Reihaneh Rabbany k.
Osmar R. Zaïane
Abstract
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.
- Blackboard. http://en.wikipedia.org/wiki/Blackboard\_Learning\_System.Google Scholar
- A. Calvani, A. Fini, M. Molino, and M. Ranieri. Visualizing and monitoring effective interactions in online collaborative groups. British Journal of Educational Technology, 2009.Google Scholar
- J. Chen, J. Fagnan, R. Goebel, R. Rabbany, F. Sangi, M. Takaffoli, E. Verbeek, and O. Zaiane. Meerkat: Community mining with dynamic social networks. In Data Mining Workshops (ICDMW), 2010 IEEE International Conference on, pages 1377--1380, dec. 2010. Google ScholarDigital Library
- J. Chen, O. R. Zaïane, and R. Goebel. An unsupervised approach to cluster web search results based on word sense communities. In Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01, pages 725--729, Washington, DC, USA, 2008. IEEE Computer Society. Google ScholarDigital Library
- J. Chen, O. R. Zaïane, and R. Goebel. Detecting communities in large networks by iterative local expansion. In CASoN, pages 105--112, 2009. Google ScholarDigital Library
- H. Cho, G. Gay, B. Davidson, and A. Ingraffea. Social networks, communication styles, and learning performance in a CSCL community. Computers & Education, 49(2):309--329, 2007. Google ScholarDigital Library
- A. Clauset, M. E. J. Newman, and C. Moore. Finding community structure in very large networks. Phys. Rev. E, 70:066111, 2004.Google ScholarCross Ref
- T. Daradoumis, A. Martínez-Monés, and F. Xhafa. A layered framework for evaluating on-line collaborative learning interactions. Int. J. Hum.-Comput. Stud., 64(7):622--635, 2006. Google ScholarDigital Library
- S. Dawson. Seeing the learning community: An exploration of the development of a resource for monitoring online student networking. British Journal of Educational Technology, 41(5):736--752, 2010.Google ScholarCross Ref
- S. Dawson, A. Bakharia, and E. Heathcote. SNAPP: Realising the affordances of real-time sna within networked learning environments. In L. Dirckinck- Holmfeld, V. Hodgson, C. Jones, M. De Laat, D. Mc- Connell, and T. Ryberg, editors, Proceedings of the 7th International Conference on Networked Learning, pages 125--133, 2010.Google Scholar
- M. de Laat, V. Lally, L. Lipponen, and R.-J. Simons. Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for social network analysis. International Journal of Computer-Supported Collaborative Learning, 2(1):87--103, March 2007.Google ScholarCross Ref
- Erlin, N. Yusof, and A. A. Rahman. Students' interactions in online asynchronous discussion forum: A social network analysis. Education Technology and Computer, International Conference on, 0:25--29, 2009. Google ScholarDigital Library
- A. Gruzd. Automated Discovery of Social Networks in Online Learning Communities. PhD thesis, University of Illinois at Urbana-Champaign, June 2009. Google ScholarDigital Library
- A. Gruzd and C. A. Haythornthwaite. The analysis of online communities using interactive content-based social networks. extended abstract. In Proceedings of the American Society for Information Science and Technology (ASIS&T) Conference, pages 523--527, Columbus, OH, USA, October 2008.Google Scholar
- C. Haythornthwaite. Building social networks via computer networks: Creating and sustaining distributed learning communities, pages 159--190. Cambridge University Press, 2002.Google ScholarCross Ref
- S. Hrastinski. What is online learner participation? a literature review. Computers & Education, 51(4):1755--1765, December 2008. Google ScholarDigital Library
- A. Laghos and P. Zaphiris. Sociology of student-centred e-learning communities: A network analysis. In IADIS international conference, Dublin, Ireland, July 2006. e-Society.Google Scholar
- A. Lancichinetti and S. Fortunato. Community detection algorithms: A comparative analysis. Phys. Rev. E, 80:056117, Nov 2009.Google ScholarCross Ref
- A. Marin and B. Wellman. Handbook of Social Network Analysis. Sage, forthcoming, 2010.Google Scholar
- Moodle. http://en.wikipedia.org/wiki/Moodle.Google Scholar
- NetMiner. http://www.netminer.com/NetMiner/.Google Scholar
- M. E. J. Newman. Detecting community structure in networks. Eur. Phys. J.B, 38:321--330, 2004.Google ScholarCross Ref
- M. E. J. Newman and M. Girvan. Finding and evaluating community structure in networks. Physical Review E, 69, 2004.Google Scholar
- K. Nurmela, E. Lehtinen, and T. Palonen. Evaluating CSCL log files by social network analysis. Computer Support for Collaborative Learning, 1999. Google ScholarDigital Library
- OpenNlp. http://opennlp.sourceforge.net/README.html.Google Scholar
- G. Palla, I. Derenyi, I. Farkas, and T. Vicsek. Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435:814--818, 2005.Google ScholarCross Ref
- C. Reffay and T. Chanier. How social network analysis can help to measure cohesion in collaborative distancelearning. In Computer Supported Collaborative Learning, pages p. 343--352. Kluwer Academic Publishers, May 2003.Google Scholar
- B. Sundararajan. Emergence of the most knowledgeable other (mko): Social network analysis of chat and bulletin board conversations in a CSCL system. Electronic Journal of e-Learning, 8:191--208, 2010.Google Scholar
- UCINET. http://www.analytictech.com/ucinet/.Google Scholar
- WebCT. http://en.wikipedia.org/wiki/WebCT.Google Scholar
- P. A. Willging. Using social network analysis techniques to examine online interactions. US-China Education Review, 2(9):46--56, 2005.Google Scholar
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