计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (8): 1742-1763.DOI: 10.3778/j.issn.1673-9418.2111054

• 综述·探索 • 上一篇    下一篇

面向智慧教育的知识追踪模型研究综述

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
  • 收稿日期:2021-11-10 修回日期:2022-03-24 出版日期:2022-08-01 发布日期:2022-08-19
  • 通讯作者: +E-mail: xuluqian1002@163.com.
  • 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。
    许露倩(1995—),女,河南南阳人,硕士研究生,主要研究方向为计算机视觉、数据挖掘。
    周燕(1979—),女,江西抚州人,硕士,教授,硕士生导师,CCF会员,主要研究方向为计算机视觉、压缩感知、三维模型检索。
    周月霞(1978—),女,湖北监利人,硕士,讲师,主要研究方向为信息采集与处理。
    廖俊玮(1996—),男,广东梅州人,硕士研究生,主要研究方向为计算机视觉、场景文字检测与识别。
  • 基金资助:
    国家自然科学基金(61972091);广东省自然科学基金(2022A1515010101);广东省自然科学基金(2021A1515012639);广东省普通高校重点研究项目(2019KZDXM007);广东省普通高校重点研究项目(2020ZDZX3049);佛山市科技创新项目(2020001003285)

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)

摘要:

知识追踪(KT)作为智慧教育领域的重点研究方向之一,利用智能辅导系统(ITS)提供的大量学习轨迹信息对学生进行建模,自动衡量学生的知识水平,为其提供个性化的学习方案,达到人工智能辅助教育的目的。全面回顾了面向智慧教育的知识追踪模型研究进展,三类具有代表性的模型分别为基于贝叶斯的知识追踪、基于Logistic模型的知识追踪以及近年来迅速发展并且表现出更好性能的深度学习知识追踪。基于贝叶斯的知识追踪分为贝叶斯知识追踪(BKT)以及结合个性化、知识相关性、节点状态与现实问题扩展的BKT模型;基于Logistic模型的知识追踪分为项目反应理论(IRT)与因子分析模型两类;基于深度学习的知识追踪分为深度知识追踪(DKT)及其改进模型以及设计网络结构与引入注意力机制。介绍了目前可供研究者们使用的国际公开教育数据集与常用的模型评估指标,比较和分析了不同类型方法的性能、特点以及应用场景,并对当前研究所存在的问题以及未来发展方向进行探讨与展望。

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

Abstract:

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

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