Modeling and Predicting Learning Behavior in MOOCs

Abstract

Massive Open Online Courses (MOOCs), which collect complete records of all student interactions in an online learning environ-ment, offer us an unprecedented opportunity to analyze students’ learning behavior at a very fine granularity than ever before. Using dataset from xuetangX, one of the largest MOOCs from China, we analyze key factors that influence students ’ engagement in MOOCs and study to what extent we could infer a student’s learning effectiveness. We observe significant behavioral hetero-geneity in students ’ course selection as well as their learning pat-terns. For example, students who exert higher effort and ask more questions are not necessarily more likely to get certificates. Addi-tionally, the probability that a student obtains the course certificate increases dramatically (3 × higher) when she has one or more “cer-tificate friends”. Moreover, we develop a unified model to predict students ’ learn-ing effectiveness, by incorporating user demographics, forum ac-tivities, and learning behavior. We demonstrate that the proposed model significantly outperforms (+2.03-9.03 % by F1-score) several alternative methods in predicting students ’ performance on assign-ments and course certificates. The model is flexible and can be applied to various settings. For example, we are deploying a new feature into xuetangX to help teachers dynamically optimize the teaching process