Predicting student success in MOOCs: a comprehensive analysis using machine learning models

The study found that simple indicators, such as a student's activity level on a given day, could be as effective as more complex data combinations or personal information in predicting student success. The logistic regression model achieved an accuracy of 72.1% for four-class classification and 92.4% for 2-class classification, while the random forest classifier achieved an accuracy of 74.6% for four-class classification and 95.7% for two-class classification. These findings highlight the potential of machine learning models in predicting and understanding students' course outcomes in MOOCs, offering valuable insights for improving student engagement and success in online learning environments.