1. Identifying At-Risk Students Gary R. Pike Information Management & Institutional Research Indiana University Purdue University Indianapolis

2. Using Student Groups At both the University of Missouri and Mississippi State we made use of student groups in enrollment management. At both the University of Missouri and Mississippi State we made use of student groups in enrollment management. We used these groups to assess the effectiveness of our recruitment efforts, advise students about appropriate courses, and assess progress in improving retention. We used these groups to assess the effectiveness of our recruitment efforts, advise students about appropriate courses, and assess progress in improving retention.

3. Creating Student Groups My preference is to use “predicted GPA” to create student groups. My preference is to use “predicted GPA” to create student groups. –It is empirically derived from a measure of student success (GPA). –It uses multiple measures of incoming quality (e.g., SAT/ACT & High School GPA). –It creates a “sliding scale” classification system where low performance in one area (SAT/ACT scores) can be offset by high performance in another area (H.S. grades).

4. First Things First In order to create student groups, you have to: In order to create student groups, you have to: –First, create groups based on existing cohorts of students; and –Second, validate the groups against the performance of the students in the cohorts.

5. Creating Student Groups In order to create student groups, I used all first-time, full-time freshmen (21 or younger) who began in Fall 2005. In order to create student groups, I used all first-time, full-time freshmen (21 or younger) who began in Fall 2005. I only included those students that had complete data for I only included those students that had complete data for –Freshman-Year GPA –ACT/SAT –High School Grade Point Average

6. Creating Student Groups IUPUI first-year GPA was regressed on IUPUI first-year GPA was regressed on –SAT/ACT and –HS GPA Based on the regression results, predicted GPAs were calculated for each student. Based on the regression results, predicted GPAs were calculated for each student.

7. Predicted GPA Results Model 1: PGPA = –0.730 + 0.001 * SAT + 0.838 * HSGPA PGPA = –0.730 + 0.001 * SAT + 0.838 * HSGPA R 2 = 0.23

8. Creating Student Groups Quartiles of predicted GPAs were used to create four student groups. Quartiles of predicted GPAs were used to create four student groups. –A fifth student group consists of those students with no predicted GPA. In order to evaluate the predictive validity of the student groups, I looked at differences in retention and success rates by advising group. In order to evaluate the predictive validity of the student groups, I looked at differences in retention and success rates by advising group. –Conditional admits was used as a baseline.

9. Student Groups Student Groups (Quartiles) Student Groups (Quartiles) –Group 1:3.211 – 4.000 –Group 2:2.832 – 3.210 –Group 3:2.530 – 2.831 –Group 4:0.000 – 2.529

10. Outcome Measures Retention: Retention: –Students who began in Fall 2005 and were still enrolled in Fall 2006 Success: GPA >= 2.00 Success: GPA >= 2.00 –First-year GPA of 2.00 or greater.

11. Student Groups & Conditional Admits Regular Admit Conditional Group 1 98.7%1.3% Group 2 92.0%8.0% Group 3 65.8%34.2% Group 4 21.8%78.2%

12. Predictive Validity - Retention Retained Not Retained Group 1 74.6%25.4% Group 2 71.5%28.5% Group 3 57.5%42.5% Group 4 54.1%45.9% Retention of Regular Admits = 68.4%; Conditional Admits = 55.5%

13. Predictive Validity: GPA ≥ 2.00 GE 2.00 LT 2.00 Group 1 90.7%9.3% Group 2 79.8%20.2% Group 3 65.5%34.5% Group 4 55.2%44.8% GPA ≥ 2.00 for Regular Admits = 79.2%; Conditional Admits = 58.3%

14. Success Rates by Predicted GPA

15. Numbers of At-Risk Students

16. Setting A Cut Score

17. A Multivariate Analysis Success in college is a result of a variety of factors. Success in college is a result of a variety of factors. Important to try to isolate the unique contributions of those factors to student success. Important to try to isolate the unique contributions of those factors to student success. Multivariate analyses (logistic regression) can be used to identify the unique contributions and relative importance of factors contributing to student success. Multivariate analyses (logistic regression) can be used to identify the unique contributions and relative importance of factors contributing to student success.

18. Factors Associated with Success Gender Gender First-Generation Student First-Generation Student Institutional Commitment (Intent to Transfer) Institutional Commitment (Intent to Transfer) Amount of Time Spent Working Amount of Time Spent Working Student Groups Student Groups Ethnicity (minority status) was not significantly related to student success.

19. Results BProb. ΔPΔPΔPΔP Constant (Intercept) 2.752 2.7520.940 Male Student –0.2700.923–0.017 First-Generation–0.4520.909–0.031 Intend to Transfer –0.2160.927–0.013 Intend Work 20+ Hours –0.5440.901–0.039 Group 2 –0.7670.879–0.061 Group 3 –1.5400.771–0.169 Group 4 –1.8950.702–0.238

20. Probabilities of Success Overall probability of success (i.e., GPA ≥ 2.00) for the sample: 0.718. Overall probability of success (i.e., GPA ≥ 2.00) for the sample: 0.718. The probability of success for a female, second generation student, in Group 1, who intends to graduate from IUPUI, and intends to work 20 hours per week or less: 0.940. The probability of success for a female, second generation student, in Group 1, who intends to graduate from IUPUI, and intends to work 20 hours per week or less: 0.940. The probability of success for a male, first- generation student, in Group 4, who is not certain he will graduate from IUPUI, and intends to work more than 20 hours per week: 0.349. The probability of success for a male, first- generation student, in Group 4, who is not certain he will graduate from IUPUI, and intends to work more than 20 hours per week: 0.349.

21. Grades, Financial Aid, & Retention Outcome Measure: Fall-to-Fall Retention. Outcome Measure: Fall-to-Fall Retention. Predictors Predictors –First-Generation Student –Intent to Transfer –Financial Need ($1,000) –Total Gift Aid ($1,000) –Total Loans ($1,000) –GPA < 2.00

22. Results BProb. ΔPΔPΔPΔP Constant 1.460 1.4600.812 First-Generation Student –0.1570.786–0.026 Intends to Transfer –0.3540.751–0.061 GPA < 2.00 –2.1200.341–0.526 Financial Need ($1,000) –0.0170.809–0.003 Total Gift Aid ($1,000) 0.039 0.0390.817 0.005 0.005 Total Loans ($1,000) –0.0500.804–0.008

23. Probability of Being Retained Overall probability of being retained: 0.633. Overall probability of being retained: 0.633. The probability of being retained if the student is second generation, intends to graduate from IUPUI, has no financial need, no gift aid, no loans, and has a first- year GPA of 2.00 of greater: 0.812. The probability of being retained if the student is second generation, intends to graduate from IUPUI, has no financial need, no gift aid, no loans, and has a first- year GPA of 2.00 of greater: 0.812.

24. Effects of Financial Aid The probability of being retained if the student is second generation, intends to graduate from IUPUI, has no financial need, no gift aid, no loans, and has a first-year GPA of 2.00 of greater: 0.812. The probability of being retained if the student is second generation, intends to graduate from IUPUI, has no financial need, no gift aid, no loans, and has a first-year GPA of 2.00 of greater: 0.812. Need=$15,000; Gift=$5,000; Loans=$10,000: 0.711. Need=$15,000; Gift=$5,000; Loans=$10,000: 0.711. Need=$15,000; Gift=$7,500; Loans=$7,500: 0.754. Need=$15,000; Gift=$7,500; Loans=$7,500: 0.754. Need=$15,000; Gift=$10,000; Loans=$5,000: 0.793. Need=$15,000; Gift=$10,000; Loans=$5,000: 0.793. Need=$10,000; Gift=$0; Loans=$10,000: 0.688. Need=$10,000; Gift=$0; Loans=$10,000: 0.688.