Aprender a gostar de poesia: contributos para uma didática do texto poético em contexto de Educação Pré-Escolar e Ensino do 1º Ciclo

Relatório de estágio de mestrado em Educação Pré-Escolar e Ensino do 1º Ciclo do Ensino Básico

Ensino, aprendizagem e avaliação no Ensino Superior: Perspetivas e práticas

Tese de Doutoramento em Ciências da Educação - Especialidade de Desenvolvimento Curricular

Encontros e desencontros da educação de adultos numa associação de teatro amador

Dissertação de mestrado em Ciências da Educação (área de especialização em Educação de Adultos)

Implementación de una metodología docente basada en las TIC y orientada al autoaprendizaje en asignaturas con un elevado contenido iconográfico en la Licenciatura de Veterinària de la UAB

La adaptación de los estudios universitarios al Espacio Europeo de Educación Superior (EEES) pretende conseguir un nuevo modelo educativo basado en el aprendizaje activo del estudiante. En este sentido, las Tecnologías de la Información y la Comunicación (TICs) pueden desempeñar un papel importante en la renovación de la metodología docente, y muy especialmente en asignaturas donde la carga iconográfica es fundamental, tal como ocurre en las Ciencias morfológicas y en algunas materias clínicas. En la Licenciatura en Veterinària de la UAB la carga presencial del alumno es muy elevada, lo que deja poco tiempo para el autoaprendizaje activo y el estudio autónomo. Para intentar paliar este problema, en nuestra Titulación se han elaborado en los últimos años diversos atlas y otros documentos virtuales cuyos contenidos didácticos están relacionados con materias como la Anatomía, Parasitología, Radiología y Anatomía Patológica. Estos materiales, algunos de los cuales ya están publicados on line en la plataforma Veterinària Virtual (http://quiro.uab.es), y que están a disposición de los estudiantes, posibilitan reducir en parte la carga presencial, sirven de ayuda en el proceso de enseñanza y aprendizaje, facilitan el aprendizaje no presencial, autónomo y activo y permiten la evaluación continuada, consiguiendo en definitiva un aumento del protagonismo del alumno en el proceso educativo, lo que constituye una de las metas de la adaptación al EEES. Los alumnos valoran muy positivamente la publicación on line de material educativo, ya que representa un recurso didáctico fácilmente disponible, de acceso permanente y de bajo coste económico. La duración del proyecto ha sido de dos años.

Mapping of Environmental Data Using Kernel-Based Methods

Recently, kernel-based Machine Learning methods have gained great popularity in many data analysis and data mining fields: pattern recognition, biocomputing, speech and vision, engineering, remote sensing etc. The paper describes the use of kernel methods to approach the processing of large datasets from environmental monitoring networks. Several typical problems of the environmental sciences and their solutions provided by kernel-based methods are considered: classification of categorical data (soil type classification), mapping of environmental and pollution continuous information (pollution of soil by radionuclides), mapping with auxiliary information (climatic data from Aral Sea region). The promising developments, such as automatic emergency hot spot detection and monitoring network optimization are discussed as well.

Ler melhor através dos temas e mitos na literatura cabo-verdiana : uma análise comparativista

Esta dissertação teve como ponto de partida a pergunta “Como se comportaram alguns temas e mitos ao longo da história da literatura de Cabo Verde? Que caminho percorreram?” Para tentar dar-lhe resposta recorremos à metodologia comparativista, com o objetivo de analisar as evoluções e possíveis mudanças ocorridas ao longo dos tempos, a partir do cruzamento transversal entre temas e mitos em épocas diferentes. Foi assim necessário abordar a literatura cabo-verdiana, na sua relação profunda com a história do país, considerando os seus três grandes momentos: um tempo “ indefinido” que vai até 1936, um segundo que vai de 1936 até 1975 e, por último, o que vai desde essa data até à atualidade. Ao longo dessa análise, e a partir de um corpus textual necessariamente circunscrito, fomos detetando as variantes, as diferentes atualizações ocorridas nesta literatura, geralmente provocadas por fatores de ordem geográfica, pela evolução histórico-social, por fatores ideológicos que se prenderam sobretudo com a falta de liberdade vivida até 1975. Esta abordagem comparativista, a análise intertextual que lhe é inerente, proporcionou, não uma simples comparação, mas o desvendar de relações múltiplas existentes entre as obras, o corpus literário escolhido, e os tempos que lhes deram origem, permitindo assim conhecer as partes e, consequentemente o todo através do “desocultamento do oculto”. Constatou-se uma migração dos temas e dos mitos entre as literaturas dos três momentos. Em certos casos, alguns elementos permaneceram, mas com novas representações, noutros assumem significados radicalmente diferentes dados os atuais contextos da sua recepção. Relativamente aos mitos, observou-se a sua crescente dessacralização, em sintonia com o progresso que o país foi conhecendo após a independência nacional. A leitura que fomos fazendo conduziu-nos deste modo a uma viagem ao passado do país. Esta leitura transversal, alicerçada na história, dando conta das transformações ocorridas ao longo dos tempos, mostrou-nos ainda de forma cabal a necessidade de se introduzirem mudanças no ensino da literatura nas escolas cabo-verdianas. Uma mudança que deverá passar por uma abordagem comparativista, que privilegia as relações entre textos de diferentes épocas, a sua perspetivação interdisciplinar com outras formas de expressão, capaz de transformar o aluno-mero-receptor num leitor ativo, implicado numa realidade que lhe diz respeito.

Novel methods improve prediction of species' distributions from occurrence data

Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.

Data-driven clustering : new methods and applications

Abstract : This work is concerned with the development and application of novel unsupervised learning methods, having in mind two target applications: the analysis of forensic case data and the classification of remote sensing images. First, a method based on a symbolic optimization of the inter-sample distance measure is proposed to improve the flexibility of spectral clustering algorithms, and applied to the problem of forensic case data. This distance is optimized using a loss function related to the preservation of neighborhood structure between the input space and the space of principal components, and solutions are found using genetic programming. Results are compared to a variety of state-of--the-art clustering algorithms. Subsequently, a new large-scale clustering method based on a joint optimization of feature extraction and classification is proposed and applied to various databases, including two hyperspectral remote sensing images. The algorithm makes uses of a functional model (e.g., a neural network) for clustering which is trained by stochastic gradient descent. Results indicate that such a technique can easily scale to huge databases, can avoid the so-called out-of-sample problem, and can compete with or even outperform existing clustering algorithms on both artificial data and real remote sensing images. This is verified on small databases as well as very large problems. Résumé : Ce travail de recherche porte sur le développement et l'application de méthodes d'apprentissage dites non supervisées. Les applications visées par ces méthodes sont l'analyse de données forensiques et la classification d'images hyperspectrales en télédétection. Dans un premier temps, une méthodologie de classification non supervisée fondée sur l'optimisation symbolique d'une mesure de distance inter-échantillons est proposée. Cette mesure est obtenue en optimisant une fonction de coût reliée à la préservation de la structure de voisinage d'un point entre l'espace des variables initiales et l'espace des composantes principales. Cette méthode est appliquée à l'analyse de données forensiques et comparée à un éventail de méthodes déjà existantes. En second lieu, une méthode fondée sur une optimisation conjointe des tâches de sélection de variables et de classification est implémentée dans un réseau de neurones et appliquée à diverses bases de données, dont deux images hyperspectrales. Le réseau de neurones est entraîné à l'aide d'un algorithme de gradient stochastique, ce qui rend cette technique applicable à des images de très haute résolution. Les résultats de l'application de cette dernière montrent que l'utilisation d'une telle technique permet de classifier de très grandes bases de données sans difficulté et donne des résultats avantageusement comparables aux méthodes existantes.

Organizational learning from customer interaction

In the fierce competition of today‟s business world an organization‟s capacity to learn maybe its only competitive advantage. This research aims at increasing the understanding on how organizational learning from the customer happens in technology companies. In doing so it provides a synthesized definition of organizational learning and investigates processes of organizational learning within technology companies. A qualitative research method and in-depth interviews with different sized high technology companies, as applied here, enables in-depth study of the learning processes. Research contributes to the understanding of what type of knowledge firms acquire, how new knowledge is transferred and used in a learning firm‟s routines and processes. Research findings show that SMEs and large size companies also, depending on their position in the software value chain, consider different knowledge types as most important and that they use different learning methods to acquire knowledge from their customers.

Sentence Compression with Supervised Machine Learning

The subject of the thesis is automatic sentence compression with machine learning, so that the compressed sentences remain both grammatical and retain their essential meaning. There are multiple possible uses for the compression of natural language sentences. In this thesis the focus is generation of television program subtitles, which often are compressed version of the original script of the program. The main part of the thesis consists of machine learning experiments for automatic sentence compression using different approaches to the problem. The machine learning methods used for this work are linear-chain conditional random fields and support vector machines. Also we take a look which automatic text analysis methods provide useful features for the task. The data used for machine learning is supplied by Lingsoft Inc. and consists of subtitles in both compressed an uncompressed form. The models are compared to a baseline system and comparisons are made both automatically and also using human evaluation, because of the potentially subjective nature of the output. The best result is achieved using a CRF - sequence classification using a rich feature set. All text analysis methods help classification and most useful method is morphological analysis. Tutkielman aihe on suomenkielisten lauseiden automaattinen tiivistäminen koneellisesti, niin että lyhennetyt lauseet säilyttävät olennaisen informaationsa ja pysyvät kieliopillisina. Luonnollisen kielen lauseiden tiivistämiselle on monta käyttötarkoitusta, mutta tässä tutkielmassa aihetta lähestytään television ohjelmien tekstittämisen kautta, johon käytännössä kuuluu alkuperäisen tekstin lyhentäminen televisioruudulle paremmin sopivaksi. Tutkielmassa kokeillaan erilaisia koneoppimismenetelmiä tekstin automaatiseen lyhentämiseen ja tarkastellaan miten hyvin erilaiset luonnollisen kielen analyysimenetelmät tuottavat informaatiota, joka auttaa näitä menetelmiä lyhentämään lauseita. Lisäksi tarkastellaan minkälainen lähestymistapa tuottaa parhaan lopputuloksen. Käytetyt koneoppimismenetelmät ovat tukivektorikone ja lineaarisen sekvenssin mallinen CRF. Koneoppimisen tukena käytetään tekstityksiä niiden eri käsittelyvaiheissa, jotka on saatu Lingsoft OY:ltä. Luotuja malleja vertaillaan Lopulta mallien lopputuloksia evaluoidaan automaattisesti ja koska teksti lopputuksena on jossain määrin subjektiivinen myös ihmisarviointiin perustuen. Vertailukohtana toimii kirjallisuudesta poimittu menetelmä. Tutkielman tuloksena paras lopputulos saadaan aikaan käyttäen CRF sekvenssi-luokittelijaa laajalla piirrejoukolla. Kaikki kokeillut teksin analyysimenetelmät auttavat luokittelussa, joista tärkeimmän panoksen antaa morfologinen analyysi.

An exploratory study of nurses' perceptions of their action learning experience /

Each year, the College of Nurses of Ontario (CNO) requires all registered nurses and registered practical nurses in Ontario to complete a Reflective Practice learning activity. In doing so, nurses are expected to perform a self- assessment, identify a practice problem or issue, create and implement a personal learning plan, and evaluate the learning and outcomes accomplished. The process and components of CNO's Reflective Practice program are very similar to an Action Learning activity. The purpose of this qualitative research was to explore the perceptions of 1 1 nurses who completed at least 1 Action Learning activity. Data analysis of their comments provided insight into their perceptions of the Action Learning experience, perceptions of the negative and positive characteristics of various activities within the Action Learning process, and perceptions of barriers or challenges within this experience. The author concluded that participants perceived their Action Learning activities to be a positive experience because the process focused on practice problems and issues, enhanced thinking about practice problems, and achieved practice-relevant outcomes. However, the results indicated that self-directed learning and journal writing were difficult activities for some participants, and some experienced negative emotional responses during reflection. The research concluded that barriers to implementation of Action Learning include a lack of understanding of the process and a perceived lack of support from employers.

Creating a Space for Self-Transformation: Factors of Success for Adult Literacy Learners with Specific Learning Difficulties

A significant number of adults in adult literacy programs in Ontario have specific learning difficulties. This study sought to examine the holistic factors that contributed to these learners achieving their goals. Through a case study design, the data revealed that a combination of specific learning methods and strategies, along with particular characteristics of the instructor, participant, and class, and the evidence of self-transformation all seemed to contribute to the participant's success in the program. Instructor-directed teaching and cooperative learning were the main learning methods used in the class. General learning strategies employed were the use of core curriculum and authentic documents, and using phonics, repetition, assistive resources, and using activities that appealed to various learning styles. The instructor had a history of both professional development in the area of learning disabilities as well as experience working with learners who had specific learning difficulties. There also seemed to be a goodness of fit between the participant and the instructor. Several characteristics of the participant seemed to aid in his success: his positive self-esteem, self-advocacy skills, self-determination, self-awareness, and the fact that he enjoyed learning. The size (3-5 people) and type of class (small group) also seemed to have an impact. Finally, evidence that the participant went through a self-transformation seemed to contribute to a positive learner identity. These results have implications for practice, theory, and further research in adult education.

Intervention éducative visant le transfert des résultats probants par les infirmières dans la pratique du contact peau à peau en post-partum immédiat

Rapport de stage présenté à la Faculté des sciences infirmières en vue de l’obtention du grade de Maître ès sciences (M. Sc.) en sciences infirmières option formation en sciences infirmières