Humans’ ability to transfer knowledge through teaching is one of the essential aspects for human intelligence. A human teacher can track the knowledge of students to customize the teaching on students’ needs. With the rise of online education platforms, there is a similar need for machines to track the knowledge of students and tailor their learning experience. This is known as the Knowledge Tracing (KT) problem in the literature. Effectively solving the KT problem would unlock the potential of computer-aided education applications such as intelligent tutoring systems, curriculum learning, and learning materials’ recommendation. Moreover, from a more general viewpoint, a student may represent any kind of intelligent agents including both human and artificial agents. Thus, the potential of KT can be extended to any machine teaching application scenarios which seek for customizing the learning experience for a student agent (i.e., a machine learning model). In this paper, we provide a comprehensive survey for the KT literature. We cover a broad range of methods starting from the early attempts to the recent state-of-the-art methods using deep learning, while highlighting the theoretical aspects of models and the characteristics of benchmark datasets. Besides these, we shed light on key modelling differences between closely related methods and summarize them in an easy-to-understand format. Finally, we discuss current research gaps in the KT literature and possible future research and application directions.

人类通过教学传递知识的能力是人类智能的重要方面之一。真人教师可以跟踪学生的知识，根据学生的需求定制教学。随着在线教育平台的兴起，机器也有类似的需求来跟踪学生的知识并定制他们的学习体验。这被称为知识追踪（KT）文献中的问题。有效解决 KT 问题将释放计算机辅助教育应用的潜力，如智能辅导系统、课程学习和学习材料推荐。此外，从更一般的角度来看，学生可以代表任何类型的智能代理，包括人类和人工智能代理。因此，KT 的潜力可以扩展到任何寻求为学生代理（即机器学习模型）定制学习体验的机器教学应用场景。在本文中，我们对 KT 文献进行了全面的调查。我们涵盖了从早期尝试到最近使用深度学习的最先进方法的广泛方法，同时突出模型的理论方面和基准数据集的特征。除此之外，我们阐明了密切相关方法之间的关键建模差异，并以易于理解的格式对其进行了总结。最后，我们讨论了 KT 文献中当前的研究差距以及未来可能的研究和应用方向。