Abstract
Current Learning Management Systems (LMS) generically provide virtual places to conduct interactions between students and educators. Chats, forums and other communication mechanisms usually are present in any LMS. In this paper, we propose a tool that can be embedded in any LMS that features some sort of hierarchical communication mechanisms. The proposed system is capable of depicting and analyzing online interactions in an easy to understand social graph. The vertex positioning algorithm is based on social network analysis statistics, taken from the collected interactions, and is able to smoothly present the temporal evolution in order to find communicational patterns and report them to the educator and the students.

Abstract
We present a system for a dynamic graphical representation of the interactions captured in educational online environments. The system goes beyond interaction between students and teachers, also addressing resource usage or any other entity for which it is possible to create a relation which binds two entities. By defining these relationships between pairs of entities in an online learning environment (Moodle, in our case) our tool creates a graph, where it is possible to apply techniques of social network analysis. This system brings up new possibilities for e-learning as a tool capable of helping the teacher assorting and illustrating the degree of participation and to find the implicit relations between participants, or participants and resources or events. © 2017 Association for Computing Machinery.

Abstract
Euclidean distance and cosine similarity are frequently used measures to implement the k-means clustering algorithm. The cosine similarity is widely used because of it's independence from document length, allowing the identification of patterns, more specifically, two documents can be seen as identical if they share the same words but have different frequencies. However, during each clustering iteration new centroids are still computed following Euclidean distance. Based on a consideration of these two measures we propose the k-Communities clustering algorithm (k-C) which changes the computing of new centroids when using cosine similarity. It begins by selecting the seeds considering a network of tags where a community detection algorithm has been implemented. Each seed is the document which has the greater degree inside its community. The experimental results found through implementing external evaluation measures show that the k-C algorithm is more effective than both the k-means and k-means++. Besides, we implemented all the external evaluation measures, using both a manual and an automatic "Ground Truth", and the results show a great correlation which is a strong indicator that it is possible to perform tests with this kind of measures even if the dataset structure is unknown.

Abstract
Research concerning mining data from learning management systems have been consistently been appearing in the literature. However, in many situations there is not a clear path on the data mining procedures that lead to solid conclusions. Therefore, many studies result in ad-hoc conclusions with insufficient generalization capabilities. In this article, we describe a methodology and report our findings in an experiment which one online course which involved more than 150 students. We used the Moodle LMS during the period of one academic semester, collecting all the interactions between the students and the system. These data scales up to more than 33K records of interactions where we applied data mining tools following the procedure for data extraction, cleaning, feature identification and preparation. We then proceeded to the creation of automatic learning models based on decision trees, we assessed the models and validate the results by assessing the accuracy of the predictions using traditional metrics and draw our conclusions on the validity of the process and possible alternatives1. © 2017 Association for Computing Machinery.

Abstract
We present a new algorithm to discover overlapping communities in networks with a scale free structure. This algorithm is based on a node evaluation function that scores the local influence of a node based on its degree and neighbourhood, allowing for the identification of hubs within a network. Using this function we are able to identify communities, and also to attribute meaningful titles to the communities that are discovered. Our novel methodology is assessed using LFR benchmark for networks with overlapping community structure and the generalized normalized mutual information (NMI) measure. We show that the evaluation function described is able to detect influential nodes in a network, and also that it is possible to build a well performing community detection algorithm based on this function.

Abstract
Social media has become one of the most prolific felds for interchange of multidisciplinary expertise. In this paper, computer science, communication and management are brought together for the development of a sound strategic content analysis, in the Higher Education Sector. The authors present a study comprised of two stages: analysis of SM content and corresponding audience engagement according to a weighted scale, and a classification of content strategies, which builds on different noticeable articulations of editorial areas among organizations. Their approach is based on an automatic classification of content according to a predefned editorial model. The proposed methodology and research results offer academic and practical fndings for organizations striving on social media. Copyright © 2015,.