Predicting Student Persistence Using Machine Learning: An End-to-End Overview of Local Development to Production Deployment

This event last occurred March 2, 2022

As part of the Office of University Provost, Actionable Analytics team developed machine-learning models to predict next-term persistence. Towards this effort, we iteratively constructed a list of features for each student and built a set of predictive models to map these features to a predicted probability. This presentation will cover the end-to-end development to deployment of these models to inform decision making and student-level actionability aimed at increasing student success.

Three people of different genders and ethnicities discussing around a table while interactng with online Zoom attendees



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ASU Library
Data Science and Analytics