1. Predicting Individual Student Attrition and Fashioning Interventions to Enhance Student Persistence and Success Dr. Thomas E. Miller University of South Florida

2. Introduction Sources of concern for persistence and graduation rates Sources of concern for persistence and graduation rates - institutions – all component parts - government - college rating services - public Common approaches have been broadly implemented Common approaches have been broadly implemented - generally targeted to sub-populations - necessarily inefficient and wasteful as persistence - necessarily inefficient and wasteful as persistence enhancement tools enhancement tools - may still be sound practice for educational reasons

3. Introduction cont. This project is specific to each student based on established weighted predictors This project is specific to each student based on established weighted predictors - allows for timely response (uses pre-matriculation data) - efficient - replicable - responsive to individual needs and interests

4. Background Pascarella and Terenzini (1980) applied Tinto’s model of social integration. - findings valued the interaction between students and faculty - addressed post-matriculation issues Chapman and Pascarella (1983) studied social and academic integration. Chapman and Pascarella (1983) studied social and academic integration. -findings revealed differences in levels of social and academic integration.

5. Background cont. Canisius College model predicted attrition for specific students. Canisius College model predicted attrition for specific students. - successful, still used - freshman to sophomore persistence rate - graduation rates - variables in logistic regression formula included high school average high school average gender gender academic behaviors in high school academic behaviors in high school parents together parents together

6. CSXQ Normally used to compare how students expectations for college align with their actual experiences For this study CSXQ data are examined to determine their worth in predicting student persistence. Supplemental data such as gender, ethnicity, age, academic performance potential will be used along with the CSXQ data in the predictive model.

11. Methodology The CSXQ was administered to First Time in College (FTIC) freshman prior to matriculation in the fall of 2006. Participants were 3,998 student on Tampa campus Slightly fewer than 1,000 completed the survey and gave identifying information The sample was representative of the larger population in every demographic measure.

12. Results The PROC LOGISTIC procedure in SAS was run using set-wise inclusion of variables. Two blocks of independent variables; dependent variable: persist/not persist Block One Block Two

16. Predicting New Cases Focusing on Block Two variables, predictors are Focusing on Block Two variables, predictors are 1. High School GPA (+) 2. Being Black vs being white (+) 3. Expecting to participate in clubs/student organizations (+) 4. Expecting to read many textbooks or assigned books in college (+) 5. Expecting to read many non-assigned books in college (-) 6. Expecting to work off campus while in college (-)

17. Other variables that may prove useful Institutional data Institutional data - Gender - Honors Program - Early enrollment summer programs - Residence - Number of guests at summer orientation - Date of summer orientation program - Date of application for admission - Permanent residence out of state - Major is pre-nursing or pre-education

18. Other variables cont. CSXQ data CSXQ data - plan to be employed on campus - intended effort scale related to course learning - intended effort scale related to scientific and quantitative experiences.

19. Interventions Model will identify approximately 500-700 FTIC students at risk of attrition in their first year, of the total 4,200 enrolled. Model will identify approximately 500-700 FTIC students at risk of attrition in their first year, of the total 4,200 enrolled. Levels of intervention Levels of intervention Employing those already interacting with student sub-sets Employing those already interacting with student sub-sets - athletics - undeclared majors - residence halls - summer programs - Honors College - Others – First-Year Student Connections

20. Referral points (post-intervention) Career services Academic advising Financial aid Residence halls

21. Conclusion & Next Steps Model refinement Increasing and refining interventions Predicting sophomore persistence Transfer students