9 resultados para Psycho-social learning environments

em Universidade do Minho


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Dissertação de mestrado em Educação Especial (área de especialização em Dificuldades de Aprendizagem Específicas)

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Forming suitable learning groups is one of the factors that determine the efficiency of collaborative learning activities. However, only a few studies were carried out to address this problem in the mobile learning environments. In this paper, we propose a new approach for an automatic, customized, and dynamic group formation in Mobile Computer Supported Collaborative Learning (MCSCL) contexts. The proposed solution is based on the combination of three types of grouping criteria: learner’s personal characteristics, learner’s behaviours, and context information. The instructors can freely select the type, the number, and the weight of grouping criteria, together with other settings such as the number, the size, and the type of learning groups (homogeneous or heterogeneous). Apart from a grouping mechanism, the proposed approach represents a flexible tool to control each learner, and to manage the learning processes from the beginning to the end of collaborative learning activities. In order to evaluate the quality of the implemented group formation algorithm, we compare its Average Intra-cluster Distance (AID) with the one of a random group formation method. The results show a higher effectiveness of the proposed algorithm in forming homogenous and heterogeneous groups compared to the random method.

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There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.

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Relatório de estágio de mestrado em Educação Pré-Escolar

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Tese de Doutoramento em Ciências da Educação - Especialidade de Desenvolvimento Curricular

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Tese de Doutoramento em Ciências da Educação (área de especilização em Desenvolvimento Curricular).

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Dissertação de mestrado em Estudos da Criança (área de especialização em Intervenção Psicossocial com Crianças, Jovens e Famílias)

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This article argues for a cultural perspective to be brought to bear on studies of climate change risk perception. Developing the “circuit of culture” model, the article maintains that the producers and consumers of media texts are jointly engaged in dynamic, meaning-making activities that are context-specific and that change over time. A critical discourse analysis of climate change based on a database of newspaper reports from three U.K. broadsheet papers over the period 1985–2003 is presented. This empirical study identifies three distinct circuits of climate change—1985–1990, 1991–1996, 1997–2003—which are characterized by different framings of risks associated with climate change. The article concludes that there is evidence of social learning as actors build on their experiences in relation to climate change science and policy making. Two important factors in shaping the U.K.’s broadsheet newspapers’ discourse on “dangerous” climate change emerge as the agency of top political figures and the dominant ideological standpoints in different newspapers.

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Students have different ways for learning and processing information. Some students prefer learning through seeing while others prefer learning through listening; some students prefer doing activities while other prefer reflecting.Some students reason logically, while others reason intuitively, etc. Identifying the learning style of each student, and providing learning content based on these styles represents a good method to enhance the learning quality. However, there are no efforts onhow to detect the students’ learning styles in mobile computer supported collaborative learning (MCSCL) environments. We present in this paper new ways for automatically detecting the learning styles of students in MCSCL environments based on the learning style model of Felder-Silverman. The identified learning styles of students could be then stored and used at anytime toassign each one of them to his/her appropriate learning group.