Engaging with Massive Online Courses
This work addresses the challenge of improving engagement in MOOCs, which have high dropout rates, but it is incremental as it builds on existing data analysis methods without introducing a new paradigm.
The study analyzed trace data from massive open online courses (MOOCs) to understand user engagement patterns, including behavioral differences between high- and low-achieving students and the impact of forum participation, and found that making badges more salient increased forum engagement.
The Web has enabled one of the most visible recent developments in education---the deployment of massive open online courses. With their global reach and often staggering enrollments, MOOCs have the potential to become a major new mechanism for learning. Despite this early promise, however, MOOCs are still relatively unexplored and poorly understood. In a MOOC, each student's complete interaction with the course materials takes place on the Web, thus providing a record of learner activity of unprecedented scale and resolution. In this work, we use such trace data to develop a conceptual framework for understanding how users currently engage with MOOCs. We develop a taxonomy of individual behavior, examine the different behavioral patterns of high- and low-achieving students, and investigate how forum participation relates to other parts of the course. We also report on a large-scale deployment of badges as incentives for engagement in a MOOC, including randomized experiments in which the presentation of badges was varied across sub-populations. We find that making badges more salient produced increases in forum engagement.