计算机科学 ›› 2021, Vol. 48 ›› Issue (4): 213-222.doi: 10.11896/jsjkx.200600044

• 人工智能 • 上一篇    下一篇

学习者知识追踪研究进展综述

张暖1, 江波2   

  1. 1 浙江工业大学教育科学与技术学院 杭州310023
    2 华东师范大学教育信息与技术学系(上海数字化教育装备工程技术研究中心) 上海200062
  • 收稿日期:2020-06-24 修回日期:2020-10-09 出版日期:2021-04-15 发布日期:2021-04-09
  • 通讯作者: 江波(bjiang@deit.ecnu.edu.cn)
  • 基金资助:
    国家自然科学基金(61977058); 上海市“科技创新行动计划”人工智能科技支撑专项(20511101600);中央高校基本科研业务费项目华东师范大学引进人才启动费项目(41300-20101-222696)

Review Progress of Learner Knowledge Tracing

ZHANG Nuan1, JIANG Bo2   

  1. 1 College of Educational Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
    2 Department of Educational Information and Technology(Shanghai Engineering Research Center of Digital Education Equipment),East China Normal University,Shanghai 200062,China
  • Received:2020-06-24 Revised:2020-10-09 Online:2021-04-15 Published:2021-04-09
  • About author:ZHANG Nuan,born in 1995,postgra-duate.Her main research interests include big data of education,personalized learning and knowledge tracing.(353211550@qq.com)
    JIANG Bo,born in 1985,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include educational data mining,learning analytics and machine lear-ning.
  • Supported by:
    National Natural Science Foundation of China(61977058),“Science and Technology Innovation Action Plan” Artificial Intelligence Technology Support Special Project of Shanghai(20511101600) and Fundamental Research Funds for the Central Universities of East China Normal University(41300-20101-222696).

摘要: 学习者建模是自适应学习系统的支撑技术之一,其中以知识追踪为代表的学习者知识状态建模研究最为广泛。3种代表性的知识追踪技术分别为基于隐马尔可夫模型的贝叶斯知识追踪、基于逻辑回归模型的可加性因素模型、基于循环神经网络的深度知识追踪。通过综述发现,贝叶斯知识追踪模型适用于含单一知识点的学习任务的知识追踪,可加性因素模型和深度知识追踪模型适用于含多知识点的学习任务的知识追踪,但深度知识追踪模型的教学可解释性不佳。在综述现有研究的基础上,受到知识空间理论的启发,将知识点之间的先决关系融入到知识追踪模型是未来的一个重要研究方向,并初步提出了一种融合知识点先决关系的可加因素模型。

关键词: 贝叶斯知识追踪, 可加性因素模型, 深度知识追踪, 知识空间理论, 知识追踪, 自适应学习系统

Abstract: Learner modeling is one of the supporting techniques of adaptive learning systems,among which learner knowledge state modeling represented by knowledge tracing is the most widely studied.Three representative knowledge tracing techniques are Bayesian Knowledge Tracing(BKT) based on hidden Markov model,Additive Factor Model(AFM) based on logistic regression model and Deep Knowledge Tracing(DKT) based on recurrent neural network.It is found that the BKT is suitable for know-ledge tracing in learning tasks that only contain single knowledge skill,AFM and DKT can be used for tracing students’ know-ledge state in learning tasks that have more than one knowledge skills.However,the DKT is hard to be interpreted from the perspective of pedagogy.Based on reviewing the existing research and inspired by the knowledge space theory,this paper argues that the integration of the prerequisite relationship among knowledge skills and the knowledge tracing is a promising research direction.Finally,this paper proposes a protype of additive factor model integrating knowledge prerequisite relationship.

Key words: Adaptive learning system, Additive factor model, Bayesian knowledge tracing, Deep knowledge tracing, Knowledge space theory, Knowledge tracing

中图分类号: 

  • TP391.6
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