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

2. Introduction Sources of concern for persistence and graduation rates Institutions Government College ranking services public USF persistence experience Common approaches have been broadly implemented Generally targeted to sub-populations Necessarily inefficient and wasteful as persistence enhancement tools (yet may still be sound educational practice)

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 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

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

6. 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.

7. 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

8. 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 (-)

9. 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

10. 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.

11. Next Research Steps Model refinement Predicting sophomore persistence Transfer students

12. A Call to Action Theoretical Background Challenge/Support Mattering Theory First-year Student Development Involvement Theory

13. Starting Place Office of New Student Connections Week of Welcome Website, Blackboard, Connections newsletter UConnect New Student Socials Information and services for families Transfer student connections

14. Intervention Model identified approximately 450 FTIC students at risk of attrition in their first year, of the total 4,200 enrolled. Model identified approximately 450 FTIC students at risk of attrition in their first year, of the total 4,200 enrolled. Guiding Questions: What are real opportunities for impact? What are real opportunities for impact? What scale and scope can we manage? What scale and scope can we manage? Are multiple levels of intervention possible? Are multiple levels of intervention possible? Result: A pilot mentoring program

15. Mentoring Program Selection of Mentors Who? Who? How many? How many? What makes a good mentor? What makes a good mentor? Where are natural points of connection? Where are natural points of connection? Who else needs to be involved? Who else needs to be involved?

16. Mentoring Program Other opportunities for impact with current student sub-populations: - Intercollegiate Athletics - Freshman Summer Institute - Student Support Services - Honors Program

17. Mentoring Program Training of Mentors How to Connect/Engage Best Practices, Collecting Information Problem Solving Making Referrals Following Up

18. Mentoring Program Why Students Drop Out – Clues to which we need be alert Unclear or unreasonable goals Social isolation Insufficient academic preparation Stress Academic disengagement or boredom Financial concerns Challenges of new freedom Unmet expectations or transition shock Distraction of conflicting commitments

19. Mentoring Program Points of Referral Counseling Center Career Center (including on-campus employment) Financial Aid Office Tutoring and Learning Services/Writing Center Center for Student Involvement Housing and Residential Education

20. Mentoring Program Expectations of Mentors Five to fifteen students Initial Contact Notify NSC Office of non-respondents Meet monthly Maintain log of contacts Use Contact Checklist

21. Mentoring Program Early lessons learned Next Steps Revised model Full implementation

22. New Model High School GPA (+) Being Asian vs. being white (+) Being Black vs. being White (+) Higher combined SAT (-)

23. New Model (cont.) Expecting to use library (+) Expecting to read non-assigned books (-) Being enthusiastic about college (+) Belief in emphasis of aesthetic/creative qualities (-) Expecting to work off campus (-)

24. Citations Miller, T.E. 2007. Will they stay or will they go? Predicting the risk of attrition at a large public university. College and University. 83(2): 2-7. Miller, T.E. and Herreid, C.H. 2008. Analysis of Variables to Predict First-Year Persistence at the University of South Florida Using Logistic Regression Analysis. College and University. 83(4): 2-11. Miller, T.E. and Tyree, T.M. 2009. Using a Model that Predicts Individual Student Attrition to Intervene with Those Who are Most at Risk. College and University. 84(3): 12-19.

25. Questions/Comments