879 resultados para Action Learning Cycle
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The Response of the Northern Ireland Executive to the Bamford Review of Mental Health and Learning Disability - åÊNovember 2012
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The Bamford Review of Mental Health and Learning Disability, an independent and comprehensive review of legislation, policy and service provision, concluded in August 2007.
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Making life better for people with a learning disability and people with menal health prblems who live in Northern ireland
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THE RESPONSE OF NORTHERN IRELAND EXECUTIVE TO THE BAMFORD REVIEW OF MENTAL HEALTH AND LEARNING DISABILITY åÊ
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A Workforce Learning Strategy for the Northern Ireland Health and Social Care Services 2009-2014
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Evidence Review 4 - Adult learning services Briefing 4 - Adult learning services This pair of documents, commissioned by Public Health England, and written by the UCL Institute of Health Equity, address the role of participation in learning as an adult in improving health. There is evidence that involvement in adult learning has both direct and indirect links with health, for example because it increases employability. There is some evidence that those who are lower down the social gradient benefit most, in health terms, from adult learning. However, there is a gradient both in participation in adult learning and skill level, whereby the more someone would benefit from adult learning, the less likely they are to participate, and the lower their literacy and numeracy skills are likely to be. This is due to a range of barriers, including prohibitively high costs, lack of personal confidence, or lack of availability and access. These papers also show that there are a number of actions local authorities can take to increase access to adult learning, improve quality of provision and increase the extent to which it is delivered and targeted proportionate to need. The full evidence review and a shorter summary briefing are available to download above. This document is part of a series. An overview document which provides an introduction to this and other documents in the series, and links to the other topic areas, is available on the ‘Local Action on health inequalities’ project page. A video of Michael Marmot introducing the work is also available on our videos page.
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The Framework for Junior Cycle (The Framework) was published in October 2012. It is based on the National Council for Curriculum and Assessment’s advice to the Department as set out in Towards a Framework for Junior Cycle (November 2011) and on research into education for our young people aged 12 to 15 / 16 years. The Framework document can be downloaded from www.education.ie and www.ncca. The mission of the Department of Education and Skills (DES) is “to enable learners to achieve their full potential and contribute to Ireland’s economic, social and cultural development”. By placing students at the centre of the educational experience, the DES wants to ensure that junior cycle education will improve learning experiences and outcomes. The implementation of the Framework for Junior Cycle will enable post-primary schools to provide a quality, inclusive and relevant education with improved learning outcomes for all students, including those with special educational needs
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This document, the Framework for Junior Cycle (2015), outlines the key educational changes that the Department of Education and Skills (DES) is putting in place for young people in the first three years of their post-primary education. It builds on and advances the vision for junior cycle reform that was outlined in the Framework for Junior Cycle (2012). The Framework for Junior Cycle (2015) incorporates a shared understanding of how teaching, learning and assessment practices should evolve to support the delivery of a quality, inclusive and relevant education that will meet the needs of junior cycle students, both now and in the future. This shared understanding is informed by engagement with stakeholders and by national and international research.
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This is Ireland’s first White Paper on Adult Education and marks the adoption of lifelong learning as the governing principle of educational policy. The Paper reflects on the role of adult education in society, builds on the consultation process following publication of the Green Paper, and sets out the Government’s policies and priorities for the future development of the sector. It does not aim to provide a policy blueprint for the training sector given that this work is being advanced through the National Employment Action Plans and previous publications, and the work of the Task Force on Lifelong Learning recently established by the Department of Enterprise, Trade and Employment. Rather, it seeks to ensure that there is a fit and complementarity between education and training provision, so as to ensure that learners can move progressively and incrementally within an over-arching co-ordinated and learner-centred framework.
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Aquest projecte s'emmarca dins de l'àmbit de l'assessorament psicopedagògic, concretament, en la formació del professorat per a l'ensenyament d'estratègies d'aprenentatge. El tema d'estratègies d'ensenyament/aprenentatge és massa ampli per a poder tractar-lo en un projecte com aquest, amb una limitació temporal que exigeix concretar l'actuació en els punts següents: fer una selecció prèvia d'un determinat conjunt de procediments d'aprenentatge vinculats amb la lectura, situar-lo dins d'un context escolar concret de manera que la formació vagi dirigida als docents del cicle mitjà d'educació primària d'un CEIP del Masnou i centrar l'experiència en dos d'aquests docents.
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Peer-reviewed
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This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs
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This paper proposes a field application of a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot in cable tracking task. The learning system is characterized by using a direct policy search method for learning the internal state/action mapping. Policy only algorithms may suffer from long convergence times when dealing with real robotics. In order to speed up the process, the learning phase has been carried out in a simulated environment and, in a second step, the policy has been transferred and tested successfully on a real robot. Future steps plan to continue the learning process on-line while on the real robot while performing the mentioned task. We demonstrate its feasibility with real experiments on the underwater robot ICTINEU AUV
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Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task
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This paper proposes a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot. Although the dominant approach, when using RL, has been to apply value function based algorithms, the system here detailed is characterized by the use of direct policy search methods. Rather than approximating a value function, these methodologies approximate a policy using an independent function approximator with its own parameters, trying to maximize the future expected reward. The policy based algorithm presented in this paper is used for learning the internal state/action mapping of a behavior. In this preliminary work, we demonstrate its feasibility with simulated experiments using the underwater robot GARBI in a target reaching task