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Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil

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  • Fernandes, Eduardo
  • Holanda, Maristela
  • Victorino, Marcio
  • Borges, Vinicius
  • Carvalho, Rommel
  • Erven, Gustavo Van
Abstract
In this article, we present a predictive analysis of the academic performance of students in public schools of the Federal District of Brazil during the school terms of 2015 and 2016. Initially, we performed a descriptive statistical analysis to gain insight from data. Subsequently, two datasets were obtained. The first dataset contains variables obtained prior to the start of the school year, and the second included academic variables collected two months after the semester began. Classification models based on the Gradient Boosting Machine (GBM) were created to predict academic outcomes of student performance at the end of the school year for each dataset. Results showed that, though the attributes ‘grades' and ‘absences' were the most relevant for predicting the end of the year academic outcomes of student performance, the analysis of demographic attributes reveals that ‘neighborhood’, ‘school’ and ‘age’ are also potential indicators of a student's academic success or failure.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jbrese:v:94:y:2019:i:c:p:335-343
    DOI: 10.1016/j.jbusres.2018.02.012
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    References listed on IDEAS

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    1. Stefan Slater & Srećko Joksimović & Vitomir Kovanovic & Ryan S. Baker & Dragan Gasevic, 2017. "Tools for Educational Data Mining," Journal of Educational and Behavioral Statistics, , vol. 42(1), pages 85-106, February.
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    Cited by:

    1. Boto Ferreira, Mário & Costa Pinto, Diego & Maurer Herter, Márcia & Soro, Jerônimo & Vanneschi, Leonardo & Castelli, Mauro & Peres, Fernando, 2021. "Using artificial intelligence to overcome over-indebtedness and fight poverty," Journal of Business Research, Elsevier, vol. 131(C), pages 411-425.
    2. Zhang, Weidong & Zuo, Na & He, Wu & Li, Songtao & Yu, Lu, 2021. "Factors influencing the use of artificial intelligence in government: Evidence from China," Technology in Society, Elsevier, vol. 66(C).
    3. Tatiana Tutunaru, 2023. "Improving Assessment and Feedback in the Learning Process: Directions and Best Practices," Research & Education, Weik Press SRL, issue 8, pages 38-60, July.
    4. Joyce de Souza Zanirato Maia & Ana Paula Arantes Bueno & Joao Ricardo Sato, 2023. "Applications of Artificial Intelligence Models in Educational Analytics and Decision Making: A Systematic Review," World, MDPI, vol. 4(2), pages 1-26, May.
    5. 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.
    6. Farrukh Saleem & Zahid Ullah & Bahjat Fakieh & Faris Kateb, 2021. "Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning," Mathematics, MDPI, vol. 9(17), pages 1-22, August.
    7. Feng, Yi & Yin, Yunqiang & Wang, Dujuan & Dhamotharan, Lalitha, 2022. "A dynamic ensemble selection method for bank telemarketing sales prediction," Journal of Business Research, Elsevier, vol. 139(C), pages 368-382.
    8. Touria Hamim & Faouzia Benabbou & Nawal Sael, 2022. "Student Profile Modeling Using Boosting Algorithms," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), IGI Global, vol. 17(5), pages 1-13, September.
    9. Jesús García-Jiménez & Javier Rodríguez-Santero & Juan-Jesús Torres-Gordillo, 2020. "Influence of Contextual Variables on Educational Performance: A Study Using Hierarchical Segmentation Trees," Sustainability, MDPI, vol. 12(23), pages 1-10, November.

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