An analysis of student behavior in two massive open online courses

Abstract

Massive open online courses (MOOCs) have high potential for improving education worldwide, but understanding of student behavior and situations is difficult to achieve in online settings. Network analytics and visualizations can assist instructors with supporting understanding of student behavior as courses unfold. In this work, we perform a visual comparative analysis of two different MOOC courses to analyze the impacts of course structure differences and demonstrate the benefits of visual network analysis in this context. We present several insights: (1) behavior features that are best for prediction of student attrition varied with course structure, (2) a large proportion (about 35%) of students never received a reply to their original post and this was correlated with an eventual dropout, and (3) students that received a reply to their original post were twice as likely to post again. We contribute several information visualizations of student network data and draw recommendations for MOOC instructors and designers of course systems.