tutors enable students to participate in online learning?

● Peer Support: Is sensitive and encouraging support provided online by fellow students?

● Interpretation: Do students and tutors make good sense of each other's online

communications?

This survey was administered to students as part of the consent and recruitment form, and

before participants were interviewed. The data generated by this survey presents a quantitative

insight into students’ experiences learning online at Minerva. This data helps understand which

social elements of the learning environment are most present in students’ perceptions of the

learning environment and which elements are relatively lacking. Further, Taylor and Maor’s

study (as well as other utilizations of the COLLES) offers a point of comparison for the data I

collected through this study.


Second, I conducted semi-structured interviews with students to garner a more detailed

and specific set of insights into students’ experiences at Minerva. In part, these interviews asked

questions to gain an understanding of how students generally perceive Minerva in order to build

a context for the students’ experiences. More importantly, however, I utilized the critical incident

technique (Flanagan, 1954) to ask about students’ individual experiences with learning online at

Minerva. Using this technique, I was able to collect specific and significant behavioral facts

about the standard operation of the online learning environment. The critical incidents portion of

my interview protocol sought to help identify the social dimensions of the online learning

environment which students mention in their responses and the relationships that these elements

have to the perceived productivity of each student’s learning.

In developing this portion of the interview, I combined the qualitative methodologies

used in other studies which share some of the goals of my inquiry. Patrick and Middleton (2002)

utilized open-ended-question-based interviews which asked students about whether they

discussed their ideas with others, if they liked working in a group, whether working with others

helped them learn science, and other questions about the students’ collaboration while learning. I

will pair the types of questions this study asked about the impact of others’ actions on a student’s

learning with the types of questions asked in the Critical Incidents Questionnaire. This tool,

originally developed by Stephen Brookfield, presents a starting point for identifying moments

during class sessions which were critical to a student’s learning and can be further explored

through follow-up questions about the specific incident (Keefer, 2009). By combining these two

types of questioning, I will focus my methodology on understanding when critical learning

incidents occur at Minerva for the participants in this study and whether they specifically occur


as part of productive social interactions between individuals in the online learning environment.

The script of my interview protocol can be found in Appendix B.

Data Analysis

After conducting interviews with my participants, I coded my interviews using NVivo.

During the process of coding, I used what Glaser (1965) refers to as the constant comparative

method. This method is useful for the purposes of this project because it is “concerned with

generating and plausibly suggesting (not provisionally testing) many properties and hypothesis

about a general phenomenon” (Glaser, 1965, p. 438). Applying the constant comparative method

to my data, I generated hypotheses about the processes of social online learning and the factors

which influence it. The hypotheses suggested by the results of this analysis of Minerva’s novel

and largely unexplored learning environment can then be provisionally tested by future research.

In using the constant comparative method, I first coded my interviews to organize

sections of the interviews into relevant categories including elements of community codes drawn

from the elements Rovai (2002a) identified as the essential elements of a sense of community,

facets of constructivism drawn from my review of the literature on constructivism, types of

actions performed by students and Minerva staff, and features of the Minerva online learning

platform. During this pass of coding, I allowed the incidents I was coding to dynamically inform

the definition of my code categories as well as to create new categories which were previously

undefined. To keep track of these developments, I used a codebook to store the code categories I

was currently using and their accompanying definitions as well as memos which documented the

developments to my codebook as they occurred. Additionally, I used these memos to document

interpretive ideas I was having about my data based on the themes drawn out by the process of

coding.


After completing the first pass, I used my memos and codebook to construct a final set of

coding categories and category definitions. Using this final set of categories, I performed a

second pass of coding to ensure that all of the interviews were coded with the full set of codes.

After reviewing my memos and codebook, I then reconstructed my coding categories into

theories about my data along the lines of my research questions. To identify the social

dimensions of the Minerva online learning environment, I pulled out each of the major features

that students mentioned in interviews: discussions in the Active Learning Forum, class polls, and

breakout groups. There were also elements which, based on students’ discussion, seemed

essential to the learning environment at Minerva but which occur outside of the online space (in

full or in part). These elements included the student community at Minerva and learning

interactions outside of class. At this time, it became clear that some elements were more directed

by Minerva while others were more directed by students. This distinction was one of the theories

I developed at this point in my analysis. I then tested this theory by returning to my coded

references for each element in the theory and identifying references that marked the elements as

student- or Minerva-driven.

In addressing my second research question, I turned to the interpretative ideas I had been

logging in my memos throughout the process of coding data. At this point, the sense of

artificiality in Minerva’s online learning environment was clear, but I developed the theory that

the sense of artificiality came, at least in part, from the scripted collaboration which occurred in

the learning environment. At the same time, there was evidence that there were also benefits of

the scripted collaboration in the ALF. This produced a two-part theory: that Minerva’s scripting

of collaboration offered both risks and benefits for social learning in the ALF.


To test this second set of theories, it became essential to connect them with the first

theory that some elements of the ALF were more student-directed while other were Minerva-

directed. To do this, I mapped out the connections between the elements of the Minerva learning

environment I had identified, the intermediary ideas I had documented in my memos, and my

new theories about the risks and benefits of scripting (Appendix D). Using these connections, I

returned to my coded references for each of the connected categories and evaluated the

references as potential evidence of the scripting theories.

For the purposes of the research of this thesis, the quantitative data collected through my

survey instrument was most useful in helping understand students’ overall perceptions of various

social constructivist elements of Minerva’s learning environment. The data are, thus, most useful

in relating the frequency of specific social constructivist elements with other social constructivist

elements. To achieve this comparison, I analyzed the data using a Gantt bar representation to

offer a summative comparison between the categories (Fig 4.3) and question (Fig 4.2). This

Gantt bar representation converts student Likert-scale responses to each of the prompts into

numeric responses where 1 represents the “Almost never” response and 5 represents the “Almost

always” response. Using this scale of numeric responses, I created bars to represent the

percentage of students who selected each response within a particular prompt. The bars are

centered around the middle of the bar representing the 3 (Sometimes) response, the middle or

neutral response. The responses indicating a frequency less than 3 are positioned to the left of the

center and the responses indicating a frequency higher than 3 are positioned to the right of the

center. This allows for a comparison of the distribution of the responses greater than neutral and

less than neutral. Superimposed on top of the response bars is the mean response to each


category or question, distributed proportionally left to right such that the averages can be

compared with each other.


Chapter 4 – Results

Though this thesis aims to understand the relation between the social dimensions of an

online learning environment and the learning which occurs in that learning environment, a

significant portion of the Minerva experience occurs in IRL (In-Real-Life) interactions due to the

residential nature of the program. Over the course of the interviews, it became clear that the

digital places where students interact represent a single manifestation of the Minerva community

which, in full, spans a variety of digital and physical media from Facebook group chats to dorm

hallways. In this way, it is important to acknowledge that the Minerva community, and the

Minerva experience as a whole, cannot be constrained to or described by solely the digital,

online realm, a sentiment expressed by many of the students interviewed.

As such, this chapter will focus on students’ perceptions of the online social dimensions

of the Minerva learning environment while also including IRL elements of the Minerva

experience which are essential to understanding the environment. By combining aggregated

responses to the Constructivist Online Learning Environment Survey (COLLES) and students’

experiences shared in interviews, these results offer an insight into the ways students experience

the online learning community and how Minerva’s strict facilitation structures both contribute to

and impede students’ learning.

The Social Dimensions of the Minerva Learning Environment

The crux of the Minerva online classroom experience is the Active Learning Forum

(ALF), a digital platform which serves as both a classroom space and a tool to manage the

logistics of a course, like recording grades, housing course materials, and facilitating course

communication. To attend class, students join the digital classroom by synchronously sharing


live video streams with their classmates and the instructor for ninety minutes two times per week

for each of the three to four classes students take per semester. Generally, there are ten to

eighteen students in a class, though more advanced classes can be limited to three or four

students. Much like other video conferencing software, each participant’s video is displayed

simultaneously on the screen with the current speaker’s video feed expanded to take up a larger

portion of the screen. Additionally, there are also expanded functionalities and tools in this

digital environment which students and instructors can use to facilitate activities and interactions.

These tools include features like a whiteboard, live polling, chats, and digital emoji reactions

(i.e., snaps, smiley faces, frowny faces, etc.) (Katzman et al., 2017).

Each ninety-minute session in the ALF follows a lesson plan which incorporates content-

specific activities into a general active learning structure in order to achieve a specific set of

learning objectives (Chandler, Kosslyn, Holman, & Genone, 2017). According to the students

interviewed, the general structure of classes consists of the following elements: a pre-class poll

where students draw on the content covered in the pre-class readings or pre-class work, time to

submit pre-class work, an overview of the objectives of the class session, a series of

collaborative and/or active-learning activities, a closing summary of the class session, and a

reflection poll where students are asked to respond to a concept or peer-comment from class. The

activity portion of the class is the largest element of the class structure and is broken down into

three primary pieces: full-class discussion-based activities, breakout sessions where groups of

two to three students collaboratively complete an activity, and individual polling activities.

Full-class discussions occur multiple times throughout the class (broken up by breakout

sessions) and in multiple forms based on the type of activity the discussion is based around.

Students described some activities which are built around traditional seminar activities like


analyzing paragraphs of a text to interpret their meaning and other activities which involve more

strict structures like student relays where an instructor will call on one student after another and

each student must respond directly to the comment made by the previous student. Throughout

the interviews, students mentioned a variety of activities instructors employed during discussions

including students being selected to share their responses to pre-class polls, students debating

and moving between two sides of an argument, and open calls for examples where students are

asked to share their personal experiences with course content from their lives outside of class.

Breakout groups occur one to two times in most class sessions. To begin, students are

divided into groups of two to four. In these groups, students spend eight to fifteen minutes

working on an activity assigned by the instructor. As in full-group discussion, breakout activities

differ based on the content and goals of the class, but students shared that some breakout

activities included solving practice problems, collectively interpreting pieces of literature, or

addressing real-life issues using theories from the class. Additionally, students noted that

activities will often contain multiple parts which build upon each other, and breakout activities

will often focus on creating a product of some kind like an analysis, program, or presentation.

After the allotted time is over, the full class reassembles to discuss the product each group

created.

At the beginning of a lesson, end of a lesson, and at various points throughout a lesson,

students will be asked to engage in individual activities which take the form of voting and polls

(Katzman et al., 2017). In these activities, students must respond to prompts in multiple choice,

short answer, or long form responses. While at first glance these individual activities may not

seem like social activities as they only involve the work of a single student, these activities are

often used as social reflection activities. In polls throughout the class, the results of polls will


sometimes be displayed and then the poll will be rerun. In the second run, students will be

allowed to change their opinions based on the opinions of others. Additionally, the end-of-class

polls are known as reflection polls, and frequently ask students to engage with other students’

contributions during the class. For example, a student, Alya, shared the prompts to discuss

“someone's comment that changed [their] perspective on something,” and “mention something

someone said that exhibited a very good... learning outcome.” Both the rerun polls and the

reflection polls motivate students to reflect on how other students influenced their learning,

pushing students toward reflective abstraction: the “[organization] and [generalization] across

experiences in a representational form” (Fosnot, 2005, p. 34).

In addition to the structured lessons which Minerva creates for classes, nearly every

student interviewed mentioned meaningful learning interactions which occurred outside of the

class structure. In most cases, students described informal and spontaneous IRL interactions

which referenced academic material from their classes. Many students also discussed informal

but planned interactions which explicitly built upon learning experiences from their classes like

going to students’ rooms after class to continue a discussion from class or meeting before class to

discuss the pre-class materials in preparation for a class session. Additionally, students described

more formal learning experiences which occur outside of class. Faculty and course designers

regularly include assignments in courses that require students to work in groups with civic

partners. Minerva also employs a Student Experience Team which organizes community

building and educational events in the residence as well as field trips to organizations,

employers, and culturally significant sites in the city. Given Minerva’s large investment in

residential spaces as well as organized IRL activities, it is not surprising that students mention

these IRL interactions as some of their primary learning experiences at Minerva.


However, a smaller but still significant portion of students – nearly half of the students

interviewed – also mentioned how they used digital technologies, both Minerva-provided and

external, to create spaces for discussion and reflection outside of the class structure. The first of

these spaces, and the one mentioned most often, is actually a space that occurs parallel to class

sessions: chats on the Active Learning Forum (ALF). During each class session, the ALF

maintains a chat box which all students and the instructor can see and send messages to. During

class, this chat space is used to fork class discussions so that threads of discussion can continue

outside of the official instructor-directed class space when the discussion moves on or the

activity changes. In some cases, this forking of the discussion occurs when students want to

continue hashing out a disagreement or building upon an idea which was shared in the instructor-

directed discussion. In other cases, students will write explanations responding to the questions

of confused students which were unaddressed in the instructor-directed discussion.

Similarly, chats and messaging also occur on other platforms outside of the ALF.

Sometimes these types of interactions occur at the same time as class sessions (like when

students text each other during class), but more often these occur outside of class time. In

particular, a few students mentioned the use of Facebook group chats centered around specific

topics or identity affinity groups which create a space to discuss both formal and informal

learning. Henri & Pudelko (2003) identifies these types of online communities as communities of

interest. For example, one student, Thomas, described a chat that was “created for all women that

are in computer science across classes,” and another chat to “post things that are interesting and

share from both professional development things to ‘Oh, this is interesting, look at this,’ or

‘maybe this is happening in the city.’” Another student, Sofia, mentioned that she had “never

used Facebook so much as [she had] at Minerva.” Further, this engagement on Facebook was not


limited to group chats; a few students mentioned the many Facebook groups, such as the one

which served as a recruitment tool for participants in this research, where students engage in

discussions and arguments about issues relevant to Minerva and beyond. For example, one

student, Zara, mentioned students using Facebook pages to make memes about elements of the

Minerva experience, and another student, Rudo, mentioned students using Facebook groups as a

platform for debates about current events.

Together, the online and IRL learning experiences described above make up a significant

part if not all of the learning environment at Minerva (students also mentioned learning through

other online platforms or summer employment, but these experiences are outside the scope of

this thesis). However, the way the students experience each of these elements of the learning

environment varies based on the agency students have to direct the interaction or activity. Like

traditional colleges, Minerva’s residential components allow it to influence, directly or indirectly,

almost all of a student’s learning experiences during their time at Minerva. Yet, during

interviews with students, a distinction arose between learning interactions which were directly

facilitated by a Minerva instructor or employee and those which relied on students to drive the

interaction. This distinction was explicit when students like Nor commented, “I personally feel

like the primarily time [I interact with my peers during class] is during the breakout. There's a lot

of interaction outside as well. But it's more like professor to student and I feel like student to

student happens more in the breakouts.” Further, this distinction between Minerva- and student-

directed learning arose less explicitly as students described the sense that student-directed

interactions were more natural and dynamic than those facilitated by Minerva faculty. One

student, Erika, describes a sort of hierarchy of interactions where full class discussions are the

most forced, IRL study groups are the most dynamic, and class breakout sessions are a hybrid:


The two main different types of interactions, which are either in the really large group,

which often seem... like they sometimes have a very forced feeling because it's like,

everyone is listening to you. And it's a little bit scary… The breakouts are usually very

nat- normal. You just talk. Sometimes you also unmute yourself and you just... it's like

you were actually sitting next to the person when you're doing work together. Yeah. And

then the in study group… it's even more dynamic in that sense, because you also end up

talking about other things that, I don't know, you share food and you decide to take a

break and go out for dinner, and it's more of a friendship interaction, I guess than it is

necessarily in the breakouts.”

Erika’s gradation of “forced,” “normal,” and “even more dynamic” between full group class

activities, class breakout sessions, and out-of-class study groups points to a spectrum of the ways

students experience learning interactions along the lines of students’ agency to direct the

interaction. Whereas in the full class discussion interactions are, as Nor mentioned, “professor to

student,” in the breakout sessions interactions are “student to student.”

To guide the results of the students’ discussion of the different elements of the Minerva

learning environment, I created a spectrum between student-directed elements (elements where

students have agency to direct the interaction or activity) and Minerva-directed elements

(elements where instructors, curriculum designers, or the features of the ALF impose a strict

structure on interactions and activities) (Fig 4.1). Using this spectrum of student agency, this

chapter will discuss students’ perceptions of the social dimensions of the Minerva learning

environment.


Fig 4.1: Online and IRL (In Real Life) elements of the Minerva learning environment on a spectrum of

student-directed vs Minerva-directed. Whereas Facebook groups/chats, spontaneous interactions in the

residence, class follow-ups, chats, breakout sessions, and study groups are more directed by the student

participants, community building events, full class activities, individual activities, location-based assignments,

and field trips are more directed by Minerva instructors and staff. Elements of the ALF (Active Learning

Forum) span the spectrum with some elements being more Minerva-directed and some elements being more

student-directed.

Minerva-Directed Learning

In designing its classes, Minerva uses a “team teaching” approach which iterates on the

contributions of deans, curriculum designers, subject matter experts, and instructors in creating

the syllabus and lesson plans for a course (Fost, Chandler, Gardner, & Gale, 2017). The lesson

plans for each course pull from a large set of activities developed from active learning


approaches including “peer instruction, collaborative work in small breakouts, debates, Socratic

method discussion, task- or problem-based learning, role-playing, and game-based activities”

(Fost, Levitt, et al., 2017, p. 169). This section will focus on two social dimensions of the

Minerva learning environment which are the most heavily directed by the teaching team: the full-

class discussions and the reflection polls. The teaching team direction occurs through both the

design of lessons and the learning environment as well as through the active facilitation of the

instructor.

Full-class discussions.

Though some of the activities which are used in full-class discussions (like student

relays) have more strict structures than others, all discussions are built upon Minerva’s active

learning principles which introduce a sort of interactional “script” which influences (and

sometimes dictates) how students are to collaborate in discussions (Dillenbourg, 2002).

Specifically, Minerva’s definition of fully active learning “requires all students to be engaged at

least 75 percent of the time while in class” (Fost, Levitt, et al., 2017, p. 166). To achieve this

level of engagement, instructors are encouraged to craft the discussion such that each student has

an approximately equal amount of time speaking. This principle of equal participation manifests

in two technical features of the ALF. The first is known as “talk time” and indicates to the

instructor how much time each student has spoken in a class session by superimposing shades of

red, yellow, and green on top of each student’s video feed when the instructor presses a key. A

red shade indicates that a student has talked relatively frequently in the current class session, a

green shade indicates that a student has talked relatively little in class, and a yellow shade

indicates that a student’s talk time is close to the average talk time. The second feature is known

as the “‘feature quiet students’ tool” and offers the instructor the option to automatically select a


quiet student or group of quiet students to participate in a discussion or respond to a question

(Fost, Levitt, et al., 2017).

These guidelines on participation intentionally manipulate the interactional sequence of

full-class discussions in order to create a discussion experience which, in principle, engages all

students (Goodwin & Heritage, 1990). Students notice the effect this scripting structure has on

the discussion, often mentioning the “flow” of discussions. Alya describes how an instructor

calling on a student who was not paying attention can “cut the flow of the conversation.” Zara

draws out the importance of the instructor’s “gauge of the flow” and notes that calling on

students in the wrong order can be “destructive to the flow” of the discussion. However, students

expressed contrasting opinions about the effects of scripting on class discussion. Sara described

how “creating structures to make students pay attention” can lead to “fake active learning.”

Other students also described some very heavily facilitated discussion in the ALF as

“mechanical,” “artificial,” “synthetic,” and “forced.”

On the other hand, some students felt that the participation structures improve students’

focus and give them the confidence to participate in class. Adriana notes how, despite being a

non-native English speaker, she feels comfortable sharing her opinion because required

participation makes students want to understand what she is saying:

You need to be really paying attention because your professor who called you, you need

to answer… So everyone pays a lot of attention to what is said. So, you kind of feel that

you are heard in your classroom. For example, I kind of have problems with my English,

but I never feel bad about that. When people don't understand what I'm talking about or if

I say something really wrong, people just ask me to repeat.


Unfortunately, the benefits and the limitations of the required participation structure come with a

trade-off. As a student who participates in discussions frequently, Carol notes that the required

participation structures keep her from “going back and forth more than a couple of times with

someone before [the class moves] on.” While Carol says that this makes her feel like she cannot

deeply engage with class activities, she acknowledges that this means that other students have

more opportunities to participate in class discussions.

The effect the instructor and the features of ALF lessons have on the interactional

sequence of a discussion is compounded by the grading system Minerva utilizes. Because

students are almost entirely graded based on their performance in classes, students feel

particularly motivated to meet the instructor’s prompts to participate. One student, Hubert,

described this pressure to participate saying,

During the lecture like teacher is calling on you "you go, you go, you go," like, "what is

the answer?" And if you don't answer, it can actually get a bad grade on it. And every

lecture is like an exam. So that's our exams. And because every lecture is graded, and you

don't know what's going to be graded, it might be a prep poll, reflection poll, or might be

actually a part of recording which they put a bookmark on and they tell you "Oh, you got

it two, or you got a three, you got a four." So, everything is graded.

Further, the influence of significantly graded participation seems to have an effect on the

implied, but mutually understood rules of discourse over time in a class, a concept identified first

by Goodwin and Heritage (1990). In answering the interview question about the actions of other

students during discussions which impeded her learning, one student, Farida, praised an

instructor’s ability to keep students from repeating points made by others by grading them down:


I would make a point and some other person might repeat the point but word it in a

different way and I'll be like, "I just said the same thing," but I feel like that didn't

impede, like it didn't happen like in future classes, because that person probably got

graded for it like a low score because that's what happens.

In this case, the Minerva instructor is using grading as a tool to influence what students perceive

as allowable actions to make in full group discourse.

However, grades are not the only source of feedback which influences full-class

discussions in the ALF. Because the ALF is a synchronous video-conference-style learning

experience, students are able to see the reactions of their peers and of the instructor as they make

contributions to the discussion. Additionally, the built-in digital emoji reactions to the platform

allow for students to explicitly express a defined array of emotions including agreement,

disagreement, confusion, happiness, and frustration to the entire class while another student is

speaking and without verbally interrupting the speaker. Many students mention how these

reactions, both physical and digital, contribute to their confidence (or lack of confidence) as

speakers and affect how they contribute to the discussion in the future. Gloria describes how, “if

[she is] speaking, and [she sees] people putting a check. Or if [she sees] people like nodding or

like with a smile on, [she’s] like, "Yeah, hell yeah, I'm saying something, right.”

Still, some students notice and mention a sense of “distance” from others which comes

from trying to gauge non-verbal communication in the ALF full-group discussions. Material

limitations on the learning space can cause elements of interactions to be lost. One prominent

example comes from students sometimes taking classes in public spaces like coffee shops.

Technically, a student’s audio feed is only turned on when they click a key and choose to speak,


presumably to limit background noise from the physical environment the student is in. As such,

Erika describes,

You only unmute yourself when you have something to say. So, you'll never hear when

someone laughs at something, or any of those things. We do have the half-emojis,

whatever... that helps a little bit, but it's still- sometimes it's very like synthetic. I wouldn't

say forced, but synthetic.

Along these lines, a number of students mention feeling that, in some cases, in-person

learning has beneficial affordances which have not been replicated in the ALF online

environment. As a student, Sara, describes it, “whatever [the students are] learning could benefit

from in-person.”

Reflection polls.

Reflection is a key part of social constructivist learning and, based on the inclusion of

reflection polls at the end of every class, a key part of the Minerva class experience. However,

despite the theoretical and institutional value of reflection, students do not seem to value the

reflection polls. Significantly fewer than half of the students interviewed mentioned the

reflection polls, and none of the students talked about the reflections polls as a productive

learning experience (only as a fact of the class structure). Somewhat contradictorily, however,

the results from the COLLES (Fig. 4.2) show that students responded that reflective thinking

occurred with a relatively average frequency compared to the other elements polled by the

COLLES. According to the survey data, the prompt “My teacher modeled critical self-

reflection,” was one of the lowest frequency elements of all the items in the survey. This lack of

modelled reflection could indicate a rift between participating in reflection and actively

perceiving learning benefits which results from reflection. However, the single insight of low-


frequency reflection modelling cannot fully explain the students’ lack of emphasis on reflection

as a productive learning experience. This is one area of inquiry which would benefit from

investigation into more objective measures of learning outcomes at Minerva, an issue discussed

in the future work section of Chapter 6.


Fig 4.2: Frequency of social constructivist learning elements in Minerva learning environment as assessed by

the Constructivist On-Line Learning Environment Survey. Student responses (N = 67) to Likert response are

converted to Likert scores where “Almost Never” corresponds to a score of 1 and “Almost always corresponds

to a score of 5. The lowest frequency element over all (based on the average Likert scores) is “Other students

praised my contribution” in the Peer Support category. This category contains 3 out of 4 of the lowest elements

overall. Further, the elements involving other students in the Interpretation category (I made good sense of


other students’ contributions and Other students made good sense of my contributions) are notably lower than

the other elements in that category. Additionally, the element “My teacher modeled critical self-reflection” in

the Teacher Support category is the second lowest despite the other elements in the category being relatively

average.

Fig 4.3: Frequency of social constructivist learning categories in Minerva learning environment as assessed by

the Constructivist On-Line Learning Environment Survey. While the responses to all questions have a lean

significantly to the “Often” and “Almost Always” side, the Peer Support category has more “Sometimes,”

“Rarely/Seldom,” and “Almost Never” responses than the other categories. The Peer Support category also has

the lowest average Likert score with a score of 3.63.

Student-Directed Learning

Minerva-directed learning through discussions and activities facilitated in the ALF is

only part of the learning environment. Students spend a significant amount of time learning in

situations where students direct the discourse without the direct, real-time facilitation of an

instructor. These more student-driven learning situations (because of the residential nature of

Minerva, all of a student’s experiences are Minerva-directed in some ways) occur during class

sessions through breakout groups, through the chat feature of the ALF, and outside of class in

study groups, group chats, and spontaneous interactions. This section will discuss the more

student-directed interactions which occur in breakout groups learning outside of class structures,

both digitally and IRL.


Breakout groups.

Breakout groups during class sessions fall near the middle of the student-

directed/Minerva-directed gradient because they are still a part of the ALF and have strict

requirements for the goal of the interactions which occur in them. Still, unlike in full class

activities, students have control over the interactional sequence of the interactions in the breakout

groups because the instructor is largely absent from the interactions. Additionally, the time spent

in the breakout group is also graded differently than the other components of a class session.

While other portions of a class session could be used to determine a student’s grade based on the

student’s participation, only the deliverable from the breakout session and the related full-class

discussion are used to inform students’ grades. Still, instructors can enter the breakout groups at

any time during the breakout session to assist and refocus students on the activity. This model

creates a hybrid student-directed/Minerva-directed place which offer more agency to students in

the ways they interact and is less strictly graded but is still significantly scripted by the activity

and the instructor’s potential presence.

Though these breakout spaces can feel more normal, their productivity as learning spaces

is variable, as several quotations below exemplify. Students cite two major contributing factors

to the productivity of breakout sessions. First, most students who discuss breakout sessions

mention the impact that an individual’s approach to the breakout groups can have on the

productivity of the breakout group. Some students characterize this as a need for the style of

interaction of each member of a breakout group to align with that of the other members. These

students discuss how their peers who were more individualist or less prone to contribute in a

discussion could limit the productivity of the session. One student, Chloe, described

unsuccessfully attempting to engage in breakout groups with these types of students:


There's still students that when we go into break out groups, they just want to work by

themselves or they are not talkative so they either have this like huge individualistic

mindset where they want to do the whole breakout activity by themselves when there's

like two other people there and I think that's just like a personality choice. Or there's the

people that do not speak and when people don't speak and contribute to the conversation.

And I'm always having to ask "How- What do you think about this? What do you think

about this? What do you think about this?"... if you have nothing to contribute than I

can't speak with myself, or I can't speak with just one other person. When you add more

people together. That's when the conversation gets interesting. And when we're actually

able to have a thoughtful, well thought out conversation.

Alternative to the “style of interaction” comments, other students like Nor note that some

of their peers approaches to breakout sessions is not an approach at all, but rather a

disengagement with class:

Some people totally zone out. Like they will... there's no way to explain it... there is

actually, they just kind of become unresponsive, and maybe very obviously, are looking

at their phones, or, you know, they just don't do anything.

Students who mention this kind of breakout group member are often more frustrated with their

peers. Nor expresses this sentiment, saying, “some students definitely… don't do anything during

break out” and “it’s one thing to be confused...”

Second, many students mention how students preparing for class is essential for

productivity in breakout sessions. As Minerva utilizes a flipped classroom model where students

learn class content before class and then use class sessions to clarify and apply the content, all

elements of class are dependent upon students preparing for class (Katzman et al., 2017). The


small, highly interdependent nature of breakout groups, however, makes these spaces especially

sensitive to unprepared students. These unprepared students are hindered in making meaningful

contributions to the group’s work and can, at times, hinder others by requiring explanations of

content which was supposed to be learned before class. Due to the problems caused by

unprepared students, students like Erika expressed a similar frustration with unprepared students

as with disengaged students saying things like “the other thing that really frustrates me is when

you are in one-on-one interactions, or like maybe two or three people interactions and breakout

groups, and the other people didn't do their work.” When students repeatedly come to class

unprepared (and when their peers repeatedly have to spend time catching them up), the dynamic

between students can become toxic and students can lose faith in the learning potential of the

course.

In line with these concerns about their peers’ modes of participation in breakout groups,

some students reported experiences in breakout sessions which were unproductive or altogether

detrimental to their learning. Carol described a breakout session during a computer science class

where she was paired with another student who had previously worked as a software engineer for

several years prior to attending Minerva:

So, I could be in a breakout group with this person, way more advanced, and just feel

like… "I'm sorry, I don't get it. I didn't..." right? And [the software engineer student] ends

up just explaining everything, but I don't get it better. And he's kind of carrying it. And he

knows he's carrying it. And it just becomes this kind of, resentful, toxic situation.

Carol, and multiple others in other interviews, went on to describe how such drastic differences

in classmates’ experiences are quite common in classes at Minerva and especially in engineering

classes. For example, Adriana described how, in the mandatory Python class which students take


in their first year, “you can clearly see that some students that know Python understood

everything. And the student that don't know Python, they don't understand anything. So, you

kind of see a gap on those moments in class.”

As another example, in breakout groups primarily in her computer science classes, Nor

will sometime ask her more knowledgeable peers multiple questions to understand a breakout

activity. But sometimes she doesn’t “want to bother [her] classmates as well, because they're

trying to solve the problem,” and so she asks questions only “if they are finished solving the

problems [because she doesn’t] want to interrupt them.” Like in these examples, the meeting of

different experience levels was mentioned in multiple interviews as one of the more challenging

elements of the Minerva experience. From these students experiences, the inequality of

experience at Minerva seems to be the result of many interacting factors: the small student

population, the alternative nature of Minerva and the type of student that it attracts, the dearth of

classes that focus on foundational mathematical concepts, and the lack of leveled courses.

Simultaneously, these differences in experiences are also one of the primary sources of

learning which students cite when talking about breakout sessions as experiences which are

productive for their learning. Carol, who experienced the resentful, toxic breakout session with

the software engineer, also described other breakout sessions where “[she] was in a group with

someone who's far less frustrated, who maybe does get something that [she doesn’t], and they

can explain it.” In situations like these, where one student is capable of acting as a teacher to

another student, students say that breakout sessions are useful and productive. Additionally, Nor

from the previous paragraph went on to say that students are generally very willing to help each

other in these situations. Further, some students describe situations where group members have

different experiences, but no one is seen as more knowledgeable or advanced than the others. In


these cases, students combine their varied perspectives to generate new insights and engage with

class concepts at a deeper level than can be reached in full-class discussions. One student,

Francisco, related one such instance of combining perspectives from a multimodal

communications class:

One of [my groupmates] told us that he won a poetry competition, and then he helped us

to understand how a poem was structured... then there was another girl that already, she

was a junior journalist for her high school. So, she helped us to analyze the text in a more

practical and objective way. So when we came out with the analysis of the poem, we

came – even if our analysis, it wasn't accurate, like what the professor wanted – we came

up with a creative and a different perspective of the poem that my professor didn't see

because we mixed the objective perspective of a journalist and the subjective and artistic

perspective of a poetry writer. So yeah, they help us to figure out that with many

perspectives.

Francisco’s experience seems unique to the breakout session (compared to the full class

activities) as the students in the group were allowed to explore and solve the problem in a way

that made natural sense to them based on their prior experiences. That the student could sense

that their analysis “wasn’t accurate, like what the professor wanted,” indicates that the instructor

had a particular expectation for the activity which could have stifled the group’s natural approach

to problem solving in a full group discussion.

Chats.

Students regularly mention how chat-based interactions are helpful to their learning and

how the chat feature is a valuable asset to the ALF. Despite being a feature of the ALF which

occurs during a class session, the place which arises in the chat is notably different from the


place which arises in the instructor-led element of the class. Though the instructor can see what

goes on in the chat, the space serves as an opportunity for students to interact outside of the

regular and direct facilitation of the instructor. Erika described how the chat space in the ALF

expands the interactive capacity of the ALF, acting as a substitute in some cases for side

conversations and interactions that would normally occur in IRL class sessions:

[The chat] makes it possible to have a little bit of offside conversations. And professors

aren't super opposed to that, which I think is nice, because sometimes you just need to,

you know, make a joke, or laugh about something, or share a thought. And I don't know,

if you have a shared experience with someone, reference it. It's just nice to have that

ability.

Though these chat-based interactions do occur on the ALF and at the same time class is

occurring, they occur outside of the normal rules of discourse in ALF discussions. Sofia alludes

to this saying, “the chat during class gets pretty fun, because you’re using that to kind of have

those social interactions that you don't normally have on the ALF.”

However, the ALF chat is not the only space which students use to communicate during

class. Based on the types of messages students want to send, they may opt for a third-party

messaging tool which does not allow the entire class to see their messages to their peers. Zara

describes this distinction in usage:

The ALF is definitely much more for like content specific… When I text through [third-

party apps], it's because I don't want the whole class to see… If it's just small innocent

jokes, I like don't mind putting in class chat, but for more like, somebody's obviously on

their phone all class or being a dick or something, I text… on third party chat apps.


Because of their more informal, fun nature, both the ALF chat and third-party chat apps create

opportunities for students to form in-class relationships with their peers. Speaking about third

party chat apps in particular, Zara goes on to talk about the people she messages in class:

I usually have a couple of people in class who I consider my class besties. It's just class-

specific, not like I'm super close to them. It's just, we're in this together. Like, this activity

sucks or this reading is really difficult, let's help each other out kind of thing.

The interactions which occur in the chat arise purely out of students’ interest in engaging with

each other. They are essential to in-class relationship building, and they also help students who

have an interest in or confusion about a concept which could not be resolved in the regular class

discussion. However, that this interest arises during class but cannot be fulfilled through the

facilitated class structure has important implications for the places where collaborative learning

occurs and what students must do to experience this collaborative learning, an issue which will

be further explored in chapter 5.

Learning Community

Because of the ways they relate to classes at Minerva, the previous sections have focused

exclusively on digital dimensions of the Minerva learning community. However, as mentioned

above, the learning community at Minerva does not follow a strict boundary created by online

and offline learning. As such this section will focus on the learning community at Minerva, an

element of Minerva mentioned by almost every student interviewed, as a social dimension of

both the physical and digital portions of the Minerva environment.


Online and IRL community.

In highlighting the interconnectedness of the Minerva learning community, I do not mean

to obscure the different manifestations of the community in different spaces. Though some

students talk about the ways their learning experiences will extend from instructor-directed

discussion to chat to Saturday morning chats at a local coffee shop, other students like Jana

describe how the relationships built in classes do not necessarily translate to IRL interactions:

Even if I take classes with [other students in the same city], like online… even if we talk

in class, it doesn't mean that we talk in real life, which I thought was kind of strange,

because if I took a class in real life somewhere else, and I talked to those people, I would

probably greet them on the hallway or something. But here, it's not so much.

This sentiment of a distance between relationships in class and relationships in “real life,” echoes

Sofia’s discussion of distinctions between the way relationships form in the ALF and the way

they form online, saying that “interactions in real life… will always have a deeper connection

right away because you kind of, you can't log off and get away from each other.”

Further, while students often talk about the experience of taking classes with their friends

and how the experience differs when they take classes with friends, almost no students talk about

making friends in their classes. Combined with the COLLES survey results (Fig. 4.1) showing

peer support as the lowest frequency category of social constructivist elements, this distinction

between IRL friends and class friends is a significant finding which offers another means of

evaluating students’ feelings of inauthenticity in the ALF learning environment, discussed above.

The distinction between IRL relationships and relationships formed online has been explored in

other works (e.g. Parks & Floyd, 1996). While some authors claim that there is essentially no

difference from IRL and online relationships (Carter, 2005), others assert that online


relationships are ultimately limited to shallow interactions (Beniger, 1987). As with most

opposing beliefs, the truth seems to be somewhere in between: there are distinctions between

IRL and online relationships, but both offer affordances that the other could not achieve

(Wellman & Gulia, 1997). Further, online relationships are often taken offline and IRL

relationships often move to online spaces as well (Carter, 2005).

Given the extensive literature noting the differences and similarities between IRL and

online relationships, sentiments of social distance in the ALF expressed by students in this study

and the Peer Support results of the COLLES are particularly interesting when contrasted with

many ways students mentioned the elements of a “sense of community” defined in Chapter 2:

connectedness, cohesion, spirit, trust, and mutual interdependence among members. For

example, Farida likened participating in class to playing a team sport where each individual must

train (prepare) for a game (a class session) or risk missing a pass (not productively contributing

to discussion) and letting the team down. Along similar lines, Zara talked about how, when

another student resurfaces a point she made earlier in the discussion, it makes her “feel like we're

all in this together. And we're like, all learning, we're all talking about the same things. And it's

really exciting.” The sentiments expressed by these students seem to result from many

experiences students shared in which they supported and relied on their peers. These included

experiences like tutoring their peers in unfamiliar coding languages, challenging their peers’

ideas in constructive ways, offering feedback on job applications, and teaching their peers about

foods from their home. The contrast between a sense of social distance and a sense of

community in the ALF (and IRL) raises questions about how students can have such variable

experiences or even feel both senses simultaneously.


Community formation.

To foster the sense of community expressed by the students above, Minerva employs a

variety of community building structures and practices. Many of these structures are an

intentional piece of the Minerva environment and are described above like the residential system

and the community building events facilitated by the student Experience Team. While Minerva is

obviously intentional in crafting its model of community building, community building also

occurs in ways which are presumably unintentional. Specifically, multiple students discussed

bonding over stressful situations which arose from Minerva’s early-stage development. This type

of community formation is one which bridges the IRL and online manifestations of the

community, as Carol describes:

I think we've been through a lot of, I don't want to say trauma... we've been through so

much. Whenever the administration rolls out a new policy, there's a huge debate about it

online, sometimes even like a public forum via our school government and by public

forum, it's, you know, 30 people in a room, just kind of trying to talk about it.

In parallel, Carol described the experience of two classes, each with a large assignment due at the

same time every weekend, as “this weird communal marathon of like the same like 10 folks in

the same cafes, working through this stuff. It was kind of nice. It was awful but kind of nice.” As

such, by its very nature of being a new university actively developing its model, Minerva

motivates community formation for some.

However, these means of community formation are not exclusively successful and, for

some, caused more harm than benefit. José shares:

For me, [the interconnectedness of the community] played very negatively during my

sophomore year. And the movement that I'm seeing… in the third year is that most


people are finding that they can have better lifestyles, better mental health, and a better

approach to school when they detach themselves from the community. So, a lot of people

are choosing to like make friends in the city, to spend less time in community events,

spend less time trying to make as many friends in the community as possible.

Though only a few students interviewed conveyed this sentiment, this comment is significant

because it offers contrast to the largely positive feelings about Minerva and the Minerva

community expressed by the other interviewees. Especially when many students cite shared

stressful situations as agents in community building, this serves as a reminder that the experience

of community is not monolithic amongst all members but rather nuanced in its effects. The

experiences these students offered about the stress of the learning community at Minerva begins

to unveil the interpersonal politics of Minerva, an essential and interesting factor in students’

experiences and one which warrants further exploration.


Chapter 5 – Discussion

Given the data presented in the results section, there are two primary points of interest

regarding students’ social learning experiences in Minerva’s online learning environment. First,

though the distinction between Minerva’s online community and the IRL community is blurry,

there is a difference in the way students form relationships in the two different manifestations of

the community. Specifically, the online community suffers from some degree of impersonality

and social distance. This social distance is compounded by a sense that discussion-based learning

interactions in the Active Learning Forum (ALF) feel mechanical, artificial, and shallow. At the

same time, the experiences students shared indicate that there are benefits of learning on the ALF

which co-occur with the challenges. As a highly regulated space, opportunities are created for all

students to contribute to classes, including students who would not normally feel comfortable

contributing or be given the chance to contribute.

This chapter will analyze the features of the ALF which create both the benefits and

challenges associated on the ALF. These features revolve around the scripting of collaboration in

the learning environment, both on the level of specific collaborative activities in lessons and of

the combination of activities to create an entire lesson built upon principles of collaborative and

active learning.

Scripting Collaborative Learning

Throughout the interviews, students shared how discussions in the Active Learning

Forum are heavily directed through the facilitation of the instructor and the extrinsic motivation

to participate in class in order to get a good grade. Online learning environments are no stranger

to tools of facilitation and external motivation. As discussed in Chapter 2, both extended


MOOCs (xMOOCs) and connectivist MOOCs (cMOOCs) employ directive structures to achieve

the particular form of pedagogy they utilize and have employed these structures from their

inception. xMOOCs rely on practices which lean towards behaviorism like knowledge transfer

from instructor to student through lectures and checks on knowledge acquisition like quizzes.

Alternatively, cMOOCs attempt to develop pedagogies based on constructivism and

constructionism in an online space through student-driven discussion forums where grading

schemes require students to post in these forums. Though Minerva employs some elements of

both xMOOCs and cMOOCs, Minerva’s pedagogical structure is more closely related to that of

cMOOCs where student contributions are meant to drive learning interactions, at least in

principle.

However, achieving this sort of active, collaborative learning is no small feat, and, as

O’Donnell and Dansereau (1992) note, students will not always choose the most effective

strategies in collaborative learning situations when left to their own devices. As such, learning

environments, including Minerva, are required to facilitate interactions with “scripted

cooperation” where scripts direct the roles in a collaborative interaction as well as the nature and

timing of the activities that make up the interaction. O’Donnell and Dansereau largely discuss

how scripts can improve collaborative interactions by helping participants choose effective

strategies and can allow instructors to selectively activate particular cognitive activities.

However, with scripting comes the risk of over-scripting, imposing structure on collaborative

interactions to the point that they feel unnatural and/or impede learning in some way

(Dillenbourg, 2002). As a highly scripted learning environment, Minerva experiences both the

risks and benefits of scripting.


Risks of overscripting.

Dillenbourg (2002) identifies five major risks of scripting collaboration: (1) disturbing

natural interactions by manipulating the interactional sequence, (2) disturbing natural problem

solving sequences, (3) increasing cognitive load by requiring students to think about the script as

well as the problem, (4) didactising collaboration by standardizing interactions, and (5) goalless

interactions which are reliant on the script to generate purpose for the interaction. These risks

were identified in the context of computer supported collaborative learning environments

(CSCL) where scripts defined multiphase interactions in which each phase has a particular

activity with a defined task, grouping of students, distribution of labor, mode of interaction, and

timing of interaction. Like in Minerva classes, this multiphase structuring creates scripts that

work at two levels: in the way activities are sequenced to form a collaborative interaction over

the course of a class and the way contributions are sequenced to form a collaborative interaction

in each activity. In the context of Minerva, these risks are apparent and relevant at both the class

and activity level, though some risks are mentioned more explicitly or more frequently than

others. Additionally, another risk which Dillenbourg did not identify arose relatively frequently

in the student interview data: the propensity to make opportunistic contributions which would

allow students to “participate” but not contribute substantively to the overall collaborative

interaction.

The first and second risks Dillenbourg identifies can be difficult to distinguish in the

context of the ALF because students often describe the scripting at the activity and class level

simultaneously. As discussed in the results, many students described the feeling that the “flow”

of classes was unnatural, using terms like “mechanical,” “artificial,” and “synthetic.” At the


activity level, this sentiment better aligns with the risk that natural interaction will be disturbed.

Sara expressed this sentiment describing the way the instructor calls on students to contribute:

I think the kind of spontaneous generative aspect, especially in the normal class model, in

which, depending on the professor, can sometimes be like, "Sara, can you answer this

question?" And you're like, "yeah, I'm active learning..." You don't have the opportunity

to kind of speak when you feel the energy to speak, the impetus, right? You're just

speaking in an active learning way.

Alternatively, at the activity level, the sentiment of an unnatural flow better aligns with the risk

of disturbing natural problem-solving sequences. Students like Carol explicitly described this

risk in reference to the ways that classes are structured:

I have never really been a fan of the structure of Minerva's LPs – lesson plans – which

tend to be pretty rigid and don't allow for much diving deep, I think. It's like going by this

weird script. I never liked it. I think my better classes have been ones where professors

are willing to kind of go off script, and actually maybe the classes I've enjoyed the most

tend to be.

Here, Carol begins by describing how the overall script of lesson plans -- how the structuring of

various activities to make a full lesson -- limits the depth that a class can reach. However, she

goes on to describe how the nature of the individual activities is what causes the shallowness she

previously mentioned. She compares what she sees as a more traditional activity in a philosophy

class with the “weird activities” which are more common in a Minerva lesson plan:

In some philosophy classes… we read a text and then pull out paragraphs and just talk

about what the paragraphs are saying, which has never been a broken model of education

but that's really not the bread and butter of the Minerva lesson plan. They're really


oriented around these weird activities. It's oriented around activities that are meant to

refine our understanding of the material.

Carol’s sentiment is particularly interesting because she goes on to describe how these “weird

activities” fail because of a significant difference in expertise between the students participating

in the course. In this way, the issue of mismatched levels of expertise (presented in Chapter 4)

interacts with the scripting of Minerva’s classes to make collaboration challenging at times. Most

students who mentioned the flow of the conversation, however, did not make an explicit

distinction between unnatural flows at the class and activity level. José’s comments about the

structure of discussion-based classes represent this ambiguity:

So, they are discussion-based, but they're heavily preprogrammed in the sense that… they

have a predetermined structure that they're supposed to follow. And oftentimes,

especially depending on the quality of the facilitation, then that can become like very

mechanic and artificial.

These two risks of disturbing natural interaction and problem-solving sequences are discussed

first because they are at the heart of the risks which come with overscripting collaboration.

Disturbing natural sequences of collaboration are the root of increased cognitive load, creating a

need to redefine the sequence of interactions, the way students participate in interactions, and the

goals of interactions. As such, these two risks are expressed frequently when students talk about

the risks of overscripting, both explicitly like the students quoted above and implicitly as

students talked about the other risks of overscripting.

The third risk Dillenbourg identifies is also expressed relatively frequently by students,

that scripting collaboration increases the cognitive load required by the interaction. Dillenbourg

describes this happening in two ways, in requiring students to understand, memorize, and


execute the script and in forcing students to find ways to collaborate despite the script. Some

students like Adriana discussed the increased cognitive load that executing the script requires:

You need to be really paying attention because your professor who called you, you need

to answer. This is kind of hard as well. It is a lot of pressure to be 100% paying attention

all the time.

However, more frequently students discuss finding ways to collaborate despite the script. Some

of these elements are presented in Chapter 4 like chats which co-occur with class discussions and

emoji reactions which expand students’ interactional capacity. These divisions of attention

inevitably increase students’ cognitive load during class. Nevertheless, some students cite this as

a benefit to a highly scripted class, a sentiment which will be further explored below. However,

the most significant increase in cognitive load comes from an element which Dillenbourg does

not associate with scripting per se: the stresses associated with the pervasiveness of one’s

activities being graded. In the context of ALF classes, grading is heavily associated with

scripting because students’ motivation to follow the participation scripts largely comes

extrinsically from the grades they receive on their contributions to class. Multiple students

mentioned how grades don’t make Minerva competitive. Chloe went so far as to say:

There is no competition at Minerva at all… The grades don't even really allow us to be

competitive, because we get graded on a one to five scale. And so, if you get a three,

that's good, and most people get threes, so there's not a good three or a bad three, just a

three. And so, you really can't compete with each other students.

Nevertheless, students frequently reference how grades influence the ways they engage with

class. Sara explicitly discussed the fear which comes with grading and motivates students to

participate in class:


And then I've experienced fake active learning, which is like, "we're going to make sure

that you're paying attention the whole time by making you feel a little bit afraid that like,

if you don't answer correctly, we're going to score you bad.”

Many students also mentioned thinking about grades as they planned and executed their

contributions during class, a cognitive load which, at the very least, is likely to have

consequences for the ways in which they contribute in class. As a good student in high school,

Jordan described the stress the participation structure caused them when they began Minerva:

The way that you get cold called – so like called on without warning – was absolutely

terrifying to me in the beginning, because I was like terrified to say something wrong

because I was not used to being called on in a situation where I might be wrong because I

didn't totally understand because I hadn't already been taught it.

Despite the stress of the initial transition to Minerva, Jordan describes developing attitudes and

strategies that help them deal with the fear of being wrong. Though most students interviewed

seemed motivated to learn and develop skills to learn (a quality Minerva explicitly selects for),

the influence of grades on students’ development was obvious. Students like Dara described how

grades provided a “stick” to motivate changes in behavior. After getting a bad grade on an

assignment because his group failed to compromise, Dara said he “reflected a lot after that

experience. And [he] kind of had to learn that lesson. Because those bad grades right there, they

are a stick and so they helped shape your behavior.”

While Dara talked about this development in a positive way, the influence of grades on

behavior were sometimes detrimental to productive collaboration. Jordan describes how students

manipulate the participation structure by making a contribution early in the course of the class.

Then, “people kind of zone out. And then it feels like people aren't really answering questions or


aren't really trying to further the discussion.” Because students are required to participate during

class (and, in particular, because there are structures which attempt to produce equal

participation), students develop strategies to contribute at opportune times and then stop paying

attention during class. This opportunistic participation is a risk of overscripting which

Dillenbourg did not identify and seems to be particularly relevant in large classes where the

required participation means that each student only needs to make a few contributions to produce

the needed grade. However, opportunistic participation is also evident in student’s understanding

of the Minerva participation structure at large. While talking about why Minerva students are

more comfortable speaking in class (a benefit of scripting explored in the next section), Chloe

mentions opportunistic participation as a fact of the Minerva environment:

If you don't just speak when you feel like you have a moment to speak or when you think

it's best for you to speak or you actually have a good idea and you can contribute

something really great, then you're going to be asked to contribute when you don't feel as

confident.

Like the risks that Dillenbourg identified, opportunistic participation limits the collaborative

potential of an interaction by shifting the focus from effectively collaborating to meeting the

requirements of the script in order to maximize a grade.

The final two risks of overscripting that Dillenbourg mentions, didactising collaboration

and goalless interactions, are also interwoven in students’ responses. Because Minerva’s

participation structure encourages instructors to engage students who have not participated

recently, instructors can sometimes create conversations where students do not meaningfully

engage with previous contributions. Bela describes how the quality of a class can be decreased

when instructors create such discussions:


I've had professors that would just say, "Oh, Bela, what do you think about this thing?"

And then after I put forward my point, they would just say, "Hmm, so this other person,

what do you think about it?" There has to be a certain level of engagement in between the

students' points, and it's mostly the professors.

By following too closely to the script of interactions, instructors limit students’ ability to engage

meaningfully with each other. These limitations on interactions can also contribute to goalless

interactions which prioritize following the script over the goal of reaching shared understanding.

Students such as Adriana discuss this interaction between didactising collaboration and goalless

interactions:

But even though having those kinds of tools and trying to balance the conversation,

sometimes I feel that people raise their hand to say not very deep things like sometimes

empty words, you know? And the professor calls them, and it just wastes like 30 seconds

of the class.

Though Adriana describes students raising their hands to talk (which may not be immediately

apparent as didactising collaboration), it is important to remember that the significance placed on

contributing to class by the grading structure in one-way collaboration is scripted at Minerva.

One student, Alya, reminds us that “encouraging people who haven't spoken to speak is also

encouraging opinions that aren't necessarily the best because you're just speaking for the sake of

speaking.” With these last two risks, we can see the most tangible impacts of overscripting on

students’ experiences in the ALF. While students may notice disturbances in interactional

sequence, experiencing a goalless interaction in a learning environment could easily lead

students to describe that interaction as shallow, like Carol did when she said that Minerva’s

lesson plans “don't allow for much like diving deep.” Further, experiencing a collaborative


activity that feels overly didactic could easily lead students to describe the interaction as

“synthetic,” like Erika did when describing how limited student-to-student interactions could be

based on the nature of the ALF.

In this way, the risks of overscripting can help explain, at least in part, the two major

problems identified at the beginning of this chapter, that learning in the ALF feels artificial and

shallow at times and that the online community suffers from a social distance which is not

present to the same degree in the IRL community. While some risks cause students to disengage

from collaboration, others cause students to make contributions to discussions which do not

advance or, worse, even hinder the collaboration. In Minerva’s heavily scripted environment,

these risks present a significant concern for the efficacy of collaboration in the ALF and for the

online manifestation of the learning community at Minerva at large. To quote Dillenbourg

(2002), “Scripted collaboration may appear superficially as genuine collaboration, but may fail to

trigger the cognitive, social and emotional mechanisms that are expected to occur during

collaboration” (p. 27).

Benefits of scripting.

It would be convenient to end with the risks associated with overscripting described

above which nicely explain many of the issues discussed by students at Minerva. However, it is

unlikely that any analysis which is so straightforward is entirely accurate. Despite the risks of

overscripting described above, there are meaningful benefits of a highly scripted learning

environment which students mentioned in their discussions of Minerva. Though it would seem

that the benefits of scripting occur when overscripting is mitigated by developing less directive

scripts, this is not the case. Instead, the three benefits discussed below come simultaneously with

the risks of overscripting (and in one case because of the risks of overscripting).


First, scripting at Minerva does achieve, in some regards, the original goal of scripting:

improved collaborative interactions. When first writing about the benefits of scripting,

O’Donnell and Dansereau (1992) were concerned that in some interactions, students were not

able to “choose appropriate strategies” when left to direct learning interactions entirely on their

own (p. 122). One such inappropriate strategy is a student over-explaining their idea or

perspective and thus limiting other students’ opportunities to contribute. One student, Farida,

describes the way Minerva instructors avoid this inappropriate strategy by actively scripting

contributions in an activity:

If someone is droning on and on forever and not really making a concise point or getting

to the point, professors will be like "Okay, this is not exactly what I'm looking for," or

"Okay, does someone else want to add anything?" So, the professors might intervene as

well and make it a better experience if they feel like the student is not getting to the point.

This intervention saves the interaction from becoming dominated by a single student and gives

other students the opportunity to contribute to the discussion. However, this benefit arises out of

the same structure that could potentially increase a student’s cognitive load: grading. In Chapter

4, Farida also shared that students stopped repeating the points she made (another inappropriate

strategy for collaboration) because the instructor graded them down. This combination of a

benefit of improved collaboration with a risk of increased cognitive load showcases a paradox in

motivating effective collaborative discourse in learning environments: if instructors impose

effective collaboration by grading students in particular ways, they can improve collaboration in

interactions. However, grading also increases students’ cognitive load which reduces their ability

to participate effectively in collaborative interactions.


In some cases (and potentially in the second example Farida shared about another student

restating her point), “inappropriate strategies” for collaboration go beyond simple strategy

choices. Instead, they begin to embody imbalanced power structures in which some students’

contributions to discussions are undervalued. In these situations where power imbalances

manifest, certain students repeatedly take opportunities to contribute to a discussion (or, in

Farida’s case, take the content of other student’s contributions) and thereby limit the potential for

others to contribute. Carol described this occurring at the college she attended before transferring

to Minerva:

I remember, a couple times I was in a seminar where I was having an amazing time and I

felt like I learned a lot, but… I realized… I was one of only a couple students who felt

like that because the class was a dialogue between me and maybe like one or two other

people and the teacher and I don't know what everyone else was doing. But I felt really

engaged and active.

Carol went on to describe how this kind of interaction dominated by a few students could never

happen in a class at Minerva because of the strict collaborative script which Minerva employs to

make sure all students participated in equal amounts.

This benefit of scripting co-occurs with the risk of opportunistic participation. Previously,

Chloe described how Minerva’s participation structure motivated students to participate when

they had ideas to contribute in order to avoid being called on when they did not. Though on the

opportunistic participation side this caused students to disengage, for Chloe this pushed her to

see herself as someone who could meaningfully contribute to discussions despite previously

feeling like she could not contribute:


[Getting called on at any time] was really like a huge problem for me because I was

never– I was used to think, but not speak, and there's not a time where I'm not cold called

right in the middle... of class. I have to always be on my feet, and because they always

are encouraging you to speak, and they actually have to make you speak, would be a huge

reason why, now, I love active learning. And I can see the benefits of it.

In this way, the required participation can be empowering for those students who have not

traditionally been allowed equal space to contribute to class discussion, a significant contribution

to accessibility in education which will be discussed further below.

In Chapter 4, students shared how they would use chats and outside of class discussions

to continue learning experiences which could not be completed within the frame of the class

experience. One student, Thomas, describes how he would sometimes continue interesting

discussions started in breakout groups over coffee on the weekends. This kind of interaction

where class experiences extend beyond the class was mentioned frequently by many different

students. There are certainly many factors which contribute to a learning environment where

students engage with class content outside of class, but one can be seen in the way overscripting

limits students’ capacity to collaborate during class. In one instance, a student, Gloria, shared

how one of her friends went to confront someone in his class face-to-face while class was still

occurring in the ALF after the person made a racist comment. Though Gloria has never gone up

to a peer face-to-face during a class session, she related her friend’s action to students talking

with each other about concepts and occurrences in class about which they were passionate,

something which Gloria said happened frequently. In explaining why students would go to each

other during or after class to have extended discussion about class, Gloria shared:


We cannot be raising our hands all the time and keep going back to a point and I often

think of stuff that I want to answer later on. And so, it doesn't make sense. Sometimes we

use chat, but I think some thoughts are better conveyed by speaking. And I think what

was also important for this friend of mine, and for me, is that English is not our first

language. So, it's often much easier to try to talk and use words that, when you write, you

might not use, or you may not express very well. So maybe that could also be feeling

passionate about what we are talking about. In that case, my friend was really passionate

about the sociological stuff. They were talking about sociology or whatever, and he felt

passionate about it in the way that he wanted to go and tell that person that like, "hey,

what you're saying is, like, wrong or right." In a way that I am usually not that passionate.

So that's why I don't think I would go during class like he did. But definitely passionate

to the point of going and talking to the people about it after class happens, not during

class, because that moment is for the class itself. But after class is the moment that I can

explore that a bit further with people. And it's easier to just go and talk instead of being

typing everything on email or Facebook or in the chat.

This type of interaction which extends class experiences shows a benefit of what might be

otherwise be critically viewed as overscripting: when students do not feel that they can fully

express their ideas (and are passionate about them), they find ways to “collaborate despite the

script” (Dillenbourg, 2002, p. 26). Though Dillenbourg discusses this as a risk of overscripting,

in the case of Minerva it has become a benefit in some ways that students report. In the types of

interactions described by Gloria, students will extend their learning to spaces outside of the

formal learning environment, allowing them to be engaged more often and in more varied

situations with course material. This reflective and frequent application of ideas is an essential


way that knowledge is developed in constructivist learning, and, in some cases, overscripting

promotes this kind of learning.

Balancing overscripting and underscripting.

The effects of scripting are further complicated by the problems which arise with

underscripting. One student in particular, José, noted that overscripting and underscripting posed

a challenge for learning, creating a spectrum from “too organic” to “truth official.” According to

José, classes which are too organic are frustrating because each student is “entitled to their own

opinion” and the lecturer and the “underground structure” are unable to direct the discussion

towards a ground truth. Alternatively, in a “truth official” class, the instructor is constantly

directing a class back toward a predetermined set of learning outcomes such that “the professor

is not mapping back to the feeling of the classroom or how people are reacting to what is

happening.” Indicating the severity of underscripting, José noted that “when the discussion-based

gets really bad, it's because it's too organic.” When talking about classes which are “too organic,”

José and the other students who mention this issue identify a significant barrier to the adoption of

pedagogies founded in social constructivism: the concern that “the ground truth” of concepts will

be missed when, in the words of José, “unsophisticated” or “unqualified” opinions are not

checked by the authority of the instructor.

However, this is not the only manifestation of underscripting which students mentioned

in interviews. Scripting was initially identified by O’Donnell and Dansereau (1992) as a need in

collaborative learning because members of collaborative groups do not always have the

knowledge which allows them to choose appropriate strategies which promote learning. In the

context of Minerva, this lack of knowledge and direction is particularly prevalent in students’

discussion of the challenges of learning in groups in which members have different levels of


experience. Nor notes that the worst-case scenario for unproductive classes is when “one or two

people who are really good or have already learned this material, and the professor is trying to

prompt everyone else to join in. But everyone else is just like, ‘I’m sorry, I’m just so confused.’”

At first glance, this may not seem like a problem of underscripting because the instructor Nor

mentioned attempts to prompt others in the class to join in the discussion. However, this

represents a problem of underscripting effective collaborative scripts when certain students have

greater experience with the topic at hand. Carol’s comments included above which describe how

the “weird activities” which Minerva uses in its lesson plans amplify the inequality of experience

between students in groups show how, even in the presence of collaborative scripts, a lack of

scripts which explicitly acknowledge students’ differences in experience can lead to ineffective

collaborative experiences. This ineffectiveness manifested in students’ classroom experiences as

a free-rider problem when certain students neglect to prepare for class, but also in a status

differential problem where some students dominate the group because they have more

experience or socio-cultural power.

It is important to note here that O’Donnell’s and Dansereau’s (1992) original conception

of scripted collaboration was intentionally differentiated from peer tutoring because the members

of the collaborative group were supposed to be equal with respect to the task they were

attempting to accomplish. In reality, this is not how collaborative learning groups are formed.

Students will inevitably have different levels of experience with the task and sometimes even

different levels of experience with different subtasks which make up the task. The Minerva

students who shared their experiences draw out these inevitable differences in status as well as

the challenges they pose for scripting collaborative learning.


Chapter 6 – Conclusions

This thesis began with the problematization of accessibility in online education, and it

will close in a similar fashion. In seeking to achieve authentic accommodation – accommodation

that goes beyond mere access to education – we must consider how our learning technologies

include students who have not traditionally been accommodated in education spaces. A

significant gap in our consideration of accommodation in online learning is the tension which

exists between authentic accommodation and progressive pedagogies. As the entanglement of the

development of the MOOC and the history of constructivism indicate, social learning, defined in

the context of social constructivism, is one such progressive pedagogy which is important to

understand in the context of online education. Developing a theory of social online learning

requires us to understand how students experience social learning online and how students can

be motivated to engage in social learning online.

Chapters 4 and 5 contributed to such a theory of social online learning by exploring the

ways in which scripting collaboration in Minerva’s Active Learning Forum influenced students’

perceptions of their learning and their overall experience in the Minerva learning environment.

Scripting can have significant negative effects on students’ learning by limiting the depth to

which they can engage with a topic, causing students to rely too heavily on grades to determine

how they contribute, and increasing students’ cognitive load. Simultaneously, however, scripting

creates opportunities for students to be more effective collaborators, to contribute in ways they

would not normally, and to create additional places to collaborate beyond class structures.

Furthermore, despite the risks of overscripting, scripting collaboration plays an important

role in creating effective learning environments. With a dearth of scripting, underscripting can

also lead to situations where students feel that what they have learned is unfounded, or where


students do not know how to engage with knowledge or power imbalances they experience in

collaborative groups. The benefits of scripting collaboration to both the productivity of learning

interactions and the authentic accommodation of a learning environment indicates that there is

potential for productive social learning in online spaces. However, the risks of overscripting as

well as the need to balance underscripting and overscripting show that there is still work (and

further research) to be done to refine scripted collaboration in online learning environments. The

implications of the risks and benefits of scripting, the limitations of the current research, and the

potential direction of future research is further discussed below.

Implications

First, the fact that collaboration scripting exists and offers benefits to collaborative

learning provides evidence that there are problems which underlie educational discourse from

which casual day-to-day discourse does not appear to suffer. Though I will not develop the

theory in full here, the philosopher Paul Grice’s (1991) maxims of the cooperation principle in

conversation are helpful in understanding the problem which the need for scripting reveals.

Essentially, Grice identifies four maxims which underlie all conversation in order to make it a

meaningful exchange between individuals. Two such maxims will be drawn from here.

First, the maxim of quantity directs speakers to share enough and just enough information

as is required for the other participants in a conversation to understand the speaker’s

contribution. Second, the maxim of quality directs speakers to say what they believe is true (both

things that are not false and things which they have adequate evidence to reasonably believe).

The motivation behind scripting collaboration is that we cannot expect students to always be able

to spontaneously act in accord with these maxims while participating in educational discourse.


Further, we can see that students do not always follow these maxims. As is evident in the

results and discussions, at times students are motivated to speak (or explicitly prompted by the

instructor) when they do not have information which will help develop the conversation.

Sometimes this type of unproductive contribution manifests in students making contributions

other students have already made in the conversation (a violation of the maxim of quantity

because the information did not need to be shared twice), and sometimes this manifests as

students sharing information that they may not actually believe, simply to have something to talk

about (a violation of the maxim of quality). Minerva’s scripting structures such as required

participation motivate students to violate Grice’s conversational maxims. Then, to repair these

violations, further scripting structures are developed to do what Grice claims should naturally

occur in productive discourse (for example, grading down students who repeat the points of

others). However, violations and attempted repairs through scripting are not unique to Minerva;

they occur in other online learning environments and in IRL learning environments: imagining a

discussion section where a student has obviously not done the readings but talks anyway is not a

difficult feat.

The results of this thesis offer some preliminary insights into how instructors and

curriculum designers might address this. In particular, because students were so aware of the

grades that they were being assigned based on their contributions to class, it would seem that the

constant evaluative nature of the full class and individual spaces of the ALF influence the ways

that students participate. In this way, it is a design hypothesis worth testing that more spaces like

breakout groups where students’ individual contributions are not recorded and used for

evaluative purposes could give students more space to act and collaborate freely. If breakouts

prove to be spaces where students feel more comfortable taking risks and authentically


collaborating, restructuring the learning environment to focus more on breakout groups could

shift the responsibility of learning from the individual level to the collective level making each

student responsible for the learning of every student in the class. To use the community of

learners framework developed by Brown and Campione (1994), this shift could help create an

environment where the unit of learning is not the individual but the class as a whole.

However, it is also clear from the results and discussion that collaborative spaces created

by breakout groups are not perfectly effective collaborative spaces either. Specifically, the

prevalence of knowledge imbalances where one student will be more prepared or experienced

with the topic at hand than other students continues to present challenges to the collaborative

potential of these spaces. The two types of knowledge imbalance evident in the results of this

research are defined by Salomon and Globerson (1989): the “free rider” is a student who does

not prepare for class and instead relies on the other group members to accomplish the task (p. 94)

and the “thinkist” is a student who has more background experience with class concepts than

other students and uses the imbalance to dominate the group (p. 96). These two different

situations affect students in breakout groups quite differently. However, in both situations of

knowledge imbalance, instructors must be better equipped to support students in choosing

strategies for collaborative work which account for knowledge imbalances. As Barron (2003)

wrote her article, “When Smart Groups Fail,” “How participants manage these [joint problem

solving spaces] is critical to the outcome of their work” (p. 307).

One such strategy is to allow students with less knowledge to be seen as competent and

valuable assets to the success of the group as a whole. Cohen, Lotan, Scarloss, and Arellano’s

(1999) instructional approach of Complex Instruction offers two strategies to promote this

competence and value. First, the multiple-abilities treatment calls for group tasks which


incorporate a variety of skills. Then while doing group tasks, each student should expect to be

stronger in some of the skills and weaker in others. Second, after tasks are completed, instructors

must assign competence to students who experienced a knowledge imbalance by publicly

acknowledging the contributions they made to their group in a specific and positive way. Here it

should be noted that this type of strategy relies on an accountability structure where instructors

publicly review the work of students, a challenge for large learning environments with high

student to instructor ratios. This presents an area of inquiry for future design and research. In

general, the findings of both risks and benefits associated with scripting collaboration indicate

that instructors and students must be made aware of these risks and benefits in order to allow

them to aim for the benefits and avoid the risks when choosing engagement strategies.

Finally, Chapter 1 explored online education’s roots in a history of educational equity as

an extension of distance education. The aim was improving access to educational experiences for

those who had traditionally been excluded from educational opportunity. With this ambition for

equity arose the notion of authentic accommodation, accommodation that goes beyond students

simply being able to access educational experiences and which pushes colleges to accommodate

the needs of disadvantaged students during their time at the institution. As Minerva is attempting

to redefine higher education using a tool that has historically been associated with educational

equity, returning to the question of Minerva’s propensity for authentic accommodation will allow

us to explore the implications of Minerva’s work on the future of accessibility in online

education.

In some regards, Minerva still does not meet the standard of authentic accessibility.

Specifically, the data from student interviews shows that, currently, the student experience is

dominated by large amounts of out-of-class, IRL learning in both formal and informal settings.


In this way, the Minerva experience is limited to traditional college students who can afford to

attend a full-time, immersive experience. As such, it is not surprising that all of the students

interviewed were traditional college students: they were young (going to Minerva directly after

high school, transferring to Minerva after some time in another college, or attending Minerva

after taking a short gap period), they were independent (in that they did not have families who

they were directly caring for), and they were looking for a broad, non-technical education.

Despite this focus on traditional students, however, Minerva’s model does offer some

authentic accommodations to those students: there are many tutoring and advising structures to

help students who enter Minerva at different experience levels. Further, Minerva’s unique

participation model challenges power dynamics in discussions which could limit the learning

opportunities of students whose contributions are undervalued based on an element of their

identity like race or gender. In this regard, Minerva’s model potentially works to more equally

distribute learning opportunities.

Limitations

In evaluating the contributions of this thesis to the field of social online learning, it is

important to acknowledge the limitations of this project. Identifying these limitations will allow

future work in this area to take them into consideration when planning the work. First, a set of

limitations arose due to constraints on sampling participants for this research. Participants in this

study were limited to students at Minerva. However, it is clear from students’ experiences that

instructors and course designers play a significant role in how students learn at Minerva; they are

key elements in Minerva’s learning ecosystem. Differential pedagogical implementation

strategies by both course designers and instructors are likely to have consequences for students’

learning experiences, and this would also be likely to vary by the Minerva course subject matter,


which this investigation has not explored as a research topic (e.g., considering physics and

philosophy as one disparate comparison).

Further, Minerva’s team-teaching approach creates situations where instructors are

responsible for executing lesson plans that they did not create, and where instructor teamwork

could itself vary in quality of provision. In future research, gaining insight into the perspectives

and practices of Minerva instructors and course designers could shed light on how the

participation and scripting structures discussed in this thesis are perceived by those tasked with

implementing them. Specifically, this thesis unearthed a thread of the influence of evaluation

structures on students’ actions in Minerva’s online space. A better understanding of how

instructors do these evaluations would complement research into how students respond to being

nearly constantly evaluated. To address these limitations of having little insight into classes

beyond students’ perceptions, in-situ observations and recordings of online instructional

activities and follow-up interviews with Minerva technology platform developers, course

designers, and instructors, could add vital dimensions to an understanding of the contributors to

Minerva students’ learning experiences.

In a similar vein, only current students at Minerva were interviewed for this project

because Minerva will only graduate its first class of students this year. Though the perspective of

students who are closest to the experience is obviously valuable, it is likely that graduates who

have tested the skills and knowledge Minerva taught them in outside settings could provide

additional insight into the benefits and challenges of Minerva’s educational model.

Finally, this study chose to collect limited demographic data from survey respondents.

As such, only limited claims can be made about differences in experience based on commonly-

researched factors like race, gender, and socioeconomic status. Future research which


encompasses these variables for study could more rigorously evaluate the claim that Minerva’s

required participation structures create more opportunities for historically disenfranchised groups

to participate in collaborative learning.

This research also experienced limitations in the methods it employed. Due to its focused

nature as an undergraduate honors thesis, this study evaluated students’ perceptions at a single

point in time rather than longitudinally and did not capture recordings or observations of the

interactional dynamics of students’ in-situ Minerva online learning participation. An equal

sample from all class years was taken which allowed for some comparison between students who

had experienced Minerva for different amounts of time. However, a longitudinal study could be

valuable in discerning more rigorously how students’ perceptions of Minerva change over time

as well as how Minerva’s model changes the ways students learn during their time at Minerva.

Further, this thesis relied on students’ perceptions of their learning to garner a better

understanding of the process of learning. However, evaluating the effectiveness of learning

experiences at Minerva would require research on more objective measures of students’ learning

like evaluations, graduate school admissions, or post-graduation employment data.

Additionally, as a new university, Minerva offers an interesting insight into the pivotal

decisions which create cultures and structures in an online university. Simultaneously though,

Minerva has changed drastically over the four years that the oldest group of students have been

at the school. As such, the experience of a senior is likely very different from the experience of a

freshman. Further, because Minerva values iteration and responding to feedback from students,

the negative experiences of older students has informed the changes to the experiences of the

younger students, in principle creating a better experience for the younger students.


Finally, the survey utilized in this study polled students about the frequency at which they

experienced certain elements of constructivism at Minerva. Results from the survey can be

compared between categories, but the survey could offer even richer data if it also polled

students about the frequency at which they wished each element occurred at Minerva. This

additional dimension would allow for greater insight into which elements students perceive to be

lacking, which elements they are satisfied with, and which elements they feel are overexpressed.

Future Work

This thesis presents an initial investigation into a previously unexplored type of online

learning environment. Future work in this domain can continue to develop the rich lines of

inquiry exposed by this project. Specifically, Minerva’s highly structured, information rich

environment makes it a particularly interesting data source for research on the effects of creating

digital social learning experiences on students’ cognitive load. Because students must

simultaneously pay attention to a variety of communication media (video streams, chats, digital

reactions) while also following a high structured collaborative script, students are asked to divide

their attention between a variety of content streams. This expectation of multitasking is ripe for

research on the effects of digital multitasking on learning and social well-being (e.g. Pea et al.,

2012). Additionally, deep inclusion of scripted collaboration into Minerva’s lesson plans

provides an opportunity to research and develop collaborative scripts which explicitly address

the issues of knowledge and power imbalances in collaborative groups.

Further, Minerva’s unique hybrid model combining online and IRL learning experiences

would allow for much-needed comparisons between online and IRL elements of learning

environments. One such comparison could come in the form of comparing the relationships

students form in online learning environments to the relationships which students form in IRL


learning environments. This hybrid structure could also offer insight into the way online

relationships translate to IRL relationships and vice versa.

Finally, due to its strength and intensity, the learning community formed at Minerva

provides a case study of the ways in which community formation, politics, and tension impact

students’ social learning strategies and overall learning outcomes. Further investigation could

reveal how dimensions of community (as well as the factors that influence their formation) are

experienced differently by different members of the community.

On the tension between quality and accessibility

As this work was coming to a close, Minerva announced that it would begin marketing its

Active Learning Forum software to educational institutions, corporations, governments, and

NGOs. This marketed software builds on the preexisting software and active learning pedagogies

Minerva uses in its own classes but with a critical distinction: the marketed software can

accommodate classes of up to 400 students (Minerva Announces Expansion of Its Educational

System To Enable Innovation at Institutions of All Sizes, 2019). According to Minerva’s CEO,

Ben Nelson, this capacity expansion was essential when attempting to sell the software to other

institutions which could not afford to teach classes of 15 students at a time (Young, 2019). To

host up to 400 students, the new version of the software allows instructors to divide students into

breakout groups of up to 12 students (note that these are significantly larger than the breakout

groups discussed in this research). In these breakout groups, students will engage in a small

group discussion while each student “fills out a ‘structured worksheet’ that can be graded later

by a TA following a rubric to make sure each student was following along and participating”

(Young, 2019). Based on the findings of this thesis, the next stage of Minerva’s life cycle as

foreshadowed by their new software necessitates some degree of skepticism.


Minerva is looking to expand the reach of its online learning software and, with it,

Minerva’s approach to learning. That Minerva is confident that they will be able to successfully

market the ALF and the active learning pedagogies it is built upon is an indicator that there is

demand for online learning to change. Minerva believes that active learning is the way online

learning must change. However, as this research has shown, active learning, at least as it is

manifested at Minerva is not without its imperfections. Overscripting presents significant risks to

authentic and deep social learning experiences, to the extent of threatening social online learning.

Though it is not yet known how students will experience Minerva’s new software, the emphasis

on monitoring breakout groups through worksheets seems to imply more intense, grade-enforced

scripting applied to breakout groups. Breakout groups the size of current full classes at Minerva

which are based upon graded worksheets which serve to force students to engage in classes do

not address the concerns identified in this research. Further, it is easy to imagine these changes

worsening student’s feelings of inauthenticity and distance in their learning environment.

The tension uncovered by Minerva’s most recent development is reminiscent of the

tension between accessibility and quality identified in the first chapter of this thesis. To make its

learning environment potentially more accessible, Minerva is potentially sacrificing the quality

of the social learning it has developed in its current model. However, based on the results of this

thesis, I claim that this sacrifice is not inevitable. If scripting is the way to make learning more

accessible by reducing the number of instructors needed to facilitate a class, the benefits of

scripting found in Minerva’s current learning environment offer evidence that scripting can also

increase the quality of social learning in online spaces. Rather than forcing them into making

certain actions, effective scripts empower students to productively navigate collaborative


learning settings. Quality and accessibility in online learning are achievable, and student agency

is the tool that will bring them together.


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Appendix A – Constructivist On-Line Learning Environment Survey (COLLES)

NOTE: This survey will be represented in digital form in Qualtrics.

Demographic Information

Name:

Year of Study:

Major:

Gender:

Please circle the symbol that best describes your perception of your experiences at Minerva.

• -- Almost Never

• - Rarely/Seldom

• -/+ Sometimes

• + Often

• ++ Almost Always

A. Relevance

In my experiences at Minerva…

| -- | - | +/- | + | ++ | My learning focused on issues that interested me.

| -- | - | +/- | + | ++ | What I learned is important for my interests.

| -- | - | +/- | + | ++ | I learned how to improve skills which relate to my interests.

| -- | - | +/- | + | ++ | What I learned connects well with my interests.


B. Reflective Thinking


| -- | - | +/- | + | ++ | I thought critically about how I learn.

| -- | - | +/- | + | ++ | I thought critically about my own ideas.

| -- | - | +/- | + | ++ | I thought critically about other students' ideas.

| -- | - | +/- | + | ++ | I thought critically about ideas in the readings.

C. Interactivity


| -- | - | +/- | + | ++ | I explained my ideas to other students.

| -- | - | +/- | + | ++ | I asked other students to explain their ideas.

| -- | - | +/- | + | ++ | Other students asked me to explain my ideas.

| -- | - | +/- | + | ++ | Other students responded to my ideas.

D. Teacher Support


| -- | - | +/- | + | ++ | My teacher stimulated my thinking.

| -- | - | +/- | + | ++ | My teacher encouraged me to participate.

| -- | - | +/- | + | ++ | My teacher modeled good discourse.


| -- | - | +/- | + | ++ | My teacher modeled critical self-reflection.

E. Peer Support


| -- | - | +/- | + | ++ | Other students encouraged my participation.

| -- | - | +/- | + | ++ | Other students praised my contribution.

| -- | - | +/- | + | ++ | Other students valued my contribution.

| -- | - | +/- | + | ++ | Other students empathized with my struggle to learn.

F. Interpretation


| -- | - | +/- | + | ++ | I made good sense of other students' contributions.

| -- | - | +/- | + | ++ | Other students made good sense of my contributions.

| -- | - | +/- | + | ++ | I made good sense of the teacher's contributions.

| -- | - | +/- | + | ++ | The teacher made good sense of my contributions.


Appendix B – Semi-Structured Interview Protocol

● Thank you so much for offering to talk with me. I’m doing this research to better

understand how social learning happens online, and I’m excited to learn about your

experiences at Minerva. This interview should take about 40 minutes. I’ll be recording

our conversation so I can make sure I don’t miss anything important you say. I just want

to get your consent that it’s still ok to record you. [Wait for response]

● You don’t have to answer any questions you don’t want to answer, and you can stop the

interview at any time, as well as decide that you no longer want to participate in this

study. Any questions?

● [10 Minutes] First, I’d like to know about you and how you came to be a Minerva

student.

○ Tell me about your educational experiences prior to attending Minerva.

○ How did you find out about Minerva? (Possible follow-up questions: When? What

did you initially find out? How did you first feel about it? Previous online

learning communities?)

○ Why did you choose Minerva? (Possible follow-up questions: What is it about

Minerva that influenced your decision to attend? What led you to apply to

Minerva? Was it the only place you applied? As you were deciding, what put

Minerva at the top?)

● [10 minutes] I’d like to get a sense of what your learning experiences at Minerva have

been like.

○ How is Minerva the same or different from your past educational experiences?

(Possible follow-up questions: Did you go to public or private high school? Have


you done blended/online learning before Minerva? Get educational background?

Previous online learning communities?)

○ I am kind of aware of what Minerva is, but could you tell me what Minerva is like

for your learning? (Possible follow-up questions: What some examples of the most

important elements of that? Why are these important?)

● [15 minutes] Now, I’d like to hear about some of your specific learning experiences at

Minerva.

○ Give me three examples of learning experiences that felt particularly productive

for your learning. (Possible follow-up questions: Why does this moment stand out

to you? What were you or others doing that led to your understanding in this

moment? How often do moments like this happen?)

○ Please describe an action from anyone in the class that you found particularly

helpful. (Possible follow-up questions: Who did this? Why do you think they did

it? Why does this action stand out to you? How did this moment change the way

you approached the class or learning in the future? How often are the actions of

other students particularly helpful like with the action you just described?)

○ Give me three examples of course experiences that felt unproductive for your

learning. (Possible follow-up questions: Why does this moment stand out to you?

How would you change the activity to be more productive to you in the future?

How often do moments like this happen?)

○ Please describe an action from anyone in the class that impeded your learning.

(Possible follow-up questions: Who did this? Why do you think they did it? Why

does this action stand out to you? How did this moment change the way you


approached the class or learning in the future? How often are the actions of other

students particularly puzzling like with the action you just described?)

○ If you were to estimate what overall percentage of your learning achieved was

taking place in-person or while online, what percentages would you suggest?

(Possible follow-up question: how do you think that affects your learning?)

● [3 minutes] Before we end, I’d like to debrief this interview. My goal with this interview

was to better understand how the learning community at Minerva affects your learning

process.

○ Do you have any comments or thoughts that we haven’t already discussed about

the impact of the learning community on your learning processes and experiences

at Minerva?

○ If you’d be comfortable sharing, I’d like to ask some demographic information.

You do not need to answer any of these questions if you don’t feel comfortable.

■ What gender do you identify with?

■ What race or ethnicity do you identify with?

■ Did your parents go to college/university?

■ Would your family identify as low-income?

■ Where you are from?

● [2 minutes] So I have one last question: knowing what you know now, are you satisfied

with your experience at Minerva? Would you do it again?

● Thank you so much for sharing your time with me. This will be really helpful in helping

me understand the learning community at Minerva. I will send you the $30 gift card as

thanks for your time in the next 24 hours.


Appendix C – Facebook Participant Recruitment Post


Appendix D – Theory Mapping During Qualitative Data Analysis

scripting social learning: investigating students

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