Dropout Prediction in MOOCs: Moving Toward Intervention Personalization Through Deep Learning

• In Event: Poster Session 15
  In Poster Session: Research on Online and Mobile Learning and Digital Literacies

Abstract

In this study, we take an initial step to optimize the dropout prediction model performance toward intervention personalization for at-risk of dropping out 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.

Author

• Wanli Xing, Texas Tech University