EduRiskX：结合F-Logic推理的神经符号框架用于早期学业风险预测
原标题：EduRiskX: A Neuro-Symbolic Framework with F-Logic Reasoning for Early Academic Risk Prediction
AI 摘要
EduRiskX 是一个神经符号框架，结合了时间 Transformer 预测器与 F-Logic 符号推理，用于在线教育中的早期学业风险预测。在 OULAD 数据集上，它实现了 0.900 的准确率和 0.894 的 F1 分数，平均早期检测周为 9.32，检测率为 94.30%。该框架通过 F-Logic 模块提供基于规则的解释，增强了可解释性，并优于 PatchTST 和 iTransformer 等基线模型。
正文节选
EduRiskX: A Neuro-Symbolic Framework with F-Logic Reasoning for Early Academic Risk Prediction Abstract Predicting students’ academic risk in online education is crucial for enabling timely interventions that can improve retention and learning outcomes. However, existing models often suffer from limited early detection capability and insufficient interpretability, leading to a “black-box” trust crisis that hinders their adoption in real-world pedagogical settings. To address these challenges, we