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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 11

Dynamic knowledge tracking driven by multi-view comparison learning

Xie Wanzhen
Liu Wei
Hu Diwei
Cui Zihan
Zhao Yubo
School of Computer Science & Engineering, Wuhan Institute of Technology, Wuhan 430205, China
DOI: 10.19734/j.issn.1001-3695.2025.05.0118

Abstract

Knowledge tracing aims to dynamically assess students’ mastery of knowledge. Factors such as changes in learners’ knowledge structures and cognitive interactions influence this process. Existing methods suffer from two limitations. At the static level, these approaches simplify heterogeneous interactions among students, questions, and knowledge points into homogeneous treatment. This simplification hinders the capture of high-order dependencies. At the dynamic level, current techniques reduce continuously evolving knowledge states to discrete binary jumps. This reduction disconnects the model from real learning feedback. To address these issues, this paper proposed a model based on multi-view contrastive learning for knowledge tracing (MCLKT). First, it jointly modelled heterogeneous and homogeneous graphs to capture multidimensional interactions among students, questions, and knowledge points. Second, it employed designed bipartite graphs and hypergraphs to extract question weights and student proficiencies. This design explicitly captured high-order dependencies in answer behaviors. Finally, it defined a Q-Pattern subgraph to model local subgraphs and their interactions and applied a cross-view contrastive loss to maximize structural consistency. The model underwent evaluation on three public datasets, demonstrating AUC improvements of 0.69%~1.58% and ACC gains of 0.21%~1.78%, alongside an RMSE reduction of 1.17%~1.75%. These results demonstrate the effectiveness of multi-view contrastive learning in enhancing knowledge tracing performance.

Foundation Support

国家自然科学基金面上项目(52371373)
湖北省高等学校优秀中青年科技创新团队计划项目(T2023009)
武汉工程大学第十六届研究生教育创新基金资助项目(CX2024153)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.05.0118
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 11

Publish History

[2025-07-18] Accepted Paper

Cite This Article

谢宛真, 刘玮, 胡棣威, 等. 多视图对比学习驱动的动态知识追踪 [J]. 计算机应用研究, 2025, 42 (11). (2025-07-24). https://doi.org/10.19734/j.issn.1001-3695.2025.05.0118. (Xie Wanzhen, Liu Wei, Hu Diwei, et al. Dynamic knowledge tracking driven by multi-view comparison learning [J]. Application Research of Computers, 2025, 42 (11). (2025-07-24). https://doi.org/10.19734/j.issn.1001-3695.2025.05.0118. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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