An Ensemble Learning Model for Early Dropout Prediction of MOOC Courses

Massive open online courses(MOOCs) have become a way of online learning across the world in the past few years. However, the extremely high dropout rate has brought many challenges to the development of online learning. Most of the current methods have low accuracy and poor generalization ability when dealing with high-dimensional dropout features. They focus on the analysis of the learning score and check result of online course, but neglect the phased student behaviors. Besides, the status of student participation at a given moment is necessarily impacted by the prior status of learning. To address these issues, this paper has proposed an ensemble learning model for early dropout prediction(ELM-EDP) that integrates attention-based document representation as a vector(A-Doc2vec), feature learning of course difficulty, and weighted soft voting ensemble with heterogeneous classifiers(WSV-HC). First, A-Doc2vec is proposed to learn sequence features of student behaviors of watching lecture videos and completing course assignments. It also captures the relationship between courses and videos. Then, a feature learning method is proposed to reduce the interference caused by the differences of course difficulty on the dropout prediction. Finally, WSV-HC is proposed to highlight the benefits of integration strategies of boosting and bagging. Experiments on the MOOCCube2020 dataset show that the high accuracy of our ELM-EDP has better results on Accuracy, Precision, Recall, and F1.