Commenced in January 2007
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Ontology Development of e-Learning Moodle for Social Learning Network Analysis

Authors: Norazah Yusof, Andi Besse Firdausiah Mansur

Abstract:

Social learning network analysis has drawn attention for most researcher on e-learning research domain. This is due to the fact that it has the capability to identify the behavior of student during their social interaction inside e-learning. Normally, the social network analysis (SNA) is treating the students' interaction merely as node and edge with less meaning. This paper focuses on providing an ontology structure of e-learning Moodle that can enrich the relationships among students, as well as between the students and the teacher. This ontology structure brings great benefit to the future development of e-learning system.

Keywords: Ontology, e-learning, © Learning Network, Moodle.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1058867

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References:


[1] Mike, P. 2007. Social Networks And The Semantic Web Xiv, 234 P.74 Illus., Hardcover Isbn 978-0-387-71000-6.
[2] Li, T ., et al. A Social Network Analysis Methods based on Ontology. in 3rd Interational Symposium on Kowledge Acquisition and Modeling, IEEE. 2010.
[3] Martinez, A., et al., Combining qualitative evaluation and social network analysis for the study of classroom social interactions. Computer and Education, Elsevier, 2003. 41: p. 353-368.
[4] Al-Fayoumi, M., Soumya Banerjee, J. & Mahanti, P. K. (2009). Analysis Of Social Network Using Clever Ant Colony Metaphor, World Academy Of Science, Engineering And Technology, 29
[5] Erlin, B., Yusof, N. and Rahman, A.A. Integrating Content Analysis and Social Network Analysis for Analyzing Asynchronous Discussion Forum in IEEE. 2008.
[6] Erlin, B., Yusof, N. and Rahman, A.A. Analyzing Online Asynchronous Discussion Using Content and Social Network Analysis in IEEE-Ninth International Conference on Intelligent Systems Design and Applications. 2009.
[7] Agarwal, N., Galan, M., Liu, H. & Subramanya, S. 2010. Wiscoll: Collective Wisdom Based Blog Clustering. Information Sciences, Elsevier 180 39-61.
[8] Cantador, I. & Castells, P. Year. Multilayered Semantic Social Network Modeling By Ontology-Based User Profiles Clustering: Application To Collaborative Filtering. In: The 15th International Conference On Knowledge Engineering And Knowledge Management (Ekaw 2006), 2006 Podebrady, Czech Republic. Springer Verlag Lecture Notes In Computer Science, Vol. 4248.October 2006, Issn: 3-540-46363-1, Pp. 334-349.
[9] Yunianta, A., Yusof, N., Othman, M. S. & Octaviani, D. 2012. Analysis And Categorization Of E-Learning Activities Based On Meaningful Learning Characteristics. Johor Bahru: Universiti Teknologi Malaysia
[10] Huang, Y. M., Chiu, P. S., Liu, T. C. & Chen, T. S. 2011. The Design And Implementation Of A Meaningful Learning-Based Evaluation Method For Ubiquitous Learning. Computer And Education, 57, 2291- 2302.
[11] Hamulic, I. and N. Bijedic, Social network analysis in virtual learning community at faculty of information technologies (fit), Mostar. Elsevier-Procedia Social and Behavioral Sciences, 2009. 1: p. 2269- 2273.
[12] Drazdilova, P., et al. Analysis of Relations in eLearning. in IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. 2008.
[13] Spadavecchia, C. and C. Giovannella. Monitoring learning experiences and styles: the socio-emotional level. in 10th IEEE International Conference on Advanced Learning Technologies. 2010.