用子图注意力提升知识追踪效率，降低长序列计算开销。
Efficient Knowledge Tracing Leveraging Higher-Order Information in Integrated Graphs
- 基于子图构建注意力机制，只处理相关局部结构。
- 相比全图模型，内存与计算量显著降低，保持高精度。
- 适合大规模在线学习系统中实时追踪学生能力。
在线学习的兴起催生了多种知识追踪（KT）方法。然而，现有方法在使用大规模图和长学习序列时忽略了计算成本上升的问题。为此，我们提出双图注意力知识追踪（DGAKT），一种图神经网络模型，利用表示学生-习题-知识点（KC）关系的子图中的高阶信息。DGAKT采用基于子图的方法，仅对每个目标交互处理相关子图，显著降低了内存与计算开销，相比全图全局模型表现更优。大量实验表明，DGAKT不仅超越现有KT模型，在资源效率上也树立了新标准，解决了以往方法普遍忽视的关键问题。
原文摘要 · Abstract (English)
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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