Research Article
Development of a Classification Model for Predicting Student Payment Behavior Using Artificial Intelligence and Data Science Techniques
@ARTICLE{10.4108/eetsis.3489, author={Henry Villarreal-Torres and Julio \^{A}ngeles-Morales and William Mar\^{\i}n-Rodriguez and Daniel Andrade-Gir\^{o}n and Edgardo Carre\`{o}o-Cisneros and Jenny Cano-Mej\^{\i}a and Carmen Mej\^{\i}a-Murillo and Mariby C. Bosc\^{a}n-Carroz and Gumercindo Flores-Reyes and Oscar Cruz-Cruz}, title={Development of a Classification Model for Predicting Student Payment Behavior Using Artificial Intelligence and Data Science Techniques}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={10}, number={5}, publisher={EAI}, journal_a={SIS}, year={2023}, month={9}, keywords={Automated Machine Learning, Higher Education, Data Mining, Delinquency}, doi={10.4108/eetsis.3489} }- Henry Villarreal-Torres
Julio Ángeles-Morales
William Marín-Rodriguez
Daniel Andrade-Girón
Edgardo Carreño-Cisneros
Jenny Cano-Mejía
Carmen Mejía-Murillo
Mariby C. Boscán-Carroz
Gumercindo Flores-Reyes
Oscar Cruz-Cruz
Year: 2023
Development of a Classification Model for Predicting Student Payment Behavior Using Artificial Intelligence and Data Science Techniques
SIS
EAI
DOI: 10.4108/eetsis.3489
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.
Copyright © 2023 Villarreal-Torres et al., licensed to EAI. This is an open access article distributed under the terms of the CC BYNC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
