Early prediction of MOOC dropout in self-paced students using deep learning

Abstract

ABSTRACT To address three issues identified in previous research this study proposes a clustering-based MOOC dropout identification method and an early prediction model based on deep learning. The MOOC learning behavior of self-paced students was analyzed, and two well-known MOOC datasets were used for analysis and validation. The findings are as follows: Firstly, the dropout rate among self-paced students in MOOCs exceeds 90%, with over 50% of students participating in online learning activities for only one day. Furthermore, the starting dates for students in the same course differ significantly. Secondly, leveraging early learning behavior and relevant background features, the proposed early prediction model accurately predicts over 98% of dropout cases and identifies over 50% of engaged students. Through training, the model's convolutional kernels capture meaningful weights for different days and activities. Lastly, background features related to students and courses have a more significant impact on dropout rates. The utilization of resources such as videos and active participation in learning activities, like asking questions, demonstrate a particularly significant influence on dropout rates. Notably, there is no fixed period that consistently affects dropout rates. These method and findings provide effective strategies for decreasing dropout rates and improving student engagement.