662 resultados para Learning in action


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Recurso basado en observaciones de niños con dificultades matemáticas y en métodos probados para ayudarlos. Pone ejemplos de problemas que los niños pueden encontrar con la manipulación de los números. Proporciona a los profesores orientación sobre cómo evitar la confusión de los alumnos y cómo guiarlos a través del currículo de matemáticas más complejas.

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The aim of this thesis was to investigate the respective contribution of prior information and sensorimotor constraints to action understanding, and to estimate their consequences on the evolution of human social learning. Even though a huge amount of literature is dedicated to the study of action understanding and its role in social learning, these issues are still largely debated. Here, I critically describe two main perspectives. The first perspective interprets faithful social learning as an outcome of a fine-grained representation of others’ actions and intentions that requires sophisticated socio-cognitive skills. In contrast, the second perspective highlights the role of simpler decision heuristics, the recruitment of which is determined by individual and ecological constraints. The present thesis aims to show, through four experimental works, that these two contributions are not mutually exclusive. A first study investigates the role of the inferior frontal cortex (IFC), the anterior intraparietal area (AIP) and the primary somatosensory cortex (S1) in the recognition of other people’s actions, using a transcranial magnetic stimulation adaptation paradigm (TMSA). The second work studies whether, and how, higher-order and lower-order prior information (acquired from the probabilistic sampling of past events vs. derived from an estimation of biomechanical constraints of observed actions) interacts during the prediction of other people’s intentions. Using a single-pulse TMS procedure, the third study investigates whether the interaction between these two classes of priors modulates the motor system activity. The fourth study tests the extent to which behavioral and ecological constraints influence the emergence of faithful social learning strategies at a population level. The collected data contribute to elucidate how higher-order and lower-order prior expectations interact during action prediction, and clarify the neural mechanisms underlying such interaction. Finally, these works provide/open promising perspectives for a better understanding of social learning, with possible extensions to animal models.

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Despite the increasing importance of, and interest in, documenting the impact of environmental education programs on students' learning for sustainability, few tools are currently available to measure young students' environmental learning across all the dimensions of knowledge, skills, attitudes and behaviours. This paper reports on the development of such a tool, using an iterative action research process with 134 students, aged six to eleven, attending programs at an Environmental Education Centre in Queensland. The resulting instrument, the Environmental Learning Outcomes Survey (ELOS) incorporates observations of students' engagement in learning processes as well as measuring learning outcomes, and allows both of these aspects to be linked to particular components of the environmental education program. Test data using the instrument are reported to illustrate its potential usefulness. It is envisaged that the refined instrument will enable researchers to measure student environmental learning in the field, investigate environmental education program impacts and identify aspects of programs that are most effective in facilitating student learning. [Author abstract]

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Early human development offers a unique perspective in investigating the potential cognitive and social implications of action and perception. Specifically, during infancy, action production and action perception undergo foundational developments. One essential component to examine developments in action processing is the analysis of others’ actions as meaningful and goal-directed. Little research, however, has examined the underlying neural systems that may be associated with emerging action and perception abilities, and infants’ learning of goal-directed actions. The current study examines the mu rhythm—a brain oscillation found in the electroencephalogram (EEG)—that has been associated with action and perception. Specifically, the present work investigates whether the mu signal is related to 9-month-olds’ learning of a novel goal-directed means-end task. The findings of this study demonstrate a relation between variations in mu rhythm activity and infants’ ability to learn a novel goal-directed means-end action task (compared to a visual pattern learning task used as a comparison task). Additionally, we examined the relations between standardized assessments of early motor competence, infants’ ability to learn a novel goal-directed task, and mu rhythm activity. We found that: 1a) mu rhythm activity during observation of a grasp uniquely predicted infants’ learning on the cane training task, 1b) mu rhythm activity during observation and execution of a grasp did not uniquely predict infants’ learning on the visual pattern learning task (comparison learning task), 2) infants’ motor competence did not predict infants’ learning on the cane training task, 3) mu rhythm activity during observation and execution was not related to infants’ measure of motor competence, and 4) mu rhythm activity did not predict infants’ learning on the cane task above and beyond infants’ motor competence. The results from this study demonstrate that mu rhythm activity is a sensitive measure to detect individual differences in infants’ action and perception abilities, specifically their learning of a novel goal-directed action.

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Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.

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This paper provides a longitudinal, empirical view of the multifaceted and reciprocal processes of organizational learning in a context of self-managed teams. Organizational learning is seen as a social construction between people and actions in a work setting. The notion of learning as situated (Brown & Duguid 1989, Lave& Wenger 1991, Gherardi & al. 1998, Easterby-Smith & Araujo 1999, Abma 2003) opens up the possibility for placing the focus of research on learning in the community rather than in individual learning processes. Further, in studying processes in their social context, we cannot avoid taking power relations into consideration (Contu & Willmott 2003). The study is based on an action research with a methodology close to the ‘democratic dialogue’ presented by Gustavsen (2001). This gives a ground for research into how the learning discourse developed in the case study organization over a period of 5 years, during which time the company abandoned a middle management level of hierarchy and the teams had to figure out how to work as self-managed units. This paper discusses the (re)construction of power relations and its role in organizational learning. Power relations are discussed both in vertical and horizontal work relations. A special emphasis is placed on the dialectic between managerial aims and the space for reflection on the side of employees. I argue that learning is crucial in the search for the limits for empowerment and that these limits are negotiated both in actions and speech. This study unfolds a purpose-oriented learning process, constructing an open dialogue, and describes a favourable context for creative, knowledge building communities.

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Artificial Intelligence has been applied to dynamic games for many years. The ultimate goal is creating responses in virtual entities that display human-like reasoning in the definition of their behaviors. However, virtual entities that can be mistaken for real persons are yet very far from being fully achieved. This paper presents an adaptive learning based methodology for the definition of players’ profiles, with the purpose of supporting decisions of virtual entities. The proposed methodology is based on reinforcement learning algorithms, which are responsible for choosing, along the time, with the gathering of experience, the most appropriate from a set of different learning approaches. These learning approaches have very distinct natures, from mathematical to artificial intelligence and data analysis methodologies, so that the methodology is prepared for very distinct situations. This way it is equipped with a variety of tools that individually can be useful for each encountered situation. The proposed methodology is tested firstly on two simpler computer versus human player games: the rock-paper-scissors game, and a penalty-shootout simulation. Finally, the methodology is applied to the definition of action profiles of electricity market players; players that compete in a dynamic game-wise environment, in which the main goal is the achievement of the highest possible profits in the market.