Title: A Bayesian belief network model of a virtual learning community

Authors: Ben K. Daniel, Richard A. Schwier

Addresses: Virtual Learning Community Research Laboratory, University of Saskatchewan, 28 Campus Drive, Saskatoon, Saskatchewan S7N 0X1, Canada. ' Virtual Learning Community Research Laboratory, University of Saskatchewan, 28 Campus Drive, Saskatoon, Saskatchewan S7N 0X1, Canada

Abstract: This article proposes a Bayesian methodology for modelling a virtual learning community, and illustrates one application of the multi-step approach. The article describes metrics and techniques for modelling fundamental variables that constitute a virtual learning community. The variables used for constructing the Bayesian model were drawn from a grounded theory analysis of transcripts of online discussions and an empirical study that used Thurstone analysis to assign weights and rankings to variables based on their comparative significance according to participants in the communities. The results of the Thurstone analysis were then used to infer causality among the variables and to assign the strength of relationships among the variables. Finally, scenario-based reasoning, grounded on practice, was used to query the model and observe its impact on the other constituent variables and how they relate to one major variable of interest – learning in virtual communities.

Keywords: virtual learning communities; virtual communities; modelling; Bayesian belief networks; BBN; Thurstone scaling; scenario-based reasoning; Bayesian models; online learning; e-learning; web based communities.

DOI: 10.1504/IJWBC.2007.014077

International Journal of Web Based Communities, 2007 Vol.3 No.2, pp.151 - 169

Published online: 18 Jun 2007 *

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