High-quality education is one of the keys to achieving a more sustainable
world. In contrast to traditional face-to-face classroom education, online
education enables us to record and research a large amount of learning data for
offering intelligent educational services. Knowledge Tracing (KT), which aims
to monitor students' evolving knowledge state in learning, is the fundamental
task to support these intelligent services. In recent years, an increasing
amount of research is focused on this emerging field and considerable progress
has been made. In this survey, we categorize existing KT models from a
technical perspective and investigate these models in a systematic manner.
Subsequently, we review abundant variants of KT models that consider more
strict learning assumptions from three phases: before, during, and after
learning. To better facilitate researchers and practitioners working on this
field, we open source two algorithm libraries: EduData for downloading and
preprocessing KT-related datasets, and EduKTM with extensible and unified
implementation of existing mainstream KT models. Moreover, the development of
KT cannot be separated from its applications, therefore we further present
typical KT applications in different scenarios. Finally, we discuss some
potential directions for future research in this fast-growing field.

本文研究在线教育与智能教育服务，提出了基于知识跟踪的算法模型，分类 KT 模型及其应用场景，并开放 KT 相关数据集和模型库，最后讨论了 KT 领域未来的发展方向。