922 resultados para Practice as a curriculum component
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Mestrado (PES II) em Educação Pré-Escolar e Ensino do 1.º Ciclo do Ensino Básico.
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Mestrado (PES II) em Educação Pré-Escolar e Ensino do 1.º Ciclo do Ensino Básico.
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ECER 2014 "The Past, the Present and Future of Educational Research in Europe" will take place at the University of Porto from 1 - 5 September 2014.
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Inflammatory bowel diseases (IBDs) are lifelong disorders predominantly present in developed countries. In their pathogenesis, an interaction between genetic and environmental factors is involved. This practice guide, prepared on behalf of the European Society of Pathology and the European Crohn's and Colitis Organisation, intends to provide a thorough basis for the histological evaluation of resection specimens and biopsy samples from patients with ulcerative colitis or Crohn's disease. Histopathologically, these diseases are characterised by the extent and the distribution of mucosal architectural abnormality, the cellularity of the lamina propria and the cell types present, but these features frequently overlap. If a definitive diagnosis is not possible, the term indeterminate colitis is used for resection specimens and the term inflammatory bowel disease unclassified for biopsies. Activity of disease is reflected by neutrophil granulocyte infiltration and epithelial damage. The evolution of the histological features that are useful for diagnosis is time- and disease-activity dependent: early disease and long-standing disease show different microscopic aspects. Likewise, the histopathology of childhood-onset IBD is distinctly different from adult-onset IBD. In the differential diagnosis of severe colitis refractory to immunosuppressive therapy, reactivation of latent cytomegalovirus (CMV) infection should be considered and CMV should be tested for in all patients. Finally, patients with longstanding IBD have an increased risk for the development of adenocarcinoma. Dysplasia is the universally used marker of an increased cancer risk, but inter-observer agreement is poor for the categories low-grade dysplasia and indefinite for dysplasia. A diagnosis of dysplasia should not be made by a single pathologist but needs to be confirmed by a pathologist with expertise in gastrointestinal pathology.
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Mestrado (PES II), Educação Pré-Escolar e Ensino do 1.º Ciclo do Ensino Básico, 24 de Junho de 2015, Universidade dos Açores.
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Dissertação de Mestrado, Educação Pré-Escolar e Ensino do 1.º Ciclo do Ensino Básico, 16 de Junho de 2015, Universidade dos Açores.
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Mestrado (PES II), Educação Pré-Escolar e Ensino do 1.º Ciclo do Ensino Básico, 22 de Junho de 2015, Universidade dos Açores.
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Mestrado (PES II), Educação Pré-Escolar e Ensino do 1.º Ciclo do Ensino Básico, 18 de Junho de 2015, Universidade dos Açores.
Narração de histórias na educação de valores na Educação Pré-Escolar e no 1.º Ciclo do Ensino Básico
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Mestrado (PES II), Educação Pré-Escolar e Ensino do 1.º Ciclo do Ensino Básico, 22 de Junho de 2015, Universidade dos Açores.
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The development of children's school achievements in mathematics is one of the most important aims of education in Poland. The results of research concerning monitoring of school achievements in maths is not optimistic. We can observe low levels of children’s understanding of the merits of maths, self-developed strategies in solving problems and practical usage of maths skills. This article frames the discussion of this problem in its psychological and didactic context and analyses the causes as they relate to school practice in teaching maths
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II European Conference on Curriculum Studies. "Curriculum studies: Policies, perspectives and practices”. Porto, FPCEUP, October 16th - 17th.
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Mestrado, Ensino de História e Geografia no 3.º Ciclo do Ensino Básico e no Ensino Secundário, 10 de Março de 2016, Universidade dos Açores (Relatório de Estágio).
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Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção do grau de Mestre em Educação - Especialização em Educação Especial
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Independent component analysis (ICA) has recently been proposed as a tool to unmix hyperspectral data. ICA is founded on two assumptions: 1) the observed spectrum vector is a linear mixture of the constituent spectra (endmember spectra) weighted by the correspondent abundance fractions (sources); 2)sources are statistically independent. Independent factor analysis (IFA) extends ICA to linear mixtures of independent sources immersed in noise. Concerning hyperspectral data, the first assumption is valid whenever the multiple scattering among the distinct constituent substances (endmembers) is negligible, and the surface is partitioned according to the fractional abundances. The second assumption, however, is violated, since the sum of abundance fractions associated to each pixel is constant due to physical constraints in the data acquisition process. Thus, sources cannot be statistically independent, this compromising the performance of ICA/IFA algorithms in hyperspectral unmixing. This paper studies the impact of hyperspectral source statistical dependence on ICA and IFA performances. We conclude that the accuracy of these methods tends to improve with the increase of the signature variability, of the number of endmembers, and of the signal-to-noise ratio. In any case, there are always endmembers incorrectly unmixed. We arrive to this conclusion by minimizing the mutual information of simulated and real hyperspectral mixtures. The computation of mutual information is based on fitting mixtures of Gaussians to the observed data. A method to sort ICA and IFA estimates in terms of the likelihood of being correctly unmixed is proposed.
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Linear unmixing decomposes a hyperspectral image into a collection of reflectance spectra of the materials present in the scene, called endmember signatures, and the corresponding abundance fractions at each pixel in a spatial area of interest. This paper introduces a new unmixing method, called Dependent Component Analysis (DECA), which overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical properties of hyperspectral data. DECA models the abundance fractions as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. The performance of the method is illustrated using simulated and real data.