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Maximizing University Enrollment Using Institutional-Based Aid Scholarship

The overall student enrollment has declined over the past eight years in US. The administrators in enrollment management are applying ever more sophisticated analytical techniques in efforts to determine the characteristics of students that are most probable to enroll upon offering them monetary incentives (i.e., financial aid and/or scholarship). In this paper we present a framework that reveals the characteristics of such students using different machine learning models. Particularly, we use a genetic algorithm optimization method along with three different classification models: logistic regression (LR), support vector machines (SVMs) and bayesian networks (BNs). The results show that students with particular gender, ethnicity, socioeconomic and academic backgrounds are most probable to enroll upon offering them institutional money. We validated our results using data of actual students from a large public Tier 1 research university.