Knowledge Graph-Enhanced Interleaved Multi-Head Attention Knowledge Tracing

Zehan Guo, Honghai Guan, Chungang He, Ye Xu and Rui Liu
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Zehan Guo: Mudanjiang Normal University, China
Honghai Guan: Mudanjiang Normal University, China
Chungang He: Mudanjiang Normal University, China
Ye Xu: Heilongjiang Preschool Education College, China
Rui Liu: Heilongjiang Preschool Education College, China

International Journal of Data Warehousing and Mining (IJDWM), 2025, vol. 21, issue 1, 1-20

Abstract: The rise of online education demands improved learning assessment and personalization. Current knowledge tracing methods struggle with feature extraction, limited information interaction within learning data, and insufficient utilization of structured relationships between knowledge points. To address these challenges, this article proposes a knowledge graph-enhanced interleaved multi-head attention knowledge tracing model. The model integrates bidirectional long short-term memory networks, an interleaved multi-head attention mechanism, and graph convolutional networks into a deep learning framework. The interleaved multi-head attention mechanism enhances the model's ability to capture long-distance dependencies, while the knowledge graph encoding module utilizes graph convolutional networks to mine structured relationships between knowledge points. This architecture considers both the dynamic learning process and integrates structured information from the knowledge system. Experiments on multiple public datasets validate the model's effectiveness.

Date: 2025
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