THE SOFTWARE ‘RULER AND COMPASS’ AS A TOOL FOR TEACHING GEOMETRY

Este artigo tem como objetivo relatar a experiência vivenciada com o uso da ferramenta (software) de ensino Régua e Compasso (RC) na 18º Semana Nacional de Ciência e Tecnologia do Instituto Federal Goiano Campus Urutaí, ocorrido em 2021. Usamos o software para o desenvolvimento de uma oficina do evento no formato remoto devido ao distanciamento social. A oficina foi organizada pelos bolsistas do subprojeto de Matemática do Programa Institucional de Bolsas de Iniciação à Docência (PIBID/Matemática). Primeiramente, estudamos o software, em seguida, criamos o roteiro da oficina, elaboramos as atividades, apresentamos o roteiro e as atividades à coordenação do PIBID para as devidas correções e orientações, realizamos a simulação e nos dias 07 e 08 de outubro de 2021 realizamos a oficina com o apoio do supervisor do subprojeto. Participaram da oficina 13 alunos. Todo o trabalho realizado foi importante para a nossa formação profissional. Consideramos o momento da realização das atividades o mais significativo da oficina, pois foi o momento de interação dos participantes conosco. Pudemos acompanhar o raciocínio deles e intervir provocando-os com questionamentos até que conseguissem chegar no resultado correto. Foi desafiador, prazeroso e enriquecedor.This article aims to report the experience using Compass and Ruler (C.a.R.) – a tool for teaching and experiencing geometry by René Grothmann–, in a workshop at the 18th National Week of Science and Technology of the Goiano Federal Institute (IFGoiano) - Campus Urutaí, 2021. Due to the social distance caused by the Sars-CoV-2 virus, the software was used in a remote workshop, which was organized by scholarship students of the Institutional Program of Initiation to Teaching Scholarship (PIBID/Mathematics) mathematics subproject. We studied the software, created the workshop session script, developed the activities, presented the workshop project to the PIBID coordination for corrections and guidelines, carried out a simulation and on October 7th, 8th, 2021, we held the workshop with thirteen students and the support of the subproject supervisor. The planning process and the pedagogical practice of the workshop were important for our professional training, notably the interaction between the participants and the scholarship students, as we followed their reasoning and intervened, provoking them with questions so that they could achieve the appropriate result

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost