R.: Social Network Analysis and Mining to Support the Assessment

There is a growing number of courses delivered using e-learning environments and their online discussions play an important role in collaborative learning of students. Even in courses with a few number of students, there could be thou-sands of messages generated in a few months within these forums. Manually evaluating the participation of students in such case is a significant challenge, considering the fact that current e-learning environments do not provide much information regarding the structure of interactions between students.There is a recent line of research on applying social network analysis (SNA) techniques to study these interac-tions. Here we propose to exploit SNA techniques, including com-munity mining, in order to discover relevant structures in social networks we generate from student communications but also information networks we produce from the content of the exchanged messages. With visualization of these dis-covered relevant structures and the automated identification of central and peripheral participants, an instructor is pro-vided with better means to assess participation in the online discussions. We implemented these new ideas in a toolbox, named Meerkat-ED. Which automatically discovers relevant network structures, visualizes overall snapshots of interac-tions between the participants in the discussion forums, and outlines the leader/peripheral students. Moreover, it cre-ates a hierarchical summarization of the discussed topics, which gives the instructor a quick view of what is under dis-cussion. We believe exploiting the mining abilities of this toolbox would facilitate fair evaluation of students ’ partici-pation in online courses. 1