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System Development & Application
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 5
1496-1500

TCN-KT：temporal convolutional knowledge tracking model based on fusion of personal basis and forgetting

Wang Can1a
Liu Zhaohui1a
Wang Bei1b
Zhao Zhongyuan1a
Tang Kun2
1. a. School of Computer Science, b. School of Language & Literature, University of South China, Hengyang Hunan 421001, China
2. Teachers College for Vocational & Technical Education, Guangxi Normal University, Guilin Guangxi 541004, China
DOI: 10.19734/j.issn.1001-3695.2021.10.0466

Abstract

KT is a popular area of wisdom education and is a typical sequence modeling task. Its main focus and solutions are focused on RNN. However, RNN's training time and equipment requirements are too strict, which usually leads to problems such as gradient disappearance or gradient explosion. In response to the above problems, this paper proposed temporal convolutional network knowledge tracing model(TCN-KT) that integrated the learner's personal prior basis and forgetting factors. Firstly, the method used the RNN model to calculate the student's personal prior basis. Then, the model used the gradient-stable and lower memory usage TCN to predict the initial probability of the student's next question. Finally, the model got the final result by integrating the forgetting factors based on the student's foundation. Experimental results show that TCN-KT has the best performance and reduces calculation time.

Foundation Support

湖南省教育厅基金资助项目(180SJY044)
2020年湖南省普通高等学校教学改革研究项目(HNJG-2020-0477)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.10.0466
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 5
Section: System Development & Application
Pages: 1496-1500
Serial Number: 1001-3695(2022)05-035-1496-05

Publish History

[2021-12-24] Accepted Paper
[2022-05-05] Printed Article

Cite This Article

王璨, 刘朝晖, 王蓓, 等. TCN-KT：个人基础与遗忘融合的时间卷积知识追踪模型 [J]. 计算机应用研究, 2022, 39 (5): 1496-1500. (Wang Can, Liu Zhaohui, Wang Bei, et al. TCN-KT：temporal convolutional knowledge tracking model based on fusion of personal basis and forgetting [J]. Application Research of Computers, 2022, 39 (5): 1496-1500. )

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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