Prediction of user temporal interactions with online course platforms using deep learning algorithms

Abstract

The analysis of learning interactions during online studying is a necessary task for designing online courses and sequencing key interactions, which enables online learning platforms to provide users with more efficient and personalized service. However, the research on predicting the interaction itself is not sufficient and the temporal information of interaction sequences hasn't been fully investigated. To fill in this gap, based on the interaction data collected from Massive Open Online Courses (MOOCs), this paper aims to simultaneously predict a user's next interaction and the occurrence time to that interaction. Three different neural network models: the long short-term memory, the recurrent marked temporal point process, and the event recurrent point process, are applied on the MOOC interaction dataset. It concludes that taking the correlation between the user action and its occurrence time into consideration can greatly improve the model performance, and that the prediction results are conducive to exploring dropout rates or online learning habits and performances.

Keywords

• User action analysis
• Temporal interaction prediction
• Deep learning in education
• Marked temporal point process
• Recurrent neural networks