Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 4
1070-1074

F-TCKT: deep temporal convolutional knowledge tracking model with forgetting factors

Zhang Peng1a,1b
Wen Lei1a,1b,2
1. a. School of Software Engineering, b. Laboratory of Intelligent Information Technology & Service Innovation, Chongqing University of Posts & Telecommunications, Chongqing 400065, China
2. Chongqing Institute of Microelectronics Industry Technology, University of Electronic Science & Technology of China, Chongqing 401331, China
DOI: 10.19734/j.issn.1001-3695.2022.08.0448

Abstract

Tracking students' knowledge level is one of the most important techniques in intelligent education. Traditional deep knowledge tracking methods mainly focus on RNN, but RNN has the problem of gradient disappearance or gradient explosion. And many knowledge tracking methods do not take into account the impact of forgetting behavior on the results of learning. Aiming at the above problems, in order to accurately predict the knowledge level of students, this paper proposed a deep temporal convolutional knowledge tracking model, which integrated forgetting factors(F-TCKT). This method introduced three factors that affected students' forgetting behavior, including the time interval of learning the same knowledge point, the time interval of learning and the times of learning the same knowledge point. Firstly, the method used the fully connected network to calculate the vector that represented the degree of forgetting of students, and then concatenated the vector with the answer record of students. Finally, the method used the TCN and attention mechanism to predict the probability of students' next answer. Experimental results show that F-TCKT has better prediction performance than traditional methods.

Foundation Support

国家自然科学基金资助项目(61936001)
重庆市自然科学基金资助项目(cstc2021jcyj-msxmX0849)
重庆市高等教育教学改革研究重大项目(221017)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.08.0448
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 4
Section: Algorithm Research & Explore
Pages: 1070-1074
Serial Number: 1001-3695(2023)04-018-1070-05

Publish History

[2022-12-07] Accepted Paper
[2023-04-05] Printed Article

Cite This Article

张鹏, 文磊. F-TCKT:融合遗忘因素的深度时序卷积知识追踪模型 [J]. 计算机应用研究, 2023, 40 (4): 1070-1074. (Zhang Peng, Wen Lei. F-TCKT: deep temporal convolutional knowledge tracking model with forgetting factors [J]. Application Research of Computers, 2023, 40 (4): 1070-1074. )

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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