App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 9

Structure-aware and cognition-enhanced modeling for knowledge tracing

Rui Jiawen
Xie Peizhong
School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
DOI: 10.19734/j.issn.1001-3695.2026.02.0013

Abstract

Most existing knowledge tracing methods have limitations in feature modeling. They often rely only on fixed sequences or static graph structures, which makes it difficult to characterize the dynamic relationships between students and problems over time. In addition, they frequently overlook the effects of temporal and behavioral factors on knowledge states. To address these issues, this paper proposed a Structure-Aware and Cognition-Enhanced Knowledge Tracing model (SACEKT) , which comprehensively considered the dynamic relationships between students and problems, response time, and behavioral features, as well as learning and forgetting mechanisms, to model the dynamic evolution of students’ knowledge states from both structural and cognitive perspectives. Specifically, SACEKT introduced a dynamic graph neural network to capture the temporal evolution information of student-problem nodes. Also, this model designed a multi-feature encoding module that distinguishes between short- and long-term time intervals and attempt behaviors. Furthermore, it proposed a Cognition-Aware Gated Recurrent Unit (CA-GRU) to explicitly model the influence of learning progress and forgetting effects on students’ knowledge states. Experiments on three public datasets show that SACEKT outperforms existing models in prediction accuracy and generalization capability.

Foundation Support

国家自然科学基金资助项目(62277032)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.02.0013
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 9

Publish History

[2026-05-21] Accepted Paper

Cite This Article

芮佳雯, 解培中. 结构感知与认知增强的知识追踪模型 [J]. 计算机应用研究, 2026, 43 (9). (2026-06-02). https://doi.org/10.19734/j.issn.1001-3695.2026.02.0013. (Rui Jiawen, Xie Peizhong. Structure-aware and cognition-enhanced modeling for knowledge tracing [J]. Application Research of Computers, 2026, 43 (9). (2026-06-02). https://doi.org/10.19734/j.issn.1001-3695.2026.02.0013. )

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.

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