772 resultados para Victorian Certificate of Applied Learning


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This study is about the challenges of learning in the creation and implementation of new sustainable technologies. The system of biogas production in the Programme of Sustainable Swine Production (3S Programme) conducted by the Sadia food processing company in Santa Catarina State, Brazil, is used as a case example for exploring the challenges, possibilities and obstacles of learning in the use of biogas production as a way to increase the environmental sustainability of swine production. The aim is to contribute to the discussion about the possibilities of developing systems of biogas production for sustainability (BPfS). In the study I develop hypotheses concerning the central challenges and possibilities for developing systems of BPfS in three phases. First, I construct a model of the network of activities involved in the BP for sustainability in the case study. Next, I construct a) an idealised model of the historically evolved concepts of BPfS through an analysis of the development of forms of BP and b) a hypothesis of the current central contradictions within and between the activity systems involved in BP for sustainability in the case study. This hypothesis is further developed through two actual empirical analyses: an analysis of the actors senses in taking part in the system, and an analysis of the disturbance processes in the implementation and operation of the BP system in the 3S Programme. The historical analysis shows that BP for sustainability in the 3S Programme emerged as a feasible solution for the contradiction between environmental protection and concentration, intensification and specialisation in swine production. This contradiction created a threat to the supply of swine to the food processing company. In the food production activity, the contradiction was expressed as a contradiction between the desire of the company to become a sustainable company and the situation in the outsourced farms. For the swine producers the contradiction was expressed between the contradictory rules in which the market exerted pressure which pushed for continual increases in scale, specialisation and concentration to keep the production economically viable, while the environmental rules imposed a limit to this expansion. Although the observed disturbances in the biogas system seemed to be merely technical and localised within the farms, the analysis proposed that these disturbances were formed in and between the activity systems involved in the network of BPfS during the implementation. The disturbances observed could be explained by four contradictions: a) contradictions between the new, more expanded activity of sustainable swine production and the old activity, b) a contradiction between the concept of BP for carbon credits and BP for local use in the BPfS that was implemented, c) contradictions between the new UNFCCC1 methodology for applying for carbon credits and the small size of the farms, and d) between the technologies of biogas use and burning available in the market and the small size of the farms. The main finding of this study relates to the zone of proximal development (ZPD) of the BPfS in Sadia food production chain. The model is first developed as a general model of concepts of BPfS and further developed here to the specific case of the BPfS in the 3S Programme. The model is composed of two developmental dimensions: societal and functional integration. The dimension of societal integration refers to the level of integration with other activities outside the farm. At one extreme, biogas production is self-sufficient and highly independent and the products of BP are consumed within the farm, while at the other extreme BP is highly integrated in markets and networks of collaboration, and BP products are exchanged within the markets. The dimension of functional integration refers to the level of integration between products and production processes so that economies of scope can be achieved by combining several functions using the same utility. At one extreme, BP is specialised in only one product, which allows achieving economies of scale, while at the other extreme there is an integrated production in which several biogas products are produced in order to maximise the outcomes from the BP system. The analysis suggests that BP is moving towards a societal integration, towards the market and towards a functional integration in which several biogas products are combined. The model is a hypothesis to be further tested through interventions by collectively constructing the new proposed concept of BPfS. Another important contribution of this study refers to the concept of the learning challenge. Three central learning challenges for developing a sustainable system of BP in the 3S Programme were identified: 1) the development of cheaper and more practical technologies of burning and measuring the gas, as well as the reduction of costs of the process of certification, 2) the development of new ways of using biogas within farms, and 3) the creation of new local markets and networks for selling BP products. One general learning challenge is to find more varied and synergic ways of using BP products than solely for the production of carbon credits. Both the model of the ZPD of BPfS and the identified learning challenges could be used as learning tools to facilitate the development of biogas production systems. The proposed model of the ZPD could be used to analyse different types of agricultural activities that face a similar contradiction. The findings could be used in interventions to help actors to find their own expansive actions and developmental projects for change. Rather than proposing a standardised best concept of BPfS, the idea of these learning tools is to facilitate the analysis of local situations and to help actors to make their activities more sustainable.

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"Fifty-six teachers, from four European countries, were interviewed to ascertain their attitudes to and beliefs about the Collaborative Learning Environments (CLEs) which were designed under the Innovative Technologies for Collaborative Learning Project. Their responses were analysed using categories based on a model from cultural-historical activity theory [Engestrom, Y. (1987). Learning by expanding.- An activity-theoretical approach to developmental research. Helsinki: Orienta-Konsultit; Engestrom, Y., Engestrom, R., & Suntio, A. (2002). Can a school community learn to master its own future? An activity-theoretical study of expansive learning among middle school teachers. In G. Wells & G. Claxton (Eds.), Learning for life in the 21st century. Oxford: Blackwell Publishers]. The teachers were positive about CLEs and their possible role in initiating pedagogical innovation and enhancing personal professional development. This positive perception held across cultures and national boundaries. Teachers were aware of the fact that demanding planning was needed for successful implementations of CLEs. However, the specific strategies through which the teachers can guide students' inquiries in CLEs and the assessment of new competencies that may characterize student performance in the CLEs were poorly represented in the teachers' reflections on CLEs. The attitudes and beliefs of the teachers from separate countries had many similarities, but there were also some clear differences, which are discussed in the article. (c) 2005 Elsevier Ltd. All rights reserved."

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The influence of applied DC potentials on the activity and growth of Thiobacillus ferrooxidans, as well as on the dissolution behaviour of some base metal sulphides is discussed with reference to bioleaching. Selective bioleaching of zinc from sphalerite could be achieved under an applied potential of −500 mV (saturated calomel electrode) from binary mineral mixtures containing the zinc mineral and chalcopyrite or pyrite. On the other hand, bioleaching of pyrite and chalcopyrite was found to be enhanced under positive potentials of +400 mV and +600 mV, respectively. Probable mechanisms in the electrobioleaching of sulphides are examined with respect to galvanic, microbiological and applied potential effects.

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We propose an efficient and parameter-free scoring criterion, the factorized conditional log-likelihood (ˆfCLL), for learning Bayesian network classifiers. The proposed score is an approximation of the conditional log-likelihood criterion. The approximation is devised in order to guarantee decomposability over the network structure, as well as efficient estimation of the optimal parameters, achieving the same time and space complexity as the traditional log-likelihood scoring criterion. The resulting criterion has an information-theoretic interpretation based on interaction information, which exhibits its discriminative nature. To evaluate the performance of the proposed criterion, we present an empirical comparison with state-of-the-art classifiers. Results on a large suite of benchmark data sets from the UCI repository show that ˆfCLL-trained classifiers achieve at least as good accuracy as the best compared classifiers, using significantly less computational resources.

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This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN) based on multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT (N-1)(60)] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters (N-1)(60) and peck ground acceleration (a(max)/g)], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

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We propose for the first time two reinforcement learning algorithms with function approximation for average cost adaptive control of traffic lights. One of these algorithms is a version of Q-learning with function approximation while the other is a policy gradient actor-critic algorithm that incorporates multi-timescale stochastic approximation. We show performance comparisons on various network settings of these algorithms with a range of fixed timing algorithms, as well as a Q-learning algorithm with full state representation that we also implement. We observe that whereas (as expected) on a two-junction corridor, the full state representation algorithm shows the best results, this algorithm is not implementable on larger road networks. The algorithm PG-AC-TLC that we propose is seen to show the best overall performance.

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In this paper, we investigate the use of reinforcement learning (RL) techniques to the problem of determining dynamic prices in an electronic retail market. As representative models, we consider a single seller market and a two seller market, and formulate the dynamic pricing problem in a setting that easily generalizes to markets with more than two sellers. We first formulate the single seller dynamic pricing problem in the RL framework and solve the problem using the Q-learning algorithm through simulation. Next we model the two seller dynamic pricing problem as a Markovian game and formulate the problem in the RL framework. We solve this problem using actor-critic algorithms through simulation. We believe our approach to solving these problems is a promising way of setting dynamic prices in multi-agent environments. We illustrate the methodology with two illustrative examples of typical retail markets.

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When Priestley College began to plan the redevelopment of its learning resource centre, it continued the culture of student involvement that exists within the College by asking students to help plan and create the new development. This case study describes how the Jisc infoKit on 'Planning and Designing Technology-Rich Learning Spaces' was used as the starting point for ideas and planning, and how the finished development was the recognisable result of students' ideas and plans.

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[EN] The objective of this study was to test the hypothesis that cooperative learning strategies will help to increase nutrition knowledge of nurses and nursing assistants caring for the elderly in different institutional communities of the Basque Country, Spain. The target population was a sample of volunteers, 16 nurses and 28 nursing assistants. Training consisted of 12 nutrition education sessions using cooperative strategies conducted over a period of 3 consecutive weeks. The assessment instruments included two pretest and two posttest questionnaires with questions selected in multiplechoice format. The first questionnaire was about general knowledge of applied nutrition (0-88 point scale) and the second one on geriatric nutrition knowledge (0-18 point scale). Data were analyzed using SPSS vs. 11.0. The outcomes indicated a significant increase in general nutrition knowledge (difference between the pre- and posttest mean score: 14.5±10.1; P<0.001) and in geriatric nutrition knowledge for all participants (difference between the pre- and post-test mean score: 4.6±4.6; P<0.001). So the results indicated that cooperative learning strategies could improve the nutrition knowledge of nursing staff. Additionally, the results of this study provide direction to continuing nutrition education program planners regarding appropriate content and methodology for programs.

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Esse estudo teve por objetivo analisar as dinâmicas de mudanças organizacionais transcorridas em um estabelecimento de saúde. Conduzido através metodologia de estudo de caso descritivo, teve como campo de pesquisa o Instituto de Hematologia do Estado do Rio de Janeiro. Buscando conhecer o papel da preparação para acreditação na dinâmica da mudança em uma organização de saúde essa pesquisa foi assim estruturada: abordagem dos problemas encontrados nas mudanças organizacionais em estabelecimentos de saúde; quadro teórico estruturado com uma revisão de literatura; embasamento da metodologia aplicada, com definição de instrumentos de coletas de dados, de material e atores implicados no levantamento para realização da análise qualitativa. O estudo analisou as seguintes variáveis: a natureza da mudança focalizando a extensão, ritmo e trajetória; as estratégias de ação, contemplando as situações de adesões e resistência e a concepção, verificando se as mudanças foram indutivas ou dedutivas. O resultado demonstrou que a preparação para acreditação naquele hospital, proporcionou mudanças com movimentos lentos, mas com continuidade em todos os setores do estabelecimento. Foi identificada participação mais ativa de um grupo de profissionais identificados como facilitadores, funcionando como multiplicadores. A abrangência das estratégias aplicadas foram desde as reuniões em assembléias gerais, á formação de grupos de estudo por setores para entendimento do manual de padrões de acreditação. Foram realizados processos internos de auto-avaliação com base no manual de acreditação. Em relação á concepção, o processo de mudança foi motivado pela determinação da direção do hospital para obtenção do certificado de acreditação internacional. Quanto á resistência e adesão, o estudo demonstrou que a participação de uma grande maioria dos profissionais foi motivada pelo desejo de aprender e desenvolver novas práticas que proporcionasse a melhoria da qualidade da assistência. A análise de dados aponta certa resistência da categoria médica no início do processo. Do ponto de vista organizacional, foram criadas novas estruturas. A conclusão do estudo: O processo de preparação para acreditação na unidade de saúde estudada demonstrou ser um instrumento capaz de promover mudanças em organizações de saúde.

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Learning to perceive is faced with a classical paradox: if understanding is required for perception, how can we learn to perceive something new, something we do not yet understand? According to the sensorimotor approach, perception involves mastery of regular sensorimotor co-variations that depend on the agent and the environment, also known as the "laws" of sensorimotor contingencies (SMCs). In this sense, perception involves enacting relevant sensorimotor skills in each situation. It is important for this proposal that such skills can be learned and refined with experience and yet up to this date, the sensorimotor approach has had no explicit theory of perceptual learning. The situation is made more complex if we acknowledge the open-ended nature of human learning. In this paper we propose Piaget's theory of equilibration as a potential candidate to fulfill this role. This theory highlights the importance of intrinsic sensorimotor norms, in terms of the closure of sensorimotor schemes. It also explains how the equilibration of a sensorimotor organization faced with novelty or breakdowns proceeds by re-shaping pre-existing structures in coupling with dynamical regularities of the world. This way learning to perceive is guided by the equilibration of emerging forms of skillful coping with the world. We demonstrate the compatibility between Piaget's theory and the sensorimotor approach by providing a dynamical formalization of equilibration to give an explicit micro-genetic account of sensorimotor learning and, by extension, of how we learn to perceive. This allows us to draw important lessons in the form of general principles for open-ended sensorimotor learning, including the need for an intrinsic normative evaluation by the agent itself. We also explore implications of our micro-genetic account at the personal level.

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Humans have exceptional abilities to learn new skills, manipulate tools and objects, and interact with our environment. In order to be successful at these tasks, our brain has become exceptionally well adapted to learning to deal not only with the complex dynamics of our own limbs but also with novel dynamics in the external world. While learning of these dynamics includes learning the complex time-varying forces at the end of limbs through the updating of internal models, it must also include learning the appropriate mechanical impedance in order to stabilize both the limb and any objects contacted in the environment. This article reviews the field of human learning by examining recent experimental evidence about adaptation to novel unstable dynamics and explores how this knowledge about the brain and neuro-muscular system can expand the learning capabilities of robotics and prosthetics. © 2006.

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The optimization of dialogue policies using reinforcement learning (RL) is now an accepted part of the state of the art in spoken dialogue systems (SDS). Yet, it is still the case that the commonly used training algorithms for SDS require a large number of dialogues and hence most systems still rely on artificial data generated by a user simulator. Optimization is therefore performed off-line before releasing the system to real users. Gaussian Processes (GP) for RL have recently been applied to dialogue systems. One advantage of GP is that they compute an explicit measure of uncertainty in the value function estimates computed during learning. In this paper, a class of novel learning strategies is described which use uncertainty to control exploration on-line. Comparisons between several exploration schemes show that significant improvements to learning speed can be obtained and that rapid and safe online optimisation is possible, even on a complex task. Copyright © 2011 ISCA.

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The accurate recognition of cancer subtypes is very significant in clinic. Especially, the DNA microarray gene expression technology is applied to diagnosing and recognizing cancer types. This paper proposed a method of that recognized cancer subtypes based on geometrical learning. Firstly, the cancer genes expression profiles data was pretreated and selected feature genes by conventional method; then the expression data of feature genes in the training samples was construed each convex hull in the high-dimensional space using training algorithm of geometrical learning, while the independent test set was tested by the recognition algorithm of geometrical learning. The method was applied to the human acute leukemia gene expression data. The accuracy rate reached to 100%. The experiments have proved its efficiency and feasibility.