Improve Student Risk Prediction with Clustering Techniques: A Systematic Review in Education Data Mining
Student dropout rates continue to present major difficulties for educational institutions, leading to academic, operational, and financial impacts. Educational Data Mining (EDM) methods, particularly those combining clustering techniques with predictive models, have demonstrated potential in identif...
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| Principais autores: | , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
MDPI AG
2025-12-01
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| Colecção: | Education Sciences |
| Assuntos: | |
| Acesso em linha: | https://www.mdpi.com/2227-7102/15/12/1695 |
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