Modeling and Predicting Learning Behavior in MOOCs

Massive Open Online Courses (MOOCs), which collect complete records of all student interactions in an online learning environment, 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 heterogeneity in students’ course selection as well as their learning patterns. For example, students who exert higher effort and ask more questions are not necessarily more likely to get certificates. Additionally, the probability that a student obtains the course certificate increases dramatically (3× higher) when she has one or more “certificate friends”. Moreover, we develop a unified model to predict students’ learning effectiveness, by incorporating user demographics, f...

• Type: Journal
• Year: 2016
• Where: WSDM
• Authors: Jiezhong Qiu, Jie Tang, Tracy Xiao Liu, Jie Gong, Chenhui Zhang, Qian Zhang, Yufei Xue
• Keywords: Data Mining, WSDM 2016