Access to Success: Leading Indicators Workgroup

1. Access to Success:Leading Indicators Workgroup The University of Hawaiʻi at Mānoa

2. New Student Enrollment

3. Retention Rates

4. Graduation Rates

5. Focus of Study • First Time freshmen of Fall 2003 • n = 1,809 • Enough history to examine first term retention through degree completion • Transfer students entering in Fall 2009 • n = 1,804 • Currently studying retention

6. Milestone Facts (Fall 2003)

7. Milestone Facts (Fall 2003 Freshmen)

8. Milestone Facts (Fall 2003 Freshmen)

9. College-Level Math & English(Fall 2003 Freshmen)

10. Milestone Facts (Fall 2003 Freshmen)

11. Multivariate Analysis Data Collection, Causal Modeling, Results

12. Data Collection • Created a longitudinal database with over 100 data elements theoretically related to retention & graduation(see handout for list of variables): • Demographic • Geographic Origin • Pre-Collegiate Experiences • Academic and Course Experiences • Campus Experience • Financial Aid • Interaction Variables • Additional Variables from LI Research!

13. Types of Variables Analyzed Gender Age Ethnicity Residency Geographic Origin On Campus Employment Housing Student Life Activities Athletics STAR Usage Average Class Size Campus Experience Demographics High School GPA & Rank SAT AP CLEP Educational Goals Transfer GPA # Transfer Credits Need Based Aid Non-need Based Aid Pell Grant Work Study % of Aid Met Retention & Degree Completion Financial Need Pre-College Major Credit Load Credits Earned First Term GPA Distance Education Dual Enrollment High Failure Rate Courses Courses Taken (including Math & English) Ethnicity by Geographic Origin Employment by Housing High School GPA by First Term GPA Residency by Need Based Aid Ratio of Successful Adds to Drops Interactions Academic

14. Strongest Predictors of Degree Completion(Fall 2003 Freshmen) Strongest Credits Earned Yr. 1 These variables account for approximately 34% of the variance in a student’s likelihood of completing a degree (Pseudo R Square = .344). First Term GPA 145.560 (<.001)* 23.883 (<.001)* Geographic Origin 23.369 (<.001)* Degree Completion Dual Enrollment 21.084 (<.001)* 12.004 (.001)* Ethnicity 11.816 (.001)* Enrollment in College Level Math Year 1 6.177 (.013)* *Wald statistic (sig.) The Wald test statistic was used to indicate strength of the variable instead of the coefficient, standardized beta. Because of the nature of the logistic regression, the coefficient is not easily interpretable to indicate strength. High School GPA Weakest

15. Findings • Variables significant in predicting degree completion of freshmen: • “Expected” predictors emerging from model: • Ethnicity (Asian students 2x greater odds) • Geographic Origin (1.9x greater odds for HI students) • First Term GPA (1.5x greater odds per grade point increase) • Not-so obvious predictors: • >= 24 Credits Earned in Year 1 (Odds Ratio = 6x) • Dual Enrollment (Odds Ratio = 2x) • Enrollment in College-Level Math in Year 1 (Odds Ratio = 1.5x) • Prior Credits Earned (Odds Ratio = 1.5x)

16. Overall Model Performance • 73% of observations correctly classified • Sensitivity: 76% • Specificity: 70%

17. Strongest Predictors of Transfer Student Retention (Fall 2009 Transfers’ Preliminary Results) Strongest 1st Term GPA These variables account for approximately 26% of the variance in a student’s likelihood of completing a degree (Pseudo R Square = .258). Ethnicity 34.019 (<.001)* 26.995 (<.001)* Declared Major 19.174 (<.001)* Transfer Student Retention On Campus Employment 17.094 (<.001)* 8.776 (.003)* Geographic Origin 7.080 (.008)* Need-Based Aid 4.010 (.045)* *Wald statistic (sig.) The Wald test statistic was used to indicate strength of the variable instead of the coefficient, standardized beta. Because of the nature of the logistic regression, the coefficient is not easily interpretable to indicate strength. Distance Education Weakest

18. What can advisors do? • Engaging the students in understanding • Positive Psychology • i.e How taking 15 credits will help students graduate in 4 years • Showing students the cost implications of delaying their studies • The power of a phone call • Targeted interventions • Use at-risk forecasting data to predict and inform • Focus on at-risk students on the “threshold”

19. Good Examples • Florida State University • 85% retention rate in 2000 up to 91% in 2009 • Retention Task Force started at the very top level • Main efforts were in IR and Advising • U of Nevada at Reno • 76% in 2005 up to 80% in 2010. • Retention Task Force started at the very top; same focus as FSU • Quantified data in terms of $/revenue. • Covered in Media nationally

20. Mānoa Policy Implications • Facilitate credit momentum • “Do it in 4” Project • Students missing credit milestones are called in for advising • Drive enrollment in Math & English in students’ first year • Automatic (pre)registration for first year students • Examine course pressure points; course wait listing • Mandatory advising in first semester on campus • Mandatory declaration of major by second year on campus • New opportunities for on-campus employment: • Undergraduate Research, Student Success Fellowships, Legislative Internships • Fast track transfer policies • Automatic admit for UH System students • Transfer programs like “Ka’ie’ie”

21. Who are we? Mānoa Institutional Research Office www.manoa.hawaii.edu/ovcaa/mir Dr. Ron Cambra Assistant Vice Chancellor  for Undergraduate Education808-956-6231 cambra@hawaii.edu John Stanley Institutional Analyst 808-956-5366 jstanley@hawaii.edu