OULAD MOOC Dropout and Result Prediction using Ensemble, Deep Learning and Regression Techniques

Massive Open Online Courses (MOOCs) have become increasingly popular since their start in the year 2008. Universities known worldwide for their traditional confined classroom education are also changing their practices by hosting MOOCs. These are Internet-based courses where students can learn at their own pace and follow their own schedule. Study materials and videos are provided that can be used in a blended learning program. Despite its many advantages, it suffers from problems such as high dropout and failure rates. Previous studies have mostly focused on predicting student dropout. This paper contributes to the body of research by investigating both student dropout and result prediction performance of machine learning models built based on different types of attributes such as demographic info, assessment info and interaction with the VLE. An analysis on the OULAD dataset showed that models based on student’s interaction with the VLE achieved the high performance in terms of AUC, of up to 0.91 for dropout prediction and 0.93 for result prediction in case of Gradient Boosting Machine.