Personalized education, tailored to individual student needs, leverages educational technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness. The integration of AI in educational platforms provides insights into academic performance, learning preferences, and behaviors, optimizing the personal learning process. Driven by data mining techniques, it not only benefits students but also provides educators and institutions with tools to craft customized learning experiences. To offer a comprehensive review of recent advancements in personalized educational data mining, this paper focuses on four primary scenarios: educational recommendation, cognitive diagnosis, knowledge tracing, and learning analysis. This paper presents a structured taxonomy for each area, compiles commonly used datasets, and identifies future research directions, emphasizing the role of data mining in enhancing personalized education and paving the way for future exploration and innovation.

翻译：个性化教育根据学生个体需求进行定制，在数字时代借助教育技术与人工智能提升学习效果。AI与教育平台的融合可洞察学业表现、学习偏好及行为模式，从而优化个性化学习过程。在数据挖掘技术驱动下，该方法不仅使学习者受益，也为教育工作者与机构提供了打造定制化学习体验的工具。为全面梳理个性化教育数据挖掘领域的最新进展，本文聚焦四大核心场景：教育推荐、认知诊断、知识追踪与学习分析。本文针对每个领域构建了结构化分类体系，汇总了常用数据集，并明确了未来研究方向，着重强调数据挖掘在提升个性化教育中的作用，为后续探索与创新奠定基础。