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