Abstract
Social media networks' popularity has been growing in almost every context of daily human interaction. Particularly concerning the education field, organisations and teachers have been continuously recognizing social media as a rich environment with potential to benefit the teaching-learning process, classroom administration and social interactions. However, social media networks have been used as complementary environments to the mandatory adoption of an institutional LMS, leading to the development of fragmented teaching-learning environments, where mutual interchanges are not consolidated nor allow for an explicit academic legitimacy, computation and management. Also, social media networks' integration in education has been viewed as an ad-hoc initiative of some educators, who are prone to incorporate web trends in their pedagogic activity, which are evaluated, most of the times, under the lens of recreational initiates. Consequently, there is an urgent need to bridge between the consolidated adoption of LMSs and the integration of social media networks in education, not only in terms of technological infrastructure ( interface and usability) but also in terms of production and management of its pedagogical outcomes. With the intent of providing solutions for the above context, in this paper, we discuss the concept of Social Student Relationship Management and present the EduBridge system, its current stage of development and highlight the educational applicability of a thorough set of social network analysis.

Abstract
In this paper we introduce three main features extracted from Moodle logs in order to be uses a possible means to predict future student grades. We discuss the statistical analysis on these features and show how they cannot be applied isolatedly to model our data. We then apply them as a whole and use principal component analysis to derive a decision tree based on the features. With derived tree we are able to predict grades in three intervals, namely to predict failures. Our proposed analysis methodology can be incorporated in an LMS and be used during a course. As the course unfolds, the system can to trigger alarms regarding possible failure situations.

Abstract
The ability to handle large amounts of unstructured information, to optimize strategic business opportunities, and to identify fundamental lessons among competitors through benchmarking, are essential skills of every business sector. Currently, there are dozens of social media analytics' applications aiming at providing organizations with informed decision making tools. However, these applications rely on providing quantitative information, rather than qualitative information that is relevant and intelligible for managers. In order to address these aspects, we propose a semi-supervised learning procedure that discovers and compiles information taken from online social media, organizing it in a scheme that can be strategically relevant. We illustrate our procedure using a case study where we collected and analysed the social media discourse of 43 organizations operating on the Higher Public Polytechnic Education Sector. During the analysis we created an "editorial model" that characterizes the posts in the area. We describe in detail the training and the execution of an ensemble of classifying algorithms. In this study we focus on the techniques used to increase the accuracy and stability of the classifiers.

Abstract
Recent advances in mobile device technology have triggered research on using their aggregate computational and/or storage resources to form edge-clouds. Whilst traditionally viewed as simple clients, smart-phones and tablets today have hardware resources that allow more sophisticated software to be installed, and can be used as thick clients or even thin servers. Simultaneously, new standards and protocols, such as Wi-Fi Direct and Wi-Fi TDLS (Tunneled Direct Link Setup), have been established that allow mobile devices to talk directly with each other, as opposed to over the Internet or across Wi-Fi access points. This can, potentially, lead to ubiquitous, low-latency, device-to-device (D2D) communication. In this paper, we study whether D2D protocols can support mobile-edge clouds by benchmarking different protocols and configurations for a specific application. The results show that decentralized device-to-device techniques can be used to efficiently disseminate multimedia contents while diminishing contention in the wireless infrastructure, allowing for up to 65% traffic reduction at the access points.

Abstract
This paper introduces a novel ranking system for competitive sports based around the notion of subgraphs. Although the system is targeted specifically to professional tennis it could be applied to any dominance network due to its generality. The results of about 140,000 tennis matches played between Top-100 players are used to create a colored directed network where colors represent different surfaces and edge direction depends on head-to-read results between players. The main contribution of this work is a ranking system which relies on the occurrences of 4-node directed subgraphs and the positions (or orbits) where the players appear on them. Since the concept of orbit is intrinsically connected with node dominance, appearing frequently in dominant orbits indicates that the player himself is dominant. Even in a very sparse network and without any background knowledge on the tournaments or stages of the matches, our proposal is able to extract meaningful rankings which capture the intricate competitive relationships between players from different eras.

Abstract
This chapter presents the architecture and design of enki, an Integrated Development Environment (IDE) for learning programming languages on massive open online courses (moocs). this environment can be used as a tool by a learning management system (lms) and a typical lms such as moodle can launch it using the learning tool interoperability (lti) api. student authentication tokens are passed via lti, thus integrating enki in the single sign-on domain of the academic institution. the proposed tool has a web user interface similar to those of reference ides, where the learner has access to different integrated tools, from viewing tutorial videos, to solving programming exercises that are automatically evaluated. enki uses several gamification strategies to engage learners, including generic gamifications services provided by odin and the sequencing of educational resources. the course content (videos, pdfs, programming exercises) is progressively disclosed to the learner as he successfully completes exercises. this is similar to what happens in a game, where new levels are unlocked as the previous are completed, thus contributing to the sense of achievement.