Early Prediction of Success in MOOC from Video Interaction Features

The popularity of online learning, such as MOOCs (Massive Open Online Courses), continues to increase among students. However, MOOCs dropout remains high. Prediction of student performance that could feed instructors’ dashboards and help them adapt their course structure and material, or trigger help and tailor interventions to specific groups of students, is a valuable research objective. Towards that end, this paper focuses on three predictive metrics (student attendance rate: [Formula: see text], utilization rate: [Formula: see text], and watching index: [Formula: see text]) of how students interact with MOOC videos in order to predict which group of students will pass or fail the course. Results show that these metrics, taken after the first week and the midpoint, can be highly effective for predicting the students that will pass or fail the course.