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.

Next Research Steps

Model refinement

Predicting sophomore persistence

Transfer students

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

Mentoring Program

Early lessons learned

Next Steps

Revised model

Full implementation

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.

Questions/Comments