Abstract: Knowledge tracking, which aims to model students’ changing knowledge states over learning time based on their historical answer records and then predict students’ answer performance, is a core module supporting smart education systems and has received increasing attention from researchers. This paper comprehensively compares the research progress in this field, analyzes the basic theoretical research related to knowledge tracking, and analyzes the knowledge tracking models from probabilistic models, logical models, and deep learning-based models according to different research methods. Probabilistic models assume that learning follows Markov processes, logical models are a class of logic function-based models, and deep learning-based knowledge tracking models relying on the powerful feature extraction ability of deep learning have become a hot research topic in recent years. The improvement methods proposed for the problems faced by deep learning-based knowledge tracking models such as interpretability and lack of learning features are presented. The public datasets currently available to researchers are given as well as a comparison of the performance of different models. Finally, this paper concludes with a summary of this rapidly growing field on knowledge tracking, suggesting some possible future research directions for the problems of research in this area.

Key words: wisdom education, online learning, knowledge state, knowledge tracking model, deep learning

摘要： 知识追踪，旨在根据学生的历史答题记录，对学生随学习时间不断变化的知识状态进行建模，进而预测学生的答题表现，是支撑智慧教育系统的核心模块，受到越来越多研究者的关注。全面梳理了该领域的研究进展，分析了与知识追踪相关的基础理论研究，并按照研究方法的不同，将知识追踪模型分为概率模型、逻辑模型、基于深度学习的模型进行剖析，其中概率模型假设学习遵循马尔可夫过程，逻辑模型是一类基于逻辑函数的模型，而基于深度学习的知识追踪模型依赖于深度学习强大的特征提取能力成为近年来研究的热点。对基于深度学习的知识追踪模型面临的可解释性、缺少学习特征等问题提出的改进方法进行了介绍。给出了目前可供研究者们使用的公共数据集以及不同模型的性能比较。最后，对知识追踪这个快速发展起来的领域进行了总结，针对该领域研究存在的问题，提出了一些未来可能的研究方向。

关键词: 智慧教育, 在线学习, 知识状态, 知识追踪模型, 深度学习