Predicting Dropouts Before Enrollments in MOOCs: An Explainable and Self-Supervised Model

Massive Open Online Courses (MOOCs) belong to a new cloud-based service in education that suffers from low completion rates. Effective pre-learning intervention services, such as recommending courses with a high probability of completion or filtering courses with a very low probability of completion, will encourage students to spend more time and energy on proper courses, thus can reduce the dropout ratio. In practice, intervention services are introduced when students are predicted to drop out. However, existing methods concentrate on analyzing students' learning actions and predicting final dropout after a period of enrollment, which are insufficient in preventing students from enrolling in unsuitable courses and withdrawing mid-way. This paper presents a neural network-based Explainable Self-supervised Model (ESM) to predict MOOC dropout before enrollment. Specifically, the student's learning actions on an unenrolled course are estimated using previous logs by the neural network. And then, the action's contribution to the completion of a course is calculated in a similar way. Therefore, the probability of completion for an unenrolled course is predicted by aggregating the learning actions and their contribution to the completion. To train the neural network, a self-supervised training strategy is proposed, where enrolled courses in the training data are randomly selected as validation in each epoch. The ESM outperforms existing methods in terms of prediction accuracy and efficiency. The average increment of Area Under the ROC Curve (AUC) and F-score (F1) in the two MOOCs datasets, XuetangX and KDDCUP, are 8.3% and 0.6%, respectively. Furthermore, the two pre-learning intervention services named courses recommendation and courses filtration are proposed. When courses are recommended, the completion rate increased from 22% to 60% in XuetangX, and from 27% to 45% in KDDCUP. By filtering courses predicted with low completion probability, 40% wasted time in uncompleted courses will be saved in XuetangX.