Contrastive Cross-Course Knowledge Tracing via Concept Graph Guided Knowledge Transfer

• URL: http://arxiv.org/abs/2505.13489v1
• Date: Wed, 14 May 2025 10:38:30 GMT
• Title: Contrastive Cross-Course Knowledge Tracing via Concept Graph Guided Knowledge Transfer
• Authors: Wenkang Han, Wang Lin, Liya Hu, Zhenlong Dai, Yiyun Zhou, Mengze Li, Zemin Liu, Chang Yao, Jingyuan Chen,
• Abstract summary: We propose TransKT, a contrastive cross-course knowledge tracing method.<n>It builds on concept graph guided knowledge transfer to model the relationships between learning behaviors across different courses.<n>TransKT employs a contrastive objective that aligns single-course and cross-course knowledge states.
• Score: 12.34590941832835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge tracing (KT) aims to predict learners' future performance based on historical learning interactions. However, existing KT models predominantly focus on data from a single course, limiting their ability to capture a comprehensive understanding of learners' knowledge states. In this paper, we propose TransKT, a contrastive cross-course knowledge tracing method that leverages concept graph guided knowledge transfer to model the relationships between learning behaviors across different courses, thereby enhancing knowledge state estimation. Specifically, TransKT constructs a cross-course concept graph by leveraging zero-shot Large Language Model (LLM) prompts to establish implicit links between related concepts across different courses. This graph serves as the foundation for knowledge transfer, enabling the model to integrate and enhance the semantic features of learners' interactions across courses. Furthermore, TransKT includes an LLM-to-LM pipeline for incorporating summarized semantic features, which significantly improves the performance of Graph Convolutional Networks (GCNs) used for knowledge transfer. Additionally, TransKT employs a contrastive objective that aligns single-course and cross-course knowledge states, thereby refining the model's ability to provide a more robust and accurate representation of learners' overall knowledge states.

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