1. 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 University Stephenville, Texas

2. 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 …

3. 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

4. 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

5. The findings… of 67 variables, the top 6 predictors of year 1 to year 2 persistence were: Model VariableRisk CategoryRisk 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??[MH1] High School RankAcademic PreparationValues below 54.0077655.8 % Class Rank (Academic Preparation) Less than 54% less likely to persist Indicator of long-term academic performance Validated by 2012 internal Tarleton analysis on persistence, class rank, and SAT scores

6. The findings… of 67 variables, the top 6 predictors of year 1 to year 2 persistence were: Model VariableRisk CategoryRisk 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??[MH1] High School RankAcademic PreparationValues below 54.0077655.8 % Number of Days as Applicant Less than 180 days as applicants less likely to persist Early applicants more decided in their college choice No. of Days as ApplicantEducational AspirationValues below 181.3572558.2 %

7. The findings… of 67 variables, the top 6 predictors of year 1 to year 2 persistence were: Model VariableRisk CategoryRisk 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??[MH1] High School RankAcademic PreparationValues below 54.0077655.8 % Percent of Unmet Financial Need Below 61.85% less likely to persist Ability to pay for college Considerations about early packaging? Possibly packaging in consideration of other risk factors? No. of Days as ApplicantEducational AspirationValues below 181.3572558.2 % Percent of Need MetFinancial NeedsValues below 61.8571861.0 %

8. The findings… of 67 variables, the top 6 predictors of year 1 to year 2 persistence were: Model VariableRisk CategoryRisk 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??[MH1] High School RankAcademic PreparationValues below 54.0077655.8 % Counties with High Attrition Rates Identified Could be indicative of school districts within counties Bridge opportunities with feeder secondary schools for better college preparation No. of Days as ApplicantEducational AspirationValues below 181.3572558.2 % Percent of Need MetFinancial NeedsValues below 61.8571861.0 % Primary County Code of Student InstitutionalCategories with persistence rates below 63.6% 107160.6 %

9. The findings… of 67 variables, the top 6 predictors of year 1 to year 2 persistence were: Model VariableRisk CategoryRisk 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??[MH1] High School RankAcademic PreparationValues below 54.0077655.8 % Department or Program Area Use caution in interpretation of programs ability to matriculate from year 1 to year 2 What are the characteristics of students selecting these program areas? No. of Days as ApplicantEducational AspirationValues below 181.3572558.2 % Percent of Need MetFinancial NeedsValues below 61.8571861.0 % Primary County Code of Student InstitutionalCategories with persistence rates below 63.6% 107160.6 % Department or Program Area Educational AspirationCategories with persistence rates below 63.4% 68455.7 %

10. The findings… of 67 variables, the top 6 predictors of year 1 to year 2 persistence were: Model VariableRisk CategoryRisk 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??[MH1] High School RankAcademic PreparationValues below 54.0077655.8 % Number of Self-Initiated Contacts with Institution Students with 2 or less contacts less likely to persist Indicator of students commitment in college selection process Personal stake in the institution; looking forward to the experience! No. of Days as ApplicantEducational AspirationValues below 181.3572558.2 % Percent of Need MetFinancial NeedsValues below 61.8571861.0 % Primary County Code of Student InstitutionalCategories with persistence rates below 63.6% 107160.6 % Department or Program Area Educational AspirationCategories with persistence rates below 63.4% 68455.7 % No. of Self-Initiated Contacts (Optimal Binning) Educational AspirationCategories with persistence rates below 65.2% 104361.9 %

11. 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

12. 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…

13. 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)

14. 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)

15. 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)

16. 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.