Abstract: With the in-depth advancement of informational education and the rapid development of online education, a large amount of fragmented educational data are generated during the learning process of students. How to mine and analyze these educational big data has become an urgent problem in the education and the knowledge engineering with big data fields. As for the dynamic education data, knowledge tracing models trace the cognitive status of students over time by analyzing the students’ exercising data generated in the learning process, so as to predict the exercising performance of students in the future time. In this paper, knowledge tracing models in educational big data are reviewed, analyzed, and discussed. Firstly, knowledge tracing models are introduced in detail from the perspective of their principles, steps, and model variants, including two mainstream knowledge tracing models based on Bayesian methods and deep learning methods. Then, the application scenarios of knowledge tracing models are explained from five aspects: student performance prediction, cognitive state assessment, psychological factor analysis, exercise sequence, and programming practice. The strengths and weaknesses in Bayesian knowledge tracing models and Deep Knowledge Tracing models are discussed through the two classic models BKT and DKT. Finally, some future directions of knowledge tracing models are given.