The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil
Abstract
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.
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Cites
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Analyzing undergraduate students' performance using educational data mining
Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017) · Computers & Education · doi:10.1016/j.compedu.2017.05.007 · Cited by 586
A Systematic Review on Educational Data Mining
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Metadata
- Authors
- Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., & Erven, G. V.
- Venue
- Journal of Business Research
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 94
- Pages
- 335-343
- ISSN
- 0148-2963 (ISSN-L)
- OpenAlex
- W2789308566
- Cited by
- 316
- References
- 18
- FWCI
- 44.74
- Type
- journal article
- Language
- en
- Open access
- closed
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- Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., & Erven, G. V. (2019). Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2018.02.012
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
