O ensino de línguas para crianças no contexto educacional brasileiro: breves reflexões e possíveis provisões

This article aims at discussing about the foreign language teaching to young learners, taking the principles of the Sociocultural Theory (Vygotksy, 1978) and of the Communicative Approach (Almeida Filho, 1993, 2005) related to Primary English teaching (Cameron, 2001; Brewster, Ellis & Girard, 2002) as a theoretical references. Considerations about the importance of language learning in childhood will be made, as well as about the role of the grammar, oral language and mother tongue in the process. Likewise, the importance of Teacher Education will be briefly approached. This work is ended with the discussion about some possible procedures in the language teaching processes followed by a brief presentation of possible guidelines based on the bakhtinian notion of discourse genres.

Insulin and leptin relations in obesity: a multimedia approach

Obesity has been recognized as a worldwide public health problem. It significantly increases the chances of developing several diseases, including Type II diabetes. The roles of insulin and leptin in obesity involve reactions that can be better understood when they are presented step by step. The aim of this work was to design software with data from some of the most recent publications on obesity, especially those concerning the roles of insulin and leptin in this metabolic disturbance. The most notable characteristic of this software is the use of animations representing the cellular response together with the presentation of recently discovered mechanisms on the participation of insulin and leptin in processes leading to obesity. The software was field tested in the Biochemistry of Nutrition web-based course. After using the software and discussing its contents in chatrooms, students were asked to answer an evaluation survey about the whole activity and the usefulness of the software within the learning process. The teaching assistants (TA) evaluated the software as a tool to help in the teaching process. The students' and TAs' satisfaction was very evident and encouraged us to move forward with the software development and to improve the use of this kind of educational tool in biochemistry classes.

Proof Explanation in the DR-DEVICE System

Trust is a vital feature for Semantic Web: If users (humans and agents) are to use and integrate system answers, they must trust them. Thus, systems should be able to explain their actions, sources, and beliefs, and this issue is the topic of the proof layer in the design of the Semantic Web. This paper presents the design and implementation of a system for proof explanation on the Semantic Web, based on defeasible reasoning. The basis of this work is the DR-DEVICE system that is extended to handle proofs. A critical aspect is the representation of proofs in an XML language, which is achieved by a RuleML language extension.

Context-free and context-sensitive dynamics in recurrent neural networks

Continuous-valued recurrent neural networks can learn mechanisms for processing context-free languages. The dynamics of such networks is usually based on damped oscillation around fixed points in state space and requires that the dynamical components are arranged in certain ways. It is shown that qualitatively similar dynamics with similar constraints hold for a(n)b(n)c(n), a context-sensitive language. The additional difficulty with a(n)b(n)c(n), compared with the context-free language a(n)b(n), consists of 'counting up' and 'counting down' letters simultaneously. The network solution is to oscillate in two principal dimensions, one for counting up and one for counting down. This study focuses on the dynamics employed by the sequential cascaded network, in contrast to the simple recurrent network, and the use of backpropagation through time. Found solutions generalize well beyond training data, however, learning is not reliable. The contribution of this study lies in demonstrating how the dynamics in recurrent neural networks that process context-free languages can also be employed in processing some context-sensitive languages (traditionally thought of as requiring additional computation resources). This continuity of mechanism between language classes contributes to our understanding of neural networks in modelling language learning and processing.

CysView: protein classification based on cysteine pairing patterns

CysView is a web-based application tool that identifies and classifies proteins according to their disulfide connectivity patterns. It accepts a dataset of annotated protein sequences in various formats and returns a graphical representation of cysteine pairing patterns. CysView displays cysteine patterns for those records in the data with disulfide annotations. It allows the viewing of records grouped by connectivity patterns. CysView's utility as an analysis tool was demonstrated by the rapid and correct classification of scorpion toxin entries from GenPept on the basis of their disulfide pairing patterns. It has proved useful for rapid detection of irrelevant and partial records, or those with incomplete annotations. CysView can be used to support distant homology between proteins. CysView is publicly available at http://research.i2r.a-star.edu.sg/CysView/.

Perceptions of kanji learning strategies: Do they differ among Chinese character and alphabetic background learners

This study investigates three important issues in kanji learning strategies; namely, strategy use, effectiveness of strategy and orthographic background. A questionnaire on kanji learning strategy use and perceived effectiveness was administered to 116 beginner level, undergraduate students of Japanese from alphabetic and character backgrounds in Australia. Both descriptive and statistical analyses of the questionnaire responses revealed that the strategies used most often are the most helpful. Repeated writing was reported as the most used strategy type although alphabetic background learners reported using repeated writing strategies significantly more often than character background learners. The importance of strategy training and explicit instruction of fundamental differences between character and alphabetic background learners of Japanese is discussed in relation to teaching strategies. [Author abstract]

Hibridizando o ensino de inglês na Escola de Aprendizes- Marinheiros do Espírito Santo

Esta pesquisa teve por objetivo avaliar o desempenho em inglês como L2 de alunos submetidos a atividades em uma abordagem híbrida com o uso de ferramentas da internet na Escola de Aprendizes-Marinheiros do Espírito Santo. Quarenta alunos de duas turmas intactas foram divididos em dois grupos, o experimental (ensino tradicional + ambiente virtual) e o controle (ensino tradicional apenas). O referencial teórico revisou a literatura sobre a abordagem de ensino de línguas estrangeiras baseada em tarefas, metodologias híbridas de ensino de L2, multiletramentos e novas tecnologias. A metodologia de pesquisa usada foi mista com dados qualitativos e quantitativos. O estudo avaliou o impacto de atividades on-line na aprendizagem de L2 e no desenvolvimento da autonomia e do letramento digital. Três tarefas usando variados sítios da internet foram administradas ao grupo experimental, seguidas de um questionário para cada uma, usado para a avaliação qualitativa. A análise quantitativa foi feita por meio de pré e pós-testes analisados estatisticamente. Para esta pesquisa-ação, foram utilizados questionários, entrevistas semiestruturadas e o diário da professora-pesquisadora como instrumentos de coleta de dados. De forma geral, os resultados não mostraram ganhos estatisticamente significativos no tratamento, porém a análise qualitativa das impressões dos alunos-participantes sobre as tarefas realizadas revelou que a abordagem híbrida de ensino de L2 pode ajudar os alunos a desenvolver a autonomia, motivação, um maior contato com a língua-alvo e o letramento digital.

Web 2.0 and its impact on knowledge and business organizations

Today, information overload and the lack of systems that enable locating employees with the right knowledge or skills are common challenges that large organisations face. This makes knowledge workers to re-invent the wheel and have problems to retrieve information from both internal and external resources. In addition, information is dynamically changing and ownership of data is moving from corporations to the individuals. However, there is a set of web based tools that may cause a major progress in the way people collaborate and share their knowledge. This article aims to analyse the impact of ‘Web 2.0’ on organisational knowledge strategies. A comprehensive literature review was done to present the academic background followed by a review of current ‘Web 2.0’ technologies and assessment of their strengths and weaknesses. As the framework of this study is oriented to business applications, the characteristics of the involved segments and tools were reviewed from an organisational point of view. Moreover, the ‘Enterprise 2.0’ paradigm does not only imply tools but also changes the way people collaborate, the way the work is done (processes) and finally impacts on other technologies. Finally, gaps in the literature in this area are outlined.

Agent Inferencing Meets the Semantic Web

We provide all agent; the capability to infer the relations (assertions) entailed by the rules that, describe the formal semantics of art RDFS knowledge-base. The proposed inferencing process formulates each semantic restriction as a rule implemented within a, SPARQL query statement. The process expands the original RDF graph into a fuller graph that. explicitly captures the rule's described semantics. The approach is currently being explored in order to support descriptions that follow the generic Semantic Web Rule Language. An experiment, using the Fire-Brigade domain, a small-scale knowledge-base, is adopted to illustrate the agent modeling method and the inferencing process.

Self-optimization module for scheduling using case-based reasoning

Metaheuristics performance is highly dependent of the respective parameters which need to be tuned. Parameter tuning may allow a larger flexibility and robustness but requires a careful initialization. The process of defining which parameters setting should be used is not obvious. The values for parameters depend mainly on the problem, the instance to be solved, the search time available to spend in solving the problem, and the required quality of solution. This paper presents a learning module proposal for an autonomous parameterization of Metaheuristics, integrated on a Multi-Agent System for the resolution of Dynamic Scheduling problems. The proposed learning module is inspired on Autonomic Computing Self-Optimization concept, defining that systems must continuously and proactively improve their performance. For the learning implementation it is used Case-based Reasoning, which uses previous similar data to solve new cases. In the use of Case-based Reasoning it is assumed that similar cases have similar solutions. After a literature review on topics used, both AutoDynAgents system and Self-Optimization module are described. Finally, a computational study is presented where the proposed module is evaluated, obtained results are compared with previous ones, some conclusions are reached, and some future work is referred. It is expected that this proposal can be a great contribution for the self-parameterization of Metaheuristics and for the resolution of scheduling problems on dynamic environments.

Multi-apprentice learning for meta-heuristics parameter tuning in a multi agent scheduling system

The scheduling problem is considered in complexity theory as a NP-hard combinatorial optimization problem. Meta-heuristics proved to be very useful in the resolution of this class of problems. However, these techniques require parameter tuning which is a very hard task to perform. A Case-based Reasoning module is proposed in order to solve the parameter tuning problem in a Multi-Agent Scheduling System. A computational study is performed in order to evaluate the proposed CBR module performance.

Case-based reasoning for meta-heuristics self-parameterization in a multi-agent scheduling system

A novel agent-based approach to Meta-Heuristics self-configuration is proposed in this work. Meta-heuristics are examples of algorithms where parameters need to be set up as efficient as possible in order to unsure its performance. This paper presents a learning module for self-parameterization of Meta-heuristics (MHs) in a Multi-Agent System (MAS) for resolution of scheduling problems. The learning is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. In the end, some conclusions are reached and future work outlined.

Self-optimizing through CBR learning

In this paper, we foresee the use of Multi-Agent Systems for supporting dynamic and distributed scheduling in Manufacturing Systems. We also envisage the use of Autonomic properties in order to reduce the complexity of managing systems and human interference. By combining Multi-Agent Systems, Autonomic Computing, and Nature Inspired Techniques we propose an approach for the resolution of dynamic scheduling problem, with Case-based Reasoning Learning capabilities. The objective is to permit a system to be able to automatically adopt/select a Meta-heuristic and respective parameterization considering scheduling characteristics. From the comparison of the obtained results with previous results, we conclude about the benefits of its use.

Literacia familiar: contributos para a promoção e desenvolvimento de boas práticas literácitas em contexto socio económico desfavorecido

Dissertação apresentada à Escola Superior de Educação de Lisboa para a obtenção de grau de Mestre em Didática da Língua Portuguesa no 1.º e 2.º Ciclos do Ensino Básico

Web GIS to support irrigation management: a prototype for SAGRA network, Alentejo Portugal

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies