Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 3
732-738

Deep knowledge tracking optimization model based on self-attention mechanism and bidirectional GRU neural network

Li Haojun1
Fang Xuan1
Dai Hairong2
1. College of Education Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China
2. College of Business Administration, Zhejiang Finance College, Hangzhou 310018, China
DOI: 10.19734/j.issn.1001-3695.2021.08.0345

Abstract

This paper proposed an optimization model of deep-knowledge tracking(KTSA-BiGRU) based on self-attention mechanism and bidirectional GRU neural networks owing to the existing deep-knowledge tracking models with weak capture of complex relationships between input exercises and inability to effectively handle long-sequence input data. Firstly, it mapped the learner's historical learning interaction sequence data to the real value vector sequence. Then, it trained the bidirectional GRU neural network as input to model the learner's learning process, and finally used, the self-attention mechanism to calculate the probability of the learner correctly answering the next question based on the hidden vectors of the bidirectional GRU neural network output and the attention weight. The performance analysis on the three public datasets can improve the prediction accuracy of deep knowledge tracking.

Foundation Support

浙江省哲学社会科学规划交叉学科重点支持课题(22JCXK05Z)
国家自然科学基金资助项目(62077043)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.08.0345
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 3
Section: Algorithm Research & Explore
Pages: 732-738
Serial Number: 1001-3695(2022)03-015-0732-07

Publish History

[2021-11-16] Accepted Paper
[2022-03-05] Printed Article

Cite This Article

李浩君, 方璇, 戴海容. 基于自注意力机制和双向GRU神经网络的深度知识追踪优化模型 [J]. 计算机应用研究, 2022, 39 (3): 732-738. (Li Haojun, Fang Xuan, Dai Hairong. Deep knowledge tracking optimization model based on self-attention mechanism and bidirectional GRU neural network [J]. Application Research of Computers, 2022, 39 (3): 732-738. )

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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