2019 – Machine Learning and Decision Intelligence: Predicting Student Retention and Drivers

Presentation Summary

Data warehouse and business intelligence practitioners in higher education are now managing more data from more areas of the university than ever before. Going beyond BI and applying both predictive and prescriptive analytic techniques is the next logical progression in effectively leveraging this data, as has been seen across a variety of other industries. We discuss multiple case studies of applying machine learning and decision intelligence approaches to various points in student’s educational journey. One particular focus will be on student retention: predicting retention likelihood for individual students as well as the main drivers of potential non-retention. This information can be used to accurately identify at-risk students and better address their individual needs.