Work

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

Year: 2025

Type: article

Abstract: This paper proposes the novel application of group counterfactual explanations to the problem of predicting student at risk of drop-out. Our objective is to provide explanations for trying to re-cover the largest number of students with less effort and cost. Using group counterfactuals, in-structors and institutions could recover large groups of students with minimal remedial actions. For testing it, we have used the well-known public educational OULAD dataset that contains student’s clicks made throughout interaction with online courses. We have modified and adapted the only existed algorithm for generating group counterfactual named GROUP-CF. We also used the DICE individual counterfactual algorithm with the K-means clustering method and new op-tions for discovering the most representative counterfactuals for a group of students. The results obtained are very promising and they show that our approach can be successfully applied to re-cover 99.3% of students at risk of failing in a shorter time in comparison to traditional individual counterfactuals. And although, a group counterfactual proposes to change a greater number of student’s features, the values are lighter and therefore seem easier to apply than the ones obtained with individual counterfactuals. This work opens a new line of research in education.

Source: Electronics

DOI: 10.3390/electronics15010051

Authors: Pamela Buñay Guisñan, Alberto Cano Turnes, Áurea María Anguera De Sojo Hernández, Juan Alfonso Lara Torralbo, Cristobal Romero Morales

Institutions: University of Córdoba, Universidad Politécnica de Madrid

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $2,896