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

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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Bibliographic Details
Main Authors: Yu-Huei Cheng, Mu-Hsin Shih, Che-Nan Kuo
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Counseling resource allocation
multilayer perceptron (MLP)
predictive modeling
student suspension and dropout
Online Access:https://ieeexplore.ieee.org/document/11075759/
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