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...
The paper presents SC2R, a semantics‑constrained counterfactual recourse framework designed to support educational decision making. SC2R integrates a calibrated predictive model, integer‑programming recourse generation, an RDF vocabulary for intervention plans, and SHACL validation to enforce constraints such as timing, budget, and availability. Evaluation on the OULAD dataset shows that the framework can generate compact, feasible intervention plans at scale and that semantic validation filters out infeasible recommendations that would otherwise be accepted by simpler optimization approaches.
arXiv:2608. 03673v1 Announce Type: new Abstract: Many critical reasoning tasks, including clinical diagnosis, legal judgment, and industrial fault diagnosis, require step-dependent causal chains in which early errors propagate and correct conclusions can mask invalid reasoning.
By Jian Zhang, Bingyi Wang, Yizhi Liu
Many critical reasoning tasks, including clinical diagnosis, legal judgment, and industrial fault diagnosis, require step-dependent causal chains in which early errors propagate and correct conclusions can mask invalid reasoning. Although large language models perform well on such tasks, privacy, latency, and controllability motivate distillation into locally deployable models.
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
arXiv:2606. 29911v1 Announce Type: new Abstract: Decision theory provides a formal framework for how agents should make choices under uncertainty, drawing on ideas from philosophy, probability, and causality.
By Arvid Sj\"olander