Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (8): 1742-1763.DOI: 10.3778/j.issn.1673-9418.2111054

• Surveys and Frontiers • Previous Articles     Next Articles

Review of Knowledge Tracing Model for Intelligent Education

ZENG Fanzhi, XU Luqian(), ZHOU Yan, ZHOU Yuexia, LIAO Junwei   

  1. Department of Computer Science, Foshan University, Foshan, Guangdong 528000, China
  • Received:2021-11-10 Revised:2022-03-24 Online:2022-08-01 Published:2022-08-19
  • About author:ZENG Fanzhi, born in 1965, Ph.D., professor, M.S. supervisor, member of CCF. His research interests include computer vision, image processing and data mining.
    XU Luqian, born in 1995, M.S. candidate. Her research interests include computer vision and data mining.
    ZHOU Yan, born in 1979, M.S., professor, M.S. supervisor, member of CCF. Her research interests include computer vision, compressive sensing and 3D model retrieval.
    ZHOU Yuexia, born in 1978, M.S., lecturer. Her research interests include information acquisition and processing.
    LIAO Junwei, born in 1996, M.S. candidate. His research interests include computer vision, scene text detection and recognition.
  • Supported by:
    the National Natural Science Foundation of China(61972091);the Natural Science Foundation of Guangdong Province(2022A1515010101);the Natural Science Foundation of Guangdong Province(2021A1515012639);the Key Research Project of University of Guangdong Province(2019KZDXM007);the Key Research Project of University of Guangdong Province(2020ZDZX3049);the Science and Technology Innovation Project of Foshan(2020001003285)


曾凡智, 许露倩(), 周燕, 周月霞, 廖俊玮   

  1. 佛山科学技术学院 计算机系,广东 佛山 528000
  • 通讯作者: +E-mail:
  • 作者简介:曾凡智(1965—),男,湖北洪湖人,博士,教授,硕士生导师,CCF会员,主要研究方向为计算机视觉、图像处理、数据挖掘。
  • 基金资助:


As one of the key research directions in the field of intelligent education, knowledge tracing (KT) makes use of a large amount of learning trajectory information provided by the intelligent tutoring system (ITS) to model students, measure their knowledge level automatically, and provide personalized learning programs for them, to achieve the purpose of AI-assisted education. The research progress of knowledge tracing models for intelligent education is reviewed comprehensively. Three representative models are knowledge tracing based on Bayes, knowledge tracing based on Logistic regression model, and deep learning knowledge tracing which has developed rapidly in recent years and shows better performance. Knowledge tracing based on Bayes is divided into Bayesian knowledge tracing (BKT) and BKT model combining personalization, knowledge correlation, node state and real problem expansion. Knowledge tracing based on Logistic regression model is divided into item response theory (IRT) and factor analysis model. Knowledge tracing based on deep learning can be divided into deep knowledge tracing (DTK) and its improved model, designing network structure and introducing attention mechanism. The international open education datasets available to researchers and the commonly used model evaluation indicators are introduced. The performance, characteristics and application scenarios of different types of methods are compared and analyzed. It also discusses the existing problems of the current research and looks forward to future develop-ment direction.

Key words: knowledge tracing (KT), intelligent education, Bayesian network, Logistic regression, deep learning



关键词: 知识追踪(KT), 智慧教育, 贝叶斯网络, Logistic模型, 深度学习

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