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：近年来，序列化教育数据的收集与分析取得显著进展，使时间序列分析在教育研究中占据关键地位，凸显其在促进数据驱动决策中的核心作用。然而，目前尚缺乏系统整合这些进展的综合性综述。据我们所知，本文首次针对教育领域的时间序列分析技术进行了全面回顾。我们首先探讨教育数据分析的现状，对教育相关的各类数据源与类型进行系统分类。随后，我们综述了四种主流时间序列方法——预测、分类、聚类与异常检测，并阐述其在教育场景中的具体应用要点。接着，我们通过一系列教育场景与应用案例，重点说明这些方法如何被用于解决多样化的教育任务，其中特别展示了多种时间序列方法协同解决复杂教育问题的实践整合模式。最后，我们展望了未来研究方向，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：提出教育数据的详细分类体系，综合梳理时间序列技术及其具体教育应用，并对教育分析领域的新兴趋势与未来研究机遇提出前瞻性观点。相关论文与资源已在项目页面公开并定期更新。