DeustoTeka
DeustoTeka recoge la producción científica del personal docente e investigador de la Universidad de Deusto. Su propósito es reunir, archivar, preservar y aumentar la visibilidad en acceso abierto de los resultados de investigación.
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Using remote labs to serve different teacher's needs: a case study with VISIR and RemotElectLab
(International Federation of Engineering Education Societies (IFEES), 2012-11-28) Fidalgo, André; Alves, Gustavo R.; Marques, María Arcelina; Viegas, Clara; Costa-Lobo, Cristina; Hernández Jayo, Unai; García-Zubía, Javier; Gustavsson, Ingvar
Remote Laboratories are an emergent technological and pedagogical tool at all education levels, and their widespread use is an important part of their own improvement and evolution. This paper describes several issues encountered on laboratorial classes, on higher education courses, when using remote laboratories based on PXI systems, either using the VISIR system or an alternate inhouse solution. Three main issues are presented and explained, all reported by teachers that gave support to students use of remote laboratories. The first issue deals with the need to allow students to select the actual place where an ammeter is to be inserted on electric circuits, even incorrectly, therefore emulating real world difficulties. The second one deals with problems with timing when several measurements are required at short intervals, as in the discharge cycle of a capacitor. And the last issue deals with the use of a multimeter in DC mode when reading AC values, a use that collides with the lab settings. All scenarios are presented and discussed including the solution found for each case. The conclusion derived from the described work is that the remote laboratories area is an expanding field, where practical use leads to improvement and evolution of the available solutions, requiring a strict cooperation and information sharing between all actors, i.e. developers, teachers and students.
A tool to evaluate the level of inclusion of digital learning objects
(Elsevier B.V., 2012) Guenaga Gómez, Mariluz; Mechaca, Iratxe; Romero Yesa, Susana; Eguíluz, Andoni
Inclusive learning integrates pedagogical and technological variables. It means to adapt to students according to their characteristics (personal, physical, cognitive, social...), needs and interests. With the aim of evaluating the level of inclusion of digital learning objects we have developed a questionnaire that collects information about pedagogical and technological aspects that promote or discourage inclusion. Features like the pedagogical model, function of activities, type of assessment, multimedia resources, human-computer interaction, accessibility and usability criteria, etc. are variables that impact in the level of inclusion of educational objects and items that have been included in the questionnaire. This is the second version of the tool, refined with the conclusions obtained from the application of the draft version to more than 50 learning objects developed under the funding and supervision of the Basque Government. These objects are available under Creative Commons license in the Agrega web page of the Education Department, and represent the style and characteristics of more than 350 objects.
Assessing ambiguity of context data in intelligent environments: towards a more reliable context managing system
(MDPI, 2012-04-17) Almeida, Aitor; López de Ipiña González de Artaza, Diego
Modeling and managing correctly the user context in Smart Environments is important to achieve robust and reliable systems. When modeling reality we must take into account its ambiguous nature. Considering the uncertainty and vagueness in context data information it is possible to attain a more precise picture of the environment, thus leading to a more accurate inference process. To achieve these goals we present an ontology that models the ambiguity in intelligent environments and a data fusion and inference process that takes advantage of that extra information to provide better results. Our system can assess the certainty of the captured measurements, discarding the unreliable ones and combining the rest into a unified vision of the current user context. It also models the vagueness of the system, combining it with the uncertainty to obtain a richer inference process.
Enabling flexible and continuous capability invocation in mobile prosumer environments
(MDPI, 2012-06-28) Alcarria Garrido, Ramón; Robles Valladares, Tomás; Morales, Augusto; López de Ipiña González de Artaza, Diego; Aguilera, Unai
Mobile prosumer environments require the communication with heterogeneous devices during the execution of mobile services. These environments integrate sensors, actuators and smart devices, whose availability continuously changes. The aim of this paper is to design a reference architecture for implementing a model for continuous service execution and access to capabilities, i.e., the functionalities provided by these devices. The defined architecture follows a set of software engineering patterns and includes some communication paradigms to cope with the heterogeneity of sensors, actuators, controllers and other devices in the environment. In addition, we stress the importance of the flexibility in capability invocation by allowing the communication middleware to select the access technology and change the communication paradigm when dealing with smart devices, and by describing and evaluating two algorithms for resource access management.
A distributed reasoning engine ecosystem for semantic context-management in smart environments
(MDPI, 2012-07-30) Almeida, Aitor; López de Ipiña González de Artaza, Diego
To be able to react adequately a smart environment must be aware of the context and its changes. Modeling the context allows applications to better understand it and to adapt to its changes. In order to do this an appropriate formal representation method is needed. Ontologies have proven themselves to be one of the best tools to do it. Semantic inference provides a powerful framework to reason over the context data. But there are some problems with this approach. The inference over semantic context information can be cumbersome when working with a large amount of data. This situation has become more common in modern smart environments where there are a lot sensors and devices available. In order to tackle this problem we have developed a mechanism to distribute the context reasoning problem into smaller parts in order to reduce the inference time. In this paper we describe a distributed peer-to-peer agent architecture of context consumers and context providers. We explain how this inference sharing process works, partitioning the context information according to the interests of the agents, location and a certainty factor. We also discuss the system architecture, analyzing the negotiation process between the agents. Finally we compare the distributed reasoning with the centralized one, analyzing in which situations is more suitable each approach.