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 subpopulations. We find that making badges more salient produced increases in forum engagement.
Recently, Anderson et al. REF have developed a taxonomy of individual learner behaviors related specifically to assignments, designed to examine the different behavior patterns aggregated across a student's entire experience in the course to distinguish high-and low-achieving students.
Engaging with Massive Online Courses
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ArXiv
Computer Science Physics Mathematics