Topic Evolution Models for Long-Running MOOCs

Abstract

Massive open online courses (MOOCs) have emerged as a powerful platform for imparting education in the last few years. Discussion forums in online courses connect various geographically separated MOOC participants and serve as the primary means of communication between them. The text in the forums reflects many important aspects of the course such as the student population and their changing interests, parts of the course that were well received and parts needing attention, and common misconceptions faced by students. In order to improve the quality of online courses and students’ interaction and learning experience, instructors need to actively monitor and discern patterns in previous iterations of the course and mold the course to suit the needs of the ever-changing student population. To enable this, in this work, we perform a systematic detailed analysis of the evolution of fine-grained topics in online course discussion forums across repeated MOOC offerings using seeded topic models and draw important insights on the nature of students, types of issues, and student satisfaction. We present topic evolution results on two successful long-running MOOCs: (i) a business course, and (ii) a computer science course. Our models uncover interesting topic trends in both courses including the decline of logistic issues in both courses as iterations unfold, decline in grading related issues when automatic grading is adopted in the business course, and prevalence of technical issues in the computer science course in comparison to the business course. Our models throw light on the different ways students interact on MOOCs and their changing needs, and are useful for instructors to understand the progression of courses and accordingly fine-tune courses to meet student expectations.