ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (12): 2523-2546.doi: 10.7544/issn1000-1239.2020.20190767

• 人工智能 • 上一篇    下一篇

教育大数据中认知跟踪模型研究进展

胡学钢,刘菲,卜晨阳   

  1. (大数据知识工程教育部重点实验室(合肥工业大学) 合肥 230601) (合肥工业大学计算机与信息学院 合肥 230601) (合肥工业大学大知识科学研究院 合肥 230601) (jsjxhuxg@hfut.edu.cn)
  • 出版日期: 2020-12-01
  • 基金资助: 
    国家重点研发计划项目(2016YFB1000901);国家自然科学基金项目(61806065);中央高校基本科研业务费专项资金项目(JZ2020HGQA0186)

Research Advances on Knowledge Tracing Models in Educational Big Data

Hu Xuegang, Liu Fei, Bu Chenyang   

  1. (Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), Ministry of Education, Hefei 230601) (School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601) (Research Institute of Big Knowledge, Hefei University of Technology, Hefei 230601)
  • Online: 2020-12-01
  • Supported by: 
    This work was supported by the National Key Research and Development Program of China (2016YFB1000901), the National Natural Science Foundation of China (61806065), and the Fundamental Research Funds for the Central Universities (JZ2020HGQA0186).

摘要: 教育信息化的不断推进和在线教育的蓬勃发展产生了海量的教育数据,如何挖掘和分析教育大数据成为了教育领域和大数据知识工程领域亟待解决的问题.认知跟踪模型通过获取学生作答习题的得分表现,追踪学生随时间变化的认知状态,从而预测学生在未来时间的作答表现.对教育大数据中认知跟踪模型进行了回顾、分析和展望.首先从模型的原理、步骤和方法等维度详细介绍了认知跟踪模型,包括基于贝叶斯方法和深度学习方法2类认知跟踪模型.同时,从学生作答表现预测、认知状态评估、心理因素分析、习题序列分析和编程练习5个方面阐述认知跟踪模型的应用情景.最后,以经典的贝叶斯认知跟踪模型和深度认知跟踪模型为例分析了2类模型的优缺点,并探讨和展望认知跟踪模型未来可能的研究方向.

关键词: 教育大数据, 认知跟踪, 学生模型, 贝叶斯认知跟踪, 深度学习

Abstract: With the in-depth advancement of informational education and the rapid development of online education, a large amount of fragmented educational data are generated during the learning process of students. How to mine and analyze these educational big data has become an urgent problem in the education and the knowledge engineering with big data fields. As for the dynamic education data, knowledge tracing models trace the cognitive status of students over time by analyzing the students’ exercising data generated in the learning process, so as to predict the exercising performance of students in the future time. In this paper, knowledge tracing models in educational big data are reviewed, analyzed, and discussed. Firstly, knowledge tracing models are introduced in detail from the perspective of their principles, steps, and model variants, including two mainstream knowledge tracing models based on Bayesian methods and deep learning methods. Then, the application scenarios of knowledge tracing models are explained from five aspects: student performance prediction, cognitive state assessment, psychological factor analysis, exercise sequence, and programming practice. The strengths and weaknesses in Bayesian knowledge tracing models and Deep Knowledge Tracing models are discussed through the two classic models BKT and DKT. Finally, some future directions of knowledge tracing models are given.

Key words: educational big data, knowledge tracing, student model, Bayesian knowledge tracing, deep learning

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