Predictive modeling of dropout in MOOCs using machine learning techniques

Abstract

The advent of massive open online courses (MOOCs) has revolutionized education, offering unprecedented access to high-quality learning materials globally. However, high dropout rates pose significant challenges to realizing the full potential of MOOCs. This study explores machine learning techniques for predicting student dropout in MOOCs, utilizing the open university learning analytics dataset (OULAD). Seven algorithms, including decision tree, random forest, Gaussian naïve Bayes, AdaBoost classifier, extra tree classifier, XGBoost classifier, and multilayer perceptron (MLP), are employed to predict student outcomes and dropout probabilities. The XGBoost classifier emerges as the top performer, achieving 87% accuracy in pass/fail prediction and 86% accuracy in dropout prediction. Additionally, the study proposes personalized interventions based on individual dropout probabilities to enhance student retention. The findings underscore the potential of machine learning in addressing dropout challenges in MOOCs and offer insights for instructors and educational institutions to proactively support at-risk students.