Ensino de estatística descritiva com Python : uma proposta aplicada ao ensino médio

Abstract

The teaching of Descriptive Statistics in High School faces challenges related to data interpretation, tables, graphs, and the understanding of measures of central tendency and dispersion, difficulties frequently observed in school practice. Considering the importance attributed by the Brazilian National Common Core Curriculum to the reading, interpretation, and critical analysis of statistical information, this study aimed to propose, apply, describe, analyze, and reflect on a sequence of activities for Descriptive Statistics mediated by the Python programming language, aiming to integrate calculations, graphical representations, and data interpretation. This study is an experience report with a qualitative approach, developed in High School Mathematics classes at Colégio Estadual de Tempo Integral Castro Alves, located in Adustina, Bahia, Brazil. The methodological proposal consisted of integrating conventional instruction of statistical content with computational practices in Python, in which students simulated data, organized information into tables, constructed graphs, and calculated statistical measures, relating these results to the analyzed data sets. The data were analyzed through triangulation involving teacher observations, students' productions, and a feedback questionnaire, aiming to understand the perceptions and reflections emerging from the developed experience. As an educational product, a sequence of activities mediated by Python was developed, aiming at the development of statistical interpretation. The results indicate that the proposed approach has the potential to foster the understanding of Descriptive Statistics concepts and expand the possibilities for data interpretation, in line with the demands of Mathematics Education. It is not intended to claim that the use of Python, by itself, guarantees better learning outcomes, but rather that its use as a mediating tool showed potential to foster statistical interpretation processes, expand possibilities for data analysis, and stimulate students' engagement in the proposed activities.