让教育干预建议既科学又可行，自动排除不可行方案。
From Student Risk Prediction to SC2R: Semantics-Constrained Counterfactual Recourse for Educational Decision Support
- 用整数规划生成符合教育约束的可操作干预方案。
- 在OULAD数据集上验证，能批量生成紧凑且合规的计划。
- 通过语义校验识别出传统方法忽略的不可行建议，适合教育决策者使用。
学习分析模型能识别学业风险学生，但难以说明哪些干预措施实际可行、可执行且符合教育约束。本文提出SC2R框架，结合校准的预测模型、基于整数规划的离散动作变量优化、轻量级RDF干预计划表示语言及SHACL语义校验，用于强制执行时间、预算、不可更改性和可用性等约束。在OULAD数据集上，以每次评估为基准构建两个决策时点的快照进行离线评估。结果表明：预测组件表现良好，可大规模生成紧凑的干预计划，且语义校验能揭示传统仅依赖优化的设定所接受的不可行计划。本工作不宣称对学生结果有因果提升，而是证明：当推荐不仅模型有效，且语义可行、机器可验证时，反事实建议在教育中更具操作意义。
原文摘要 · Abstract (English)
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.
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