A interdisciplinaridade no desempenho da nota de matemática: um olhar para evolução do processo de ensino por meio de modelos regressivos
Resumo
O acesso a um grande volume de dados abertos ampliou as possibilidades de melhoria da gestão dos sistemas públicos. Uma dessas bases é o Exame Nacional do Ensino Médio (ENEM). Nela há informações relevantes do candidato e de seu desempenho nas provas. Nesse cenário, este trabalho teve como objetivo analisar a relação das notas de matemática com outras notas em diferentes áreas, incluindo a escrita, da base do ENEM 2019. Os resultados obtidos apresentam que a nota de matemática (MT) é influenciada pelas demais notas e dentre os oito modelos aplicados o melhor foi o Gradient Boosting com 7,4% de erro na previsão da MT. Essa análise é relevante porque podemos orientar políticas públicas que podem melhorar o desempenho acadêmico geral.
Palavras-chave:
Mineração de Dados Educacionais, Desempenho Estudantil, ENEM
Referências
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INEP. Microdados do ENEM 2019. Technical report, https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/enem, 2020.
R.A. Lazowski and C.S. Hulleman. Motivation Interventions in Education: A Meta-Analytic Review. Review of Educational Research, 86(2):602–640, 2016.
MEC. Parâmetros Curriculares Nacionais. Technical report, http://basenacionalcomum.mec.gov.br/implementacao, 2021.
OECD. Programme for International Student Assessment. Technical report, https://www.oecd.org/pisa/, 2021.
E. Oppermann and R. Lazarides. Elementary school teachers’ self-efficacy, studentperceived support and students’ mathematics interest. Teaching and Teacher Education, 103, 2021.
C. Romero and S. Ventura. Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 40 (6):601–618, 2010.
Carla Madalena Santos, Rubia Amanda Franco, Diego Leon, Daniel Bovolenta Ovigli, and Pedro Donizete Colombo. Interdisciplinarity in Education: Overcoming Fragmentation in the Teaching-Learning Process. International Education Studies, 10(10): 71–77, 2017. ISSN 1913-9020.
P. Shayan and M. van Zaanen. Predicting student performance from their behavior in learning management systems. International Journal of Information and Education Technology, 9(5):337–341, 2019.
Augusto Simon and Sílvio Cazella. Mineração de dados educacionais nos resultados do ENEM de 2015. In Anais dos Workshops do Congresso Brasileiro de Informática na Educação, volume 6, page 754, October 2017.
S.D. Simpkins, P.E. Davis-Kean, and J.S. Eccles. Math and science motivation: A longitudinal examination of the links between choices and beliefs. Developmental Psychology, 42(1):70–83, 2006.
Svein Sjøberg. PISA: A success story? In Roman Langer and Thomas Brüsemeister, editors, Handbuch Educational Governance Theorien, Educational Governance, pages 653–690. Springer Fachmedien, Wiesbaden, 2019. ISBN 978-3-658-22237-6.
B. Stearns, F. Rangel, F. Rangel, F.F. De Faria, and J. Oliveira. Scholar performance prediction using boosted regression trees techniques. In ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pages 329–334, 2017.
G.C. Wiedenhöft, E.M. Luciano, and G.V. Pereira. Information Technology Governance Institutionalization and the Behavior of Individuals in the Context of Public Organizations. Information Systems Frontiers, 22(6):1487–1504, 2020.
UM Fayyad, G Piatetsky-Shapiro, and P Smyth. Knowledge Discovery and Data Mining: Towards a Unifying Framework. In International Conference on Knowledge Discovery and Data Mining, pages 82–88, 1996. ISBN 1-57735-004-9. URL http://www.aaai.org/Papers/KDD/1996/KDD96-014.
INEP. Microdados do ENEM 2019. Technical report, https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/enem, 2020.
R.A. Lazowski and C.S. Hulleman. Motivation Interventions in Education: A Meta-Analytic Review. Review of Educational Research, 86(2):602–640, 2016.
MEC. Parâmetros Curriculares Nacionais. Technical report, http://basenacionalcomum.mec.gov.br/implementacao, 2021.
OECD. Programme for International Student Assessment. Technical report, https://www.oecd.org/pisa/, 2021.
E. Oppermann and R. Lazarides. Elementary school teachers’ self-efficacy, studentperceived support and students’ mathematics interest. Teaching and Teacher Education, 103, 2021.
C. Romero and S. Ventura. Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 40 (6):601–618, 2010.
Carla Madalena Santos, Rubia Amanda Franco, Diego Leon, Daniel Bovolenta Ovigli, and Pedro Donizete Colombo. Interdisciplinarity in Education: Overcoming Fragmentation in the Teaching-Learning Process. International Education Studies, 10(10): 71–77, 2017. ISSN 1913-9020.
P. Shayan and M. van Zaanen. Predicting student performance from their behavior in learning management systems. International Journal of Information and Education Technology, 9(5):337–341, 2019.
Augusto Simon and Sílvio Cazella. Mineração de dados educacionais nos resultados do ENEM de 2015. In Anais dos Workshops do Congresso Brasileiro de Informática na Educação, volume 6, page 754, October 2017.
S.D. Simpkins, P.E. Davis-Kean, and J.S. Eccles. Math and science motivation: A longitudinal examination of the links between choices and beliefs. Developmental Psychology, 42(1):70–83, 2006.
Svein Sjøberg. PISA: A success story? In Roman Langer and Thomas Brüsemeister, editors, Handbuch Educational Governance Theorien, Educational Governance, pages 653–690. Springer Fachmedien, Wiesbaden, 2019. ISBN 978-3-658-22237-6.
B. Stearns, F. Rangel, F. Rangel, F.F. De Faria, and J. Oliveira. Scholar performance prediction using boosted regression trees techniques. In ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pages 329–334, 2017.
G.C. Wiedenhöft, E.M. Luciano, and G.V. Pereira. Information Technology Governance Institutionalization and the Behavior of Individuals in the Context of Public Organizations. Information Systems Frontiers, 22(6):1487–1504, 2020.
Publicado
23/11/2021
Como Citar
FERREIRA, Mirley Bitencourt; AMORIM, Myrna; OGASAWARA, Eduardo; BARBASTEFANO, Rafael.
A interdisciplinaridade no desempenho da nota de matemática: um olhar para evolução do processo de ensino por meio de modelos regressivos. In: ESCOLA REGIONAL DE INFORMÁTICA DO RIO DE JANEIRO (ERI-RJ), 4. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 41-48.
DOI: https://doi.org/10.5753/eri-rj.2021.18773.
