Failure Risk Prediction in a MOOC: A Multivariate Time Series Analysis Approach

TL;DR

This paper compares multivariate time series classification methods to predict at-risk learners in MOOCs, demonstrating promising results and emphasizing the importance of rich behavioral data for accurate predictions.

Contribution

It introduces a comparative analysis of multivariate time series methods for early failure prediction in MOOCs using real-world behavioral data.

Findings

Approaches show promise in predicting learner failure.

Prediction accuracy depends on behavioral data richness.

Early-stage predictions are feasible with available methods.

Abstract

MOOCs offer free and open access to a wide audience, but completion rates remain low, often due to a lack of personalized content. To address this issue, it is essential to predict learner performance in order to provide tailored feedback. Behavioral traces-such as clicks and events-can be analyzed as time series to anticipate learners' outcomes. This work compares multivariate time series classification methods to identify at-risk learners at different stages of the course (after 5, 10 weeks, etc.). The experimental evaluation, conducted on the Open University Learning Analytics Dataset (OULAD), focuses on three courses: two in STEM and one in SHS. Preliminary results show that the evaluated approaches are promising for predicting learner failure in MOOCs. The analysis also suggests that prediction accuracy is influenced by the amount of recorded interactions, highlighting the importance of rich and diverse behavioral data.