Efficient Knowledge Tracing Leveraging Higher-Order Information in Integrated Graphs

TL;DR

This paper introduces DGAKT, a graph neural network for knowledge tracing that efficiently leverages high-order information from subgraphs, reducing computational costs while outperforming existing models.

Contribution

The paper presents a novel subgraph-based GNN model, DGAKT, that improves computational efficiency and predictive performance in knowledge tracing tasks.

Findings

DGAKT outperforms existing KT models in accuracy.

DGAKT significantly reduces memory and computational requirements.

DGAKT sets a new standard in resource-efficient knowledge tracing.

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.