Early Prediction of Student Suspension and Dropout for Counseling Resource Optimization Using Multilayer Perceptron

The sub-replacement fertility rate in Taiwan has caused many educational institutions to face significant challenges, making student suspension and dropout critical issues for sustainable operations. Addressing these challenges requires effective prediction tools to identify at-risk students and all...

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Principais autores:	Yu-Huei Cheng, Mu-Hsin Shih, Che-Nan Kuo
Formato:	Artigo
Idioma:	Inglês
Publicado em:	IEEE 2025-01-01
coleção:	IEEE Access
Assuntos:	Counseling resource allocation multilayer perceptron (MLP) predictive modeling student suspension and dropout
Acesso em linha:	https://ieeexplore.ieee.org/document/11075759/
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