The paper introduces SC2R, a semantics‑constrained counterfactual recourse framework designed to provide actionable, feasible intervention plans for students identified as at risk by learning analytics models. SC2R integrates a calibrated predictive model, integer‑programming recourse generation over discrete actions, an RDF vocabulary for representing intervention plans, and SHACL validation to enforce constraints such as timing, budget, immutability, and availability. Evaluated on the OULAD dataset, the framework demonstrates strong predictive performance, scalable generation of compact intervention plans, and the ability to detect infeasible plans that would otherwise be accepted by simpler optimization approaches.
By Ngoc Luyen Le, Marie-H\'el\`ene Abel, Bertrand Laforge
The paper proposes a structural causal modelling framework for student competency assessment, moving beyond traditional probabilistic models like item response theory. It introduces a protocol for constructing such models, emphasizing the explicit representation of interventions (e.g., hints) and counterfactual analysis. The authors illustrate the approach with data from an assessment of compulsory school students’ algorithmic skills.
By Francesca Mangili, Alessandro Antonucci, Rafael Caba\~nas
arXiv:2606. 08696v1 Announce Type: cross Abstract: Counterfactual recourse aims to provide actionable feature changes that would alter an unfavorable decision made by a predictive model.
By Yasuo Tabei
Accurate assessment of student competencies is essential for enabling educators to identify individual needs, design targeted interventions, and evaluate the effectiveness of educational strategies. E...
arXiv:2607. 01306v1 Announce Type: new Abstract: Counterfactual explanations explain machine learning predictions by identifying minimal input changes that would alter a model's decision.
By Pavel Iakovets, Liyanapathiranage Sudeepika Wajirakumari Samarathunga, Martin Thomas Horsch, Fadi Al Machot
arXiv:2606. 30650v1 Announce Type: cross Abstract: Educational support services often face a qualified-capacity problem: staff time is scarce, qualifications decay, new support needs can appear before anyone is prepared for them, and training consumes the same hours needed by current students.
By Carlos Eduardo Sanoja, Oscar Enrique Moreno Mayz