Enhancing the Early Student Dropout Prediction Model Through Clustering Analysis of Students’ Digital Traces

Educational Data Mining and learning analytics have gained significant prominence in recent years, garnering attention from researchers worldwide. This is primarily due to their potential to improve decision-making processes within higher education. This study utilizes Educational Data Mining to sug...

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Detalles Bibliográficos

Autores principales:	Janka Pecuchova, Martin Drlik
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	IEEE 2024-01-01
Colección:	IEEE Access
Materias:	Educational data mining learning analytics clustering analysis dropout prediction
Acceso en línea:	https://ieeexplore.ieee.org/document/10736593/
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