App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 11

Knowledge tracing model integrating learning ability and memory ability

Zhang Wei
Xu Mingli
Song Lingling
Zhang Deng
Huang Kangjie
Faculty of Artificial Intelligence Education, Central China Normal University, Wuhan 430079, China
DOI: 10.19734/j.issn.1001-3695.2026.03.0058

Abstract

Existing deep knowledge tracing models have not thoroughly explored individual differences in learners’ learning and memory abilities, making it difficult to accurately characterize the mechanisms of knowledge acquisition and forgetting in real learning processes. To address this issue, this study proposed a knowledge tracing model integrating learning ability and memory ability (LMKT) . First, LMKT dynamically characterized learning ability through a gated linear unit by leveraging three behavioral features, including response time, number of attempts, and number of hint usages; second, LMKT constructed a parameterized memory decay mechanism based on forgetting curve theory and characterized memory ability through the time interval between successive responses, thereby enhancing model interpretability; finally, LMKT employed a neural ordinary differential equation to model the continuous-time evolution of knowledge states, enabling more effective handling of learning sequences with irregular time intervals. Experiments on two real-world datasets showed that LMKT achieved higher AUC and ACC values than several mainstream knowledge tracing models in learner performance prediction tasks. The results indicate that integrating learning and memory abilities improves both the accuracy and interpretability of knowledge tracing models.

Foundation Support

国家自然科学基金面上项目(62377024)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.03.0058
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 11

Publish History

[2026-07-30] Accepted Paper

Cite This Article

张维, 徐明礼, 宋玲玲, 等. 融合学习能力与记忆能力的知识追踪模型 [J]. 计算机应用研究, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.03.0058. (Zhang Wei, Xu Mingli, Song Lingling, et al. Knowledge tracing model integrating learning ability and memory ability [J]. Application Research of Computers, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.03.0058. )

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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