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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 11
3272-3280

MDKT: adaptive knowledge tracing model incorporating multidimensional problem difficulty

Li Haojun
Zhong Youchun
College of Education Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China
DOI: 10.19734/j.issn.1001-3695.2024.03.0080

Abstract

Knowledge tracing aims to assess learners' mastery of knowledge, but studies have shown that question difficulty is closely related to mastery status. Models that overlook question difficulty struggle to effectively evaluate learners' actual status. To resolve this issue, this paper developed an MDKT model, incorporating multi-dimensional difficulty. This model employed BERT and CNN to extract semantic difficulty from question texts and integrates question difficulty, conceptual difficulty, and cognitive difficulty to create a multi-dimensional difficulty representation. It constructed an adaptive learning module to capture the interaction between learners and increased exercise difficulty personally. In predicting learners' future performance, the model used the Transformer's multi-head attention mechanism to focus on the importance of different prediction states. Experimentally, on two real datasets, the MDKT model improved performance by 3.99%~12.06% in AUC and 3.63%~11.15% in ACC, outperforming seven other knowledge tracing models. The results demonstrate the superior performance of the model. Furthermore, integrating this model with a knowledge point network graph accurately identifies learners' weak knowledge points, and it confirms the model's feasibility in actual teaching.

Foundation Support

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

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.03.0080
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 11
Section: Algorithm Research & Explore
Pages: 3272-3280
Serial Number: 1001-3695(2024)11-010-3272-09

Publish History

[2024-07-30] Accepted Paper
[2024-11-05] Printed Article

Cite This Article

李浩君, 钟友春. MDKT：融入多维问题难度的自适应知识追踪模型 [J]. 计算机应用研究, 2024, 41 (11): 3272-3280. (Li Haojun, Zhong Youchun. MDKT: adaptive knowledge tracing model incorporating multidimensional problem difficulty [J]. Application Research of Computers, 2024, 41 (11): 3272-3280. )

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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