Remote lab experiments: opening possibilities for distance learning in engineering fields

Remote experimentation laboratories are systems based on real equipment, allowing students to perform practical work through a computer connected to the internet. In engineering fields lab activities play a fundamental role. Distance learning has not demonstrated good results in engineering fields because traditional lab activities cannot be covered by this paradigm. These activities can be set for one or for a group of students who work from different locations. All these configurations lead to considering a flexible model that covers all possibilities (for an individual or a group). An inter-continental network of remote laboratories supported by both European and Latin American institutions of higher education has been formed. In this network context, a learning collaborative model for students working from different locations has been defined. The first considerations are presented.

Managing informal learning in engineering contexts: the learners' perspective

According to recent studies, informal learning accounts for more than 75% of our continuous learning through life. However, the awareness of this learning, its benefits and its potential is still not very clear. In engineering contexts, informal learning could play an invaluable role helping students or employees to engage with peers and also with more experience colleagues, exchanging ideas and discussing problems. This work presents an initial set of results of the piloting phase of a project (TRAILER) where an innovative service based on Information & Communication Technologies was developed in order to aid the collection and visibility of informal learning. This set of results concerns engineering contexts (academic and business), from the learners' perspective. The major idea that emerged from these piloting trials was that it represented a good way of collecting, recording and sharing informal learning that otherwise could easily be forgotten. Several benefits were reported between the two communities such as being helpful in managing competences and human resources within an institution.

Learning to manage in knowledge-based organizations: the role of learning logs or portfolios

Paper presented at the 8th European Conference on Knowledge Management, Barcelona, 6-7 Sep. 2008 URL: http://www.academic-conferences.org/eckm/eckm2007/eckm07-home.htm

Multi-Agent Based Smart Grid Management and Simulation: Situation Awareness and Learning in a Test Bed with Simulated and Real Installations and Players

The rising usage of distributed energy resources has been creating several problems in power systems operation. Virtual Power Players arise as a solution for the management of such resources. Additionally, approaching the main network as a series of subsystems gives birth to the concepts of smart grid and micro grid. Simulation, particularly based on multi-agent technology is suitable to model all these new and evolving concepts. MASGriP (Multi-Agent Smart Grid simulation Platform) is a system that was developed to allow deep studies of the mentioned concepts. This paper focuses on a laboratorial test bed which represents a house managed by a MASGriP player. This player is able to control a real installation, responding to requests sent by the system operators and reacting to observed events depending on the context.

Evolution of decision-making and learning under fluctuating social environments

The capacity to learn to associate sensory perceptions with appropriate motor actions underlies the success of many animal species, from insects to humans. The evolutionary significance of learning has long been a subject of interest for evolutionary biologists who emphasize the bene¬fit yielded by learning under changing environmental conditions, where it is required to flexibly switch from one behavior to another. However, two unsolved questions are particularly impor¬tant for improving our knowledge of the evolutionary advantages provided by learning, and are addressed in the present work. First, because it is possible to learn the wrong behavior when a task is too complex, the learning rules and their underlying psychological characteristics that generate truly adaptive behavior must be identified with greater precision, and must be linked to the specific ecological problems faced by each species. A framework for predicting behavior from the definition of a learning rule is developed here. Learning rules capture cognitive features such as the tendency to explore, or the ability to infer rewards associated to unchosen actions. It is shown that these features interact in a non-intuitive way to generate adaptive behavior in social interactions where individuals affect each other's fitness. Such behavioral predictions are used in an evolutionary model to demonstrate that, surprisingly, simple trial-and-error learn¬ing is not always outcompeted by more computationally demanding inference-based learning, when population members interact in pairwise social interactions. A second question in the evolution of learning is its link with and relative advantage compared to other simpler forms of phenotypic plasticity. After providing a conceptual clarification on the distinction between genetically determined vs. learned responses to environmental stimuli, a new factor in the evo¬lution of learning is proposed: environmental complexity. A simple mathematical model shows that a measure of environmental complexity, the number of possible stimuli in one's environ¬ment, is critical for the evolution of learning. In conclusion, this work opens roads for modeling interactions between evolving species and their environment in order to predict how natural se¬lection shapes animals' cognitive abilities. - La capacité d'apprendre à associer des sensations perceptives à des actions motrices appropriées est sous-jacente au succès évolutif de nombreuses espèces, depuis les insectes jusqu'aux êtres hu¬mains. L'importance évolutive de l'apprentissage est depuis longtemps un sujet d'intérêt pour les biologistes de l'évolution, et ces derniers mettent l'accent sur le bénéfice de l'apprentissage lorsque les conditions environnementales sont changeantes, car dans ce cas il est nécessaire de passer de manière flexible d'un comportement à l'autre. Cependant, deux questions non résolues sont importantes afin d'améliorer notre savoir quant aux avantages évolutifs procurés par l'apprentissage. Premièrement, puisqu'il est possible d'apprendre un comportement incorrect quand une tâche est trop complexe, les règles d'apprentissage qui permettent d'atteindre un com¬portement réellement adaptatif doivent être identifiées avec une plus grande précision, et doivent être mises en relation avec les problèmes écologiques spécifiques rencontrés par chaque espèce. Un cadre théorique ayant pour but de prédire le comportement à partir de la définition d'une règle d'apprentissage est développé ici. Il est démontré que les caractéristiques cognitives, telles que la tendance à explorer ou la capacité d'inférer les récompenses liées à des actions non ex¬périmentées, interagissent de manière non-intuitive dans les interactions sociales pour produire des comportements adaptatifs. Ces prédictions comportementales sont utilisées dans un modèle évolutif afin de démontrer que, de manière surprenante, l'apprentissage simple par essai-et-erreur n'est pas toujours battu par l'apprentissage basé sur l'inférence qui est pourtant plus exigeant en puissance de calcul, lorsque les membres d'une population interagissent socialement par pair. Une deuxième question quant à l'évolution de l'apprentissage concerne son lien et son avantage relatif vis-à-vis d'autres formes plus simples de plasticité phénotypique. Après avoir clarifié la distinction entre réponses aux stimuli génétiquement déterminées ou apprises, un nouveau fac¬teur favorisant l'évolution de l'apprentissage est proposé : la complexité environnementale. Un modèle mathématique permet de montrer qu'une mesure de la complexité environnementale - le nombre de stimuli rencontrés dans l'environnement - a un rôle fondamental pour l'évolution de l'apprentissage. En conclusion, ce travail ouvre de nombreuses perspectives quant à la mo¬délisation des interactions entre les espèces en évolution et leur environnement, dans le but de comprendre comment la sélection naturelle façonne les capacités cognitives des animaux.

A Negotiation Style Recommender Based on Computational Ecology in Open Negotiation Environments

The system described herein represents the first example of a recommender system in digital ecosystems where agents negotiate services on behalf of small companies. The small companies compete not only with price or quality, but with a wider service-by-service composition by subcontracting with other companies. The final result of these offerings depends on negotiations at the scale of millions of small companies. This scale requires new platforms for supporting digital business ecosystems, as well as related services like open-id, trust management, monitors and recommenders. This is done in the Open Negotiation Environment (ONE), which is an open-source platform that allows agents, on behalf of small companies, to negotiate and use the ecosystem services, and enables the development of new agent technologies. The methods and tools of cyber engineering are necessary to build up Open Negotiation Environments that are stable, a basic condition for predictable business and reliable business environments. Aiming to build stable digital business ecosystems by means of improved collective intelligence, we introduce a model of negotiation style dynamics from the point of view of computational ecology. This model inspires an ecosystem monitor as well as a novel negotiation style recommender. The ecosystem monitor provides hints to the negotiation style recommender to achieve greater stability of an open negotiation environment in a digital business ecosystem. The greater stability provides the small companies with higher predictability, and therefore better business results. The negotiation style recommender is implemented with a simulated annealing algorithm at a constant temperature, and its impact is shown by applying it to a real case of an open negotiation environment populated by Italian companies

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.

Maintaining unlinkability in group based P2P environments

In the wake of the success of Peer-to-Peer (P2P) networking, security has arisen as one of its main concerns, becoming a key issue when evaluating a P2P system. Unfortunately, some systems' design focus targeted issues such as scalabil-ity or overall performance, but not security. As a result, security mechanisms must be provided at a later stage, after the system has already been designed and partially (or even fully) implemented, which may prove a cumbersome proposition. This work exposes how a security layer was provided under such circumstances for a specic Java based P2P framework: JXTA-Overlay.

Developing Processes and Learning in a Project-based Firm

Tämän diplomityön tavoitteena on kuvata tiedonkulkua projektiliiketoimintaa harjoittavassa yrityksessä sekä analysoida kuvausta määrittäen mahdolliset kehityskohdat. Työssätuotetut kuvaukset ja kehityskohtien määrittäminen toimivat pohjana yrityksen kehittäessä projektien hallintaansa tulevaisuudessa. Työssä valitaan tietojohtamisen näkökulma sopivaksi lähestymistavaksi yrityksen toiminnananalysointiin. Haastatteluin kerätyn tutkimusmateriaalin perusteella luodaan prosessikuvaukset jotka mallintavat tietovirtoja yrityksen projektien aikana tapahtuvien prosessien välillä. Kuvausta peilataan tietämyksen luomisen sekä projektien tietojohtamisen teoriaan ja määritetään kehityskohteita. Kehityskohteiden määrittämisen lisäksi ehdotetaan mahdollisia toimenpiteitä tiedon ja tietämyksen hallinnan kehittämiseksi. Kokemusten ja opittujen asioiden sekäpalautteen kerääminen projektien aikana sekä niiden jälkeen havaittiin tärkeimmäksi kehityskohdaksi. Näiden keräämisen voidaan todeta vaativan järjestelmällisyyttä jotta projektien onnistumiset sekä niissä saavutetut parannukset voidaan toistaa jatkossa ja virheet sekä epäonnistumiset sitä vastoin välttää.

A model for the evolution of reinforcement learning in fluctuating games

Many species are able to learn to associate behaviours with rewards as this gives fitness advantages in changing environments. Social interactions between population members may, however, require more cognitive abilities than simple trial-and-error learning, in particular the capacity to make accurate hypotheses about the material payoff consequences of alternative action combinations. It is unclear in this context whether natural selection necessarily favours individuals to use information about payoffs associated with nontried actions (hypothetical payoffs), as opposed to simple reinforcement of realized payoff. Here, we develop an evolutionary model in which individuals are genetically determined to use either trial-and-error learning or learning based on hypothetical reinforcements, and ask what is the evolutionarily stable learning rule under pairwise symmetric two-action stochastic repeated games played over the individual's lifetime. We analyse through stochastic approximation theory and simulations the learning dynamics on the behavioural timescale, and derive conditions where trial-and-error learning outcompetes hypothetical reinforcement learning on the evolutionary timescale. This occurs in particular under repeated cooperative interactions with the same partner. By contrast, we find that hypothetical reinforcement learners tend to be favoured under random interactions, but stable polymorphisms can also obtain where trial-and-error learners are maintained at a low frequency. We conclude that specific game structures can select for trial-and-error learning even in the absence of costs of cognition, which illustrates that cost-free increased cognition can be counterselected under social interactions.

Student Journalism 2.0 : Testing Models for Participatory Learning in the Digital Age

Many educators and educational institutions have yet to integrate web-based practices into their classrooms and curricula. As a result, it can be difficult to prototype and evaluate approaches to transforming classrooms from static endpoints to dynamic, content-creating nodes in the online information ecosystem. But many scholastic journalism programs have already embraced the capabilities of the Internet for virtual collaboration, dissemination, and reader participation. Because of this, scholastic journalism can act as a test-bed for integrating web-based sharing and collaboration practices into classrooms. Student Journalism 2.0 was a research project to integrate open copyright licenses into two scholastic journalism programs, to document outcomes, and to identify recommendations and remaining challenges for similar integrations. Video and audio recordings of two participating high school journalism programs informed the research. In describing the steps of our integration process, we note some important legal, technical, and social challenges. Legal worries such as uncertainty over copyright ownership could lead districts and administrators to disallow open licensing of student work. Publication platforms among journalism classrooms are far from standardized, making any integration of new technologies and practices difficult to achieve at scale. And teachers and students face challenges re-conceptualizing the role their class work can play online.

Model-based objects recognition in man-made environments

We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using CEES, the C++ embedded expert system shell developed in the Systems Engineering and Automatic Control Laboratory (University of Girona) as a specific rule-based problem solving tool. It has been especially conceived for supporting cooperative expert systems, and uses the object oriented programming paradigm

Habilitats comunicatives per a l’aprenentatge en l’entorn virtual = Communicative skills for learning in a virtual environment

The virtual learning environments are an option in permanent training with great possibilities for adults who look for studies that are compatible with their jobs and with their family life. So as to participate in determined learning as much in attitudes as knowledge and skills. The article is dedicated to analysing the necessary linguistic habits for moving within an environment of this type and offers didactic proposals that can facilitate the active participation in a virtual course and widen the perspectives of the control of new channels of communication with objectives that are different to learning