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.

翻译：高质量教育是实现更可持续世界的关键之一。相较于传统面对面课堂教学，在线教育使我们能够记录并研究大量学习数据，从而提供智能化教育服务。知识追踪（Knowledge Tracing, KT）旨在监控学习过程中学生知识状态的动态演变，是支撑这些智能服务的核心任务。近年来，这一新兴领域的研究日益增多，并取得了显著进展。本综述从技术角度对现有知识追踪模型进行分类，并系统化地梳理了这些模型。随后，我们考察了考虑更严格学习假设的丰富知识追踪模型变体，这些变体涵盖学习前、学习中和学习后三个阶段。为更好地服务该领域的研究者与实践者，我们开源了两个算法库：EduData用于下载和预处理知识追踪相关数据集，以及EduKTM提供现有主流知识追踪模型的可扩展统一实现。此外，知识追踪的发展离不开其应用场景，因此我们进一步介绍了知识追踪在不同场景下的典型应用。最后，我们探讨了这一快速发展领域未来研究中的若干潜在方向。