El aprendizaje en una comunidad en internet de profesores de español como segunda lengua: estudio etnográfico virtual de la comunidad Todoele

In the digital era the availability of resources for online learning has multiplied along with personal learning environments. Proof of this is the proliferation of phenomena in the internet like Open Educational Resources or the Massive Open Online Course (MOOC) and the vast diversity of Online Communities of Practice (OCoP). The former is the object of study in the present Thesis. As Juan Freire (2012: 71) wrote: “The education is already happening, specially outside of educational institutions and formal process of education.” The OCoP garnered attention in the research community during the last few years. And, according to published research, online communities are turning into an emerging phenomena not only for “digital natives” (White, 2011) but also for lifelong learning (Wenger et al, 2002; Dubé et al, 2006; Lai et al, 2006) and specially as a tool for teacher training (Garrido, 2003; Murua, 2015). Teachers develop, through these tools, networks of self support, share didactic material, and look for solutions to common problems while undertaking initiatives towards even more collaboration. The present thesis is a study of Comunidad Todoele (CT) one of the most reputable networks for Spanish as a Foreign Language Teachers (ELE, from it’s Spanish acronym). Currently the community has near 11,000 members and it’s main purpose is researching, describing, and theorising the formative processes that happen inside the network from the perspective of those experiencing it. Debate is centred on the questions: What relevant knowledge do the teachers get from the community? In which way are these processes of learning developed? In other words, How, and what, do teachers learn in these environment?..

Digital literacy matters. Increasing workforce productivity through blended English language programmes

Current workplace demands newer forms of literacies that go beyond the ability to decode print. These involve not only competence to operate digital tools, but also the ability to create, represent, and share meaning in different modes and formats; ability to interact, collaborate and communicate effectively using digital tools, and engage critically with technology for developing one’s knowledge, skills, and full participation in civic, economic, and personal matters. This essay examines the application of the ecology of resources (EoR) model for delivering language learning outcomes (in this case, English) through blended classroom environments that use contextually available resources. The author proposes the implementation of the EoR model in blended learning environments to create authentic and sustainable learning environments for skilling courses. Applying the EoR model to Indian skilling instruction contexts, the article discusses how English language and technology literacy can be delivered using contextually available resources through a blended classroom environment. This would facilitate not only acquisition of language and digital literacy outcomes, but also consequent content literacy gain to a certain extent. This would ensure satisfactory achievement of not only communication/language literacy and technological literacy, but also active social participation, lifelong learning, and learner autonomy.

Aprendizaje de la escritura en lengua no materna: Casos de niños tarahumaras

Recibido 01 de diciembre de 2010 • Aceptado 09 de marzo de 2011 • Corregido 19 de abril de 2011   Resumen. Esta investigación analiza el desarrollo de la escritura en español de niños indígenas tarahumaras bilingües (del Estado de Chihuahua, México), desde varias perspectivas: la psicogenética, relacionada con el avance en el proceso de construcción del sistema de escritura de los niños investigados; la social, mediante al análisis del contexto familiar, cultural y el entorno del asentamiento de convivencia de los mismos; y la pedagógica, aunque más breve, a través del ambiente áulico dentro de dos escuelas muy diferentes: una regular y otra indígena. El planteamiento central de la investigación giró en torno a la percepción de los múltiples factores que se relacionan con el aprendizaje de la escritura, en un intento de escudriñar analíticamente los elementos posibles de afectación en el proceso referido. La metodología cualitativa utilizada posibilitó, mediante el estudio de casos, la observación, la entrevista, la videograbación y el análisis de los cuadernos de los niños, percibir situaciones y rescatar evidencias que, mediante el estudio transversal de eventos, personas y contextos, dieron como resultado interpretaciones sobre los factores sociales, culturales, cognitivos y pedagógicos que se percibieron asociados al aprendizaje de la escritura de una lengua que no es la materna, en niños indígenas migrantes establecidos en un medio distinto al de sus ancestros. Se atiende principalmente a la hipótesis de que, en las circunstancias de los casos estudiados, es más conveniente que aprendan a escribir primero en su segunda lengua y, posteriormente, en la materna, si así lo requieren.

Ambientes virtuais de aprendizagem

Texto que compõe a unidade 2 do módulo “Introdução a EAD” do curso de especialização em Nefrologia Multidisciplinar, produzido pela UNA-SUS/UFMA. São abordados os conceitos básicos sobre os Ambientes Virtuais de Aprendizagem – AVA e a sua função dentro da EAD, sendo discutido também o uso e a adoção do MOODLE (Modular Object-Oriented Dynamic Learning Environment), um software livre de apoio a aprendizagem executado no ambiente virtual.

Modeling and exploiting behavior patterns in dynamic environments

This paper presents a new approach to improving the effectiveness of autonomous systems that deal with dynamic environments. The basis of the approach is to find repeating patterns of behavior in the dynamic elements of the system, and then to use predictions of the repeating elements to better plan goal directed behavior. It is a layered approach involving classifying, modeling, predicting and exploiting. Classifying involves using observations to place the moving elements into previously defined classes. Modeling involves recording features of the behavior on a coarse grained grid. Exploitation is achieved by integrating predictions from the model into the behavior selection module to improve the utility of the robot's actions. This is in contrast to typical approaches that use the model to select between different strategies or plays. Three methods of adaptation to the dynamic features of the environment are explored. The effectiveness of each method is determined using statistical tests over a number of repeated experiments. The work is presented in the context of predicting opponent behavior in the highly dynamic and multi-agent robot soccer domain (RoboCup)

Enriched behavioral prediction equation and its impact on structured learning and the dynamic calculus

This theoretical note describes an expansion of the behavioral prediction equation, in line with the greater complexity encountered in models of structured learning theory (R. B. Cattell, 1996a). This presents learning theory with a vector substitute for the simpler scalar quantities by which traditional Pavlovian-Skinnerian models have hitherto been represented. Structured learning can be demonstrated by vector changes across a range of intrapersonal psychological variables (ability, personality, motivation, and state constructs). Its use with motivational dynamic trait measures (R. B. Cattell, 1985) should reveal new theoretical possibilities for scientifically monitoring change processes (dynamic calculus model; R. B. Cattell, 1996b), such as encountered within psycho therapeutic settings (R. B. Cattell, 1987). The enhanced behavioral prediction equation suggests that static conceptualizations of personality structure such as the Big Five model are less than optimal.

Dynamic equilibrium through reinforcement learning

Reinforcement Learning is an area of Machine Learning that deals with how an agent should take actions in an environment such as to maximize the notion of accumulated reward. This type of learning is inspired by the way humans learn and has led to the creation of various algorithms for reinforcement learning. These algorithms focus on the way in which an agent’s behaviour can be improved, assuming independence as to their surroundings. The current work studies the application of reinforcement learning methods to solve the inverted pendulum problem. The importance of the variability of the environment (factors that are external to the agent) on the execution of reinforcement learning agents is studied by using a model that seeks to obtain equilibrium (stability) through dynamism – a Cart-Pole system or inverted pendulum. We sought to improve the behaviour of the autonomous agents by changing the information passed to them, while maintaining the agent’s internal parameters constant (learning rate, discount factors, decay rate, etc.), instead of the classical approach of tuning the agent’s internal parameters. The influence of changes on the state set and the action set on an agent’s capability to solve the Cart-pole problem was studied. We have studied typical behaviour of reinforcement learning agents applied to the classic BOXES model and a new form of characterizing the environment was proposed using the notion of convergence towards a reference value. We demonstrate the gain in performance of this new method applied to a Q-Learning agent.

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.

Self-optimization for dynamic scheduling in manufacturing systems

Scheduling is a critical function that is present throughout many industries and applications. A great need exists for developing scheduling approaches that can be applied to a number of different scheduling problems with significant impact on performance of business organizations. A challenge is emerging in the design of scheduling support systems for manufacturing environments where dynamic adaptation and optimization become increasingly important. In this paper, we describe a Self-Optimizing Mechanism for Scheduling System through Nature Inspired Optimization Techniques (NIT).

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.

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.

Adaptive learning in agents behaviour: a framework for electricity markets simulation

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.

Adaptive learning in agents behaviour: A framework for electricity markets simulation

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 (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.

A middleware for service oriented computing in dynamic environments

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do Grau de Mestre em Engenharia Informática