The Crowd in MOOCs: A Study of Learning Patterns at Scale

TL;DR

This study analyzes massive MOOC learning data to uncover patterns in learner behavior, revealing insights into temporal activity, course co-enrollment, and enabling efficient recommendation systems.

Contribution

It introduces a large-scale analysis of MOOC learning patterns using mutual information and sequential pattern mining, and develops a fast, effective course recommendation model.

Findings

Learners' activity intervals follow power-law and cosine distributions.

Most co-enrolled courses are in the same category or university.

The proposed recommendation model is competitive and significantly faster.

Abstract

The increasing availability of learning activity data in Massive Open Online Courses (MOOCs) enables us to conduct a large-scale analysis of learners' learning behavior. In this paper, we analyze a dataset of 351 million learning activities from 0.8 million unique learners enrolled in over 1.6 thousand courses within two years. Specifically, we mine and identify the learning patterns of the crowd from both temporal and course enrollment perspectives leveraging mutual information theory and sequential pattern mining methods. From the temporal perspective, we find that the time intervals between consecutive learning activities of learners exhibit a mix of power-law and periodic cosine function distribution. By qualifying the relationship between course pairs, we observe that the most frequently co-enrolled courses usually fall in the same category or the same university. We demonstrate these findings can facilitate manifold applications including recommendation tasks on courses. A simple recommendation model utilizing the course enrollment patterns is competitive to the baselines with 200$\times$ faster training time.