Abstract:

Social media is being increasingly used not only for personal and recreational activities but also as a powerful means to harness communication in education. Social networks, most notably, Facebook, Twitter and Instagram are being used all over the world by students to exchange information between themselves and the teacher. Despite different peculiarities of each social network, all of them offer great advantage over traditional learning management system, like Moodle and Blackboard. The main advantages of these social media systems rely on their ubiquitous communication capabilities, as well as their state-of-the-art graphical interfaces, which greatly influence teenagers, but also adults, to adhere to their use.

Current learning management systems cannot compete with these technologies and should not aim to compete for their use.
In this paper, we present a system which is capable to integrate the best of the two worlds: from one side, a system which is capable to store educational contents, to engage them in pedagogical activities and to keep track of their evolution; on the other side, the possibility to include advanced/instant messaging capacities, as well as mechanisms to easily adapt to different digital media formats. Current social network systems allow the exchange of photos, videos, audio, digital presentations (to name a few) in a very simple way. Actually, many of them allow this exchange in the middle of a chat. Clearly, there is an advantage to use these technologies on the pedagogical field. Communication becomes much easier.

However, the system we present goes far from allowing students to communicate through social media. We integrate analytics from the use of these communication fields gathering important information about student performance, difficulties, the pace and, generic and specific issues.

In fact, we propose the creation of a “social graph” in Moodle, built automatically from the interactions between course participants, which are tracked by the system, and the analytics associated with this graph. Namely, we identify the “connectivity degree”, and the “betweenness” of each participant finding which is most influential, which acts as an information hub and how information propagates on this network. We expand the analytics finding connected components in the graph to understand how these virtual communities relate and work together.

We conclude the article presenting the user interface form the Moodle side and withdrawing our conclusions from its effectiveness, usability and a proof of concept.

Keywords:

Social networks, learning management systems, social media graph, learning analytics.