As a student modeling technique, knowledge tracing is widely used by various intelligent tutoring systems to infer and trace the individual’s knowledge state during the learning process. In recent years, various models were proposed to get accurate and easy-to-interpret results. To make sense of the wide Knowledge tracing (KT) modeling landscape, this paper conducts a systematic review to provide a detailed and nuanced discussion of relevant KT techniques from the perspective of assumptions, data, and algorithms. The results show that most existing KT models consider only a fragment of the assumptions that relate to the knowledge components within items and student’s cognitive process. Almost all types of KT models take “quize data” as input, although it is insufficient to reflect a clear picture of students’ learning process. Dynamic Bayesian network, logistic regression and deep learning are the main algorithms used by various knowledge tracing models. Some open issues are identified based on the analytics of the reviewed works and discussed potential future research directions.
This paper was supported by National Natural Science Foundation of China (NSFC) (No.61807013), Fundamental Research Funds for the Central Universities (No. CCNU20QN028), Special Research Project on Teacher Education (No.CCNUTEIII 2021-07) and Teaching Innovation Research Project (No. ZNXBJY202115)
Dai, M., Hung, J., Du, X., Tang, H., & Li, H. (2021). Knowledge tracing: A review of available technologies. Journal of Educational Technology Development and Exchange (JETDE), 14(2), 1-20. https://doi.org/10.18785/jetde.1402.01
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