Profiling MOOCs from viewing perspective

TL;DR

This paper analyzes MOOC viewing behaviors to classify learners, measure course attraction, and understand teaching and learning order, providing new algorithms and indices for better course and learner assessment.

Contribution

Introduces a non-parametric algorithm for learner classification and an entropy-based index for course attraction from viewing data.

Findings

Effective learner categorization method developed.

New index captures course attraction considering viewing time and video views.

Insights into the correlation between teaching order and learning order.

Abstract

We profiled three aspects of MOOCs from the perspective of viewing behaviors, the most prominent and common ones of MOOC learning. They were learner classification, course attraction, teaching order and learning order. Based on viewing behavior data, we provided a non-parametric algorithm to categorize learners, which helped to narrow the scope of finding potential all-rounders, and a method to measure the correlations between teaching order and learning order, which helped to assign teaching contents. Using information entropy, we provided an index to measure course attraction, which integrated the viewing time invested on courses and the number of viewed course videos. This index describes the diminishing marginal utility of repeated viewing and the increasing information of viewing new videos. It has potential to be an auxiliary method of assessing course achievements.