Enabling Insights into Student Success for Academic Advisors with Machine Learning

On Demand
Session Type: Breakout Session
Delivery Format: On-Demand
This session discusses how machine learning has been leveraged for academic advisors at Penn State University. This session discusses two approaches to leveraging institutional data: monitoring student engagement in online courses and predicting how well a student will do in a course. This session will include a demonstration of prototypes designed for early warning systems and discuss lessons learned.

Outcomes: Learn how models of student success are built with machine learning * Evaluate the practical implications of student engagement in academic advising * Consider scenarios of ethical use of predictive models in academic advising

Presenters

  • Benjamin Hellar

    Team Lead for Learning Analytic Applications, The Pennsylvania State University
  • Bart Pursel

    Chief Technology Officer, Unizin, Ltd.
  • Drew Wham

    Data Scientist, The Pennsylvania State University