Mining Data and Modelling Social Capital in Virtual Learning Communities

Frontiers in Artificial Intelligence and Applications

Volumn 125, Issue , 2005, Pages 200-208

  Daniel, Ben K ^a   McCalla, Gordon I ^a   Schwier, Richard A ^a  

^a UNIVERSITY OF SASKATCHEWAN   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

E-LEARNING; SOCIAL NETWORKING (ONLINE);

BAYESIAN BELIEF NETWORK MODELS; COMMUNITY IS; COMPUTATIONAL MODELLING; CONTENT ANALYSIS; EFFECTIVE INTERACTIONS; NETWORK TECHNIQUES; SOCIAL CAPITALS; SOCIAL ISSUES; VIRTUAL LEARNING COMMUNITY;

BAYESIAN NETWORKS;

EID: 85139071207     PISSN: 09226389     EISSN: 18798314     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (21)

1. M. Baker (2000). The roles of models in Artificial Intelligence and Education research: A prospective view. International Journal of Artificial Intelligence in Education (11),123-143.
2. B. Barros & F. Verdejo (2000). Analysing students interaction processes in order to improve collaboration. The DEGREE approach. International Journal of artificial inteligence in education, (11), pp. 221-241
3. B.K. Daniel, R.A. Schwier & G. I. McCalla (2003). Social capital in virtual learning communities and distributed communities of practice. Canadian Journal of Learning and Technology, 29(3), 113-139.
4. B.K. Daniel, D. J. Zapata-Rivera & G. I. McCalla (2003). A Bayesian computational model of social capital in virtual communities. In, M. Huysman, E.Wenger and V. Wulf Communities and Technologies, pp.287-305. London: Kluwer Publishers.
5. Freeman, L. C. (2000), Visualizing social networks, Journal of Social Structure, Available: [http://zeeb.library.cmu.edu:7850/JoSS/article.html]
6. K. Krippendorf (1980). Content analysis: An introduction to its methodology. Beverly Hills, CA: Sage Publications.
7. C. Lacave and F. J. Diez (2002). Explanation for causal Bayesian networks in Elvira. In Proceedings of the Workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-2002), Lyon, France.
8. K. Laskey and S. Mahoney (1997). Network Fragments: Representing Knowledge for Constructing Probabilistic Models, Uncertainty in Artificial Intelligence: Proceedings of the Thirteenth Conference.
9. G. I. McCalla (2000). The fragmentation of culture, learning, teaching and technology: Implications for artificial intelligence in education research. International Journal of Artificial Intelligence in Education, 11(2), 177-196.
10. J. Nahapiet & S. Ghoshal (1998). Social capital, intellectual capital and the organizational advantage. Academy of Management Review, (23)(2) 242- 266.
11. D. Niedermayer (1998) An Introduction to Bayesian Networks and their Contemporary Applications. Retrieved May, 6th 2004 from:: [http://www.niedermayer.ca/papers/bayesian/bayes.html]
12. J. Pearl (1988) Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann Publishers, San Mateo, CA.
13. L. Prusak & D. Cohen (2001). In good company: How social capital makes organizations work. Boston, MA: Harvard Business School Press.
14. R. Putnam (2000). Bowling alone: The collapse and revival of American community. New York: Simon Schuster.
15. A. Ravenscroft & R. Pilkington (2000). Investigation by design: developing dialogues models to support support reasoning and conceptual change. International journal of artificial intellignece in education, 11-273-298.
16. L. Rourke T. Anderson, D.R. Garrison. and W. Archer (2001). Methodological issues in the analysis of computer conference transcripts. International Journal of Artificial Intelligence in Education, (12) 8-22.
17. S. Stemler (2001). An overview of content analysis. Practical Assessment, Research & Evaluation, 7(17). Retrieved October 19, 2003 from [http://edresearch.org/pare/getvn.asp?v=7&n=17].
18. A. Soller and A. Lesgold (2003). A computational approach to analyzing Online knowledge sharing interaction. Proceedings of Artificial Intelligence in Education pp. 253-260., Sydney, Australia.
19. World Bank (1999). Social capital research group. Retrieved May 29, 2003, from http://www.worldbank.org/poverty/scapital/.
20. J.D. Zapata-Rivera (2002) cbCPT: Knowledge Engineering Support for CPTs in Bayesian Networks. Canadian Conference on AI 2002: 368-370
21. I. Zukerman, D. W., Albreacht and A.E. Nelson (1999). Predictiing users' requests on the WWW. In UM 1999, Proceedings of the 7th international conference on user modelling, Banf, Canada, pp, 275-284.