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Uma proposta para o ensino de matrizes utilizando a metodologia sala de aula invertida

Detalhes bibliográficos
Ano de defesa: 2020
Autor(a) principal: Sachini, Luciana
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal da Fronteira Sul
Brasil
Campus Chapecó
Programa de Pós-Graduação Profissional em Matemática em Rede Nacional
UFFS
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: https://rd.uffs.edu.br/handle/prefix/3888
Resumo: We present in this work a methodological proposal that contemplates a didactic sequence about Matrix contents, for High School, using as methodology the Inverted Classroom allied to the Digital Technologies of Information and Communication. With this proposal, we seek to become students more active in the educational process, develop their autonomy and provide problem solving with themes more related to their reality in order to promote more meaningful learning. To this end, we developed a research of a theoretical and descriptive nature. In this sense, we elaborated a didactic sequence that contemplates activities to be developed in nonface-to-face and face-to-face moments, which form the basis of the Inverted Classroom methodology, and for non-face-to-face (but online) moments students have explanatory video lessons Matrix contents, recorded by the teacher, as well as exercises / problems related to that content. In the classroom, the teacher coordinates discussions and reflections on previously studied concepts, besides solving exercises / problems related to these contents. Considering that, the didactic material we elaborate has characteristics of being potentially significant for the student, allied to his predisposition to learn, we conclude that this material has the possibility of generating significant learning of Matrix contents.