Ecological Literacy And Beyond: Problem-based Learning For Future Professionals.

Ecological science contributes to solving a broad range of environmental problems. However, lack of ecological literacy in practice often limits application of this knowledge. In this paper, we highlight a critical but often overlooked demand on ecological literacy: to enable professionals of various careers to apply scientific knowledge when faced with environmental problems. Current university courses on ecology often fail to persuade students that ecological science provides important tools for environmental problem solving. We propose problem-based learning to improve the understanding of ecological science and its usefulness for real-world environmental issues that professionals in careers as diverse as engineering, public health, architecture, social sciences, or management will address. Courses should set clear learning objectives for cognitive skills they expect students to acquire. Thus, professionals in different fields will be enabled to improve environmental decision-making processes and to participate effectively in multidisciplinary work groups charged with tackling environmental issues.

Parameter setting and statistical learning

Three main models of parameter setting have been proposed: the Variational model proposed by Yang (2002; 2004), the Structured Acquisition model endorsed by Baker (2001; 2005), and the Very Early Parameter Setting (VEPS) model advanced by Wexler (1998). The VEPS model contends that parameters are set early. The Variational model supposes that children employ statistical learning mechanisms to decide among competing parameter values, so this model anticipates delays in parameter setting when critical input is sparse, and gradual setting of parameters. On the Structured Acquisition model, delays occur because parameters form a hierarchy, with higher-level parameters set before lower-level parameters. Assuming that children freely choose the initial value, children sometimes will miss-set parameters. However when that happens, the input is expected to trigger a precipitous rise in one parameter value and a corresponding decline in the other value. We will point to the kind of child language data that is needed in order to adjudicate among these competing models.

Hyperplane navigation: A method to set individual scores in fMRI group datasets

Recent studies have demonstrated that spatial patterns of fMRI BOLD activity distribution over the brain may be used to classify different groups or mental states. These studies are based on the application of advanced pattern recognition approaches and multivariate statistical classifiers. Most published articles in this field are focused on improving the accuracy rates and many approaches have been proposed to accomplish this task. Nevertheless, a point inherent to most machine learning methods (and still relatively unexplored in neuroimaging) is how the discriminative information can be used to characterize groups and their differences. In this work, we introduce the Maximum Uncertainty Linear Discrimination Analysis (MLDA) and show how it can be applied to infer groups` patterns by discriminant hyperplane navigation. In addition, we show that it naturally defines a behavioral score, i.e., an index quantifying the distance between the states of a subject from predefined groups. We validate and illustrate this approach using a motor block design fMRI experiment data with 35 subjects. (C) 2008 Elsevier Inc. All rights reserved.

Spatial learning ability of rats following differing levels of exposure to alcohol during early postnatal life

Rats exposed to a relatively high dose (7.5 g/kg body weight) of alcohol on either the fifth or tenth postnatal day of age have been reported to have long-lasting deficits in spatial learning ability as tested on the Morris water maze task. The question arises concerning the level of alcohol required to achieve this effect. Wistar rats were exposed to either 2, 4 or 6 g/kg body weight of ethanol administered as a 10% solution. This ethanol was given over an 8-h period on the fifth postnatal day of age by means of an intragastric cannula. Gastrostomy controls received a 5% sucrose solution substituted isocalorically for the ethanol. Another set of pups raised by their mother were used as suckle controls. All surgical procedures were carried out under halothane vapour anaesthesia. After the artificial feeding regimes all pups were returned to lactating dams and weaned at 21 days of age. The spatial learning ability of these rats was tested in the Morris water maze when they were between 61-64 days of age. This task requires the rats to swim in a pool containing water made opaque and locate and climb onto a submerged platform. The time taken to accomplish this is known as the escape latency. Each rat was subjected to 24 trials over 3 days of the test period. Statistical analysis of the escape latency data revealed that the rats given 6 g/kg body weight of ethanol had significant deficits in their spatial learning ability compared with their control groups. However, there was no significant difference in spatial learning ability for the rats given either 2 or 4 g/kg body weight of ethanol compared with their respective gastrostomy or suckle control animals. We concluded that ethanol exposure greater than 4 g/kg over an 8-h period to 5-day-old rats is required for them to develop long-term deficits in spatial learning behaviour. (C) 1998 Elsevier Science Inc.

Changes in conceptions of learning for indigenous Australian university students

Background. Conceptions of learning have been investigated for students in higher. education in different countries. Some studies found that students' conceptions change and develop over time while others have found no changes. Investigating conceptions of learning for Australian Aboriginal and Torres Strait Islander university students is a relatively new area of research. Aims. This study set out to investigate conceptions of learning for Aboriginal and Torres Strait Islander university students during the first two years of their undergraduate degree courses in three Australian universities. Conceptions for each year were compared. Knowing, more about learning as conceived by this cultural group may facilitate more productive higher educational experiences. Sample. The sample comprised 17 students studying various degrees; Il were male and 6 were female. Ages ranged from 18 to 48 years; mean age was 26 years. Method. This was a phenomenographic, longitudinal study. Individual semistructured interviews were conducted each year to ascertain students' conceptions of learning. Conceptions for second year were derived independently of those From first year. A comparative analysis then took place to determine ally changes. Results. These students held conceptions of learning that were similar to those of other university students; however there were some intrinsic differences. On a group level, conceptions changed somewhat over the two years as did core conceptions reported by some individual students. Some students also exhibited a greater awareness of learning during their second year that resulted in three dimensions of changed awareness. Conclusions. We believe the changed conceptions and awareness resulted from learning at university where there is some need to understand and explain phenomena in relation to theory. This brought about new understandings which allowed students to see their own learning in a relational sense.

On learning context-free and context-sensitive languages

The long short-term memory (LSTM) is not the only neural network which learns a context sensitive language. Second-order sequential cascaded networks (SCNs) are able to induce means from a finite fragment of a context-sensitive language for processing strings outside the training set. The dynamical behavior of the SCN is qualitatively distinct from that observed in LSTM networks. Differences in performance and dynamics are discussed.

Use of web-based formative assessment to support student learning in metabolism/nutrition unit

In 2002, an integrated basic science course was introduced into the Bachelor of Dental Sciences programme at the University of Queensland, Australia. Learning activities for the Metabolism and Nutrition unit within this integrated course included lectures, problem-based learning tutorials, computer-based self-directed learning exercises and practicals. To support student learning and assist students to develop the skills necessary to become lifelong learners, an extensive bank of formative assessment questions was set up using the commercially available package, WebCT®. Questions included short-answer, multiple-choice and extended matching questions. As significant staff time was involved in setting up the question database, the extent to which students used the formative assessment and their perceptions of its usefulness to their learning were evaluated to determine whether formative assessment should be extended to other units within the course. More than 90% of the class completed formative assessment tasks associated with learning activities scheduled in the first two weeks of the block, but this declined to less than 50% by the fourth and final week of the block. Patterns of usage of the formative assessment were also compared in students who scored in the top 10% for all assessment for the semester with those who scored in the lowest 10%. High-performing students accessed the Web-based formative assessment about twice as often as those who scored in the lowest band. However, marks for the formative assessment tests did not differ significantly between the two groups. In a questionnaire that was administered at the completion of the block, students rated the formative assessment highly, with 80% regarding it as being helpful for their learning. In conclusion, although substantial staff time was required to set up the question database, this appeared to be justified by the positive responses of the students.

A Feedback Learning and Mental Models Perspective on Strategic Decision Making

This study aims to be a contribution to a theoretical model that explains the effectiveness of the learning and decision-making processes by means of a feedback and mental models perspective. With appropriate mental models, managers should be able to improve their capacity to deal with dynamically complex contexts, in order to achieve long-term success. We present a set of hypotheses about the influence of feedback information and systems thinking facilitation on mental models and management performance. We explore, under controlled conditions, the role of mental models in terms of structure and behaviour. A test based on a simulation experiment with a system dynamics model was performed. Three out of the four hypotheses were confirmed. Causal diagramming positively influences mental model structure similarity, mental model structure similarity positively influences mental model behaviour similarity, and mental model behaviour similarity positively influences the quality of the decision.

A Feedback Learning and Mental Models Perspective on Strategic Decision Making

This study aims to be a contribution to a theoretical model that explains the effectiveness of the learning and decision-making processes by means of a feedback and mental models perspective. With appropriate mental models, managers should be able to improve their capacity to deal with dynamically complex contexts, in order to achieve long-term success. We present a set of hypotheses about the influence of feedback information and systems thinking facilitation on mental models and management performance. We explore, under controlled conditions, the role of mental models in terms of structure and behaviour. A test based on a simulation experiment with a system dynamics model was performed. Three out of the four hypotheses were confirmed. Causal diagramming positively influences mental model structure similarity, mental model structure similarity positively influences mental model behaviour similarity, and mental model behaviour similarity positively influences the quality of the decision

Learning techniques for automatic email message tagging

A organização automática de mensagens de correio electrónico é um desafio actual na área da aprendizagem automática. O número excessivo de mensagens afecta cada vez mais utilizadores, especialmente os que usam o correio electrónico como ferramenta de comunicação e trabalho. Esta tese aborda o problema da organização automática de mensagens de correio electrónico propondo uma solução que tem como objectivo a etiquetagem automática de mensagens. A etiquetagem automática é feita com recurso às pastas de correio electrónico anteriormente criadas pelos utilizadores, tratando-as como etiquetas, e à sugestão de múltiplas etiquetas para cada mensagem (top-N). São estudadas várias técnicas de aprendizagem e os vários campos que compõe uma mensagem de correio electrónico são analisados de forma a determinar a sua adequação como elementos de classificação. O foco deste trabalho recai sobre os campos textuais (o assunto e o corpo das mensagens), estudando-se diferentes formas de representação, selecção de características e algoritmos de classificação. É ainda efectuada a avaliação dos campos de participantes através de algoritmos de classificação que os representam usando o modelo vectorial ou como um grafo. Os vários campos são combinados para classificação utilizando a técnica de combinação de classificadores Votação por Maioria. Os testes são efectuados com um subconjunto de mensagens de correio electrónico da Enron e um conjunto de dados privados disponibilizados pelo Institute for Systems and Technologies of Information, Control and Communication (INSTICC). Estes conjuntos são analisados de forma a perceber as características dos dados. A avaliação do sistema é realizada através da percentagem de acerto dos classificadores. Os resultados obtidos apresentam melhorias significativas em comparação com os trabalhos relacionados.

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.

Thinking over b‐learning strategy: the MIPO model approach

This article describes the main research results in a new methodology, in which the stages and strategies of the technology integration process are identified and described. A set of principles and recommendations are therefore presented. The MIPO model described in this paper is a result of the effort made regarding the understanding of the main success features of good practices, in the web environment, integrated in the information systems/information technology context. The initial model has been created, based on experiences and literature review. After that, it was tested in the information and technology system units at higher school and also adapted as a result of four cycles of an actionresearch work combined with a case study research. The information, concepts and procedures presented here give support to teachers and instructors, instructional designers and planning teams – anyone who wants to develop effective b‐learning instructions.

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.

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.