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

Eduardo Fernandes, Maristela Holanda, Marcio Victorino, Vinicius Borges, Rommel Carvalho and Gustavo Van Erven

Journal of Business Research, 2019, vol. 94, issue C, 335-343

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.

Keywords: Educational data mining; Academic performance; Predictive analysis; Decision tree; Gradient boosting machine; H2O (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0148296318300870
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:94:y:2019:i:c:p:335-343

DOI: 10.1016/j.jbusres.2018.02.012

Access Statistics for this article

Journal of Business Research is currently edited by A. G. Woodside

More articles in Journal of Business Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().