Presentation at the University of Otago in Dunedin New Zealand on research methods we have employed at the Virtual Learning Communities Research Laboratory at the University of Saskatchewan.

1. Methods for Identifying and Analysing
Learning Communities
Richard
A.
Schwier
Virtual
Community
Research
Laboratory
Educa;onal
Technology
and
Design
University
of
Saskatchewan
Higher
Educa;on
Development
Centre
University
of
Otago
Dunedin,
New
Zealand
February
7,
2011

2. Central
Concerns
• ShiNing
focus
of
research
• Atomized
view
of
communi;es
• Tools
for
analysis
• Genera;on
of
models
• Using
research
to
inform
development
of
online
learning
environments

3. Community
Modeling Cons;tuents
Comparison

6. Sense
of
Community
• Chavis’
“Sense
of
Community
Index”
• Rovai
&
Jordan’s
“Classroom
Community
Scale”
(Chronbach’s
alpha
=
.93)
– Connectedness
(.92)
– Learning
(.87)
• Pre-­‐post
design
(t-­‐Test,
p<.005)

7. Interac;on
Analysis
• Fahy,
Crawford
&
Ally
(TAT)
• Intensity
– “levels of participation," or the degree to which the
number of postings observed in a group exceed the
number of required postings
– 858 actual/490 required = 1.75

8. Interac;on
analysis
• Density
– Included
only
peripheral
interac;ons
– the
ra;o
of
the
actual
number
of
connec;ons
observed,
to
the
total
poten;al
number
of
possible
connec;ons
2a/N(N-­‐1)
=
2(122)/13(12)
=
.78

9. Reciprocity
ra;o
the parity of communication among participants

10. Plodng
Reciprocity

11. Characteris;cs
of
Community
• Transcript
analysis
• Interviews
• Focus
groups

12. Characteris;cs
• Awareness • Par;cipa;on
• Social
protocols • Trust
• Historicity • Trajectory
• Iden;ty • Technology
• Mutuality • Learning
• Plurality • Reflec;on
• Autonomy • Intensity

13. Comparison
of
characteris;cs
• Thurstone
analysis

14. Thurstone
Scale

15. Modeling
Bayesian
Belief
Network
Model
of
a
Virtual
Learning
Community

16. BBN
-­‐
Query
the
network

17. BBN
-­‐
Query
the
network

18. Sense
of
Community
Rovai
&
Jordan’s
“Classroom
Community
Scale”
(Chronbach’s
alpha
=
.93)
90.0
67.5
45.0
Formal
22.5 Non-Formal
0

19. Intensity
Fahy,
Crawford
&
Ally
(TAT)
2.0
1.5
1.0
Formal
0.5
0 Non-Formal

20. Density
Fahy,
Crawford
&
Ally
(TAT)
0.8
0.6
0.4
Formal
0.2 Non-Formal
0

21. Reciprocity
ra;o
Instructors
15.0
11.3
7.5
3.8 Non-Formal
0 Formal

22. Reciprocity
par;cipants
1.0
0.8
0.5
Mean Mean sd
0.3
sd
0
Formal
Non-Formal
0.376276399

23. Order
of
importance
-­‐
elements
Element Formal Non-­‐formal
Trust 1 7
Learning 2 3
Par;cipa;on 3 6
Mutuality 4 10
Intensity 5 7
Protocols 6 10
Reflec;on 7 2
Autonomy 8 10
Awareness 9 1
Iden;ty 10 4
Trajectory 11 13
Technology 12 4
Historicity 13 13
Plurality 14 7

24. And
lately...

25. Par;cipa;on
Pakerns

26. Interac;on
analysis
• Thread
density
and
depth
(Wiley,
2010)
– Calcula;on
of
levels
of
replies
in
conversa;on
threads
– Data
flawed,
but
useful
Mean
Reply
Depth
(MRD
crude)
=
sum
of
reply
depth
for
all
messages/messages
in
the
thread
Mean
Reply
Depth
(corrected)=
MRD
(crude)
x
((n-­‐b(childless
messages)/n)

27. Do
not
akempt
to
read
this!

28. Do
not
akempt
to
read
this!
Mulitlogue/discussion
Simple
Q&A/chit-­‐chat
Monologue/no
discussion

29. SNAPP
hkp://research.uow.edu.au/learningnetworks/seeing/snapp/

30. Keep
an
eye
on...
Technology
Enhanced
Knowledge
Research
Ins;tute
(TEKRI)-­‐
hkps://tekri.athabascau.ca/
George
Siemens
&
data
analy;cs

31. Conclusions
• Cycle
of
analysis
is
more
important
than
specific
tools
used
• Mixed
methods
seems
reasonable,
and
worked
well
in
prac;ce
• Baseline
data
are
needed
to
situate
findings
• Modeling
is
an
act
of
systema;c
specula;on
influenced
by
data
(not
limited
by
data)
• Most
enjoyable
part:
the
hunt