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Predicting Individual Student Attrition and Fashioning Interventions to Enhance
Student Persistence and Success
Thomas E. Miller
University of South Florida
IntroductionSources of concern for persistence and graduation rates
InstitutionsGovernmentCollege ranking servicespublic
USF persistence experience
Common approaches have been broadly implemented
Generally targeted to sub-populationsNecessarily inefficient and wasteful as persistence enhancement tools (yet may still be sound educational practice)
Introduction cont.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
Background
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 gender
academic behaviors in high school parents together
CSXQNormally 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.
MethodologyThe 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.
ResultsThe PROC LOGISTIC procedure in SAS was run using set-wise inclusion of variables.
Two blocks of independent variables; dependent variable: persist/not persist
Predicting New Cases
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 (-)
Other variables that may prove useful
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
Other variables cont.
CSXQ data- plan to be employed on campus- intended effort scale related to
course learning - intended effort scale related to
scientific and quantitative experiences.
A Call to Action
Theoretical Background
Challenge/SupportMattering TheoryFirst-year Student DevelopmentInvolvement Theory
Starting PlaceOffice of New Student Connections
Week of Welcome
Website, Blackboard, Connections newsletter
UConnect
New Student Socials
Information and services for families
Transfer student connections
InterventionModel 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 scale and scope can we manage?Are multiple levels of intervention possible?
Result: A pilot mentoring program
Mentoring Program
Selection of Mentors
• Who?
• How many?
• What makes a good mentor?
• Where are natural points of connection?
• Who else needs to be involved?
Mentoring Program
Other opportunities for impact with current student sub-populations:
- Intercollegiate Athletics
- Freshman Summer Institute
- Student Support Services
- Honors Program
Mentoring Program
Training of Mentors
How to Connect/EngageBest Practices, Collecting InformationProblem SolvingMaking ReferralsFollowing Up
Mentoring Program
Why Students Drop Out – Clues to which we need be alert
Unclear or unreasonable goalsSocial isolationInsufficient academic preparationStressAcademic disengagement or boredomFinancial concernsChallenges of new freedomUnmet expectations or transition shockDistraction of conflicting commitments
Mentoring Program
Points of ReferralCounseling CenterCareer Center (including on-campus employment)Financial Aid OfficeTutoring and Learning Services/Writing CenterCenter for Student InvolvementHousing and Residential Education
Mentoring Program
Expectations of MentorsFive to fifteen studentsInitial ContactNotify NSC Office of non-respondentsMeet monthlyMaintain log of contactsUse Contact Checklist
New ModelHigh School GPA (+)
Being Asian vs. being white (+)
Being Black vs. being White (+)
Higher combined SAT (-)
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 (-)
CitationsMiller, 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.