will barratt - professor, department of educational leadership christopher childs – research...
TRANSCRIPT
SEM Planning:The Role of the SEM Data
Team
• Will Barratt - Professor, Department of Educational Leadership• Christopher Childs – Research Analyst, Student Success • Linda Ferguson - Associate Director, Institutional Research• Tom Green – Managing Consultant, AACRAO
2013 AACRAO Strategic Enrollment Management Conference
1. Setting the stage2. Providing examples and references3. Facilitating meetings4. Developing appropriate organizational
structure5. Keeping the planning on track6. Keeping the committee members on track
The role of AACRAO SEM Consulting
ISU SEM Organization FrameworkSEM Steering Team
High level guidanceEnabler of cross-unit planning and action
Reviews and approves recommendations of the Councils
Student Success Council
Development of strategic goals and assessment framework
Visions opportunities for action and improved focus of activities and energy
Supports work of Subcommittees
Recruitment Council
Development of strategic goals and assessment framework
Visions opportunities for action and improved focus of activities and energy
Data Team
Environmental scanningStudent enrollment behavior research
Retention and success modelingProviding data to inform planning and action
Tactical Subcommittees
Action planning & executionTimelines and metricsActivity assessment
Tactical Subcommittees
Action planning & executionTimelines and metricsActivity assessment
ResearchIntroductionSetting the
Stage for SEM Planning
Tracking Progress
The Role of the SEM Data Team
SEM Data Team membership
Political considerations Skill considerations Credibility considerations
Linda Ferguson – Institutional Research Jerome Cline – Institutional Research Chris Childs – Student Success Charlene Shivers – Financial Aid Deirdre Mahan – Admissions Julie Cuffle – Information Technology Tess Avelis - Registrar Will Barratt – Educational Leadership Catherine Tucker – School Counseling
SEM Data Team
ResearchIntroductionSetting the
Stage for SEM Planning
Tracking Progress
The Role of the SEM Data Team
SEM Kickoff Presentation Preparation
Environmental Scan & Enrollment Behavior• Determine content• Use pictures (graphs, charts, maps)• Engage the audience • Be able to clarify (source, timeframe,
context)
• Our charge: Describe the internal and external factors that will influence our institution’s SEM planning.
• What would you include?
Environmental Scan Purpose
• State Policy• Demographics• Preparation• Affordability• Enrollment
Environmental Scan Components
Enrollment and Retention Behavior(Tell your retention story)
• Comparison to competitors & peers• Describe differences among groups
• Income (Pell-recipients)• Type of admission• Ethnicity• Region
• Survey findings• BCSSE/NSSE• Mapworks
African American White0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
43.2%
63.2%51.6%
63.0%
1-yr Retention
20072008200920102011
Challenge: Engage the audience
What percentage of all Indiana Core40 graduates require remediation?
a) 8%b) 18%c) 28%d) 38%
General Diploma Graduates - 66% Core40 Graduates - 38% Core40 with Honors - 7%
SEM Kickoff Audience Participation
High School Graduates by Region
Source: Indiana Department of Education
Year 4 Year 4+?1 of the 2 students who don’t return after the first year transfer elsewhere
ISU Retention/Graduation
Dispelling the myths
Main 2009 6-Year Grad Rate 1st Year Retention Rate
Illinois State University 69.00% 83.00%
Ball State University 58.30% 78.00%
Central Michigan University 57.20% 77.00%
East Carolina University 56.80% 76.00%
Indiana University of Pennsylvania-Main Campus 54.20% 74.00%
University of Northern Colorado 49.30% 70.00%
Louisiana Tech University 46.40% 72.00%
The University of West Florida 45.30% 71.00%
East Tennessee State University 42.50% 67.00%
Indiana State University 40.40% 66.00%
For our type of institution, our retention rates are appropriate
Source: AACRAO SEM Consulting
Potential Retention & Graduation Peers
External and Internal analysis becomes part of the SEM Plan itself
Projected Population Change 2010 to 2050
The US Chamber of Commerce also graded states on their efficiency and cost – effectiveness. Indiana received a “D” in this area of interest. The issue of cost efficiency has grown in importance as frustration grows concerning the rate of tuition increases which outpace most other goods in this country (including pharmaceuticals and health care).
Indiana did receive an “A” in the area of Policy Environment and is one of four leaders in this area: largely due to their Reaching Higher and performance-based funding initiatives. ICHE and the General Assembly will pay attention to this report card.
Demographics
Indiana is made up of 92 counties which supply the vast majority of students to Indiana State University. Over the next 40 years, it is projected that Indiana’s population will increase by 15% overall (Source: Indiana Business Research Center (IBRC)).1 However, over that time period it is also predicted that large swaths of mid-sized and rural communities in the North, East, and West-Central parts of Indiana will lose population. 49 of Indiana’s 92 counties are expected to see a population decline. Currently, Hamilton County is the fastest growing county in the state. Central Indiana’s role will become more dominant - between 2010 and 2030, this region is predicted to account for 62% of the state’s total growth.
As depicted in the chart below, Indiana’s population is getting older. Aging baby boomers are the dominant force behind this condition. Currently, this segment of the population accounts for about 13% of the state’s total population. It is predicted that this segment will continue to grow through 2030, eventually representing over 20% of the state’s total population. Other groups will continue to grow as well. Through 2050, the college-age
Table of Contents
Page
Executive Summary…………………………………….. 3
Part I: Organizing Framework…………………….. 5
Part II: Environmental Scan………………………… 8
Part III: SEM Initiatives & Strategies…………… 46
Part IV: Appendix – SEM Participants……….. 63
Endnotes…………………………………………………… 64
ResearchIntroductionSetting the
Stage for SEM Planning
Tracking Progress
The Role of the SEM Data Team
Research
• Survey results• National Survey of Student Engagement• Beginning college Survey of Student Engagement• Faculty Survey of Student Engagement• Map-Works Fall Transition Survey
• Clearinghouse Data• Regression and Cluster
Student-Faculty Inter-action Scale (Means 0
to 100)
Discussed grades or assignments with an
instructor *
Talked about career plans with a faculty member or advisor *
Discussed ideas from your classes with fac-ulty members outside
of class *
Worked with faculty members on activities other than course-
work **
Received prompt feedback from faculty on your academic performance
Entering Ex-pected
NaN 72 NaN 49 48 67
FY - ISU 35 51 32 20 19 47
FY - CarnClass 35.9 54 34 23 17 62
Seniors - ISU 41.8 63 41 28 24 64
Seniors - Carn-Class
43.8 63 44 30 24 70
Faculty NaN 29 23 12 68 92
5152535455565758595
Student Faculty Interactions
Students: % Often or Very Often
NSSE/BCSSE/FSSE
MapWorks ReportsBenchmark, Factors & Question Details
Our Select 6:
• Central Washington University• Eastern Kentucky University• Indiana University-Purdue University (Fort Wayne)• University of Akron• University of Southern Indiana• Western Kentucky University
MapWorks Fall Transition Survey
National Student ClearinghouseAdmits who did not enroll
Source: National Student Clearinghouse
61%19%
20%
Returned Not Enrolled
Transferred
National Student ClearinghouseFirst-time freshmen who don’t
return for second yearAll Students
77%
7%
15%
Good Standing
Returned
Not Enrolled
Transferred
What pre-college factors are related to fall to fall retention?
What during-college factors are related to fall to fall retention?◦ 2011 – 2012◦ 2012 – 2013
Cluster Analysis
What is a two-step Cluster Analysis?
A two step cluster is a way to find important characteristics that are different between two groups
Our cluster will find important characteristics that describe students who stay from the first fall semester to the second fall semester.
Finding the right variables for the Cluster: An iterative
Process 1. Focused on variables that the institution
could change2. Conducted a Binary Logistic Regression to
find variables that were predictive of retention
3. Ran several clusters and picked variables that were most important but did not overwhelm other variables in the cluster analysis
Cluster Analysis Variables
Core Demographi
cs
Pre-College Factors
First year Academic Factors
First year Co-curricular
Factors Retention
Core Demographic Variables
Gender Ethnicity Country of Origin State of Origin Zip code of Origin County of Origin Child of ISU Alumni Relative of ISU Alumni MeritSchol Type of Admission Oncampus/Offcampus ResHall MAP-Works: Fraternity or
Sorority
Family Income High School Name High School State High School County High School Zip 21st Century Scholar Military Pidm Cohort Year Pell Indicator Honors Program First Generation Indicator International Student
Pre-College Variables
High School Grad Date High School GPA High School Percentile
Rank High School Class Size ACT Test Scores SAT Test Scores SAT to ACT Composite
Application Date Hoosier Boys
State/Girls State Pre Application Hours High School Extra
Curricular Activities Summer Honors H.S Athlete
1st Year Academic Variables College Dept Major MAP-Works: Class Attendance MAP-Works: Class Participation MAP-Works: Communicate with professors
outside of class MAP-Works: Know what is expected of you to
be successful in class MAP-Works: Do family obligations interfere
with course work MAP-Works: Number of Courses taking MAP-Works: Class Participation MAP-Works: How many courses are you
struggling in Remedial Math Year 1 Indicator MAP-Works: Satisfied Academic Life MAP-Works: Do you know what is expected of
you in your classes?
Midterm Deficiencies Fall Term GPA Fall GPA Hours Took Remedial Math Took Math Took Communication 101 Freshmen Writing Visited Math Computer lab Participated in tutoring Fall Academic Standing First Year Academic Standing Three week attendance Fall Enrollment Hours Fall Attempted Hours Supplemental Instruction Attendee
1st Year Co-Curricular and other Variables
MAP-Works: Meeting people you like (peer connections)
MAP-Works: Time spent sleeping MAP-Works: Time spent socializing MAP-Works: Attend student functions MAP-Works: Communication with
parents MAP-Works: Choice of Institution MAP-Works: Commitment to ISU MAP-Works: Financial Means MAP-Works: Intramural Sports MAP-Works: Student functions MAP-Works: Volunteer MAP-Works: Leadership Position MAP-Works: Adjustment to Living on-
campus MAP-Works: How many roommates do
you have MAP-Works: Roommate Problems
MAP-Works: Satisfied with Social Life MAP-Works: Belong to a learning
community Community service Visited the Rec Center Attended ISU Performance Attended ISU Athletic Event Participated in Intramural Sports Financial Holds Student Judicial Holds Student Athlete 1st year Organization Count Organization Member African American Cultural Center
Attendance Isucceed Program indicator Spring Persistence Fall 1 to Fall 2 retention
Cluster Analysis FindingsMain Points
Participation in ISU events and student organization events were the most important variables in clusters
Cluster Analysis FindingsMain Points
Clusters indicate that students with the higher levels of student participation at ISU events and in student organizations had higher retention rates
Cluster Analysis FindingsMain Points
It may not be the events themselves that caused students to come back but factors associated with the event
• How could the first year at ISU better engage students with our expectations for them, academically?
Research Results Guiding questions
• Social engagement appears to have a strong impact on our first-year students:• What are the opportunities to leverage this to
improve their academic performance?• How can we identify and engage those students
who show greater risk for lack of social engagement, early in their first semester?
Research Results Guiding questions
ResearchIntroductionSetting the
Stage for SEM Planning
Tracking Progress
The Role of the SEM Data Team
• Purpose• Projects enrollment over the life of the SEM plan• Illustrates the impact of changes in cohort sizes and
retention rates
• Based on long-range enrollment goals• New freshman, transfer and/or graduate cohort sizes• Differential retention rates for groups within each cohort
Enrollment Model
One-year Retention Rates Actual Goals Fall 2012 Cohort Category Fall 2010 Fall 2011 Fall 2012 Fall 2013 Fall 2014 Fall 2015 Fall 2016 Fall 2017 ActualFirst-time, full-time bachelor's degree seekers All 58.1% 60.6% 61.0% 63.0% 65.0% 67.0% 69.0% 70.0% 63.5% African-American 43.2% 51.6% 52.0% 53.0% 54.0% 55.0% 56.0% 58.0% 52.5% Pell Recipients 51.1% 56.5% 57.0% 59.0% 61.0% 63.0% 64.5% 66.0% 57.6% 21st-Century Scholars 57.1% 56.6% 58.0% 60.0% 62.0% 64.0% 66.0% 68.0% 61.2%New BDS Transfer students 64.6% 65.2% 67.0% 69.0% 71.0% 72.0% 73.0% 74.0% 67.9%
Reviewing SEM Tactical Committee plans Getting data Helping with analyses Creating a climate of data based decisions
Oversight and Monitoring
Monitoring SEM Initiatives Metric Review
Frequency of Measurement
Reported to Whom
Expected Outcome
Data used for measurement
Location of data/system
Metric Provider Data Team Questions/Comments
Number of academic-focused student organizations
Annually AVP for Student Success & VP Student Affairs
Annual 10% increase over baseline year
Counts of academic student organizations
Banner & OrgSync records
Metrics
Are we going to use OrgSync or BANNER? How can we determine which are academic? Cocurricular data in BANNER does not specify this. (See note below)
Number of students involved in residence hall living-learning communities
Annually AVP of Student Success
Annual 10% increase in number of students involved
Counts of students involved
Department of Residential Life BANNER
Residential life This needs to be tracked and stored in BANNER and/or the data warehouse so that it can be tied to retention, performance, etc. Can we measure the level of student engagement? A lot of this should go into data warehouse when it is fully operational. We have definitional issues – themed floors vs living learning communities.
Monitoring SEM InitiativesProviding Metrics
Goal 1 Initiative 12C: Augment college-going education for admits
and families via multi-media tool entitled Sycamoreology
Questions and Discussion
Will Barratt - [email protected] Childs –
[email protected] Ferguson – [email protected]
Tom Green – [email protected]