1. 1 Predicting Success and Risk: Multi-spell Analyses of Student Graduation, Departure and Return Roy Mathew Director Center for Institutional Evaluation Research and Planning (CIERP)

2. 2 Presentation Outline  Introduction  Factors that affect graduation within six years  Factors that affect departure and reenrollment  Risk groups and associated trends  Implications, and areas for future research

3. 3 Introduction Institutional Evaluation Questions What more can the institution do to improve degree completion rates? Can we improve the effectiveness of our current initiatives? Research Questions: What factors explain graduation from UTEP within six years? What factors explain the behavior of student departure? What factors explain students’ return after initial stop- out?

4. 4 The Range of Success ACT 18

5. 5 Impact of Selectivity ACT 18

6. 6 Predictors of Graduation

7. 7 Method of Determining Predictors of Graduation  Stepwise logistic regression model  Fall 1999 and Fall 2000 first-time student cohorts  Variables were added in four steps: demographic variables academic preparation for college New student survey data first semester academic performance  21 variables were used in the model.

8. 8 Factors That Explain Graduation  Significant factors : Failing class in the first term will decrease the probability of graduation Higher first term GPA will increase the probability of graduation Working 30 or more hours per week will decrease the probability of graduation Students from bottom half of their high school class rank are less likely to graduate Placement in Math below college level will decrease the probability of graduation

9. 9 Modified Method to Determining Factors That Explain Graduation  A longitudinal approach will help assess the effect of performance over time  A proportional sub-distribution hazards model (a class of survival models) was used to examine student graduation under a competing risk setting

10. 10 Factors That Explain Graduation  Survival model results : Higher cumulative GPA increases the probability of graduation Receiving financial aid (loans, grants and work study) increases the probability of graduation Failing any class decreases the probability of graduation Enrolling and passing a developmental course did not decrease a student’s chance of graduation significantly Stopping-out decreases the probability of graduation

11. 11 Factors That Explain Departure

12. 12 Method of Determining Factors that Explain Departure  longitudinal approach  Survival model (multi-spell discrete-time logit model) The hazard is modeled using a logit link function that will capture the general shape of the hazard profile and the heterogeneity of the hazard caused by different predictor variables.

13. 13  The survival model indicates that: Low semester GPA will increase the risk of departure  The effect of semester GPA decreases over time  Failing a developmental course will increase the risk of departure Part-time enrollment will increase the risk of departure Financial aid (loans, grants, work study) will reduce the risk of departure Factors That Explain Departure

14. 14  Timing of departure is important: Risk of departure is higher in earlier semesters Risk of departure is higher in spring semester than fall semester Students who stop-out are more likely to leave again  Risk of departure for returnees is higher in earlier semesters  Risk of departure for returnees increases with the length of the stop-out period Factors That Explain Departure

15. 15 Factors That Explain Reenrollment  The chances of returning to UTEP after stopping out: are higher for students with good academic standing at the time of departure are lower for older students (20 years or older when they first enrolled) increase as a student’s initial length of enrollment increases decrease as a student’s length of stopping out increases

16. 16 Identifying Students At Risk of Departure

17. 17 Method for Identifying Students At Risk of Departure  Cox proportional hazards (PH) regression model is used to determine the risk of departure for each student  Significant factors at the time of admission that are used to identify level of risk : Mathematics placement score High school class rank percentile Intended number of hours spent to work Delayed matriculation from high school

18. 18 Assigning Risk Score  Low risk group: Students with 0 risk score with college level math placement top quartile of high school class intend to work less than 20 hours per week Directly matriculated from high school  Medium risk group: Students with a score of less than 1.15 are assigned into medium risk group  High risk group: Students with a score of 1.15 or higher is assigned into high risk group.

19. 19 Survival Trends Associated with Risk Groups (First Three Years)

20. 20  One year retention rates: High Risk: 48%, Medium Risk: 74% Low Risk: 87%  Three year retention rates: High Risk: 20% Medium Risk: 48% Low Risk 67% Retention Trends for Risk Groups

21. 21 Graduation Trends for Risk Groups  Six year graduation rates: High Risk: 9% Medium Risk: 30% Low Risk: 60%  Graduation rate upon persisting through the first year with cumulative GPA of at least 2.0 High Risk: 25% Medium Risk: 43% Low Risk: 70%

22. 22 Implications and Further Research  Implications Evaluate efficacy of current interventions for each risk group, and modify interventions Develop tools and methods to track progress during critical periods  Areas for further research Advance understanding of students within each risk group Explore how ecosystems affect student performance Explore factors that explain the success of transfer students

23. 23 Student Success Research Team  Denise Carrejo, Ph.D.  Bereket Weldeslassie, M.S.  Thomas Taylor, Ph.D. candidate  Myoung Kim, Ph.D. candidate  Roy Mathew, Ph.D.  Julia Bader, Ph.D. (UTEP Statistical Consulting Lab) For more information, please email: dcarrejo2@utep.edu We gratefully acknowledge the support of Lumina Foundation for Education