A layered framework for evaluating on-line collaborative learning interactions
International Journal of Human-Computer Studies
Evaluating CSCL log files by social network analysis
CSCL '99 Proceedings of the 1999 conference on Computer support for collaborative learning
Social networks, communication styles, and learning performance in a CSCL community
Computers & Education
What is online learner participation? A literature review
Computers & Education
An Unsupervised Approach to Cluster Web Search Results Based on Word Sense Communities
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Detecting Communities in Large Networks by Iterative Local Expansion
CASON '09 Proceedings of the 2009 International Conference on Computational Aspects of Social Networks
Students' Interactions in Online Asynchronous Discussion Forum: A Social Network Analysis
ICETC '09 Proceedings of the 2009 International Conference on Education Technology and Computer
Automated discovery of social networks in online learning communities
Automated discovery of social networks in online learning communities
Meerkat: Community Mining with Dynamic Social Networks
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
| Hi-index | 0.00 |
There is a growing number of courses delivered using elearning 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 thousands 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 interactions. Here we propose to exploit SNA techniques, including community 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 discovered relevant structures and the automated identification of central and peripheral participants, an instructor is provided 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 interactions between the participants in the discussion forums, and outlines the leader/peripheral students. Moreover, it creates a hierarchical summarization of the discussed topics, which gives the instructor a quick view of what is under discussion. We believe exploiting the mining abilities of this toolbox would facilitate fair evaluation of students' participation in online courses.