Resumo

AGUILAR-REYES, Johanna Enith; MEJIA-PENAFIEL, Edwin Fernando; MOROCHO-BARRIONUEVO, Tania Paulina  e  VELASCO CASTELO, Geoconda-Marisela. Study of academic performance through comparison of regression models and classification trees. Telos [online]. 2025, vol.27, n.1, pp.94-115.  Epub 03-Jul-2025. ISSN 1317-0570.  https://doi.org/10.36390/telos271.08.

This article aims to identify the factors that affect academic performance by comparing regression models and decision trees to determine the factors involved. The methodology adopted is quantitative in nature, focused on the collection of numerical data and its statistical analysis, in order to evaluate the relationships between different variables and determine those factors that influence academic performance. The population studied includes remedial students in the statistics career, who underwent an exploratory and descriptive analysis, using two statistical methods. Two modeling techniques were used: multinomial logistic regression and classification trees. The variables evaluated included sociodemographic factors, previous academic performance, and characteristics of the educational environment. The results showed that the logistic regression model achieved 100% accuracy with an AUC of 1, indicating perfect classification ability. In comparison, the classification tree model had an accuracy of 70.83% with an AUC of 0.7042, reflecting moderate classification ability. From these results, key factors that affect academic performance were identified, such as study habits, interest in the career and psychological aspects. In conclusion, multinomial logistic regression was more effective and accurate in analyzing the quantitative relationships between the variables that affect academic performance, outperforming the classification tree method.

Palavras-chave : Academic Performance; Logistic Regression; Classification Trees; Confusion Matrix.

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