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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 1
86-92

Knowledge tracing via reinforcement of concept representation

Zhang Kai
Zhang Huiling
Wang Zechen
Wang Xue
Fang Yangyang
School of Computer Science, Yangtze University, Jingzhou Hubei 434000, China
DOI: 10.19734/j.issn.1001-3695.2024.06.0196

Abstract

Knowledge tracing models mainly use supervised learning paradigm to model the probability distribution of answers given the question information, which cannot adjust the model immediately based on new question information, ultimately affecting the prediction performance. To address this issue, this paper proposed a knowledge tracing model with enhanced knowledge representation by integrating reinforcement learning paradigm, which mainly consisted of three parts: a basic network, a value network, and a policy network. The basic network modeled the representation of questions and knowledge points, the value network calculated the value of questions and the temporal difference error, and the policy network optimized the prediction results. Experiments conducted with five baseline models on three datasets demonstrate that the proposed model excels in terms of AUC and ACC, especially on the ASSISTments2009 dataset, where AUC is improved by 6.83%~14.34% and ACC by 11.39%~19.74%. Furthermore, the quality of model representation is improved by 2.59% compared to baseline models, and ablation experiments confirm the effectiveness of the reinforcement learning framework. Finally, applying the proposed model to learning behavior data from three real courses shows its practical usability, as evidenced by its performance compared to baseline models.

Foundation Support

国家自然科学基金资助项目(62077018)
湖北省自然科学基金资助项目(2022CFB132)
湖北本科高校省级教学改革研究项目(2023273)
长江大学2023年研究生教育教学改革研究立项项目(YJY202341)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.06.0196
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 1
Section: Algorithm Research & Explore
Pages: 86-92
Serial Number: 1001-3695(2025)01-012-0086-07

Publish History

[2025-01-05] Printed Article

Cite This Article

张凯, 张慧玲, 王泽琛, 等. 知识点表征强化的知识追踪模型 [J]. 计算机应用研究, 2025, 42 (1): 86-92. (Zhang Kai, Zhang Huiling, Wang Zechen, et al. Knowledge tracing via reinforcement of concept representation [J]. Application Research of Computers, 2025, 42 (1): 86-92. )

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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