  Modern online education has the capacity to provide intelligent educational
services by automatically analyzing substantial amounts of student behavioral
data. Knowledge Tracing (KT) is one of the fundamental tasks for student
behavioral data analysis, aiming to monitor students' evolving knowledge state
during their problem-solving process. In recent years, a substantial number of
studies have concentrated on this rapidly growing field, significantly
contributing to its advancements. In this survey, we will conduct a thorough
investigation of these progressions. Firstly, we present three types of
fundamental KT models with distinct technical routes. Subsequently, we review
extensive variants of the fundamental KT models that consider more stringent
learning assumptions. Moreover, the development of KT cannot be separated from
its applications, thereby we present typical KT applications in various
scenarios. To facilitate the work of researchers and practitioners in this
field, we have developed two open-source algorithm libraries: EduData that
enables the download and preprocessing of KT-related datasets, and EduKTM that
provides an extensible and unified implementation of existing mainstream KT
models. Finally, we discuss potential directions for future research in this
rapidly growing field. We hope that the current survey will assist both
researchers and practitioners in fostering the development of KT, thereby
benefiting a broader range of students.
