Development of a Classification Model for Predicting Student Payment Behavior Using Artificial Intelligence and Data Science Techniques

IF 1.1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS EAI Endorsed Transactions on Scalable Information Systems Pub Date : 2023-06-26 DOI:10.4108/eetsis.3489
Henry Villarreal-Torres, Julio Angeles-Morales, W. Marín-Rodriguez, Daniel Andrade Girón, Edgardo Carreño Cisneros, Jenny Cano-Mejía, Carmen Mejía-Murillo, Mariby C. Boscán-Carroz, Gumercindo Flores-Reyes, Oscar Cruz-Cruz
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引用次数: 1

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.
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使用人工智能和数据科学技术预测学生支付行为的分类模型的发展
如今,人工智能已成为一种有价值的决策工具,大学必须调整和优化其流程,提高服务质量。在这种情况下,收藏品的经济收入对可持续发展至关重要。有几个问题可以导致学生犯罪,如经济,金融,学术,家庭和个人。因此,本研究旨在建立一个分类模型来预测入学学生的付费行为。该方法是一种具有同步时间范围的渐进式创新的前瞻性技术研究。研究人群包括8,495名在2022 - II学年入学的本科生,包括学业成绩、经济状况和个人因素的信息。结果是一个使用H2O的分类模型。ai平台,离散化算法,数据平衡和R语言。数据科学算法从该机构的计算机系统中获得基础。训练和测试数据集对应70%和30%,得到性能指标AUC为0.905,AUCPR为0.926,logLoss等于0.311的GBM Grid模型;即模型有效地符合学生债务人的分类,为其提供早期干预服务,帮助其完成学业。
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来源期刊
EAI Endorsed Transactions on Scalable Information Systems
EAI Endorsed Transactions on Scalable Information Systems COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.80
自引率
15.40%
发文量
49
审稿时长
10 weeks
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