Predicting students' academic performance and modeling using data mining techniques
In educational institutions and universities, the issue of study interruptions can be addressed by using e-learning. As a result, this field has recently attracted a lot of attention. In this study, we applied four machine-learning methods to predict students' academic progress: logistic regression, decision trees, random forests, and Naive Bayes. The Open University Learning Analytics Dataset (OULAD), which contains a subset of the OU student data, was the source of the student data for all of these techniques. There is information regarding the students' VLE inter