807 resultados para Computer supported collaborative blended learning


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It has been convincingly argued that computer simulation modeling differs from traditional science. If we understand simulation modeling as a new way of doing science, the manner in which scientists learn about the world through models must also be considered differently. This article examines how researchers learn about environmental processes through computer simulation modeling. Suggesting a conceptual framework anchored in a performative philosophical approach, we examine two modeling projects undertaken by research teams in England, both aiming to inform flood risk management. One of the modeling teams operated in the research wing of a consultancy firm, the other were university scientists taking part in an interdisciplinary project experimenting with public engagement. We found that in the first context the use of standardized software was critical to the process of improvisation, the obstacles emerging in the process concerned data and were resolved through exploiting affordances for generating, organizing, and combining scientific information in new ways. In the second context, an environmental competency group, obstacles were related to the computer program and affordances emerged in the combination of experience-based knowledge with the scientists' skill enabling a reconfiguration of the mathematical structure of the model, allowing the group to learn about local flooding.

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Automation or semi-automation of learning scenariospecifications is one of the least exploredsubjects in the e-learning research area. There isa need for a catalogue of learning scenarios and atechnique to facilitate automated retrieval of stored specifications. This requires constructing anontology with this goal and is justified inthis paper. This ontology must mainlysupport a specification technique for learning scenarios. This ontology should also be useful in the creation and validation of new scenarios as well as in the personalization of learning scenarios or their monitoring. Thus, after justifying the need for this ontology, a first approach of a possible knowledge domain is presented. An example of a concrete learning scenario illustrates some relevant concepts supported by this ontology in order to define the scenario in such a way that it could be easy to automate.

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This study examined the influence of training on Asian learners' beliefs, interaction, and attitudes during collaborative learning (CL) and explored the processes of their CL in pairs. The literature contains few studies on the effect of collaborative training in language learning. In addition, it shows gaps between SLA theory and practice resulting from learners' cultural differences. Although second/subsequent language acquisition (SLA) theory assumes that CL contributes to language learning, implementing CL in a multicultural classroom is often considered to be unsuccessful by teachers. The research questions designed to address this gap explore: (a) the extent to which tra~ng affects Asian learners' attitudes towards and interaction during CL; (b) how Asian learners accomplish collaborative tasks in pairs. In the quasi-experimental research design, the learners in the treatment group received special training in CL for 5 weeks while the learners in the comparison group did not receive similar training. Data were collected from 45 McMaster University students through pre- and posttests, pre- and postintervention questionnaires, student information, and informal classroom observations. To detennine the influence of training, the frequency of communication units (c-units), Language Related Episodes (LREs), Collaborative Dialogue (CD) from audio-taped data, and the fmal draft scores were compared between pre- and posttests. The learners' pre- and postintervention questionnaires were also compared. Transcripts from audio-taped data, students' information, their responses and comments from questionnaires, and informal observations served to investigate the processes of Asian learners' CL. Overall, this study found that training had significant influence on the frequency of c-units and CD, and considerable impact on the draft scores, although little influence on the frequency of LREs was observed. The results from the questionnaires in the treatment group showed positive changes in the learners' beliefs on pair work after training. On the other hand, analyses of the transcription data showed that the learners did not conduct enough discussion for a resolution of problems with peers. In conclusion, results suggested the need for teacher intervention, a longer period of collaborative training, and an implementation of self-evaluation into the course grade to encourage the learners to succeed in collaborative learning.

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This is a study of one participant's reflective practice as she worked to develop online communities in a face-to-face science course. Her process of reflective practice was examined in order to address factors that influenced her learning path, and the benefits and challenges of collaborative action research. These research goals were pursued using a collaborative action research methodology, initially chosen for its close match with Schon's (1983) model of reflective practice. The participant's learning fit vnth Mezirow's (1991) model of transformative learning. She began with beliefs that matched her goals, and she demonstrated significant learning in three areas. First, she demonstrated instrumental learning around the constraints of workload and time, and achieving online learning community indicators. Second, she demonstrated communicative learning that helped her to see her own needs for feedback and communication more clearly, and how other process partners had been a support to her. Third, her emancipatory learning saw her revisiting and questioning her goals. It was through the reflective conversation during the planned meetings and the researcher's reframing and interrogation of that reflection that the participant was able to clarify and extend her thinking, and in so doing, critically reflect on her practice as she worked to develop online learning communities. In this way, the collaborative action research methodology was an embodiment of co-constructivism through collaborative reflective practice. Schon's (1983) model of reflective practice positions a lone practitioners moving through cycles ofplan-act-observe-reflect. The results fi"om this study suggest that collaboration is an important piece of the reflective practice model.

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This qualitative study investigated how a team of 7 hospital educators collaborated to develop e-curriculum units to pilot for a newly acquired learning -r management system at a large, multisite academic health sciences centre. A case study approach was used to examine how the e-Curriculum Team was structured, how the educators worked together to develop strategies to better utilize e-leaming in their ovwi practice, what e-curriculum they chose to develop, and how they determined their priorities for e-curriculum development. It also inquired into how they planned to involve other educators in using e-leaming. One set of semistructured interviews with the 6 hospital educators involved in the project, as well as minutes of team meetings and the researcher's journal, were analyzed (the researcher was also a hospital educator on the team). Project management structure, educator support, and organizational pressures on the implementation project feature prominently in the case study. This study suggests that implementation of e-leaming will be more successful if (a) educators involved in the development of e-leaming curriculum are supported in their role as change agents, (b) the pain of vmleaming current educational practice is considered, (c) the limitations of the software being implemented are recognized, (d) time is spent leaming about best practice, and (e) the project is protected as much as possible from organizational pressures and distractions.

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L’objectif de cette thèse par articles est de présenter modestement quelques étapes du parcours qui mènera (on espère) à une solution générale du problème de l’intelligence artificielle. Cette thèse contient quatre articles qui présentent chacun une différente nouvelle méthode d’inférence perceptive en utilisant l’apprentissage machine et, plus particulièrement, les réseaux neuronaux profonds. Chacun de ces documents met en évidence l’utilité de sa méthode proposée dans le cadre d’une tâche de vision par ordinateur. Ces méthodes sont applicables dans un contexte plus général, et dans certains cas elles on tété appliquées ailleurs, mais ceci ne sera pas abordé dans le contexte de cette de thèse. Dans le premier article, nous présentons deux nouveaux algorithmes d’inférence variationelle pour le modèle génératif d’images appelé codage parcimonieux “spike- and-slab” (CPSS). Ces méthodes d’inférence plus rapides nous permettent d’utiliser des modèles CPSS de tailles beaucoup plus grandes qu’auparavant. Nous démontrons qu’elles sont meilleures pour extraire des détecteur de caractéristiques quand très peu d’exemples étiquetés sont disponibles pour l’entraînement. Partant d’un modèle CPSS, nous construisons ensuite une architecture profonde, la machine de Boltzmann profonde partiellement dirigée (MBP-PD). Ce modèle a été conçu de manière à simplifier d’entraînement des machines de Boltzmann profondes qui nécessitent normalement une phase de pré-entraînement glouton pour chaque couche. Ce problème est réglé dans une certaine mesure, mais le coût d’inférence dans le nouveau modèle est relativement trop élevé pour permettre de l’utiliser de manière pratique. Dans le deuxième article, nous revenons au problème d’entraînement joint de machines de Boltzmann profondes. Cette fois, au lieu de changer de famille de modèles, nous introduisons un nouveau critère d’entraînement qui donne naissance aux machines de Boltzmann profondes à multiples prédictions (MBP-MP). Les MBP-MP sont entraînables en une seule étape et ont un meilleur taux de succès en classification que les MBP classiques. Elles s’entraînent aussi avec des méthodes variationelles standard au lieu de nécessiter un classificateur discriminant pour obtenir un bon taux de succès en classification. Par contre, un des inconvénients de tels modèles est leur incapacité de générer deséchantillons, mais ceci n’est pas trop grave puisque la performance de classification des machines de Boltzmann profondes n’est plus une priorité étant donné les dernières avancées en apprentissage supervisé. Malgré cela, les MBP-MP demeurent intéressantes parce qu’elles sont capable d’accomplir certaines tâches que des modèles purement supervisés ne peuvent pas faire, telles que celle de classifier des données incomplètes ou encore celle de combler intelligemment l’information manquante dans ces données incomplètes. Le travail présenté dans cette thèse s’est déroulé au milieu d’une période de transformations importantes du domaine de l’apprentissage à réseaux neuronaux profonds qui a été déclenchée par la découverte de l’algorithme de “dropout” par Geoffrey Hinton. Dropout rend possible un entraînement purement supervisé d’architectures de propagation unidirectionnel sans être exposé au danger de sur- entraînement. Le troisième article présenté dans cette thèse introduit une nouvelle fonction d’activation spécialement con ̧cue pour aller avec l’algorithme de Dropout. Cette fonction d’activation, appelée maxout, permet l’utilisation de aggrégation multi-canal dans un contexte d’apprentissage purement supervisé. Nous démontrons comment plusieurs tâches de reconnaissance d’objets sont mieux accomplies par l’utilisation de maxout. Pour terminer, sont présentons un vrai cas d’utilisation dans l’industrie pour la transcription d’adresses de maisons à plusieurs chiffres. En combinant maxout avec une nouvelle sorte de couche de sortie pour des réseaux neuronaux de convolution, nous démontrons qu’il est possible d’atteindre un taux de succès comparable à celui des humains sur un ensemble de données coriace constitué de photos prises par les voitures de Google. Ce système a été déployé avec succès chez Google pour lire environ cent million d’adresses de maisons.

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Resumen tomado de la publicaci??n

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An E-Learning Gateway for the latest news and information relating to Computer Crime for INFO2009

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En un mundo hiperconectado, dinámico y cargado de incertidumbre como el actual, los métodos y modelos analíticos convencionales están mostrando sus limitaciones. Las organizaciones requieren, por tanto, herramientas útiles que empleen tecnología de información y modelos de simulación computacional como mecanismos para la toma de decisiones y la resolución de problemas. Una de las más recientes, potentes y prometedoras es el modelamiento y la simulación basados en agentes (MSBA). Muchas organizaciones, incluidas empresas consultoras, emplean esta técnica para comprender fenómenos, hacer evaluación de estrategias y resolver problemas de diversa índole. Pese a ello, no existe (hasta donde conocemos) un estado situacional acerca del MSBA y su aplicación a la investigación organizacional. Cabe anotar, además, que por su novedad no es un tema suficientemente difundido y trabajado en Latinoamérica. En consecuencia, este proyecto pretende elaborar un estado situacional sobre el MSBA y su impacto sobre la investigación organizacional.

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Este cuadernillo quiere ser un catalizador que motive a los estudiantes a pensar y hablar sobre un tema relevante. Hablar les ofrece la oportunidad de reflexionar, pensar, y expresar con palabras, una nueva forma de aprendizaje y de probar su comprensión de una situación, o en otras palabras 'pensar en voz alta'. Este proceso de reflexión en voz alta permite a los alumnos explorar nuevos conceptos de información y construir su comprensión de ellos, al responder a las preguntas de otros estudiantes y, obtener una respuesta, a su vez, a sus propias ideas.

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Monográfico con el título: 'La universidad de la convergencia. Acreditación y certificaciones. Estrategias docentes, tutoriales y evaluadoras de reforma'. Resumen basado en el de la publicación