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Research Methods for Identifying and Analysing Virtual Learning Communities
 

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.

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 Research Methods for Identifying and Analysing Virtual Learning Communities Presentation Transcript

    • 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
    • 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
    • CommunityModeling Cons;tuents Comparison
    • 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 • Par;cipa;on• Social  protocols • Trust• Historicity • Trajectory• Iden;ty • Technology• Mutuality • Learning• Plurality • Reflec;on• Autonomy • 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  Community Rovai  &  Jordan’s  “Classroom  Community  Scale”  (Chronbach’s  alpha  =  .93)90.067.545.0 Formal22.5 Non-Formal 0
    • 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 Formal Non-Formal 0.376276399
    • 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
    • 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  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)
    • Do  not  akempt  to  read  this!
    • Do  not  akempt  to  read  this! Mulitlogue/discussion Simple  Q&A/chit-­‐chat Monologue/no  discussion
    • SNAPPhkp://research.uow.edu.au/learningnetworks/seeing/snapp/
    • 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