Engaging with Massive Online Courses

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 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.