Miguel L. Bote Lorenzo

Entorno de simulación distribuida de redes basado en la nube computacional

Las simulaciones de barridos de parametros tienen un gran potencial en el estudio de redes telematicas, especialmente en contextos docentes. Sin embargo, el elevado tiempo necesario habitualmente para completar este tipo de simulaciones es una limitacion importante para su uso. En este articulo se propone DNSE3, un entorno que permite la ejecucion distribuida de tareas de simulacion en el simulador ns-3 dentro de un entorno de nube computacional, a traves de una arquitectura de servicios RESTful. El sistema se ha disenado para ser autoescalable, aprovisionando y liberando dinamicamente recursos de la nube computacional en funcion de la carga de simulaciones demandada, y garantizando un reparto equitativo de los recursos entre los distintos usuarios. Ademas, DNSE3 se ha implementado reutilizando servicios presentes en \emph{middlewares} de nube populares, y ha sido evaluado mediante pruebas sinteticas. La implementacion de DNSE3 ha demostrando un correcto comportamiento funcional y un rendimiento considerablemente superior a otras alternativas cuando el numero de simulaciones es muy elevado.

A System for Gamifying Ubiquitous Learning Situations Supported by Multiple Technologies

The past years have witnessed an increased use of applied games for developing and evaluating communication skills. These skills benefit from in-terpersonal interactions. Providing feedback to students practicing communica-tion skills is difficult in a traditional class setting with one teacher and many students. This logistic challenge may be partly overcome by providing training using a simulation in which a student practices with communication scenarios. A scenario is a description of a series of interactions, where at each step the player is faced with a choice. We have developed a scenario editor that enables teachers to develop scenarios for practicing communication skills. A teacher can develop a scenario without knowledge of the implementation. This paper presents the implementation architecture for such a scenario-based simulation.This paper presents an initial evaluation of different forms of adaptation based on learning style and knowledge level, which were implemented in an adaptive e-learning system. An experiment conducted in a learning context with 174 participants produced significant results in terms of learning gain. They indicate that adaptation based on both learning style and knowledge level yields significantly better learning gain than adaptation based on learning style only, and better than adaptation based on knowledge level only.Technology Enhanced Learning (TEL) largely focuses on the retrieval and reuse of educational resources from Web platforms like Coursera. Unfortunately, Coursera does not provide educational metadata of its content. To overcome this limitation, this study proposes a data mining approach for discovering Teaching Contexts (TC) where resources have been delivered in. Such TCs can facilitate the retrieval of resources for the teaching preferences and requirements of teachers.Gamification is the use of game design elements in non-game contexts, and it has reported potential benefits for students. However, the proposals supporting teachers to create gamified ubiquitous learning situations are tied to specific activities and enactment technologies. To start addressing this issue, we propose a system to help teachers design and deploy these situations involving a variety of technologies frequently used in education.Adapting tasks to learner characteristics is essential when selecting appropriate tasks for learners [5]. This paper investigates how humans adapt exercise selection to learner self-esteem (SE) and performance, to allow a future Intelligent Tutoring System (ITS) to use these adaptations. Self esteem is an important factor in learning as it is a significant predictor of academic performance [4]. Previous research adapts task selection to other characteristics e.g. past performance [1], but little work focuses on task selection based on learner personality.