Methods for Identifying and Analysing      Learning Communities                       Richard	  A.	  Schwier          Virt...
Central	  Concerns•   ShiNing	  focus	  of	  research•   Atomized	  view	  of	  communi;es•   Tools	  for	  analysis•   Ge...
CommunityModeling                Cons;tuents           Comparison
Sense	  of	  Community• Chavis’	  “Sense	  of	  Community	  Index”• Rovai	  &	  Jordan’s	  “Classroom	  Community	    Scal...
Interac;on	  Analysis• Fahy,	  Crawford	  &	  Ally	  (TAT)• Intensity   – “levels of participation," or the degree to whic...
Interac;on	  analysis• Density	     – Included	  only	  peripheral	  interac;ons   – the	  ra;o	  of	  the	  actual	  numb...
Reciprocity	  ra;othe parity of communication among participants
Plodng	  Reciprocity
Characteris;cs	  of	  Community    • Transcript	  analysis    • Interviews    • Focus	  groups
Characteris;cs• Awareness             •   Par;cipa;on• Social	  protocols   •   Trust• Historicity           •   Trajector...
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	  Community       Rovai	  &	  Jordan’s	  “Classroom	  Community	  Scale”	  (Chronbach’s	  alpha	  =	  .93)90.06...
Intensity        Fahy,	  Crawford	  &	  Ally	  (TAT)2.01.51.0      Formal0.5  0                      Non-Formal
Density        Fahy,	  Crawford	  &	  Ally	  (TAT)0.80.60.4      Formal0.2                      Non-Formal  0
Reciprocity	  ra;o	                  Instructors15.011.3 7.5 3.8               Non-Formal   0   Formal
Reciprocity                    par;cipants1.00.80.5      Mean                        Mean     sd0.3             sd  0     ...
Order	  of	  importance	  -­‐	  elements           Element   Formal    Non-­‐formal   Trust                1             7...
And	  lately...
Par;cipa;on	  Pakerns
Interac;on	  analysis     • Thread	  density	  and	  depth	  (Wiley,	  2010)            – Calcula;on	  of	  levels	  of	  ...
Do	  not	  akempt	  to	  read	  this!
Do	  not	  akempt	  to	  read	  this!                      Mulitlogue/discussion                      Simple	  Q&A/chit-­‐...
SNAPPhkp://research.uow.edu.au/learningnetworks/seeing/snapp/
Conclusions• Cycle	  of	  analysis	  is	  more	  important	  than	  specific	    tools	  used• Mixed	  methods	  seems	  re...
Research Methods for Identifying and Analysing Virtual Learning Communities
Research Methods for Identifying and Analysing Virtual Learning Communities
Research Methods for Identifying and Analysing Virtual Learning Communities
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Research Methods for Identifying and Analysing Virtual Learning Communities

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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.

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Research Methods for Identifying and Analysing Virtual Learning Communities

  1. 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. 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. 3. CommunityModeling Cons;tuents Comparison
  4. 4. 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)
  5. 5. 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
  6. 6. 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
  7. 7. Reciprocity  ra;othe parity of communication among participants
  8. 8. Plodng  Reciprocity
  9. 9. Characteris;cs  of  Community • Transcript  analysis • Interviews • Focus  groups
  10. 10. Characteris;cs• Awareness • Par;cipa;on• Social  protocols • Trust• Historicity • Trajectory• Iden;ty • Technology• Mutuality • Learning• Plurality • Reflec;on• Autonomy • Intensity
  11. 11. Comparison  of  characteris;cs• Thurstone  analysis
  12. 12. Thurstone  Scale
  13. 13. ModelingBayesian  Belief  Network  Model  of  a  Virtual   Learning  Community
  14. 14. BBN  -­‐  Query  the  network
  15. 15. BBN  -­‐  Query  the  network
  16. 16. Sense  of  Community Rovai  &  Jordan’s  “Classroom  Community  Scale”  (Chronbach’s  alpha  =  .93)90.067.545.0 Formal22.5 Non-Formal 0
  17. 17. Intensity Fahy,  Crawford  &  Ally  (TAT)2.01.51.0 Formal0.5 0 Non-Formal
  18. 18. Density Fahy,  Crawford  &  Ally  (TAT)0.80.60.4 Formal0.2 Non-Formal 0
  19. 19. Reciprocity  ra;o   Instructors15.011.3 7.5 3.8 Non-Formal 0 Formal
  20. 20. Reciprocity par;cipants1.00.80.5 Mean Mean sd0.3 sd 0 Formal Non-Formal 0.376276399
  21. 21. 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
  22. 22. And  lately...
  23. 23. Par;cipa;on  Pakerns
  24. 24. Interac;on  analysis • Thread  density  and  depth  (Wiley,  2010) – Calcula;on  of  levels  of  replies  in  conversa;on   threads – Data  flawed,  but  usefulMean  Reply  Depth  (MRD  crude)  =  sum  of  reply  depth  for  all  messages/messages  in  the  threadMean  Reply  Depth  (corrected)=  MRD  (crude)  x  ((n-­‐b(childless  messages)/n)
  25. 25. Do  not  akempt  to  read  this!
  26. 26. Do  not  akempt  to  read  this! Mulitlogue/discussion Simple  Q&A/chit-­‐chat Monologue/no  discussion
  27. 27. SNAPPhkp://research.uow.edu.au/learningnetworks/seeing/snapp/
  28. 28. 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

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