Efficient Knowledge Tracing Leveraging Higher-Order Information in Integrated Graphs

• URL: http://arxiv.org/abs/2507.18668v1
• Date: Thu, 24 Jul 2025 06:12:43 GMT
• Title: Efficient Knowledge Tracing Leveraging Higher-Order Information in Integrated Graphs
• Authors: Donghee Han, Daehee Kim, Minjun Lee, Daeyoung Roh, Keejun Han, Mun Yong Yi,
• Abstract summary: We introduce Dual Graph Attention-based Knowledge Tracing (DGAKT)<n>It is a graph neural network model designed to leverage high-order information from subgraphs representing student-exercise-KC relationships.<n>It significantly reduces memory and computational requirements compared to full global graph models.
• Score: 2.4134741591214808
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The rise of online learning has led to the development of various knowledge tracing (KT) methods. However, existing methods have overlooked the problem of increasing computational cost when utilizing large graphs and long learning sequences. To address this issue, we introduce Dual Graph Attention-based Knowledge Tracing (DGAKT), a graph neural network model designed to leverage high-order information from subgraphs representing student-exercise-KC relationships. DGAKT incorporates a subgraph-based approach to enhance computational efficiency. By processing only relevant subgraphs for each target interaction, DGAKT significantly reduces memory and computational requirements compared to full global graph models. Extensive experimental results demonstrate that DGAKT not only outperforms existing KT models but also sets a new standard in resource efficiency, addressing a critical need that has been largely overlooked by prior KT approaches.

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