Abstract

In the field of education, the level of awareness of students' knowledge status largely affects students' learning efficiency. Knowledge Tracing, as a method of modeling students' learning status, obtaining their knowledge status to predict their future learning can provide more targeted learning solutions, which is extremely useful for improving students' learning efficiency and implementing tailor-made education. This paper introduces the related research methods and their core ideas in the order of probability-based models, logistic function-based models, and deep learning-based models by investigating the development of this field since its introduction. Among the probability-based models, the mainstream methods include BKT and DBKT, which are based on Hidden Markov Models with certain assumptions and are not ideal in practical application scenarios. In contrast, logistic-based models consider a variety of factors in the student learning process and have achieved good results. After that, deep learning-based model has achieved excellent assessment results through its powerful feature extraction ability, and many variants have been derived. This paper introduces DKT in detail and briefly introduces its variant models. At last, this paper summarizes some of the mainstream evaluation criteria in this field and gives the widely used datasets and their categories. Finally, this paper proposes some suggestions for the future development of the field in view of the shortcomings of the current research status for reference.