用多模态数据预测高数学习风险，提前预警并干预。
Advanced Mathematics Learning Behavior Prediction and Academic Early Warning Model Based on Multimodal Data Analysis
- 构建分层知识图谱，动态调整题目难度权重。
- 准确识别高危学生，追踪错误传播路径。
- 适合高数教学预警系统与个性化辅导者使用。
在高等数学教育中，复杂概念层级与非线性学习轨迹导致早期发现风险学生并及时干预成为难题。本研究采用多模态数据分析，构建动态学习行为预测与学业预警框架。通过建立分层知识图谱本体，根据题目难度与学生表现实现自适应边权重调整，并结合异构图注意力与时间序列建模，捕捉学生知识状态的演化过程。基于一学期多模态数据集的实证测试表明，该方法能精准识别高风险学生，有效追踪错误传播路径。针对性干预显著提升学生知识掌握度，降低学业风险。结果验证了将知识图谱分析与多模态时序建模融合，可为高等数学教育提供更高效、个性化的学习支持。
原文摘要 · Abstract (English)
Early detection of at-risk students and timely academic intervention pose major challenges in advanced mathematics education, where complex conceptual hierarchies and nonlinear learning trajectories often hold back students' academic performance. This study adopts multimodal data analytics to build a dynamic framework for learning behavior prediction and academic early warning. It constructs a hierarchical knowledge graph ontology, realizes adaptive edge weighting according to problem difficulty and student performance, and combines heterogeneous graph attention with temporal sequence modeling to capture students' evolving knowledge states. Empirical tests on semester-long multimodal datasets prove that this method can accurately identify high-risk students and effectively track error propagation. Targeted interventions greatly improve students' knowledge mastery and reduce academic risks. The results verify that integrating knowledge graph analytics with multimodal temporal modeling can deliver more efficient and personalized learning support for advanced mathematics education.
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