Deep analytic model for student dropout prediction in massive open online courses

Abstract

Predicting students' performance is critical in Massive Open Online Courses (MOOCs) in order to benefit from many aspects such as students' retention and make timely interventions. In this paper, we propose a hyper-model of Convolutional Neural Networks and Long Short-Term Memory, called CONV-LSTM, in order to automatically extract features from MOOCs raw data and predict whether each student will drop out or complete courses. We consider class imbalance problem, which means that models will be biased to yield good results on the majority of class examples and poor results on the minority of class examples. In that case, model prediction is inaccurate, which means that the false negative rate is high. To reinforce better prediction performance, a cost-sensitive technique is used in the loss function, which considers the various misclassification costs for false negatives and false positives. The proposed model shows a better performance when compared to baseline methods.