Recent advancements in the collection and analysis of sequential educational data have brought time series analysis to a pivotal position in educational research, highlighting its essential role in facilitating data-driven decision-making. However, there is a lack of comprehensive summaries that consolidate these advancements. To the best of our knowledge, this paper is the first to provide a comprehensive review of time series analysis techniques specifically within the educational context. We begin by exploring the landscape of educational data analytics, categorizing various data sources and types relevant to education. We then review four prominent time series methods-forecasting, classification, clustering, and anomaly detection-illustrating their specific application points in educational settings. Subsequently, we present a range of educational scenarios and applications, focusing on how these methods are employed to address diverse educational tasks, which highlights the practical integration of multiple time series methods to solve complex educational problems. Finally, we conclude with a discussion on future directions, including personalized learning analytics, multimodal data fusion, and the role of large language models (LLMs) in educational time series. The contributions of this paper include a detailed taxonomy of educational data, a synthesis of time series techniques with specific educational applications, and a forward-looking perspective on emerging trends and future research opportunities in educational analysis. The related papers and resources are available and regularly updated at the project page.

翻译：近期在序列化教育数据的收集与分析方面取得的进展，使时间序列分析在教育研究中占据了关键地位，突显了其在促进数据驱动决策中的核心作用。然而，目前缺乏能够整合这些进展的全面性综述。据我们所知，本文首次在教育领域内对时间序列分析技术进行了全面回顾。我们首先探讨了教育数据分析的现状，对教育相关的各类数据源和数据类型进行了分类。随后，我们回顾了四种主要的时间序列方法——预测、分类、聚类和异常检测，并阐述了它们在教育环境中的具体应用点。接着，我们展示了一系列教育场景与应用，重点说明了这些方法如何被用于解决多样化的教育任务，这突显了多种时间序列方法在实际中如何整合以解决复杂教育问题。最后，我们以对未来方向的讨论作结，包括个性化学习分析、多模态数据融合以及大语言模型（LLMs）在教育时间序列中的作用。本文的贡献包括：详细的教育数据分类体系、时间序列技术与具体教育应用的综合梳理，以及对教育分析中新兴趋势和未来研究机遇的前瞻性视角。相关论文与资源可在项目页面获取并定期更新。