FORMAÇÃO CONTINUADA DE PROFESSORES ALFABETIZADORES: COMO APRENDE O PROFESSOR?

Este estudo versa sobre a análise do processo de aprendizagem de alguns professores alfabetizadores da Rede Estadual de Educação de São Paulo e buscou compreender onde e como este grupo de profissionais aprende os conteúdos necessários para alfabetizar na perspectiva da construção de conhecimento. A hipótese que orienta esta análise é a de que um professor bem-formado tem mais condição de intervir na aprendizagem de seus alunos e, gradativamente, contribuir para a garantia do direito de todas as crianças se alfabetizarem. Parte também da hipótese de que, compreendendo o processo de aprendizagem destes educadores, as ações de formação podem ser mais eficazes. Por meio de pesquisa empírica, revisão bibliográfica e análise documental, esta dissertação organiza-se da seguinte forma: na Introdução é anunciada a trajetória profissional da proponente e sua relação com os programas de alfabetização e com as propostas de formação de educadores das Redes Públicas de Educação de São Paulo. O primeiro Capítulo tece a fundamentação teórica na qual os programas de formação de professores alfabetizadores de São Paulo são organizados. No segundo Capítulo é realizado um breve histórico sobre os programas de formação de professores alfabetizadores da Rede Estadual de Ensino, no período de 1984 a 2010, com uma análise sobre a coerência entre as concepções de ensino e aprendizagem, e as concepções que embasam os diferentes programas institucionais. No terceiro Capítulo são descritos os caminhos percorridos no processo de realização da pesquisa empírica. Nas Considerações Finais foram retomadas, em síntese, as discussões realizadas no desenvolvimento dos capítulos, recuperando a essência das análises a partir dos dados coletados, expressando os resultados da investigação. Foram aplicados questionários de perfil e realizadas entrevistas com 13 sujeitos envolvidos com o Programa Ler e Escrever da Rede Estadual de Ensino de São Paulo, envolvendo desde professores alfabetizadores até os organizadores do Programa. A fundamentação teórica deste estudo ocorreu, principalmente, a partir das contribuições de Piaget (1967); Ferreiro e Teberosky (1985); Ferreiro (1989; 1992; 2002; 2007); Becker (1993); Coll (1992); Lerner (2002) que se orientam pela concepção de construção de conhecimento e tratam da aprendizagem da leitura e escrita, da formação do professor e da didática da alfabetização e da Matemática; e Alarcão (2008), Lerner (2002) e Tardif (2002) que abordam a formação docente. A análise dos dados foi realizada a partir da fundamentação e dos procedimentos de Análise Temática do Conteúdo, conforme Bardin (2008) que resultou na elaboração de apreciações de condições que devem ser consideradas na formação de professores alfabetizadores. Os resultados obtidos apontam para algumas inferências sobre o processo de aprendizagem dos professores alfabetizadores que, se consideradas nas propostas, podem contribuir para a maior eficiência dos programas de formação, resultando em melhor apropriação pelos professores dos conteúdos envolvidos na alfabetização, na melhoria da qualidade da aprendizagem dos alfabetizandos e, consequentemente, na formação de leitores plenos e escritores eficientes.

Description and training of neural network dynamics

Attractor properties of a popular discrete-time neural network model are illustrated through numerical simulations. The most complex dynamics is found to occur within particular ranges of parameters controlling the symmetry and magnitude of the weight matrix. A small network model is observed to produce fixed points, limit cycles, mode-locking, the Ruelle-Takens route to chaos, and the period-doubling route to chaos. Training algorithms for tuning this dynamical behaviour are discussed. Training can be an easy or difficult task, depending whether the problem requires the use of temporal information distributed over long time intervals. Such problems require training algorithms which can handle hidden nodes. The most prominent of these algorithms, back propagation through time, solves the temporal credit assignment problem in a way which can work only if the relevant information is distributed locally in time. The Moving Targets algorithm works for the more general case, but is computationally intensive, and prone to local minima.

Efficient training of RBF networks for classification.

Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. We show how RBFs with logistic and softmax outputs can be trained efficiently using the Fisher scoring algorithm. This approach can be used with any model which consists of a generalised linear output function applied to a model which is linear in its parameters. We compare this approach with standard non-linear optimisation algorithms on a number of datasets.

Bayesian training of mixture density networks

Mixture Density Networks (MDNs) are a well-established method for modelling the conditional probability density which is useful for complex multi-valued functions where regression methods (such as MLPs) fail. In this paper we extend earlier research of a regularisation method for a special case of MDNs to the general case using evidence based regularisation and we show how the Hessian of the MDN error function can be evaluated using R-propagation. The method is tested on two data sets and compared with early stopping.

Efficient training of RBF networks for classification

Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. In this paper we show how RBFs with logistic and softmax outputs can be trained efficiently using algorithms derived from Generalised Linear Models. This approach is compared with standard non-linear optimisation algorithms on a number of datasets.

Education and racism:a study of the teachers' and the pupils' relations in the schooling of black boys

This thesis examines the teachers' and the pupils' relations in the schooling of black boys. The study using the methodology of participant observation focusses on one school (Kilby) in an area of black population in an English city. The thesis’s intentions are two fold: firstly, in order to examine these relations, two major aspects of their interaction are addressed, that of the absence of teachers from conventional 'race-relations' research, and, the identification and examination of the anti-school pupils' sub-cultures. Two substantive questions are asked: what is the response of the teachers to the schooling of black pupils? and, what is the meaning of the pupils' resistance to schooling? Secondly, in attempting to answer these questions and offer a critique of the dominant 'race-relations' culturalist explanation of black youth's response to schooling, a theoretical framework has been developed which takes account of both the 'economic' and the 'sociological' perspectives. Methodology allowed and pointed to the importance of examining the teachers' ideologies and practices as well as those of the black boys. It is argued that a class analysis of the racially structured British society is more adequate than the conventional ethnic approach in explaining the black boys' location within Kilby School. Hence, it is posited that the major problem in the schooling of black youth is not that of their culture but of racism, which pervasively structures the social reality at Kilby school. Racism is mediated both through the existing institutional framework that discriminates against working-class youth and through the operation of race specific mechanisms, such as the process of racist stereotyping. It is thus further argued that the Kilby school teachers are of central causal significance to the - problems that the boys encounter. Furthermore, it is in response to these racist ideologies and practices that both West Indian and Asian pupils develop specific forms of collective resistance, which are seen to be linked to the wider black community, as legitimate strategies of survival.

An initial evaluation of gellan gum as a material for tissue engineering applications

Alpha-modified minimum essential medium (αMEM) has been found to cross-link a 1% gellan gum solution, resulting in the formation of a self-supporting hydrogel in 1:1 and 5:1 ratios of polysaccharide: αMEM. Rheological data from temperature sweeps confirm that in addition to orders of magnitude differences in G' between 1% gellan and 1% gellan with αMEM, there is also a 20°C increase in the temperature at which the onset of gelation takes place when αMEM is present. Frequency sweeps confirm the formation of a true gel; mechanical spectra for mixtures of gellan and αMEM clearly demonstrate G' to be independent of frequency. It is possible to immobilize cells within a three-dimensional (3D) gellan matrix that remain viable for up to 21 days in culture by adding a suspension of rat bone marrow cells (rBMC) in αMEM to 1% gellan solution. This extremely simple approach to cell immobilization within 3D constructs, made possible by the fact that gellan solutions cross-link in the presence of millimolar concentrations of cations, poses a very low risk to a cell population immobilized within a gellan matrix and thus indicates the potential of gellan for use as a tissue engineering scaffold. © 2007 Sage Publications.

Discriminative training of the hidden vector state model for semantic parsing

In this paper, we discuss how discriminative training can be applied to the hidden vector state (HVS) model in different task domains. The HVS model is a discrete hidden Markov model (HMM) in which each HMM state represents the state of a push-down automaton with a finite stack size. In previous applications, maximum-likelihood estimation (MLE) is used to derive the parameters of the HVS model. However, MLE makes a number of assumptions and unfortunately some of these assumptions do not hold. Discriminative training, without making such assumptions, can improve the performance of the HVS model by discriminating the correct hypothesis from the competing hypotheses. Experiments have been conducted in two domains: the travel domain for the semantic parsing task using the DARPA Communicator data and the Air Travel Information Services (ATIS) data and the bioinformatics domain for the information extraction task using the GENIA corpus. The results demonstrate modest improvements of the performance of the HVS model using discriminative training. In the travel domain, discriminative training of the HVS model gives a relative error reduction rate of 31 percent in F-measure when compared with MLE on the DARPA Communicator data and 9 percent on the ATIS data. In the bioinformatics domain, a relative error reduction rate of 4 percent in F-measure is achieved on the GENIA corpus.

The use of open learning in the training of neurotechnologists:a regional case study

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The education and training of technicians in the construction industry

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The effectiveness of an organisations' equal opportunities policy for the role and training of women, particularly in the fire service

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Methodological Foundations of Fundamental Engineering Education

In the paper, methodological aspects of nowadays high engineering education are considered. Thoughts generalizing author’s long-term experience are set forth. Recommendations on the improvement of pedagogical process and training system for young teachers are given.