Modeling and interpreting user navigation patterns in MOOCs

Abstract

Over the past few years, MOOCs have trigged an education revolution. Clickstream data of user were recorded by MOOCs platform, providing valuable insights about the way user interact with the MOOCs. In this paper, we study user navigation patterns in MOOCs. We propose a metric to measure the similarity between user session-level navigation path, and build an unsupervised clustering model to capture user navigation patterns in MOOCs. Based on the user behavior clustering result, we further explore engagement of each navigation pattern from the perspective of dropout. To measure the effectiveness of our model, we conduct experiment on real world dataset with five weeks of interaction logs of 3,914 users. Through our analysis, clustering model proposed effectively identifies 13 types of user navigation patterns, which help us understand user behavior in MOOCs.