Temporal Graph-Based CNNs (TG-CNNs) for Online Course Dropout Prediction

Zoe Hancox, Samuel D. Relton

Abstract: Due to the global pandemic, the use of online courses is increasing significantly; yet the rate of student dropout from online courses is rising. The Accessible Culture & Training Massive Open Online Course (ACT MOOC) dataset is comprised of a temporal sequence of student actions and subsequent dropout information. We introduce a novel approach based upon temporal graphs, which uses the sequence of (and time between) events to predict dropout. The dataset consists of 7,047 users, with a dropout rate of 57.7%. The Temporal Graph-Based Convolutional Neural Network (TG-CNN) models developed in this study are compared against baseline models and existing models in the literature. Performance is assessed using the AUC, accuracy, precision, recall, and F1 score. Our novel TG-CNN model achieves an AUC score of 0.797, which improves upon previous literature: JODIE 0.756, TGN \(+\) MeTA 0.794, TGN 0.777, and CoPE 0.762. Our model offers a novel and intuitive formulation of this problem, with state-of-the-art performance.