Journal of East China Normal University(Natural Sc ›› 2019, Vol. 2019 ›› Issue (5): 1-15.doi: 10.3969/j.issn.1000-5641.2019.05.001

• Data-driven Computational Education • Previous Articles     Next Articles

A review of knowledge tracking

LIU Heng-yu, ZHANG Tian-cheng, WU Pei-wen, YU Ge   

  1. Computer Science and Engineering, Northeastern University, Shenyang 110819, China
  • Received:2019-07-29 Online:2019-09-25 Published:2019-10-11

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.

Key words: knowledge tracking, cognitive diagnosis, deep learning, probability map model

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