It is commonly accepted that contemporary cohorts of students witness and experience the benefits of information technologies in their learning processes. The so-called "digital natives" acquire, as a consequence of their early exposure to these technologies, different patterns of work, distinct attention conducts, new learning preferences and, generally, better skills for learning and working within rich online social contexts. So, it seems reasonable that the traditional education systems evolve and shape their practice to leverage those new patterns. Despite the fact that online social networks (osns) are widely recognized as a powerful vector for adding a new social dimension to the learning management systems (lmss), osns do not fully integrate the specific features of the learning process yet and traditional lmss do not exploit the advantages of an active social environment for reinforcing the learning experience. In this paper, we report the design, implementation, and use of a software platform (SocialWire) that leverages on the basic capabilities of an osn and extends the functionality toward its use in very general learning environments. We argue that this approach, together with gamification elements, is helpful in increasing the students' motivation, besides improving the learning experience and performance. This software system has been in use for 3 years in different subject areas in our university. Our outcomes and the feedback provided both by teachers and students support the introduction of social features, gamification elements, and informal processes into the traditional teaching practices.
LÓPEZ-ARDAO, CÁNDIDO LÓPEZ-GARCÍA, ANDRÉS SUÁREZ-GONZÁLEZ, MANUEL FERNÁNDEZ-VEIGA, and RAÚL RODRÍGUEZ-RUBIO University of Vigo, SpainSeveral recent traffic measurement studies have convincingly shown the presence of self-similarity in modern high-speed networks, involving a very important revolution in the stochastic modeling of traffic. Thus the use of self-similar processes has opened new problems and research fields in network performance analysis, mainly in simulation studies, where the efficient synthetic generation of sample paths (traces) corresponding to self-similar traffic is one of the main topics. In this article, we justify the selection of interarrival time instead of counting processes for modeling arrivals. Also, we discuss the advantages and drawbacks of the most important self-similar processes when applied to traffic modeling in simulation studies, proposing the use of models based in F-ARIMA, mainly due to their flexibility to capture both long-and short-range correlations. However, F-ARIMA processes have been little used in simulation studies, mainly because the synthetic generation methods available in the literature are very inefficient compared with those for FGN. In order to solve this problem, we propose a new method that can generate high-quality traces corresponding to a F-ARIMA(p, d, q) process. A comparison with existing methods shows that the new method is significantly more efficient, and even slightly better than the best method for FGN.
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