Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0248525.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0248525
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0248525&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0248525?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    4. 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.
    5. 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, December.
    6. 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.
    7. 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.
    8. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:ces:ifodic:v:2:y:2004:i:4:p:14567717 is not listed on IDEAS
    2. Djavad Salehi-Isfahani & Russell D. Murphy, 2006. "Labor market flexibility and investment in human capital," Working Papers e06-5, Virginia Polytechnic Institute and State University, Department of Economics.
    3. Hanushek, Eric A., 2006. "Alternative school policies and the benefits of general cognitive skills," Economics of Education Review, Elsevier, vol. 25(4), pages 447-462, August.
    4. Eric Hanushek, 2004. "United States lessons about school accountability," ifo DICE Report, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 2(04), pages 27-32, January.
    5. Griselda, Silvia, 2024. "Gender gap in standardized tests: What are we measuring?," Journal of Economic Behavior & Organization, Elsevier, vol. 221(C), pages 191-229.
    6. Eric A. Hanushek, 2005. "The economic value of improving local schools," Proceedings, Federal Reserve Bank of Cleveland, pages 59-72.
    7. Eric A. Hanushek & Margaret E. Raymond, 2006. "School accountability and student performance," Regional Economic Development, Federal Reserve Bank of St. Louis, issue Mar, pages 51-61.
    8. Mansoor, Umer & Jamal, Arshad & Su, Junbiao & Sze, N.N. & Chen, Anthony, 2023. "Investigating the risk factors of motorcycle crash injury severity in Pakistan: Insights and policy recommendations," Transport Policy, Elsevier, vol. 139(C), pages 21-38.
    9. Paul Hewson & Keming Yu, 2008. "Quantile regression for binary performance indicators," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 401-418, September.
    10. Rodrigo M. S. Moita & Claudio Paiva, 2013. "Political Price Cycles in Regulated Industries: Theory and Evidence," American Economic Journal: Economic Policy, American Economic Association, vol. 5(1), pages 94-121, February.
    11. Lee, Jong-Wha, 2005. "Human capital and productivity for Korea's sustained economic growth," Journal of Asian Economics, Elsevier, vol. 16(4), pages 663-687, August.
    12. Emanuela di Gropello, 2006. "Meeting the Challenges of Secondary Education in Latin America and East Asia : Improving Efficiency and Resource Mobilization," World Bank Publications - Books, The World Bank Group, number 7173.
    13. Patrick M. Emerson & Vladimir Ponczek & André Portela Souza, 2017. "Child Labor and Learning," Economic Development and Cultural Change, University of Chicago Press, vol. 65(2), pages 265-296.
    14. Qian, Nancy & Lagakos, David & Moll, Benjamin & Porzio, Tommaso, 2012. "Experience Matters: Human Capital and Development Accounting," CEPR Discussion Papers 9253, C.E.P.R. Discussion Papers.
    15. Carstensen Kai & Hartmann Susanne & Gundlach Erich, 2009. "The Augmented Solow Model with Mincerian Schooling and Externalities," German Economic Review, De Gruyter, vol. 10(4), pages 448-463, December.
    16. Jørn Rattsø & Hildegunn E. Stokke, 2011. "Accumulation of education and regional income growth: Limited human capital effects in Norway," Working Paper Series 11211, Department of Economics, Norwegian University of Science and Technology.
    17. Bissan Ghaddar & Ignacio Gómez-Casares & Julio González-Díaz & Brais González-Rodríguez & Beatriz Pateiro-López & Sofía Rodríguez-Ballesteros, 2023. "Learning for Spatial Branching: An Algorithm Selection Approach," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1024-1043, September.
    18. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
    19. Jewson Stephen & Penzer Jeremy, 2006. "Estimating Trends in Weather Series: Consequences for Pricing Derivatives," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-17, September.
    20. Bagella, Michele & Becchetti, Leonardo & Hasan, Iftekhar, 2004. "The anticipated and concurring effects of the EMU: exchange rate volatility, institutions and growth," Journal of International Money and Finance, Elsevier, vol. 23(7-8), pages 1053-1080.
    21. Luebke, Karsten & Czogiel, Irina & Weihs, Claus, 2004. "Latent Factor Prediction Pursuit for Rank Deficient Regressors," Technical Reports 2004,75, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0248525. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.