<![CDATA[Learning analytics and educational data mining are rapidly evolving fields that leverage data-driven methods to enhance teaching, learning, and institutional decision-making. This review provides a comprehensive overview of the key analytical techniques and tools employed in learning analytics and educational data mining, including classification, clustering, regression, association rule mining, and data visualization. It also highlights the integration of advanced methods such as deep learning and adaptive systems for personalized education. The paper examines various platforms and technologies, including learning management systems, open-source tools, and AI/ML libraries, to evaluate their capabilities, scalability, and practical adoption. Key application areas, such as dropout prediction, engagement analysis, personalized learning, and curriculum design, are examined through selected case studies spanning K–12 and higher education. The review emphasizes the growing importance of ethical considerations, interpretability, and usability in the application of educational analytics. By synthesizing current practices and trends, this work aims to inform educators, researchers, and developers seeking to harness educational data for improved learning outcomes and strategic planning.]]>
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