A Predictive Model for Improving Student Retention

Abstract

Faculty and advisors need actionable intelligence if they are to be held accountable for student retention. In order for them to effectively make decisions on how to best support at-risk students and choose intervention strategies, they need this intelligence way before the student reaches the point of no return. Lack of collaboration and little to no understanding of the factors that drive retention and influence a student’s at-risk status exacerbate the problem. This paper represents a brief exploration of current pilot programs that are experimenting with the Open Academic Analytics Initiative's (OAAI) Early Alert System, an open source predictive model-driven decision support system (DSS) designed to provide the actionable intelligence needed to support at-risk students. Initial results indicate a positive and significant impact on course grades and content mastery—primary factors that influence students' motivation to stay in school.