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Knowledge Graph-Enhanced Interleaved Multi-Head Attention Knowledge Tracing

Zibo Guo et al.

International Journal of Data Warehousing and Mining2025https://doi.org/10.4018/ijdwm.377619article
ABDC C
Weight
0.50

What the paper says

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.

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https://doi.org/https://doi.org/10.4018/ijdwm.377619

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@article{zibo2025,
  title        = {{Knowledge Graph-Enhanced Interleaved Multi-Head Attention Knowledge Tracing}},
  author       = {Zibo Guo et al.},
  journal      = {International Journal of Data Warehousing and Mining},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.4018/ijdwm.377619},
}

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Knowledge Graph-Enhanced Interleaved Multi-Head Attention Knowledge Tracing

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.