发表机构
Sichuan University（四川大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出结合F-Logic推理的神经符号框架EduRiskX，在OULAD数据集上实现了更高的早期学业风险预测性能，且可提供可解释的规则依据。
AI 中文摘要
在线教育中预测学生学业风险对及时干预、提升留存率和学习效果至关重要。然而现有模型常存在早期检测能力有限、可解释性不足的问题，导致“黑箱”信任危机，阻碍其在实际教学场景中的应用。为应对这些挑战，本文提出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。神经组件通过时间注意力、类别加权损失和动态每周截断来建模纵向学生活动序列。作为数据驱动的专家系统，F-Logic规则库基于已有的教育理论（参与理论和学生整合模型）构建，以模仿人类教育者的诊断逻辑，且该规则库仅从训练数据中生成。随后，通过基于逻辑回归的融合机制将神经风险概率与符号置信度得分相结合，该机制可学习每种信号的相对贡献。在开放大学学习分析数据集（OULAD）上，采用严格的80/10/10学生级划分进行实验，结果显示EduRiskX在学期末（第38周）的准确率达0.900，F1值为0.894，平均早期检测周数为9.32，检测率为94.30%。与最先进的时间序列模型（PatchTST、iTransformer）及常见深度学习基线（LSTM、CNN）相比，EduRiskX在相同条件下提升了召回率并实现了更早的风险识别。除预测性能外，F-Logic模块还提供基于结构化规则的解释，将预测结果与可观测的行为模式及教育理论相关联。
英文摘要
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 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. 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. 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. Beyond predictive performance, the F-Logic module provides structured rule-based explanations linking predictions to observable behavioral patterns and educational theories.
CommentsPreviously available on Research Square: https://doi.org/10.21203/rs.3.rs-8877832/v1. This version presents the complete neuro-symbolic framework, EduRiskX, integrating temporal Transformers with F-Logic reasoning
DOI：10.21203/rs.3.rs-8877832/v1
