Taking Informed Action on Student Activity in MOOCs

TL;DR

This paper introduces a method to analyze student behavior in MOOCs by deriving metrics from platform data, enabling targeted interventions through clustering of participant subgroups to improve learning outcomes.

Contribution

It presents a novel approach to segment MOOC participants based on granular activity data, allowing for more personalized and effective instructor interventions.

Findings

Derived new metrics from platform events

Enabled clustering of student subgroups

Facilitated targeted instructor actions

Abstract

This paper presents a novel approach to understand specific student behavior in MOOCs. Instructors currently perceive participants only as one homogeneous group. In order to improve learning outcomes, they encourage students to get active in the discussion forum and remind them of approaching deadlines. While these actions are most likely helpful, their actual impact is often not measured. Additionally, it is uncertain whether such generic approaches sometimes cause the opposite effect, as some participants are bothered with irrelevant information. On the basis of fine granular events emitted by our learning platform, we derive metrics and enable teachers to employ clustering, in order to divide the vast field of participants into meaningful subgroups to be addressed individually.