Development of a Classification Model for Predicting Student Payment Behavior Using Artificial Intelligence and Data Science Techniques
-
Published:2023-06-26
Issue:
Volume:
Page:
-
ISSN:2032-9407
-
Container-title:ICST Transactions on Scalable Information Systems
-
language:
-
Short-container-title:EAI Endorsed Scal Inf Syst
Author:
Villarreal-Torres Henry,Ángeles-Morales Julio,Marín-Rodriguez William,Andrade-Girón Daniel,Carreño-Cisneros Edgardo,Cano-Mejía Jenny,Mejía-Murillo Carmen,Boscán-Carroz Mariby C.,Flores-Reyes Gumercindo,Cruz-Cruz Oscar
Abstract
Artificial intelligence today has become a valuable tool for decision-making, where universities have to adapt and optimize their processes, improving the quality of their services. In this context, the economic income from collections is vital for sustainability. There are several problems that can contribute to student delinquency, such as economic, financial, academic, family, and personal. For this reason, the study aimed to develop a classification model to predict the payment behavior of enrolled students. The methodology is a proactive, technological study of incremental innovation with a synchronous temporal scope. The study population consisted of 8,495 undergraduate students enrolled in the 2022 - II academic semester, containing information on academic performance, financial situation, and personal factors. The result is a classification model using the H2O.ai platform, discretization algorithms, data balancing, and the R language. Data science algorithms obtained the base from the institution's computer system. The data sets for training and testing correspond to 70% and 30%, obtaining the GBM Grid model whose performance metrics are AUC of 0.905, AUCPR of 0.926, and logLoss equivalent to 0.311; that is, the model efficiently complies with the classification of student debtors to provide them with early intervention service and help them complete their studies.
Publisher
European Alliance for Innovation n.o.
Subject
Information Systems and Management,Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Software
Reference87 articles.
1. Abdul, M., Yusoff, M. & Mohamed, A. (2022). Survey on Highly Imbalanced Multi-class Data, International Journal of Advanced Computer Science and Applications (IJACSA), 13(6). http://dx.doi.org/10.14569/IJACSA.2022.0130627 2. Albarracín Vanoy, R. J. (2022). STEM Education as a Teaching Method for the Development of XXI Century Competencies. Metaverse Basic and Applied Research, 1, 21. https://doi.org/10.56294/mr202221 3. Andrade-Girón, D., Carreño-Cisneros, E., Mejía-Dominguez, C., Marín-Rodriguez, W., & Villarreal-Torres, H. (2023). Comparison of Machine Learning Algorithms for Predicting Patients with Suspected COVID-19. Salud, Ciencia Y Tecnología, 3, 336. https://doi.org/10.56294/saludcyt2023336 4. Angelov, P.P., Soares, E.A., Jiang, R., Arnold, N.I., & Atkinson, P.M. (2021). Explainable artificial intelligence: an analytical review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11. DOI:10.1002/widm.1424 5. Arrieta, A.B., Rodríguez, N.D., Ser, J.D., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2019). Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI. ArXiv, abs/1910.10045.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|