Interdisciplinarity in mathematics grade performance: a look at the evolution of the teaching process through regressive models
Abstract
Access to a large volume of open data has expanded the possibilities for improving public systems management. One of these bases is the National Secondary Education Examination (ENEM). It contains relevant information from test performance to socioeconomic and cultural characteristics of candidates. In this scenario, this work aimed to analyze the relationship of math gra- des with other grades in different areas, including writing, using ENEM 2019 as the basis for analysis. The results obtained show that other grades influence the math grade (MT). Among the eight models applied, the Gradient Boosting was the best, with a 7.4% error in the TM prediction. This analysis is rele- vant because we can guide public policies that can improve overall academic performance.
Keywords:
Educational Data Mining, Student Performance, ENEM
References
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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.
Published
2021-11-23
How to Cite
FERREIRA, Mirley Bitencourt; AMORIM, Myrna; OGASAWARA, Eduardo; BARBASTEFANO, Rafael.
Interdisciplinarity in mathematics grade performance: a look at the evolution of the teaching process through regressive models. In: REGIONAL SCHOOL ON INFORMATICS OF 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.
