Identifying Critical Factors When Predicting Remedial Mathematics Completion Rates

Abstract

Completion of remedial mathematics has been identified as one of the keys to college success. However, completion rates in remedial mathematics have been low and are of much debate across America. This study leverages machine learning techniques in trying to predict and understand completion rates in remedial mathematics. The purpose of this study is to build machine learning models that can predict students that are least likely to complete remedial mathematics and identify which factors are most influential when computing those predictions. The study discovers random forest as the highest performing model. Furthermore, the study reveals that the remedial course a student begins with, credit completion rate, math placement score, and high school G.P.A as the most influential predictors of completion rates. The study also offers future research directions, especially in how to improve the performance of the machine learning models.