EduRiskX: A Neuro-Symbolic Framework with F-Logic Reasoning for Early Academic Risk Prediction

EduRiskX：一种结合 F-Logic 推理的神经符号框架，用于早期学业风险预测

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.

摘要： 在在线教育中预测学生的学业风险，对于实现能够提高留存率和学习成果的及时干预至关重要。然而，现有模型往往存在早期检测能力有限和可解释性不足的问题，导致了“黑箱”信任危机，阻碍了它们在现实教学环境中的应用。

To address these challenges, we propose EduRiskX, a neuro-symbolic framework that integrates a temporal Transformer-based predictor with F-Logic symbolic reasoning. The neural component models longitudinal student activity sequences using temporal attention, class-weighted loss, and dynamic weekly truncation.

为了应对这些挑战，我们提出了 EduRiskX，这是一个将基于时间 Transformer 的预测器与 F-Logic 符号推理相结合的神经符号框架。其神经组件利用时间注意力机制、类加权损失函数和动态周截断技术，对学生的纵向活动序列进行建模。

Acting as a data-driven expert system, an F-Logic rule base — grounded in established educational theories (Engagement Theory and Student Integration Model) to mimic the diagnostic logic of human educators — is constructed exclusively from the training data. The neural risk probability and the symbolic confidence score are then combined through a logistic regression-based fusion mechanism that learns the relative contribution of each signal.

作为一个数据驱动的专家系统，F-Logic 规则库完全从训练数据中构建，并基于既定的教育理论（参与理论和学生整合模型）来模拟人类教育者的诊断逻辑。随后，神经风险概率和符号置信度分数通过一种基于逻辑回归的融合机制进行结合，该机制能够学习每个信号的相对贡献。

Experiments on the Open University Learning Analytics Dataset (OULAD) using a strict 80/10/10 student-level split show that EduRiskX achieves an accuracy of 0.900 and an F1-score of 0.894 at the end of the semester (Week 38), with an average early detection week of 9.32 and a detection rate of 94.30 percent. Compared with state-of-the-art time-series models (PatchTST, iTransformer) and common deep learning baselines (LSTM, CNN), EduRiskX yields improved recall and earlier risk identification under identical conditions.

在开放大学学习分析数据集（OULAD）上，使用严格的 80/10/10 学生级划分进行的实验表明，EduRiskX 在学期末（第 38 周）达到了 0.900 的准确率和 0.894 的 F1 分数，平均早期检测周数为 9.32 周，检测率为 94.30%。与最先进的时间序列模型（PatchTST、iTransformer）和常见的深度学习基线（LSTM、CNN）相比，EduRiskX 在相同条件下表现出更高的召回率和更早的风险识别能力。

Beyond predictive performance, the F-Logic module provides structured rule-based explanations linking predictions to observable behavioral patterns and educational theories.

除了预测性能外，F-Logic 模块还提供了结构化的基于规则的解释，将预测结果与可观察的行为模式及教育理论联系起来。