Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (21): 41-58.DOI: 10.3778/j.issn.1002-8331.2106-0552

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Review of Knowledge Tracing Preprocessing Based on Deep Learning

LIANG Kun, REN Yimeng, SHANG Yuhu, ZHANG Yiying, WANG Cong   

  1. College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin 300457, China
  • Online:2021-11-01 Published:2021-11-04



  1. 天津科技大学 人工智能学院,天津 300457


As education informatization keep deepening, knowledge tracing with the goal of predicting students’ knowledge status is becoming an important and challenging task in individualized education. As a time sequence task of educational data mining, knowledge tracing combines with the powerful feature extraction and modeling capabilities of deep learning models, and it has the unique advantage when dealing with sequential tasks. To this end, this article discusses knowledge tracing from the following four aspects. Firstly, the article briefly analyzes the characteristics and limitations of traditional knowledge tracing models. Then, through taking the development process of in-depth knowledge tracing as the main line, it summarizes knowledge tracing models based on recurrent neural networks, memory augmented neural networks, graph neural networks and their improved models; and categorizes and organizes the existing models in this field according to methods and strategies. Besides, this article sorts out the public data sets and model evaluation indicators which can be used by researchers, compares and analyzes the characteristics of different modeling methods. Finally, it discusses and prospects the future development direction of knowledge tracing based on deep learning, and lays the foundation for further in-depth knowledge tracing research.

Key words: education informatization, individualized education, knowledge tracing, deep learning, memory augmented neural network, graph neural network



关键词: 教育信息化, 个性化教育, 知识追踪, 深度学习, 记忆增强神经网络, 图神经网络