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Higher education student success: A system to evaluate degree completion
Cardona, Tatiana Cudney, Elizabeth A. Furterer, Sandra L. Elshennawy, Ahmad K.
Cardona, Tatiana
Cudney, Elizabeth A.
Furterer, Sandra L.
Elshennawy, Ahmad K.
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Issue Date
2025-03-07
Type
Conference paper
Genre
Keywords
Neural networks,Machine learning,Prediction,Higher education
Subjects (LCSH)
Citation
Cardona, T., Cudney, E. A., Furterer, S. L., & Elshennawy, A. K. (2025). Higher education student success: A system to evaluate degree completion. In Proceedings of the 31st International Conference on Industry, Engineering and Management Systems (IEMS), 31, 147-160. https://doi.org/10.62704/10057/31318
Abstract
Higher education institutions must cultivate a technically skilled workforce to meet increasing job demands. However, graduation rates remain low, with 40% of first-year students not completing their degrees. This research proposes an integrated system using machine learning, particularly neural networks, to predict student success based on multifaceted interactions. The model demonstrates a systematic approach to improving degree completion outcomes.
Table of Contents
Description
Published in SOAR: Shocker Open Access Repository by Wichita State University Libraries Technical Services, November 2025. 2025 IEMS Officers: Gamal Weheba (Conference Chair); Hesham Mahgoub (Program Chair); Dalia Mahgoub (Technical Director); Ed Sawan (Publications Editor); Wilfredo Moscoso (Proceedings Editor); Abdulaziz G. Abdulaziz (Associate Editor)
Publisher
Industry, Engineering & Management Systems Conference
Journal
Book Title
Series
Proceedings of the 2025 IEMS Conference, v.31
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Archival Collection
PubMed ID
ISSN
2690-3210 (print)
2690-3229 (online)
2690-3229 (online)