A review of knowledge tracking
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摘要: 在教育领域中，科学地、有针对性地对学生的知识状态进行有效追踪具有十分重要的意义.根据学生的历史学习轨迹，可以对学生与习题的交互过程进行建模.在此基础上，能够自动地对学生各个阶段的知识状态进行追踪，进而预测学生表现，实现个性化导学和自适应学习.首先，对知识追踪及其应用背景进行介绍，总结知识追踪所涉及的教育学与数据挖掘理论；其次，总结基于概率图、矩阵分解、深度学习的知识追踪研究现状，并根据方法的不同特点进行更为细致的分类；最后对目前的知识追踪技术进行分析比较，并对未来的研究方向进行展望.Abstract: In the field of education, scientifically and purposefully tracking the progression of student knowledge is a topic of great significance. With a student's historical learning trajectory and a model for the interaction process between students and exercises, knowledge tracking can automatically track the progression of a student's learning at each stage. This provides a technical basis for predicting student performance and achieving personalized guidance and adaptive learning. This paper first introduces the background of knowledge tracking and summarizes the pedagogy and data mining theory involved in knowledge tracking. Then, the paper summarizes the research status of knowledge tracking based on probability graphs, matrix factorization, and deep learning; we use these tools to classify the tracking methods according to different characteristics. Finally, the paper analyzes and compares the latest knowledge tracking technologies, and looks ahead to the future direction of ongoing research.
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Key words:
- knowledge tracking /
- cognitive diagnosis /
- deep learning /
- probability map model
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表 1 BKT模型参数表
Tab. 1 BKT model parameter table
参数 参数含义 $P(L_0 )$ 学生未经过练习, 掌握知识点的概率 $P(T)$ 学生经过练习后, 学会知识点的概率 $P(S)$ 学生掌握了某项知识点, 做错的概率 $P(G)$ 学生没掌握某项知识点, 做对的概率 $K$ 知识节点(1表示掌握, 0表示未掌握) $Q$ 问题节点(1表示通过, 0表示未通过) 
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表 2 模型对比
Tab. 2 Model comparison
Model Q-matrix Multi-Skill Question Repeating Answer Question text time Multi-Knowledge Proficiency Response BKT $\checkmark$ $\times $ $\checkmark$ $\times $ $\checkmark$ $\checkmark$ $\checkmark$ FuzzyCDF $\checkmark$ $\checkmark$ $\times $ $\times $ $\times $ $\checkmark$ $\checkmark$ PMF $\times $ $\times $ $\times $ $\times $ $\times $ $\times $ $\checkmark$ KPT $\checkmark$ $\checkmark$ $\times $ $\times $ $\checkmark$ $\checkmark$ $\checkmark$ DKT $\times $ $\times $ $\checkmark$ $\times $ $\times $ $\times $ $\checkmark$ EERNN $\times $ $\times $ $\checkmark$ $\checkmark$ $\times $ $\times $ $\checkmark$
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