Identifying Beneficial Sessions in an e-learning System Using Machine Learning Techniques

Abstract

The first goal of this project is to investigate the beneficial sessions for each student in an e-learning system. The second goal is to explore the relationships between student session difficulty, workload, engagement and loyalty based on the session outcome. The most difficult problem faced by e-learning instructors is finding which course sessions or what course materials are most beneficial to their students during a course. When instructors do receive insufficient feedback concerning a course session or week, the result can be that students fail the course, drop out, or receive a lower grade on the final exam. In this study, we used machine learning (ML) algorithms and regression analysis to identify beneficial sessions based on students’ workload, engagement, difficulty and loyalty during the course. The results revealed that strong relationships exist between the input student features (engagement, difficulty, workload and loyalty) and the session scores. In addition, the results show that deep learning and random forest models are appropriate ML algorithms for predicting beneficial sessions.