Abstract

In Fall 2019, Provost Paula Short charged a task force with developing evidence-based recommendations for increasing undergraduate student retention and timely degree completion in support of the University of Houston’s (UH) student success goals. As members of the data subcommittee, my colleagues and I formulated a predictive model for one-year retention. What factors predict student retention and how can we identify students at greatest risk for attrition?

In this study, we compiled student demographic, admissions, financial, and academic data to predict one-year retention for three cohorts of First-Time-in-College (FTIC) students. We split our analyses into fall, spring, and full-year retention models to evaluate the best predictive value at three different time points. Finally, we calculated predictive probabilities from our models to score students for intervention. Ultimately, we can use these models to identify students in future cohorts so that our interventions maximize the probability of retention one year later.