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Assessing the educational performance of different Brazilian school cycles using data science methods

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  • Joyce de Souza Zanirato Maia
  • Ana Paula Arantes Bueno
  • João Ricardo Sato

Abstract

Educational indicators are metrics that assist in assessing the quality of the educational system. They are often associated with economic and social factors suggested to contribute to good school performance, however there is no consensus on the impact of these factors. The main objective of this work was to evaluate the factors related to school performance. Using a data set composed by Brazilian schools’ performance (IDEB), socioeconomic and school structure variables, we generated different models. The non-linear model predicted the best performance, measured by the error and determination coefficient metrics. The heterogeneity of the importance of the variable between school cycles and regions of the country was detected, this effect may contribute to the development of public educational policies.

Suggested Citation

  • Joyce de Souza Zanirato Maia & Ana Paula Arantes Bueno & João Ricardo Sato, 2021. "Assessing the educational performance of different Brazilian school cycles using data science methods," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-14, March.
  • Handle: RePEc:plo:pone00:0248525
    DOI: 10.1371/journal.pone.0248525
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    References listed on IDEAS

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    1. Lex Borghans & Bart Golsteyn & James J. Heckman & John Eric Humphries, 2016. "What Grades and Achievement Tests Measure," Working Papers 2016-022, Human Capital and Economic Opportunity Working Group.
    2. Enriqueta Camps-Cura, 2019. "Changes in Population, Inequality and Human Capital Formation in the Americas in the Nineteenth and Twentieth Centuries," Palgrave Studies in Economic History, Palgrave Macmillan, number 978-3-030-21351-0, February.
    3. Creso Sá & Julieta Grieco, 2016. "Open Data for Science, Policy, and the Public Good," Review of Policy Research, Policy Studies Organization, vol. 33(5), pages 526-543, September.
    4. Leo Breiman & Jerome H. Friedman, 1997. "Predicting Multivariate Responses in Multiple Linear Regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(1), pages 3-54.
    5. Larissa da Silva Marioni & Ricardo Da Silva Freguglia & Naercio A Menezes-Filho, 2020. "The impacts of teacher working conditions and human capital on student achievement: evidence from brazilian longitudinal data," Applied Economics, Taylor & Francis Journals, vol. 52(6), pages 568-582, February.
    6. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    7. Eric A. Hanushek & Margaret E. Raymond, 2005. "Does school accountability lead to improved student performance?," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 24(2), pages 297-327.
    8. Jerik Hanushek & Dennis Kimko, 2006. "Schooling, Labor-force Quality, and the Growth of Nations," Voprosy obrazovaniya / Educational Studies Moscow, National Research University Higher School of Economics, issue 1, pages 154-193.
    9. Fernandes, Eduardo & Holanda, Maristela & Victorino, Marcio & Borges, Vinicius & Carvalho, Rommel & Erven, Gustavo Van, 2019. "Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil," Journal of Business Research, Elsevier, vol. 94(C), pages 335-343.
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