This article is prepared by Su-Tuan Lulee for EDDE 801 at Ed. D. Athabasca University in 2010.

1. Did We Become a Community? 1
Did We Become
a Community?
A Review of the Literature
Su-Tuan Lulee
Ed. D. Student
Professor Patric Fahy
EDDE 801, Athabasca University
September 20, 2009

2. Did We Become a Community? 2
Did We Become a Community?
A Review of the Literature
The authors, Schwier and Daniel, professors of University of Saskatchewan, Canada, employ a
variety of evaluation methods to understand the nature of formal virtual learning communities in higher
education. Over the past decade, online distance education has been growing rapidly. More and more
schools are providing courses via the Internet. The term “learning community” has become a popular
term to describe the online learning environment. A number of contributions have been given to the
methods for evaluating online learning environments (Fahy, Crawford, & Ally, 2001; Garrison, Anderson,
& Archer, 2000; Hara, Bonk, & Angeli, 1998; Henri, 1992; & Gunawardena, Lowe, & Anderson, 1998,
etc.) however; the researchers have been over-relying on transcript analysis. This paper (Schwier &
Daniel, 2007) uses mixed tools derived from previous studies to describe a full picture of the online
community. They begin from determining the existence of communities (definition), then move on
identifying the constituent elements, and the interactions among the elements (analysis), finally, they
propose a community modeling technique (prediction). The authors argue that it is necessary to use a
variety of methods when analyzing a system as complex as online learning community.
Sense of Community
Two instruments are used to define whether the online groups have become a community. The
authors first use Chavis’ Sense of Community Index (SCI, Chavis, n.d.) to measure individuals’
psychological sense of community. This 12-True/False-item Index is a classic instrument employed
broadly in community psychology. Four dimensions of the overall construction, membership, influence,
reinforcement of needs, and shared emotional connection are measured. Due to the low reliability of this
Index, the authors employ Classroom Community Scale (Rovai, & Jordan, 2004) as the second measure.
Patterns of Interaction Analysis
Techniques used to analyze patterns of interaction in this paper are basically from TAT
(Transcript Analysis Tool) developed by Fahy et al. (2001). TAT examines the structural elements (the
network exchange patterns) of online interactions including the density and the intensity.

3. Did We Become a Community? 3
Density is defined as “the ratio of the actual numbers of links to the possible total” (Fahy, 2001).
Through the calculation of density, researchers begin to understand to what extent the participants of a
network connect with each other.
Intensity is defined as “responsiveness and attentiveness of members to each other” (Fahy, 2001)
including to what degree the participants exceed the course requirement for participation; how often the
participants send and receive messages (signs of reciprocation); and by whom and to whom the messages
were sent (signs of control and leadership).
Modeling Community
To develop a model that can interpret the interactions among community variables, the authors
take the following steps.
First, they identify characteristics of community through content (transcript) analysis and confirm
their findings with participants through semi-structured interviews and the focus group. The results are 14
characteristics.
Second, they determine the relative importance of the 14 characteristics by asking students to
conduct a paired-comparison among the characteristics. The results are described using Thurstone Scale
that represents the ranking and points of the 14 characteristics clearly.
Third, they organize the 14 characteristics into a network map based on the Bayesian Belief
Network (BBN) model building technique. BBN is a graph that is composed of nodes and directional
arrows. Nodes represent the 14 characteristics as variables. Direction arrows illustrate the interactions
among the variables including the dependencies and the strength of relationships (strong, medium, or
weak) among variables. Initial probabilities for each relationship then are assigned to the network based
on the results gained from Thurstone Scale, the experts, or the raw data; however, the values of each
probability will change due to the condition of the related variables. Updating the conditional probability
table and making inferences based on new evidence can help understand the various relationships among
variables (the characteristics) in a community.
Figure 1 BBN representation of relationships among virtual learning community variables. (Schwier & Daniel, 2007)

4. Did We Become a Community? 4
Note. From Schwier, R., & Daniel, B. K. (2007). Did we become a community? Multiple methods for identifying community and
its constituent elements in formal online learning environment. In User-evaluation and online communities (p. 47).
Conclusion
While the authors use a variety of methods to illustrate the process for understanding formal
online learning community persuasively, the following questions need to be elaborated:
1. What are the connections between patterns of interactions (density, intensity,
reciprocity, etc.) and BBN modeling? Are there statistical relationships in between?
2. Before this paper, some researchers have studied on the characteristics of the
communities (Brook & Oliver, 2003 and Brown, 2001) however none of them were
mentioned in this paper. Have the study based on prior research?
3. Chavis & Acosta (2008) have published a revised version of Sense of Community
Index (SC-2) that Chavis claimed to have much higher reliability. Why didn't this
paper use the most updated version (SC-2 was published in about the same time
with this paper)?
4. How many characteristics are identified, 15 or 14? The numbers show on page 41
are not consistent.
References
Brook, C., & Oliver, R. (2003). online learning communities: a design framework. Australian Journal of

5. Did We Become a Community? 5
Educational Technology, 19(2), 139-160. Retrieved September 21, 2009, from
http://www.ascilite.org.au/ajet/ajet19/brook.html.
Brown, R. (2001). The Process of Community-building in Distance Learning Classes. Journal of
Asynchronous Learning Networks, 5(2). Retrieved from http://www.aln.org/publications/jaln/v5n2/
v5n2_brown.asp.
Chavis, D. M. (, n.d). Sense of Community Index. Association for the Study and Development of
Community. Retrieved from http://www.senseofcommunity.com/files/Sense%20of%20Community
%20Index12607.pdf.
Chavis, D. M., & Acosta, J. D. (2008). The Sense of Community (SCI) Revised: The Reliability and
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http://www.irrodl.org/index.php/irrodl/article/viewFile/36/74.
Garrison, D. Randy; Anderson, Terry; Archer, Walter. (2000). Critical Inquiry in a Text-Based
Environment: Computer Conferencing in Higher Education. The Internet and Higher Education,
2(2-3), 87-105. Retrieved February 19, 2008, from
http://communitiesofinquiry.com/files/Critical_Inquiry_model.pdf.
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Mediated Conferences as a Tool for Testing Constructivist and Social-Constructivist Learning
Theories. In Proceedings of the Annual Conference on Distance Teaching & Learning, 14th (p.
8). Presented at the Distance Learning '98, Madison, Wisconsin.

6. Did We Become a Community? 6
Hara, N., Bonk, C. J., & Angeli, C. (1998). Content Analysis of Online Discussion in an Applied
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Rovai, A. P., & Jordan, H. M. (2004). Blended Learning and Sense of Community: A comparative analysis
with traditional and fully online graduate courses. The International Review of Research in Open
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Schwier, R., & Daniel, B. K. (2007). Did we become a community? Multiple methods for identifying
community and its constituent elements in formal online learning environment. In User-
evaluation and online communities (pp. 29-53). Hershey, PA: Idea Group Publishing. Retrieved
from http://www.scribd.com/doc/3882220/Did-we-become-a-community-Multiple-methods-for-
identifying-community-and-its-constituent-elements-in-formal-online-learning-environments.