Abstract: Knowledge tracing is an important research direction in the field of educational data mining. The goal is to determine the degree of students mastery of knowledge by establishing a model of students knowledge changes over time and to mine potential learning rules from their learning trajectories. Fulfilling this goal means personalized guidance to students from the achievement of assisted education through artificial intelligence. Due to its powerful feature extraction capabilities, deep learning has been proven to significantly improve the performance of knowledge tracing models and has attracted more and more attention. Starting from the most basic deep knowledge tracing model, this paper comprehensively reviews the research progress in this field and provides both the technical improvement and an evolutionary map. The 3 main technical improvement directions have been elaborated and compared: 1) improvement of interpretable problems, 2) problems of long-term dependence, and 3) improvement for lack of learning features. At the same time, the existing models in the field have been classified, the public data sets have been sorted out, and the main areas of application are investigated for researchers. Finally, the future research direction of knowledge tracing based on deep learning is explored.

Key words: education data mining, deep learning, knowledge tracing, recurrent neural network, artificial intelligence assisted education