Studies on students’ motivation to learn Chemistry in developed countries are largely common in the literature. However, very few studies have been carried out with a view to investigating students'...

Resumo O presente estudo investigou as orientações motivacionais de estudantes do Ensino Médio em relação à disciplina de Física e buscou contribuir com o aprofundamento da discussão acerca da proposição e validação de instrumentos de medidas de variáveis psicológicas. Foi utilizada a Escala de Motivação: Atividades Didáticas de Física (EMADF) para avaliar 1155 estudantes. Foi realizada análise fatorial exploratória seguida de análise estatística descritiva. Foram encontrados cinco fatores que explicam 55,46% da variância total dos dados. Os resultados mostram uma regulação autodeterminada, com alunos majoritariamente motivados intrinsecamente e extrinsecamente por regulação identificada revelando, portanto, uma motivação autônoma ou autodeterminada. Esses resultados revelam um padrão altamente favorável, pois associa-se fortemente a comportamentos de esforço, atenção, persistência, proatividade e emoções mais positivas.

We study pruning strategies in simple perceptrons subjected to supervised learning. Our analytical results, obtained through the statistical mechanics approach to learning theory, are independent of the learning algorithm used in the training process. We calculate the post-training distribution P(J) of synaptic weights, which depends only on the overlap rho(0) achieved by the learning algorithm before pruning and the fraction kappa of relevant weights in the teacher network. From this distribution, we calculate the optimal pruning strategy for deleting small weights. The optimal pruning threshold grows from zero as straight theta(opt)(rho(0), kappa) approximately [rho(0)-rho(c)(kappa)](1/2) above some critical value rho(c)(kappa). Thus, the elimination of weak synapses enhances the network performance only after a critical learning period. Possible implications for biological pruning phenomena are discussed.

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We investigate, through statistical mechanics techniques. the problem of finding Ntuples J = (J I , h. .. . , JN) wilh a fixed fraction x of non-vanishing entries that solve 3 set of P = aN iinem equations. In particular, we determine the reginn in the plane (a, E) where these solutions exist with a probability of 1 in lhe limit of large N. We also obtain at insight into the microscopic srmcture of these solutions by calculating the distribution of the probability of an arbitmy non-vanishing enVy J,. Moreover we evaluate malytically the performances of several easy-to-implement heuristic algorithms and compare them with the optimal solution. a task that 1s suited very well to the statisticnl mechanics approach.-Kohonen T 1984 Self-organization m d Ass~ciative Memory

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