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App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 11

Exercise embeddings and learning-forgetting features boosted knowledge tracing

Zhang Wei
Li Zhixin
Gong Zhongwei
Luo Peihua
Song Lingling
Faculty of Artificial Intelligence Education, Central China Normal University, Wuhan Hubei 430079, China
DOI: 10.19734/j.issn.1001-3695.2024.04.0093

Abstract

Most existing knowledge tracing (KT) models evaluate students' future performance centered on concepts, overlooking the differences between exercises containing the same concepts, thus affecting the models' prediction accuracy. Moreover, in constructing the students' knowledge state, existing models fail to fully utilize the learning-forgetting features of students during the answering process, leading to an inaccurate modeling of students' knowledge states. To address these issues, This paper proposed an exercise embeddings and learning-forgetting features boosted knowledge tracing model. The model utilized the explicit relationships in the exercise-concept bipartite graph to calculate the implicit relationships within the graph, constructing an exercise-concept relationship heterogeneous graph. To make full use of the rich relationship information in the heterogeneous graph, the ELFBKT model introduced a relational graph convolutional network (RGCN) . Through the processing of RGCN, the model enhanced the quality of exercise embeddings and predicted students' future performance more accurately with an exercise-centric approach. Furthermore, the ELFBKT model fully utilized various learning-forgetting features to construct two gating-controlled mechanisms, modeling the students' learning and forgetting behaviors respectively, to more accurately model the students' knowledge states. Experiments on two real-world datasets show that the ELFBKT model outperforms other models in KT tasks.

Foundation Support

国家自然科学基金资助项目(62377024)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.04.0093
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 11

Publish History

[2024-08-02] Accepted Paper

Cite This Article

张维, 李志新, 龚中伟, 等. 练习嵌入和学习遗忘特征增强的知识追踪模型 [J]. 计算机应用研究, 2024, 41 (11). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.04.0093. (Zhang Wei, Li Zhixin, Gong Zhongwei, et al. Exercise embeddings and learning-forgetting features boosted knowledge tracing [J]. Application Research of Computers, 2024, 41 (11). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.04.0093. )

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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