Predictive Analytics in Higher Education: The Promises and Challenges of Using Machine Learning to Improve Student Success

Abstract

Colleges are increasingly turning to predictive analytics to identify “at-risk” students in order to target additional supports. While recent research demonstrates that the types of prediction models in use are reasonably accurate at identifying students who will eventually succeed or not, there are several other considerations for the successful and sustained implementation of these strategies. In this article, I discuss the potential challenges to using risk modeling in higher education and suggest next steps for research and practice.