Predicting Course Engagement with Machine Learning Techniques

Online Courses are one of the most popular ways to learn, but the technology used has a vital effect on the learner. In this study, we will research the prediction of students' course engagement. International surveys show that students have a 70% interest in joining online learning, and just 30% of students are interested in traditional learning. However, keeping students engaged is one of the most difficult tasks, since low engagement contributes to lower learning outcomes and higher dropout rates. We studied more than 15 papers of existing research, and were able to achieve a 96% accuracy rate, which is a very welcome improvement on previous results. This paper examines machine learning algorithms, including Decision Trees, Random Forest, Gradient Booster, Naive Bayes, and K-Nearest Neighbors (KNN), to efficiently predict engagement during online courses. By systematically examining existing published research studies, we identify gaps and limitations of existing methods, such as problems with variant datasets, chances of overtraining, and a lack of accessibility to real-time engagement data.