BIG DATA AND MACHINE LEARNING IN MATHEMATICS TEACHING: STRATEGIES BASED ON STATISTICAL ANALYSIS FOR THE DEVELOPMENT OF ALGEBRAIC AND QUANTITATIVE THINKING IN BASIC EDUCATION

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.