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.

Published in: Education, Technology

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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