research methods for identifying and analysing virtual learning communities
DESCRIPTION
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.TRANSCRIPT
Methods for Identifying and Analysing Learning Communities
Richard A. SchwierVirtual Community Research LaboratoryEduca;onal Technology and Design
University of Saskatchewan
Higher Educa;on Development CentreUniversity of Otago
Dunedin, New ZealandFebruary 7, 2011
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
Community
Cons;tuents
Comparison
Modeling
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)
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
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
Reciprocity ra;othe parity of communication among participants
Plodng Reciprocity
Characteris;cs of Community
• Transcript analysis
• Interviews
• Focus groups
Characteris;cs
• Awareness
• Social protocols
• Historicity
• Iden;ty
• Mutuality
• Plurality
• Autonomy
• Par;cipa;on
• Trust
• Trajectory
• Technology
• Learning
• Reflec;on
• Intensity
Comparison of characteris;cs• Thurstone analysis
Thurstone Scale
ModelingBayesian Belief Network Model of a Virtual
Learning Community
BBN -‐ Query the network
BBN -‐ Query the network
Sense of CommunityRovai & Jordan’s “Classroom Community Scale” (Chronbach’s alpha = .93)
0
22.5
45.0
67.5
90.0
FormalNon-Formal
IntensityFahy, Crawford & Ally (TAT)
0
0.5
1.0
1.5
2.0
Formal
Non-Formal
DensityFahy, Crawford & Ally (TAT)
0
0.2
0.4
0.6
0.8
FormalNon-Formal
Reciprocity ra;o Instructors
0
3.8
7.5
11.3
15.0
Formal
Non-Formal
Reciprocitypar;cipants
0
0.3
0.5
0.8
1.0
FormalNon-Formal
0.376276399
Mean Mean
sd
sd
Order of importance -‐ elementsElement 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
And lately...
Par;cipa;on Pakerns
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)
Do not akempt to read this!
Do not akempt to read this!
Mulitlogue/discussion
Simple Q&A/chit-‐chat
Monologue/no discussion
SNAPP
hkp://research.uow.edu.au/learningnetworks/seeing/snapp/
Keep an eye on...
Technology Enhanced Knowledge Research Ins;tute (TEKRI)-‐ hkps://tekri.athabascau.ca/
George Siemens & data analy;cs
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