Failure Risk Prediction in a MOOC: A Multivariate Time Series Analysis Approach
This addresses the problem of low completion rates in MOOCs for educators and platform developers, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled predicting learner failure in MOOCs by comparing multivariate time series classification methods on behavioral data, finding that the approaches are promising and accuracy depends on the amount of recorded interactions.
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.