A influência do e-learning no capital humano

Na Europa e nas últimas décadas do Século XX, a emergência da Sociedade de Informação veio impor às organizações a necessidade de que, para além das inovações tecnológicas, haja uma preocupação relativamente aos bens intangíveis como a informação, as novas metodologias de trabalho e o know how (Batista, 2002). Paralelamente a estas inovações, as Instituições de Ensino Superior têm contribuído para a evolução do Capital Humano, como ativo intangível intrínseco ao Homem. Em Portugal e no contexto do Ensino/Formação a Distância parecem continuar a existir, ainda, em algumas instituições, problemas de identificação, e de descriminação das vantagens no que concerne à estrutura aberta e flexível, com o estudante/formando a ter algumas dificuldades em adaptar o seu perfil e interesses profissionais ao tipo de aprendizagem que mais se lhe adequa. O e-learning surge como um método de Ensino/Formação a Distância, só possível com a especificidade dos processos pedagógicos e em complementaridade com as Tecnologias de Informação e Comunicação (TIC), uma vez que são estas que lhe dão o suporte necessário à sua concretização. O e-learning ao proporcionar novas formas de comunicação, de interação e de confronto de ideias, permite uma aprendizagem baseada na partilha de saberes, tendo em consideração as experiências e os objetivos profissionais dos formandos. Dentro destes pressupostos, achámos importante fazer uma investigação a partir de Instituições de Ensino Superior Portuguesas, de modo a percebermos qual o papel e a influência que o e-learning desempenha nos objetivos das organizações académicas em geral e no Capital Humano dos seus Estudantes/Formandos em particular. A partir da questão da investigação foram definidos os objetivos e hipóteses de investigação de modo a que ao ser enunciada uma metodologia esta englobe fatores que foquem os elementos necessários à confirmação, ou não, dos pressupostos enunciados. Foi analisada documentação diversa, criado um questionário e conduzidas entrevistas, de modo a obter e potenciar a informação necessária e suficiente para o efeito. A recolha de dados para posterior análise e os resultados depois de interpretados, permitirão responder aos propósitos expressos desde o início da investigação.

The Relationship between Teachers and Students in the Classroom: Communicative Language Teaching Approach and Cooperative Learning Strategy to Improve Learning

The aim of this research is to to investigate how a supportive relationship between teachers and students in the classroom can improve the learning process. By having a good relationship with students, teachers can offer to students chances to be motivated and feel engaged in the learning process. Students will be engaged actively in the learning instead of being passive learners. I wish to investigate how using communicative approach and cooperative learning strategies while teaching do affect and improve students’ learning performance. To achieve these goals qualitative data collection was used as the primary method. The results show that teachers and students value a supportive and caring relationship between them and that interaction is essential to the teacher-student relationship. This sense of caring and supporting from teachers motivates students to become a more interested learner. Students benefit and are motivated when their teachers create a safe and trustful environment. And also the methods and strategies teachers uses, makes students feel engaged and stimulated to participate in the learning process. The students have in their mind that a positive relationship with their teachers positively impacts their interest and motivation in school which contributes to the enhancement of the learning process.

Semantic domains and supersens tagging for domain-specific ontology learning

In this paper we propose a novel unsupervised approach to learning domain-specific ontologies from large open-domain text collections. The method is based on the joint exploitation of Semantic Domains and Super Sense Tagging for Information Retrieval tasks. Our approach is able to retrieve domain specific terms and concepts while associating them with a set of high level ontological types, named supersenses, providing flat ontologies characterized by very high accuracy and pertinence to the domain.

Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes

We present a heuristic method for learning error correcting output codes matrices based on a hierarchical partition of the class space that maximizes a discriminative criterion. To achieve this goal, the optimal codeword separation is sacrificed in favor of a maximum class discrimination in the partitions. The creation of the hierarchical partition set is performed using a binary tree. As a result, a compact matrix with high discrimination power is obtained. Our method is validated using the UCI database and applied to a real problem, the classification of traffic sign images.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Reinforcement learning utilizes proxemics: an avatar learns to manipulate the position of people in immersive virtual reality

A reinforcement learning (RL) method was used to train a virtual character to move participants to a specified location. The virtual environment depicted an alleyway displayed through a wide field-of-view head-tracked stereo head-mounted display. Based on proxemics theory, we predicted that when the character approached within a personal or intimate distance to the participants, they would be inclined to move backwards out of the way. We carried out a between-groups experiment with 30 female participants, with 10 assigned arbitrarily to each of the following three groups: In the Intimate condition the character could approach within 0.38m and in the Social condition no nearer than 1.2m. In the Random condition the actions of the virtual character were chosen randomly from among the same set as in the RL method, and the virtual character could approach within 0.38m. The experiment continued in each case until the participant either reached the target or 7 minutes had elapsed. The distributions of the times taken to reach the target showed significant differences between the three groups, with 9 out of 10 in the Intimate condition reaching the target significantly faster than the 6 out of 10 who reached the target in the Social condition. Only 1 out of 10 in the Random condition reached the target. The experiment is an example of applied presence theory: we rely on the many findings that people tend to respond realistically in immersive virtual environments, and use this to get people to achieve a task of which they had been unaware. This method opens up the door for many such applications where the virtual environment adapts to the responses of the human participants with the aim of achieving particular goals.

Metabolic activation pattern of distinct hippocampal subregions during spatial learning and memory retrieval.

Activation dynamics of hippocampal subregions during spatial learning and their interplay with neocortical regions is an important dimension in the understanding of hippocampal function. Using the (14C)-2-deoxyglucose autoradiographic method, we have characterized the metabolic changes occurring in hippocampal subregions in mice while learning an eight-arm radial maze task. Autoradiogram densitometry revealed a heterogeneous and evolving pattern of enhanced metabolic activity throughout the hippocampus during the training period and on recall. In the early stages of training, activity was enhanced in the CA1 area from the intermediate portion to the posterior end as well as in the CA3 area within the intermediate portion of the hippocampus. At later stages, CA1 and CA3 activations spread over the entire longitudinal axis, while dentate gyrus (DG) activation occurred from the anterior to the intermediate zone. Activation of the retrosplenial cortex but not the amygdala was also observed during the learning process. On recall, only DG activation was observed in the same anterior part of the hippocampus. These results suggest the existence of a functional segmentation of the hippocampus, each subregion being dynamically but also differentially recruited along the acquisition, consolidation, and retrieval process in parallel with some neocortical sites.

Case-based learning in VTLE: An effective strategy for improving learning design

This article presents preliminary research from an instructional design perspective on the design of the case method as an integral part of pedagogy and technology. Key features and benefitsusing this teaching and learning strategy in a Virtual Teaching and Learning Environment(VTLE) are identified, taking into account the requirements of the European Higher Education Area (EHEA) for a competence-based curricula design. The implications of these findings for alearning object approach exploring the possibilities of learning personalization, reusability and interoperability trough IMS LD, are also analyzed.

A methodological approach to identify teacher's ICT competencies in online learning environments in Higher Education

Aquest article pretén descriure el procés metodològic d'identificació i mesurament de les competències TIC dels professors i com a formadors en les TIC en un entorn d'aprenentatge en línia en l'Educació Superior portat a terme en el marc del Projecte Europeu Elene-TLC.La revisió de la recerca en les competències en línia del professor demostra que, en primer lloc, el mètode més utilitzat per a identificar aquestes competències és el focus group. En segon lloc, la tècnica Delphi és la tècnica més utilitzada per reunir el consens d'experts sobre quines són les competències principals per al professor en línia entre els que s'indiquen.La proposta metodològica descrita en aquest document consisteix en la creació de 7 grups de discussió en línia, l'objectiu dels quals era identificar les competències formatives dels professors en línia i les dels professos en línia. La llista de competències obtingudes posteriorment es va oferir als experts europeus que participaven en l'aplicació de la tècnica Delphi. A aquests experts se'ls va demanar que ordenessin les competències d'acord amb el seu grau d'importància.Els resultats mostren que els grups de discussió en línia i el mètode Delphi són les metodologies apropiades per a identificar les competències TIC dels professors universitaris en els entorns d'aprenentatge en línia.

Reinforcement learning utilizes proxemics: an avatar learns to manipulate the position of people in immersive virtual reality

A reinforcement learning (RL) method was used to train a virtual character to move participants to a specified location. The virtual environment depicted an alleyway displayed through a wide field-of-view head-tracked stereo head-mounted display. Based on proxemics theory, we predicted that when the character approached within a personal or intimate distance to the participants, they would be inclined to move backwards out of the way. We carried out a between-groups experiment with 30 female participants, with 10 assigned arbitrarily to each of the following three groups: In the Intimate condition the character could approach within 0.38m and in the Social condition no nearer than 1.2m. In the Random condition the actions of the virtual character were chosen randomly from among the same set as in the RL method, and the virtual character could approach within 0.38m. The experiment continued in each case until the participant either reached the target or 7 minutes had elapsed. The distributions of the times taken to reach the target showed significant differences between the three groups, with 9 out of 10 in the Intimate condition reaching the target significantly faster than the 6 out of 10 who reached the target in the Social condition. Only 1 out of 10 in the Random condition reached the target. The experiment is an example of applied presence theory: we rely on the many findings that people tend to respond realistically in immersive virtual environments, and use this to get people to achieve a task of which they had been unaware. This method opens up the door for many such applications where the virtual environment adapts to the responses of the human participants with the aim of achieving particular goals.

Cluster-based active learning for compact image classification

In this paper, we consider active sampling to label pixels grouped with hierarchical clustering. The objective of the method is to match the data relationships discovered by the clustering algorithm with the user's desired class semantics. The first is represented as a complete tree to be pruned and the second is iteratively provided by the user. The active learning algorithm proposed searches the pruning of the tree that best matches the labels of the sampled points. By choosing the part of the tree to sample from according to current pruning's uncertainty, sampling is focused on most uncertain clusters. This way, large clusters for which the class membership is already fixed are no longer queried and sampling is focused on division of clusters showing mixed labels. The model is tested on a VHR image in a multiclass classification setting. The method clearly outperforms random sampling in a transductive setting, but cannot generalize to unseen data, since it aims at optimizing the classification of a given cluster structure.

The Roles of Cultural, Functional and Value Diversity in Innovativeness and Learning

Tutkielman tavoite on tutkia kulttuurista, funktionaalista ja arvojen diversiteettiä, niiden suhdetta innovatiivisuuteen ja oppimiseen sekä tarjota keinoja diversiteetin johtamiseen. Tämän lisäksi selvitetään linjaesimiesten haastattelujen kautta miten diversiteetti case -organisaatiossa tällä hetkellä koetaan. Organisaation diversiteetin tämänhetkisen tilan tunnistamisen kautta voidaan esittää parannusehdotuksia diversiteetin hallintaan. Tutkimus- ja tiedonkeruumenetelmänä käytetään kvalitatiivista focus group haastattelumenetelmää. Tutkimuksessa saatiin selkeä kuva kulttuurisen, funktionaalisen ja arvojen diversiteetin merkityksistä organisaation innovatiivisuudelle ja oppimiselle sekä löydettiin keinoja näiden diversiteetin tyyppien johtamiseen. Tutkimuksen tärkeä löydös on se, että diversiteetti vaikuttaa positiivisesti organisaation innovatiivisuuteen kun sitä johdetaan tehokkaasti ja kun organisaatioympäristö tukee avointa keskustelua ja mielipiteiden jakamista. Case organisaation tämänhetkistä diversiteetin tilaa selvitettäessä havaittiin että ongelma organisaatiossa ei ole diversiteetin puute, vaan paremminkin se, ettei diversiteettia osata hyödyntää. Organisaatio ei tue erilaisten näkemysten ja mielipiteiden vapaata esittämistä jahyväksikäyttöä ja siksi diversiteetin hyödyntäminen on epätäydellistä. Haastatteluissa tärkeinä seikkoina diversiteetin hyödyntämisen parantamisessa nähtiin kulttuurin muuttaminen avoimempaan suuntaan ja johtajien esimiestaitojen parantaminen.

Predicting diffusion of innovations with self-organisation and machine learning

The main subject of this master's thesis was predicting diffusion of innovations. The prediction was done in a special case: product has been available in some countries, and based on its diffusion in those countries the prediction is done for other countries. The prediction was based on finding similar countries with Self-Organizing Map~(SOM), using parameters of countries. Parameters included various economical and social key figures. SOM was optimised for different products using two different methods: (a) by adding diffusion information of products to the country parameters, and (b) by weighting the country parameters based on their importance for the diffusion of different products. A novel method using Differential Evolution (DE) was developed to solve the latter, highly non-linear optimisation problem. Results were fairly good. The prediction method seems to be on a solid theoretical foundation. The results based on country data were good. Instead, optimisation for different products did not generally offer clear benefit, but in some cases the improvement was clearly noticeable. The weights found for the parameters of the countries with the developed SOM optimisation method were interesting, and most of them could be explained by properties of the products.

Therapeutic benefits of body percussion using the BAPNE method

Body percussion using to the BAPNE method is a means of cognitive stimulation with multiple applications. The aim of this research is to assess their full potential as a source of therapy. The methodology used is theoretical in nature and makes use of a wide bibliography to find evidence for its therapeutic effect. In essence, body percussion can be seen to lead to improvements in three areas. the Physical, as it stimulates awareness of the body, control of movement and muscular strength, coordination and balance; the Mental, as it improves concentration, memory and perception; and finally Socio-affective, as it helps to build egalitarian relationships and leads to a decrease in anxiety in social interactions. This means of therapy has several different uses and it is targeted at different groups. In the present investigation we categorise them into five main groups: individuals with neurodegenerative diseases like Alzheimer's or Parkinson's disease; individuals with learning disorders such as dyslexia or ADHD; patients affected by diseases of the spinal cord, cranial neuropathies and trauma (Neurorehabilitation); and for the treatment of addictive behavior (addiction); and depressive disorders or anxiety disorders.After thorough analysis, we have found scientific evidence that the therapeutic body percussion using the BAPNE method improves the quality of life of patients and it is an important factor in stabilizing the development of different diseases.In addition, evidence involving certain biological indicators (in control and experimental groups, and through a pre-test and post-test) show its effect on levels of stress and anxiety (reduction of cortisol), as well as improvement of social relations as a result of working as a group (increased levels of oxytocin), and improvements seen in self-esteem and in a variety of personal aspects through the Aspects of Identity questionnaire.

A Sparse Bayesian Approach for Joint Feature Selection and Classifier Learning

Peer-reviewed