LAUSR

Massive open online courses (MOOCs) have given global learners access to quality educational resources, but the persistent high dropout rates problem has a serious impact on their educational effectiveness. Therefore, how to predict the dropout in MOOCs and make advance intervention is a hot topic in the research of MOOCs in recent years. Traditional methods rely on handcrafted features, the workload is heavy, and it is difficult to ensure the final prediction effect. In order to solve this problem, this paper proposes an end-to-end dropout prediction model based on convolutional neural networks to predict the student dropout problem in MOOCs and it integrates feature extraction and classification into a single framework, which transforms the original timestamp data according to different time windows and automatically extracts features to achieve better feature representation. Extensive experiments on a public dataset show that our approach can achieve results comparable to other dropout prediction methods on precision, recall, F1 score, and AUC score.