1 / 24

Identifying At-Risk Students

Identifying At-Risk Students. Gary R. Pike Information Management & Institutional Research Indiana University Purdue University Indianapolis. Using Student Groups. At both the University of Missouri and Mississippi State we made use of student groups in enrollment management.

varian
Download Presentation

Identifying At-Risk Students

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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. • 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. • 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: • 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. • 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 • SAT/ACT and • HS GPA • 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 R2 = 0.23

  8. Creating 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. • Conditional admits was used as a baseline.

  9. Student Groups • 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: • Students who began in Fall 2005 and were still enrolled in Fall 2006 • Success: GPA >= 2.00 • First-year GPA of 2.00 or greater.

  11. Student Groups & Conditional Admits

  12. Predictive Validity - Retention Retention of Regular Admits = 68.4%; Conditional Admits = 55.5%

  13. Predictive Validity: GPA ≥ 2.00 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. • 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.

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

  19. Results

  20. Probabilities of Success • 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 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. • 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

  23. Probability of Being Retained • 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.

  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. • 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=$10,000; Loans=$5,000: 0.793. • Need=$10,000; Gift=$0; Loans=$10,000: 0.688.

More Related