Identifying Student Behavior for Improving Online Course Performance with Machine Learning
In this study we investigate the correlation between student behavior and performance in online courses. Based on the web logs and syllabus of a course, we extract features that characterize student behavior. Using machine learning algorithms, we build models to predict performance at end of the period. Furthermore, we identify important behavior and behavior combinations in the models. The result of prediction in three tasks reach 87% accurate on average without using any score related features in the first half of the semester.