Group Counterfactual Explanations: A Use Case to Support Students at Risk of Dropping Out in Online Education

This paper proposes the novel application of group counterfactual explanations to the problem of predicting students at risk of dropout. Our objective is to explain how to recover the largest possible number of students while minimizing effort and cost. Using group counterfactuals, instructors and institutions could recover large groups of students with minimal remedial actions. For testing, we used the well-known public educational Open University Learning Analytics Dataset (OULAD), which contains students’ clicks made throughout interactions with online courses. We modified and adapted the only existing algorithm for the generation of group counterfactuals, named GROUP-CF. We also used the Diverse Counterfactual Explanations (DiCE) individual counterfactual algorithm with the K-means clustering method and new options in discovering the most representative counterfactuals for a group of students. The results obtained are very promising; our approach can be successfully applied to recover 99.3% of students at risk of failing in a shorter time in comparison to traditional individual counterfactuals. Moreover, although a group counterfactual proposes to change a greater number of students’ features, the values are lighter and therefore seem easier to apply than the ones obtained with individual counterfactuals. This work opens up a new line of research in education.