O pensamento computacional na prática: uma experiência usando Python em aulas de Matemática Básica

• Ano de defesa: 2021
• Autor(a) principal: Grave, Leomir Augusto Severo
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Brasil; Matemática; UFSM; Programa de Pós-Graduação em Matemática em Rede Nacional; Centro de Ciências Naturais e Exatas
• Palavras-chave em Português: Ensino de matemática; Pensamento computacional; Resolução de problemas; Linguagem de programação; Python; Mathematical teaching; Computational thinking; Problem solving; Programing language; CNPQ::CIENCIAS EXATAS E DA TERRA::MATEMATICA
• Link de acesso: http://repositorio.ufsm.br/handle/1/21866

Teaching in Brazilian schools has achieved results below the desired level for years. In the most recent PISA exam (International Student Assessment Program), held in 2018, it was found that most Brazilian students do not have the ability to answer clearly defined questions that have direct data and instructions. Thus, it is urgent to look for alternatives to improve learning, especially for the Mathematics teaching. In this sense, there is a need for a greater inclusion of computing and the development of computational thinking in basic education, since this is an important tool to help students develop mathematical logical reasoning. This work aims to study alternatives and introduce programming together with computational thinking in Mathematics classes in a basic education class, following the general guidelines of the BNCC (National Common Curricular Base). Theoretical contribution was made to George Polya’s mathematical problem solving methodology and to the problem solving strategies based on computational thinking. The research reports the application of a study with students of the seventh year of elementary school in a private school in the city of Cruz Alta / RS. Three remote activities were carried out through Google Colab, using Python to solve problems, in which students developed their codes and followed the development of their colleagues’ activities. The results obtained demonstrate that students create autonomy when interacting with the machine and develop their own plans to solve mathematical problems.