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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Auteurs principaux:	Janka Pecuchova, Martin Drlik
Format:	Artigo
Langue:	Inglês
Publié:	IEEE 2024-01-01
Collection:	IEEE Access
Sujets:	Educational data mining learning analytics clustering analysis dropout prediction
Accès en ligne:	https://ieeexplore.ieee.org/document/10736593/
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