Exploring the Application of Machine Learning Techniques to Predict STEM Retention

• In Event: AERA Roundtable Session Friday 9:50 am Four Seasons Ballroom 4
  In Roundtable Session: Machine Learning Techniques I: Advancing Equity, Prediction, and Measurement (Table 4)

Abstract

This study considers how course-taking patterns might serve as a structural barrier for students minoritized within STEM disciplines. Utilizing machine learning techniques, it explores how such analytical techniques can be used to examine how course-taking patterns impact STEM retention across identity groups.

Author

• Amanda J. Davis Simpfenderfer, College of William & Mary