A multi-level analysis of factors associated with student performance: a machine learning approach to the SAEB microdata

Rodrigo Tertulino, Laércio Alencar

Open source

DOI: 10.1007/s44217-026-01699-0
Published: 2026-06-05
Container: Discover Education
Publisher: Springer Science and Business Media LLC
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1007/s44217-026-01699-0,
  title = {A multi-level analysis of factors associated with student performance: a machine learning approach to the SAEB microdata},
  author = {Rodrigo Tertulino and Laércio Alencar},
  year = {2026},
  journal = {Discover Education},
  doi = {10.1007/s44217-026-01699-0},
  url = {https://doi.org/10.1007/s44217-026-01699-0}
}

RIS

TY  - JOUR
TI  - A multi-level analysis of factors associated with student performance: a machine learning approach to the SAEB microdata
AU  - Rodrigo Tertulino
AU  - Laércio Alencar
PY  - 2026
JO  - Discover Education
DO  - 10.1007/s44217-026-01699-0
UR  - https://doi.org/10.1007/s44217-026-01699-0
ER  -

APA

Tertulino, R., & Alencar, L. (2026). A multi-level analysis of factors associated with student performance: a machine learning approach to the SAEB microdata. Discover Education. https://doi.org/10.1007/s44217-026-01699-0

Source records

crossref · retrieved 2026-09-25T05:28:42.515Z