ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2022, Vol. 59 ›› Issue (1): 81-104.doi: 10.7544/issn1000-1239.20200848

• 人工智能 • 上一篇    下一篇

基于深度学习的知识追踪研究进展

刘铁园1,2,陈威1,常亮1,古天龙1,3   

  1. 1(广西可信软件重点实验室(桂林电子科技大学) 广西桂林 541004);2(桂林电子科技大学电子工程与自动化学院 广西桂林 541004);3(暨南大学信息科学与技术学院网络安全学院 广州 510632) (yangmail2002@guet.edu.cn)
  • 出版日期: 2022-01-01
  • 基金资助: 
    国家自然科学基金项目(U1811264,61966009);广西可信软件重点实验室研究课题(KX202058);广西研究生教育创新计划项目(YCBZ2021072) Guangxi Key Laboratory of Trusted Software (KX202058), and the Innovation Project of Guangxi Graduate Education (YCBZ2021072).

Research Advances in the Knowledge Tracing Based on Deep Learning

Liu Tieyuan1,2, Chen Wei1, Chang Liang1, Gu Tianlong1,3   

  1. 1(Guangxi Key Laboratory of Trusted Software (Guilin University of Electronic Technology), Guilin, Guangxi 541004);2(School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, Guangxi 541004);3(School of Information Science and TechnologySchool of Cyber Security, Jinan University, Guangzhou 510632)
  • Online: 2022-01-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (U1811264, 61966009), the Project of

摘要: 知识追踪是教育数据挖掘领域的一个重要研究方向,其目标是通过建立学生知识状态随时间变化的模型,来判断学生对知识的掌握程度并从学生的学习轨迹中挖掘出潜在的学习规律,从而提供个性化的指导,达到人工智能辅助教育的目的.深度学习因其强大的特征提取能力,已被证明能显著提升知识追踪模型的性能而越来越受到各方重视.以最基本的深度知识追踪模型为起点,全面回顾了该研究领域的研究进展,给出了该研究领域技术改进、演化脉络图,并从针对可解释问题的改进、针对长期依赖问题的改进、针对缺少学习特征问题的改进3个主要技术改进方向做了深入阐述和比较分析,同时对该领域中的已有模型做了归类,整理了可供研究者使用的公开数据集,考察了其主要应用,最后,对基于深度学习的知识追踪的未来研究方向进行了展望.

关键词: 教育数据挖掘, 深度学习, 知识追踪, 循环神经网络, 人工智能辅助教育

Abstract: Knowledge tracing is an important research direction in the field of educational data mining. The goal is to determine the degree of students mastery of knowledge by establishing a model of students knowledge changes over time and to mine potential learning rules from their learning trajectories. Fulfilling this goal means personalized guidance to students from the achievement of assisted education through artificial intelligence. Due to its powerful feature extraction capabilities, deep learning has been proven to significantly improve the performance of knowledge tracing models and has attracted more and more attention. Starting from the most basic deep knowledge tracing model, this paper comprehensively reviews the research progress in this field and provides both the technical improvement and an evolutionary map. The 3 main technical improvement directions have been elaborated and compared: 1) improvement of interpretable problems, 2) problems of long-term dependence, and 3) improvement for lack of learning features. At the same time, the existing models in the field have been classified, the public data sets have been sorted out, and the main areas of application are investigated for researchers. Finally, the future research direction of knowledge tracing based on deep learning is explored.

Key words: education data mining, deep learning, knowledge tracing, recurrent neural network, artificial intelligence assisted education

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