Dropout Prediction in MOOCs: Using Deep Learning for Personalized Intervention

TLDR

MOOCs promise transformative education, yet their high attrition rates and the inability of traditional methods to timely identify at‑risk students limit effective intervention design. This study aims to enhance dropout prediction models to enable personalized interventions for at‑risk MOOC learners. A temporal deep‑learning framework is employed to build the prediction model and generate individual dropout probabilities. The deep‑learning approach outperforms baseline algorithms and supports prioritizing interventions based on personalized dropout risk, with implications discussed.

Abstract

Massive open online courses (MOOCs) show great potential to transform traditional education through the Internet. However, the high attrition rates in MOOCs have often been cited as a scale-efficacy tradeoff. Traditional educational approaches are usually unable to identify such large-scale number of at-risk students in danger of dropping out in time to support effective intervention design. While building dropout prediction models using learning analytics are promising in informing intervention design for these at-risk students, results of the current prediction model construction methods do not enable personalized intervention for these students. In this study, we take an initial step to optimize the dropout prediction model performance toward intervention personalization for at-risk students in MOOCs. Specifically, based on a temporal prediction mechanism, this study proposes to use the deep learning algorithm to construct the dropout prediction model and further produce the predicted individual student dropout probability. By taking advantage of the power of deep learning, this approach not only constructs more accurate dropout prediction models compared with baseline algorithms but also comes up with an approach to personalize and prioritize intervention for at-risk students in MOOCs through using individual drop out probabilities. The findings from this study and implications are then discussed.