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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 3

Multiple relations and time enhanced knowledge tracing model

Zhang Wei
Luo Peihua
Gong Zhongwei
Li Zhixin
Song Lingling
Faculty of Artificial Intelligence Education, Central China Normal University, Wuhan Hubei 430079, China
DOI: 10.19734/j.issn.1001-3695.2024.07.0301

Abstract

Existing knowledge tracing methods fail to effectively explore and utilize the multiple relations between concepts and simultaneously consider the effects of interactions between concepts as well as time on the knowledge state. This paper improves the knowledge tracing model in terms of multiple relations between concepts and learning-forgetting patterns, and proposes a Multiple Relations and Time Enhanced Knowledge Tracing Model (MRTKT) . Firstly, the relations between concepts are enriched according to assimilation theory, and a knowledge structure containing three relationships of superordinate learning, subordinate learning, and combinatorial learning is constructed using a statistical methodology. Secondly, modelling the interaction between concepts enables the aggregation of node features based on the above three relationships. This enables the model to better simulate influence propagation among concepts. Then, knowledge states are updated using a gate mechanism incorporating three temporal factors in order to simulate the learning-forgetting effect. This ensures that each node feature contains both interactions between concepts and time information, providing more comprehensive and rich information for predicting learners' responses. Experiments are conducted on three real-world datasets, and the results show that MRTKT has superior performance and better interpretability than existing models.

Foundation Support

国家自然科学基金面上项目(62377024)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.07.0301
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 3

Publish History

[2024-12-11] Accepted Paper

Cite This Article

张维, 罗佩华, 龚中伟, 等. 多关系和时间增强的知识追踪模型 [J]. 计算机应用研究, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.07.0301. (Zhang Wei, Luo Peihua, Gong Zhongwei, et al. Multiple relations and time enhanced knowledge tracing model [J]. Application Research of Computers, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.07.0301. )

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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