IDENTIFICATION OF INSTITUTIONAL DETERMINANTS OF TECHNICAL QUALIFICATION IN BRAZILIAN HEIs FROM A MATHEMATICAL PERSPECTIVE AND MACHINE LEARNING METHODS
DOI:
https://doi.org/10.56238/arev8n2-117Keywords:
Higher Education Census, Predictive Modeling, Machine Learning, Applied MathematicsAbstract
The objective of this study was to identify which institutional factors are associated with the presence of at least 30% of technical staff holding postgraduate degrees in Brazilian Higher Education Institutions (HEIs). To achieve this, microdata from the 2023 Higher Education Census, provided by the National Institute for Educational Studies and Research Anísio Teixeira (INEP), were used, considering institutional variables of an administrative, organizational, and structural nature. The research adopted the Decision Tree technique as a supervised learning method, allowing the modeling of explanatory patterns and the interpretation of relationships between predictor variables and institutional technical qualification. The results indicated that the administrative category of the HEI and the number of faculty members with doctoral degrees were the most relevant factors in distinguishing institutions that reach the established threshold. It was observed that smaller non-federal institutions presented a higher relative proportion of qualified technical staff, while larger institutions did not always demonstrate the same proportional performance. The predictive evaluation revealed moderate model performance, with accuracy higher than chance, but with reduced sensitivity. It is concluded that, although the predictive capacity is limited, the employed technique proved effective in generating interpretable insights regarding the factors associated with technical staff qualification, contributing to the structural understanding of the analyzed phenomenon.
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BECKER, Gary S. Human capital: a theoretical and empirical analysis, with special reference to education. 3. ed. Chicago: University of Chicago Press, 1993. DOI: https://doi.org/10.7208/chicago/9780226041223.001.0001
BREIMAN, Leo. Random forests. Machine Learning, v. 45, n. 1, p. 5–32, 2001. DOI: https://doi.org/10.1023/A:1010933404324
BREIMAN, Leo; FRIEDMAN, Jerome; OLSHEN, Richard; STONE, Charles. Classification and regression trees. New York: Routledge, 1984.
DIAS SOBRINHO, José. Avaliação da educação superior: democratização, qualidade e crise. Campinas: Autores Associados, 2005.
FAWCETT, Tom. An introduction to ROC analysis. Pattern Recognition Letters, v. 27, n. 8, p. 861–874, 2006. DOI: https://doi.org/10.1016/j.patrec.2005.10.010
HASTIE, Trevor; TIBSHIRANI, Robert; FRIEDMAN, Jerome. The elements of statistical learning. 2. ed. New York: Springer, 2009. DOI: https://doi.org/10.1007/978-0-387-84858-7
JAMES, Gareth; WITTEN, Daniela; HASTIE, Trevor; TIBSHIRANI, Robert. An introduction to statistical learning. New York: Springer, 2013. DOI: https://doi.org/10.1007/978-1-4614-7138-7
KUHN, Max; JOHNSON, Kjell. Applied predictive modeling. New York: Springer, 2013. DOI: https://doi.org/10.1007/978-1-4614-6849-3
MITCHELL, Tom M. Machine learning. New York: McGraw-Hill, 1997.
REZENDE, Solange Oliveira; MARCACINI, Ricardo; MOURA, Maria Fernanda. Mineração de dados. In: REZENDE, Solange Oliveira (org.). Sistemas inteligentes: fundamentos e aplicações. Barueri: Manole, 2011.
SCHULTZ, Theodore W. Investment in human capital. The American Economic Review, v. 51, n. 1, p. 1–17, 1961.
