Predicting student specializations: a Machine Learning Approach based on Academic Performance
| Autor: | Athanasios Angeioplastis, Nikolaos Papaioannou, Alkiviadis Tsimpiris, Angeliki Kamilali, Dimitrios Varsamis |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Je-LKS: Journal of E-Learning and Knowledge Society, Vol 20, Iss 2 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1971-8829 1826-6223 |
| DOI: | 10.20368/1971-8829/1135904 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|