Search for dissertations about: "learning and communication"
Showing result 1 - 5 of 543 swedish dissertations containing the words learning and communication.
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1. Play, Culture and Learning : Studies of Second-Language and Conceptual Development in Swedish Preschools
Abstract : This dissertation studies how second-language and conceptual development emerge through interactions in Swedish preschool environments. It studies how types of interaction, such as play, can scaffold children toward such developments. READ MORE
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2. Framing perceived values of education : when perspectives of learning and ICTs are related
Abstract : This thesis offers dialogue about the relations between learning and Information and Communication Technologies (ICTs). The dialogue is guided by the question of how to design education to increase perceived values of learning. READ MORE
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3. Communication as structuration : Viewing learning through the lens of communication
Abstract : This dissertation is about enlightening the relationship between organizational communication and learning. In doing so, I explore and build upon existing theories that address the relationship from a structuration perspective. Specifically, I turn to discursive communication theory and sociocultural learning theory. READ MORE
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4. Learning as a patient : What and how individuals want to learn when preparing for surgery, and the potential use of serious games in their education
Abstract : Introduction: Surgical patients need knowledge to participate in their own care and to engage in self-care behaviour in the perioperative period which is important for their recovery. Patient education facilitates such knowledge acquisition and several methods can be used to facilitate it, for example, face-to-face education and brochures or using information technology such as website or computer games. READ MORE
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5. Information-Theoretic Generalization Bounds: Tightness and Expressiveness
Abstract : Machine learning has achieved impressive feats in numerous domains, largely driven by the emergence of deep neural networks. Due to the high complexity of these models, classical bounds on the generalization error---that is, the difference between training and test performance---fail to explain this success. READ MORE