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.

Author

• Kelli Bird

Acknowledgements

I am grateful to Ben Castleman, whose continued collaboration and mentorship has been instrumental in my exploration of this topic. I am also grateful for the long-standing partnerships with the Virginia Community College System and Bloomberg Philanthropies that have enabled us to do this work.

Copyright © Association for Institutional Research 2023