The Use of Semester Course Data for Machine Learning Prediction of College Dropout Rates

Abstract

Predicting those at-risk of dropping out allows schools to assist students before it happens. Machine learning (ML) techniques can predict the likelihood of students completing a course, enrolling in future semesters, or graduating from college. This study compares four ML techniques to predict dropout rates using a student’s demographic information and performance in individual courses over all semesters enrolled. Using ten semester models the logistic regression method had the best accuracy of 84.8% versus decision trees (82.2%), neural networks (80.8%), and support vector machines (72.5%). The semester course performance data is a useful input for predicting dropout rates.