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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11075759/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849719057745444864 |
|---|---|
| author | Yu-Huei Cheng Mu-Hsin Shih Che-Nan Kuo |
| author_facet | Yu-Huei Cheng Mu-Hsin Shih Che-Nan Kuo |
| author_sort | Yu-Huei Cheng |
| collection | DOAJ |
| description | 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 allow for timely intervention. This study applies a Multilayer Perceptron to predict student suspension and dropout, with the goal of assisting schools in better monitoring these situations and proactively deploying counseling resources. By enhancing the precision of resource allocation, schools can improve the effectiveness of their interventions. The dataset used in this study includes student records from Chaoyang University of Technology spanning five academic years (2017–2021). Data from the second semester of the 2021 academic year were designated for model testing, while data from the other nine semesters were used for training and validation. The proposed model achieved an accuracy of 81.70%, demonstrating its potential to provide valuable insights. Future efforts will focus on further optimizing the model and deploying it in real-world applications to serve as a critical tool for counseling resource allocation and institutional decision-making. |
| format | Article |
| id | doaj-art-770c86cbfb3d4f5ca69975ffd27e0a85 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-770c86cbfb3d4f5ca69975ffd27e0a852025-08-20T03:12:14ZengIEEEIEEE Access2169-35362025-01-011311981011981910.1109/ACCESS.2025.358748411075759Early Prediction of Student Suspension and Dropout for Counseling Resource Optimization Using Multilayer PerceptronYu-Huei Cheng0https://orcid.org/0000-0002-1468-6686Mu-Hsin Shih1Che-Nan Kuo2https://orcid.org/0000-0001-7705-4208Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung, TaiwanDepartment of Information and Communication Engineering, Chaoyang University of Technology, Taichung, TaiwanDepartment of Artificial Intelligence, CTBC Financial Management College, Tainan, TaiwanThe 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 allow for timely intervention. This study applies a Multilayer Perceptron to predict student suspension and dropout, with the goal of assisting schools in better monitoring these situations and proactively deploying counseling resources. By enhancing the precision of resource allocation, schools can improve the effectiveness of their interventions. The dataset used in this study includes student records from Chaoyang University of Technology spanning five academic years (2017–2021). Data from the second semester of the 2021 academic year were designated for model testing, while data from the other nine semesters were used for training and validation. The proposed model achieved an accuracy of 81.70%, demonstrating its potential to provide valuable insights. Future efforts will focus on further optimizing the model and deploying it in real-world applications to serve as a critical tool for counseling resource allocation and institutional decision-making.https://ieeexplore.ieee.org/document/11075759/Counseling resource allocationmultilayer perceptron (MLP)predictive modelingstudent suspension and dropout |
| spellingShingle | Yu-Huei Cheng Mu-Hsin Shih Che-Nan Kuo Early Prediction of Student Suspension and Dropout for Counseling Resource Optimization Using Multilayer Perceptron IEEE Access Counseling resource allocation multilayer perceptron (MLP) predictive modeling student suspension and dropout |
| title | Early Prediction of Student Suspension and Dropout for Counseling Resource Optimization Using Multilayer Perceptron |
| title_full | Early Prediction of Student Suspension and Dropout for Counseling Resource Optimization Using Multilayer Perceptron |
| title_fullStr | Early Prediction of Student Suspension and Dropout for Counseling Resource Optimization Using Multilayer Perceptron |
| title_full_unstemmed | Early Prediction of Student Suspension and Dropout for Counseling Resource Optimization Using Multilayer Perceptron |
| title_short | Early Prediction of Student Suspension and Dropout for Counseling Resource Optimization Using Multilayer Perceptron |
| title_sort | early prediction of student suspension and dropout for counseling resource optimization using multilayer perceptron |
| topic | Counseling resource allocation multilayer perceptron (MLP) predictive modeling student suspension and dropout |
| url | https://ieeexplore.ieee.org/document/11075759/ |
| work_keys_str_mv | AT yuhueicheng earlypredictionofstudentsuspensionanddropoutforcounselingresourceoptimizationusingmultilayerperceptron AT muhsinshih earlypredictionofstudentsuspensionanddropoutforcounselingresourceoptimizationusingmultilayerperceptron AT chenankuo earlypredictionofstudentsuspensionanddropoutforcounselingresourceoptimizationusingmultilayerperceptron |