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Mar 22, 2024
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Aug 19, 2024
Published
Aug 28, 2024
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Abstract

Education is a cornerstone of societal progress, equipping people with essential skills and knowledge. In today’s dynamic global society, personalized learning experiences are crucial. Data-driven methodologies, especially Educational Data Mining (EDM), play pivotal roles. This study employs machine learning algorithms to predict specializations for Greek high school students based on their previous grades. The aim is to provide a practical tool for educators and parents, aiding in the optimal selection of specializations. The paper outlines the methodology, presents comparative study results, and concludes with insights into the potential impact on educational decision-making. This research advances the integration of data-driven approaches in education, enhancing students’ learning experiences and prospects.

Keywords

Machine Learning Algorithms Educational Data Mining Prediction High School Data Mining

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How to Cite
Angeioplastis, A., Papaioannou, N., Tsimpiris, A., Kamilali, A., & Varsamis, D. (2024). Predicting student specializations: a Machine Learning Approach based on Academic Performance. Journal of E-Learning and Knowledge Society, 20(2), 19-27. https://doi.org/10.20368/1971-8829/1135904

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