a data driven approach to identifying at risk students and developing retention strategies dr....
TRANSCRIPT
A Data Driven Approach to Identifying At Risk Students and Developing
Retention Strategies
Dr. Michael Haynes, Executive Director, Office of Institutional Research
Dr. Wayne Atchley, Assistant Professor, Agricultural
and Consumer Sciences
Dr. Diane Taylor, Assistant Vice President
for Academic Programs and Accreditation
Tarleton State UniversityStephenville, Texas
Tarleton’s historic first to second-year retention… between 65% & 68%... for years!
Majority of first-time in college students first-generation
Predominantly from a 42 county region, serving a region southwest of the DFW Metroplex
In 2009, contracted Noel Levitz to review recruitment and retention
Background and context …
Our involvement…
Dr. Taylor: SACS liaison
Dr. Haynes: Reports to Dr. Taylor and assists with SACS efforts
Dr. Atchley: College of Agricultural and Environmental Sciences Assessment Coordinator. Dr. Atchley was involved in the original coding of the data set used by Noel Levitz
Tarleton/Noel Levitz predictive model of retention
Myriad predictor variables identified by Tarleton staff… OVER 67!
Data set coded and submitted to Noel Levitz by Dr. Atchley
Noel Levitz used logistic regression to identify predictor variables that indicate highest likelihood of attrition/non-persistence from year one to year two
Built on 2010 & 2011 FTIC
Risk analysis used to score 2012 FTIC
The findings…
of 67 variables, the top 6 predictors of year 1 to year 2 persistence were:
Model Variable Risk Category Risk Threshold
# of students at risk for this
variable
Persistence Rate of at-risk
students
[MH1]WENDY!!! CAN WE HIGHLIGHT EACH NEW PREDICTOR AS THEY ARE ADDED??
High School Rank Academic Preparation Values below 54.00 776 55.8 %
Class Rank (Academic Preparation)
Less than 54% less likely to persistIndicator of long-term academic performanceValidated by 2012 internal Tarleton analysis on persistence, class rank, and SAT scores
The findings…
of 67 variables, the top 6 predictors of year 1 to year 2 persistence were:
Model Variable Risk Category Risk Threshold
# of students at risk for this
variable
Persistence Rate of at-risk
students
[MH1]WENDY!!! CAN WE HIGHLIGHT EACH NEW PREDICTOR AS THEY ARE ADDED??
High School Rank Academic Preparation Values below 54.00 776 55.8 %
Number of Days as Applicant
Less than 180 days as applicants less likely to persistEarly applicants more decided in their college choice
No. of Days as Applicant Educational Aspiration Values below 181.35 725 58.2 %
The findings…
of 67 variables, the top 6 predictors of year 1 to year 2 persistence were:
Model Variable Risk Category Risk Threshold
# of students at risk for this
variable
Persistence Rate of at-risk
students
[MH1]WENDY!!! CAN WE HIGHLIGHT EACH NEW PREDICTOR AS THEY ARE ADDED??
High School Rank Academic Preparation Values below 54.00 776 55.8 %
Percent of Unmet Financial Need
Below 61.85% less likely to persistAbility to pay for collegeConsiderations about early packaging? Possibly packaging in consideration of other risk factors?
No. of Days as Applicant Educational Aspiration Values below 181.35 725 58.2 %
Percent of Need Met Financial Needs Values below 61.85 718 61.0 %
The findings…
of 67 variables, the top 6 predictors of year 1 to year 2 persistence were:
Model Variable Risk Category Risk Threshold
# of students at risk for this
variable
Persistence Rate of at-risk
students
[MH1]WENDY!!! CAN WE HIGHLIGHT EACH NEW PREDICTOR AS THEY ARE ADDED??
High School Rank Academic Preparation Values below 54.00 776 55.8 %
Counties with High Attrition Rates Identified
Could be indicative of school districts within countiesBridge opportunities with feeder secondary schools for better college preparation
No. of Days as Applicant Educational Aspiration Values below 181.35 725 58.2 %
Percent of Need Met Financial Needs Values below 61.85 718 61.0 %
Primary County Code of Student
Institutional Categories with persistence rates below 63.6%
1071 60.6 %
The findings…
of 67 variables, the top 6 predictors of year 1 to year 2 persistence were:
Model Variable Risk Category Risk Threshold
# of students at risk for this
variable
Persistence Rate of at-risk
students
[MH1]WENDY!!! CAN WE HIGHLIGHT EACH NEW PREDICTOR AS THEY ARE ADDED??
High School Rank Academic Preparation Values below 54.00 776 55.8 %
Department or Program Area
Use caution in interpretation of programs ability to matriculate from year 1 to year 2What are the characteristics of students selecting these program areas?
No. of Days as Applicant Educational Aspiration Values below 181.35 725 58.2 %
Percent of Need Met Financial Needs Values below 61.85 718 61.0 %
Primary County Code of Student
Institutional Categories with persistence rates below 63.6%
1071 60.6 %
Department or ProgramArea
Educational Aspiration Categories with persistencerates below 63.4%
684 55.7 %
The findings…
of 67 variables, the top 6 predictors of year 1 to year 2 persistence were:
Model Variable Risk Category Risk Threshold
# of students at risk for this
variable
Persistence Rate of at-risk
students
[MH1]WENDY!!! CAN WE HIGHLIGHT EACH NEW PREDICTOR AS THEY ARE ADDED??
High School Rank Academic Preparation Values below 54.00 776 55.8 %
Number of Self-Initiated Contacts with InstitutionStudents with 2 or less contacts less likely to persistIndicator of students commitment in college selection processPersonal stake in the institution; looking forward to the experience!
No. of Days as Applicant Educational Aspiration Values below 181.35 725 58.2 %
Percent of Need Met Financial Needs Values below 61.85 718 61.0 %
Primary County Code of Student
Institutional Categories with persistence rates below 63.6%
1071 60.6 %
Department or ProgramArea
Educational Aspiration Categories with persistencerates below 63.4%
684 55.7 %
No. of Self-Initiated Contacts (Optimal Binning)
Educational Aspiration Categories with persistencerates below 65.2%
1043 61.9 %
OK, so what are we doing with this information?
Begin identifying FTIC cohort in spring before fall enrollment
Sort based on top 6 risk factors
Collaborate with Academic Affairs & Student Life to begin strategies for intervention
Now, what did we consider in developing a retention plan?
Increased intentional collaboration between Academic Affairs and Student Life
Attention to at-risk populations
First-year students
Transfers
Part-time students
Commuter students
Initiatives that focus on academics, financial, behaviors, etc…
Tarleton’s retention plan focuses 3 areas of student success
Academic Achievement
Early Alert programs (Student Success)
Academic advising (Advising Center)
Freshman Seminar Course(cross disciplinary)
Tarleton’s retention plan focuses 3 areas of student success
Personal Development
Diversity initiatives (Office of Diversity and Inclusion)
Financial literacy (Enrollment Management)
First-year developmental courses in areas such as math (Academic Affairs)
Tarleton’s retention plan focuses 3 areas of student success
Meaningful Engagement
Experiential learning through “Keeping It Real”…our QEP(various campus entities)
Learning Communities (Student Life and Academic Affairs)
Transition programs (Student Life)
Provost initiative to increase on-campus student employment opportunities (Financial Aid/Career Services)
Cliché, but yes…
Retention doesn’t occur in a silo, so it must be tackled outside of silos.Retention doesn’t occur in a silo, so it must be tackled outside of silos.
Retention doesn’t occur in a silo, so it must be tackled outside of silos.
