Educational Data Mining Techniques: A Review of the Potential and the Performance

Yahya Zakur

doi:10.71229/ygr02415

Authors

Yahya Asmar Zakur Department of Preparation and Training, Wasit Education Directorate, 52001 Wasit, Iraq https://orcid.org/0000-0003-0203-5875

DOI:

https://doi.org/10.71229/ygr02415

Keywords:

Data Mining , Educational Data Mining , Artificial Intelligence , Machine Learning, , Education Management

Abstract

Data mining (DM) has emerged as an important interdisciplinary research field that employs a wide range of computational and analytical techniques to discover and extract meaningful knowledge from the data. Data mining methods are becoming increasingly important in the educational sector due to their potential to support evidence based decision-making, improve of learning outcomes and enhancing of institutional effectiveness. This study presents a comprehensive review of research samples published between 2019 and 2024 in peer-reviewed scientific journals (Scopus Indexed Journals), with a special focus on the application of data mining techniques in education sector. The review study how these techniques can be used to analyze diverse educational contexts, including the student learning behaviors, engagement patterns,academic performance and etc. Also, this study investigates the most commonly used data mining algorithms, evaluation their predictive accuracy, classification performance, and practicality in the various educational environments. Also a particular attention is paid to the role of data mining approaches in the educational challenges and getting an actionable insights. The results suggest that data mining techniques serve as meaningful tools for forecasting, classification, clustering and decision support in the educational systems. In addition, this study highlights the main ethical considerations associated with (EDM), like as the privacy of the data, security, translucence, fairness, informed consent and methodological challenges faced by the researchers and practitioners when implementation the data mining solutions in educational environments effectively and responsibly.

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