Narrativas digitais para uma aprendizagem significativa no Ensino Superior: qual a percepção dos estudantes? = Digital narratives for significant learning in Higher Education: what is the perception of students?

Nos últimos anos, buscando desenvolver uma educação que atenda a demanda atual do mercado profissional, muito tem se falado sobre metodologias ativas, ensino híbrido, inovação no ambiente educacional, entre outros temas que nos remetem ao processo de ensino e aprendizagem. Mas, na literatura, pouco se encontra sobre a percepção dos estudantes do ensino superior em relação ao uso das metodologias ativas no processo de ensinagem. O estudante desse nível de ensino, em sua maioria, trabalha em parte do dia, cuida da família em outro momento e estuda em determinado turno, possuindo dezenas de ocupações e escassez de tempo. Para ele, deve-se maximizar sua aprendizagem no menor tempo possível, mesclando atividades presenciais e virtuais, mas evitando deixar excesso de atribuições acadêmicas fora do ambiente escolar, pois o mesmo não dará a devida atenção. A partir disso, torna-se importante conhecer a sua percepção sobre o uso de metodologias ativas de ensino, objetivo deste artigo. O presente trabalho buscou conhecer a percepção dos estudantes de graduação da rede particular de ensino sobre o uso de narrativas digitais para uma aprendizagem significativa. A partir de uma abordagem qualitativa, a pesquisa foi desenvolvida por meio de entrevistas individuais, grupo focal, diário de campo e observação, durante a realização de um curso de extensão universitária. Os resultados demonstraram a percepção dos estudantes em relação ao uso das narrativas digitais como metodologias ativas, destacando pontos positivos e suas objeções no tocante ao processo de ensino e aprendizagem, despertando novas inquietações e sugerindo que outras pesquisas sejam realizada

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