Engaging with Massive Online Courses
Ashton Anderson, Daniel Huttenlocher, Jon Kleinberg, Jure Leskovec

TL;DR
This paper analyzes how learners engage with MOOCs using trace data, categorizes behaviors, compares high- and low-achievers, and explores the impact of badges on participation through large-scale experiments.
Contribution
It introduces a behavioral taxonomy for MOOC learners, examines engagement patterns, and evaluates badge incentives via randomized experiments.
Findings
Badge salience increases forum engagement
Behavioral patterns differ between high- and low-achieving students
Trace data enables detailed understanding of learner interactions
Abstract
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…
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Taxonomy
TopicsOnline Learning and Analytics · Online and Blended Learning · Data Stream Mining Techniques
