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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 6

Auxiliary task enhanced knowledge tracing method

Wang Changqing1,2
Zhang Lei2
Zhang Zhenguo2
1. College of Computer Science & Technology, Harbin Engineering University, Harbin Heilongjiang 150001, China
2. Dept. of Computer Science & Technology, Yanbian University, Yanji Jilin 133002, China
DOI: 10.19734/j.issn.1001-3695.2024.10.0448

Abstract

Knowledge tracing is an effective way to achieve smart education, as it assists students in timely adjusting their learning strategies by assessing their learning status. The existing knowledge tracing methods still have shortcomings in dealing with the internal relationship between exercises and knowledge concepts and obtaining students' personalized knowledge level. To address this issue, this paper proposes an Auxiliary Task-Enhanced Knowledge Tracing (ATEKT) method. Firstly, this paper uses the group calculation method and frequency calculation method to calculate the difficulty of the exercise, the discrimination of the exercise, and the number of answers to the relevant knowledge point exercises, and obtains their embedded representation. Second, two auxiliary tasks, exercise tagging prediction tasks and prior knowledge prediction tasks, are presented to dynamically assess the relationships between exercises and knowledge points, while estimating students' knowledge levels from historical response records. Finally, the auxiliary tasks learn the characteristics of the optimization process used to enhance knowledge tracking model predicts. Compared with seven knowledge tracking models on three public datasets such as ASSIST2009, ASSIST2017 and EdNet, the AUC and ACC of the proposed method are increased by 2.4%to 15.5%and 2.0%to 9.9%respectively. The experimental results show that the proposed method can effectively extract information related to knowledge tracking tasks. And its performance is more superior. Meanwhile, ablation experiments also demonstrate the effectiveness of different auxiliary tasks.

Foundation Support

吉林省教育科学"十四五"规划2023年度一般课题"智慧教育中知识追踪模型构建及对教学的反馈研究"(GH23513)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.10.0448
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 6

Publish History

[2025-03-10] Accepted Paper

Cite This Article

王长青, 张蕾, 张振国. 辅助任务增强的知识追踪方法 [J]. 计算机应用研究, 2025, 42 (6). (2025-03-10). https://doi.org/10.19734/j.issn.1001-3695.2024.10.0448. (Wang Changqing, Zhang Lei, Zhang Zhenguo. Auxiliary task enhanced knowledge tracing method [J]. Application Research of Computers, 2025, 42 (6). (2025-03-10). https://doi.org/10.19734/j.issn.1001-3695.2024.10.0448. )

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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