发表机构
Gamaizer; Université de Technologie de Compiègne; Sorbonne Université（伽马泽公司; 贡比涅技术大学; 索邦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有学生风险预测模型无法提供可行教育干预方案的问题，提出SC2R语义约束反事实追索框架，在OULAD数据集上验证其可生成大规模紧凑可行干预计划，提升教育决策支持的操作意义。
AI 中文摘要
学习分析模型可识别存在学业表现不佳风险的学生，但无法直接指出哪些干预措施是可行、可操作且符合教育约束条件的。本文提出SC2R，这是一个面向教育决策支持的语义约束反事实追索框架。SC2R结合了校准后的预测模型、基于整数规划的离散动作变量追索生成、用于干预计划表示的轻量级RDF词汇表，以及用于强制实施时间、预算、不可变性和可用性约束的SHACL验证。该框架在OULAD数据集上进行离线评估，使用相对于两次决策时间点的每次评估构建的快照。结果表明，预测组件性能强劲，可大规模生成紧凑的干预计划，且语义验证能发现仅优化设置会接受的不可行计划。本研究未声称学生成果的因果改善，而是表明在教育领域中，当建议不仅符合模型有效性，还具备语义可行性和可机器检查性时，反事实追索更具操作意义。
英文摘要
Learning analytics models can identify students at risk of poor performance, but they do not directly indicate which interventions are feasible, actionable, and compatible with educational constraints. This paper introduces SC2R, a semantics-constrained counterfactual recourse framework for educational decision support. SC2R combines a calibrated predictive model, integer-programming-based recourse generation over discrete action variables, a lightweight RDF vocabulary for intervention-plan representation, and SHACL validation for enforcing timing, budget, immutability, and availability constraints. The framework is evaluated offline on the OULAD dataset using snapshots constructed relative to each assessment at two decision horizons. Results show that the predictive component provides strong performance, that compact intervention plans can be generated at scale, and that semantic validation reveals infeasible plans that lighter optimization-only settings would otherwise accept. Rather than claiming causal improvement in student outcomes, this work shows that counterfactual recourse becomes more operationally meaningful in education when recommendations are not only model-valid, but also semantically feasible and machine-checkable.
