Machine learning application in MOOCs: Dropout prediction

Abstract

Massive Open Online Course(MOOC) is undergoing explosive growth recently, both the number of MOOC platforms and courses are increasing dramatically during these years. One of the major concerns in MOOC is high dropout rate, we study dropout prediction in MOOCs, using student's learning activities data in a period of time to measure how likely students would drop out in next couple of days. We collect 39 courses data from XuetangX platform, which is based on the open source Edx platform. Using supervised classification approach in the machine learning field, we achieve 89% accuracy in dropout prediction task with gradient boosting decision tree model. We describe details in drop out prediction framework, including data extraction from Edx platform, data preprocessing, feature engineering and performance test on several supervised classification models.