mlsanityData to 14 Oct 2026

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

arXiv 2505.13489 ↗First posted 14 May 2025cs.AI cs.CLcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 30 of its 44 references that are in the map. First posted on arXiv 14 May 2025.

Builds on

30 of 44 references.

References · 30

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18 more
  1. 2018Addressing Two Problems in Deep Knowledge Tracing via Prediction-Consistent Regularizationcited by 55
  2. 2021TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Searchcited by 54
  3. 2019Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theorycited by 51
  4. 2020GIKT: A Graph-based Interaction Model for Knowledge Tracingcited by 51
  5. 2023simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracingcited by 47
  6. 2020Improving Knowledge Tracing via Pre-training Question Embeddingscited by 36
  7. 2023Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representationscited by 32
  8. 2023Contrastive Cross-Domain Sequential Recommendationcited by 31
  9. 2023Enhancing Deep Knowledge Tracing with Auxiliary Taskscited by 24
  10. 2024Towards Robust Knowledge Tracing Models via k-Sparse Attentioncited by 19
  11. 2024MPCODER: Multi-user Personalized Code Generator with Explicit and Implicit Style Representation Learningcited by 12
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  16. 2024Semantic Codebook Learning for Dynamic Recommendation Modelscited by 6
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  18. 2025DKT2: Revisiting Applicable and Comprehensive Knowledge Tracing in Large-Scale Datacited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 3.

7 papers.

  1. 2025Diffusion-Based Image Editing: An Unforeseen Adversary to Robust Invisible Watermarkscited by 3
  2. 2026Editing on the Generative Manifold: A Theoretical and Empirical Study of General Diffusion-Based Image Editing Trade-offscited by 1
  3. 2025FADE: Adversarial Concept Erasure in Flow Modelscited by 0
  4. 2025Robust Concept Erasure in Diffusion Models: A Theoretical Perspective on Security and Robustnesscited by 0
  5. 2025Cognitive-Level Adaptive Generation via Capability-Aware Retrieval and Style Adaptationcited by 0
  6. 2026When Denoising Becomes Unsigning: Theoretical and Empirical Analysis of Watermark Fragility Under Diffusion-Based Image Editingcited by 0
  7. 2026Editing Away the Evidence: Diffusion-Based Image Manipulation and the Failure Modes of Robust Watermarkingcited by 0