Que learning objects (LOs) utilizam as instituições de ensino superior ?

O uso das tecnologias com base na Web, no processo ensino/aprendizagem, têm obtido excelentes resultados, onde a Internet é a plataforma base de comunicação e interacção entre estudantes e professores. Assiste-se, também, a uma partilha/reutilização constante de conteúdos educativos/Learning Objects, em diferentes formatos e diferentes tipos de plataformas, incrementada pela Web 2.0. Este artigo apresenta um estudo sobre o desenvolvimento, disponibilização e utilização de Learning Objects em instituições de Ensino Superior. Conclui-se que as instituições de Ensino Superior inquiridas não desenvolvem, não reutilizam nem promovem a reutilização de LOs, que utilizam as especificações SCORM e IMS, e apresentam-se observações sobre as vantagens e desvantagens da sua utilização.

Experiencing flow while teaching and learning in individual tuition settings

Conferência anual da ISME

Human interaction with technology for working, communicating, and learning: advancements

The way humans interact with technology is undergoing a tremendous change. It is hard to imagine the lives we live today without the benefits of technology that we take for granted. Applying research in computer science, engineering, and information systems to non-technical descriptions of technology, such as human interaction, has shaped and continues to shape our lives. Human Interaction with Technology for Working, Communicating, and Learning: Advancements provides a framework for conceptual, theoretical, and applied research in regards to the relationship between technology and humans. This book is unique in the sense that it does not only cover technology, but also science, research, and the relationship between these fields and individuals' experience. This book is a must have for anyone interested in this research area, as it provides a voice for all users and a look into our future.

The Role of lifelong learning in creation of a european knowledge-based society

The dominant discourse in education and training policies, at the turn of the millennium, was on lifelong learning (LLL) in the context of a knowledge-based society. As Green points (2002, pp. 611-612) several factors contribute to this global trend: The demographic change: In most advanced countries, the average age of the population is increasing, as people live longer; The effects of globalisation: Including both economic restructuring and cultural change which have impacts on the world of education; Global economic restructuring: Which causes, for example, a more intense demand for a higher order of skills; the intensified economic competition, forcing a wave of restructuring and creating enormous pressure to train and retrain the workforce In parallel, the “significance of the international division of labour cannot be underestimated for higher education”, as pointed out by Jarvis (1999, p. 250). This author goes on to argue that globalisation has exacerbated differentiation in the labour market, with the First World converting faster to a knowledge economy and a service society, while a great deal of the actual manufacturing is done elsewhere.

Learning business strategies for competitive electronic marketplaces

This paper presents a Multi-Agent Market simulator designed for developing new agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, considering user risk preferences and game theory for scenario analysis. This tool studies negotiations based on different market mechanisms and, time and behavior dependent strategies. The results of the negotiations between agents are analyzed by data mining algorithms in order to extract rules that give agents feedback to improve their strategies. The system also includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions, and capable of considering other agent reactions.

Intelligent decision making in electricity markets: simulated annealing Q-Learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM is integrated with ALBidS, a system that provides several dynamic strategies for agents’ behavior. This paper presents a method that aims at enhancing ALBidS competence in endowing market players with adequate strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible actions. These actions are defined accordingly to the most probable points of bidding success. With the purpose of accelerating the convergence process, a simulated annealing based algorithm is included.

Adaptive learning in multiagent systems: a forecasting methodology based on error analysis

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simu-lator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM pro-vides several dynamic strategies for agents’ behaviour. This paper presents a method that aims to provide market players strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses an auxiliary forecasting tool, e.g. an Artificial Neural Net-work, to predict the electricity market prices, and analyses its forecasting error patterns. Through the recognition of such patterns occurrence, the method predicts the expected error for the next forecast, and uses it to adapt the actual forecast. The goal is to approximate the forecast to the real value, reducing the forecasting error.

A utilização da plataforma Educast@fccn, como ferramenta de gravação de aulas no ambiente de e-learning português

Dissertação apresentada à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Audiovisual e Multimédia.

O teatro de marionetas e o desenvolvimento da oralidade no 2º ciclo do ensino básico

Dissertação apresentada à Escola Superior de Educação de Lisboa para a obtenção do grau de mestre em Educação Artística - Especialização em Teatro na Educação

As formas animadas e a criatividade

Projeto de Intervenção apresentado à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Educação Artística - Especialidade Teatro na Educação

Teatro no 1º ciclo do ensino básico: relação entre formação dos professores e práticas pedagógicas

Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ciências da Educação, especialidade em Educação Artística -Teatro na Educação

O teatro na comunidade de Torres Vedras: as práticas de criação e de fruição em torno do grupo do Grémio Artístico Torreense

Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Educação Artística - Especialização em Teatro na Educação

O teatro. espaço de encontro de uma comunidade

Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Educação Artística - Especialização em Teatro na Educação

Cost dependent strategy for electricity markets bidding based on adaptive reinforcement learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents.