The Added Value of Student Affairs in Retention Prediction Models

Abstract

For this inquiry we supplemented typical academic variables to evaluate the degree to which several student affairs variables add value in a machine learning model predicting first-year retention. Findings indicated that living on campus, being Greek, as well as engaging in recreational sports all had positive contributions to predicting retention. Higher education leaders should use this study to advocate for and enact the inclusion of student affairs variables into predictive models of student success.