Improving Physics learning with virtual environments: an example on the phases of water

Usually students have misconceptions on the microscopic structure and behaviour of matter. In order to overcome these difficulties computer-based worlds seem useful to visualize physical and chemical processes allowing for a better conceptual understanding. Since more 3-D virtual environments need to be explored and evaluated in science education, we have built a 3-D virtual environment – “Virtual Water” – to support the learning of Physics and Chemistry at the final high school and first-year university levels. It is centered in the microscopic structure of water and explores concepts related to water phases and the transitions between them.Usually students have misconceptions on the microscopic structure and behaviour of matter. In order to overcome these difficulties computer-based worlds seem useful to visualize physical and chemical processes allowing for a better conceptual understanding. Since more 3-D virtual environments need to be explored and evaluated in science education, we have built a 3-D virtual environment – “Virtual Water” – to support the learning of Physics and Chemistry at the final high school and first-year university levels. It is centered in the microscopic structure of water and explores concepts related to water phases and the transitions between them

Improving Physics learning with virtual environments: an example on the phases of water

Usually students have misconceptions on the microscopic structure and behaviour of matter. In order to overcome these difficulties computer-based worlds seem useful to visualize physical and chemical processes allowing for a better conceptualunderstanding. Since more 3-D virtual environments need to be explored and evaluatedin science education, we have built a 3-D virtual environment – “Virtual Water” – tosupport the learning of Physics and Chemistry at the final high school and first-yearuniversity levels. It is centered in the microscopic structure of water and exploresconcepts related to water phases and the transitions between them.Usually students have misconceptions on the microscopic structure and behaviour of matter. In order to overcome these difficulties computer-based worlds seem useful to visualize physical and chemical processes allowing for a better conceptualunderstanding. Since more 3-D virtual environments need to be explored and evaluatedin science education, we have built a 3-D virtual environment – “Virtual Water” – tosupport the learning of Physics and Chemistry at the final high school and first-yearuniversity levels. It is centered in the microscopic structure of water and exploresconcepts related to water phases and the transitions between them

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

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

Das experiências imaginadas às experiências simuladas: o papel dos jogos sérios na aprendizagem de Física conceptual

Utilizou-se o jogo sério “Portal 2” para o estudo de conceitos de mecânica clássica de Física, com alunos de engenharia do primeiro ano do ensino superior, com insucesso escolar recorrente. A amostra do estudo foi composta por dezasseis alunos, igualmente repartidos pelos grupos experimental e de controlo. O estudo decorreu durante seis semanas, com uma duração total de vinte e duas horas. Comparativamente ao grupo de controlo, verificou-se que os alunos que jogaram o “Portal 2” aprenderam melhor os conceitos de Física, indiciando que os jogos sérios podem facilitar a aprendizagem qualitativa de conceitos de Física

Água virtual : desenvolvimento e avaliação de um ambiente virtual para o ensino e aprendizagem da Física e da Química

Tese de doutoramento em Física (História e Ensino da Física) apresentada à Fac. de Ciências e Tecnologia da Univ. de CoimbraEste trabalho descreve o uso de ambientes virtuais no ensino e aprendizagem da Física e da Química. O ambiente virtual que desenvolvemos, intitulado Água Virtual, centra-se na estrutura microscópica da água explorando os conceitos de fases da matéria, transições de fase e ainda de orbitais atómicas e moleculares. Efectuámos um estudo qualitativo com alunos dos cursos de Física, Química, Química Industrial, Engenharia Física e Engenharia Civil do primeiro ano da Universidade de Coimbra. O sistema de realidade virtual consiste num ambiente virtual composto de vários cenários, que podem ser visualizados num ecrã de computador com ou sem óculos estereoscópicos. A interacção realiza-se por meio de um rato. Averiguámos a influência de ferramentas de visualização tridimensional na compreensão de conceitos científicos por alunos com boas capacidades de aptidão espacial mas que obtiveram classificações menos boas nas disciplinas de Física e de Química do 10º ao 12º ano de escolaridade. Os resultados obtidos permitem concluir que ferramentas gráficas de visualização, que disponibilizam animações tridimensionais e possibilitam interacção e navegação, se mostraram bastante úteis na compreensão de transições de fase mas o mesmo já não aconteceu em igual medida relativamente às fases. Relativamente à compreensão conceptual de orbitais atómicas não se registaram benefícios estatisticamente significativos. O efeito de estereoscopia não se mostrou determinante na compreensão conceptual excepto no caso da melhor percepção de redes tridimensionais como a do gelo