BIG DATA AND MACHINE LEARNING IN MATHEMATICS TEACHING: STRATEGIES BASED ON STATISTICAL ANALYSIS FOR THE DEVELOPMENT OF ALGEBRAIC AND QUANTITATIVE THINKING IN BASIC EDUCATION
Álaze Gabriel do
Breviário
Master
of Theology.
Master
of Psychology.
Ivy
Enber Christian University (IECU).
Orlando,
Florida, United States.
Email:
alaze_p7sd8sin5@yahoo.com.br.
URL
Lattes: http://lattes.cnpq.br/9973998907456283.
ORCID: https://orcid.org/0000-0002-9480-6325.
Jaine Marques de Souza
Specialist
in Special Education. Portuguese Language Teacher.
ICM
School.
Email:
sousajaine9@gmail.com.
URL
Lattes: http://lattes.cnpq.br/2760508798931944.
João Batista Lucena
Master's student in Education.
Federal Institute of Education, Science and Technology of Rio
Grande do Norte, IFRN.
Natal, Rio Grande do Norte, Brazil.
Email: joao.batista.lucena@gmail.com.
URL Lattes: http://lattes.cnpq.br/2822567703207399.
Logan Faedda Rago
Master of Science in Education and Christian Ethics.
Ivy Enber Christian University, IECU.
Orlando, Florida, United States.
Email: loganfaedda@hotmail.com.
URL Lattes: https://lattes.cnpq.br/2516880221903287.
Marcelo D’Ávilla
Teixeira Gomes
Master in
Social Management, Education and Regional Development.
Vale do Cricaré
University Center (UNIVC). São Mateus, ES, Brazil.
Professor at
the Municipal Department of Education of São Mateus (ES).
E-mail: cpldavilla@gmail.com.
URL Lattes: https://lattes.cnpq.br/3740114889508259.
ORCID: https://orcid.org/0009-0003-3471-225X.
Deusirene Sousa da Silva
Fróes
PhD candidate in Education Sciences and Christian Ethics.
Ivy Enber Christian University, IECU.
Orlando, Florida, United States.
Email: deusirenesousasilvafroes@gmail.com.
URL Lattes: https://lattes.cnpq.br/0218139923264576.
ABSTRACT
This
research addresses the use of Big Data and Machine Learning in Mathematics
teaching, focusing on how these technologies can promote the development of
algebraic and quantitative thinking in Basic Education. The increasing
insertion of Digital Information and Communication Technologies (DITs) in
teaching requires new methodological approaches that integrate technological
tools efficiently. The central problem involves the resistance to the use of
these technologies by educators and the lack of infrastructure in schools. The
main objective is to investigate how Big Data and Machine Learning can be
applied in Mathematics teaching to improve student learning. The methodology
adopted is based on the Giftedean neoperspectivist paradigm, using theories
such as Critical Pedagogy, Constructivism and the Theory of Meaningful
Learning. The method used was hypothetical-deductive, combined with
bibliographic and documentary narrative review, with consultation of databases
such as Google Scholar, Scopus and ERIC, totaling the analysis of 50 works. The
main findings indicate that the use of Big Data and Machine Learning in
Mathematics teaching can contribute to personalizing learning, but faces
challenges in terms of infrastructure and teacher training. The gaps found
include the lack of consolidated theories on the use of these technologies in mathematics
education. Limitations include the qualitative approach and the restricted
scope of the research. The contributions include a better understanding of how
these technologies can transform Mathematics teaching. The added value lessons
in the promotion of more inclusive and innovative educational practices.
Keywords: Personalization of Learning. Mathematics Teaching. Digital Technologies. Educational Inclusion. Teacher Training.
Reviewed by Current Scientific Journal
on
abril 28, 2025
Rating:
