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Virtual Communities of Practice in Academia:
      An Automated Discourse Analysis

  Nicolae Nistor, Beate Baltes, George Smeaton,
 Mihai Dascălu, Dan Mihailă & Ștefan Trăușan-Matu

                 LAK13 – DCLA13
1. Rationale

 •  Increasing use of virtual communities of practice (vCoPs)
    in academia
 •  Available technology acceptance and CoP models
 •  Models are methodologically limited and
    insufficiently tested in vCoPs
 •  Participation in vCoP = technology use? If so,
    the combined acceptance x CoP model should be valid

 Ø  Validation of automated discourse analysis
 Ø  Verification of the acceptance x CoP model in an
     academic vCoP

              Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
2. Theoretical background

 Communities of practice (Lave & Wenger, 1991;
 Wenger, 1998)
 •  Groups of people sharing goals, practice and knowledge
    over lengthy periods of time
 •  Environment for knowledge construction/creation
 •  Practice and knowledge are reflected in dialogue
 •  Main factors
    •  expertise
    •  participation
    •  expert status



                Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
2. Theoretical background

 Communities of practice
 Conceptual model (Nistor & Fischer, 2012)


          Knowledge
           domain
                                                               Expert status
                                 Participation
                                                                (centrality)
          Time in the
             CoP

                                                 Role in CoP
           Expertise



              Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
2. Theoretical background

 Educational technology acceptance
 •  Unified Theory of Acceptance and Use of Technology
    (UTAUT; Venkatesh et al., 2003, 2012)

    Performance
    expectancy

        Effort                 Technology                     Technology
     expectancy               use intention                  use behavior

        Social                                   Facilitating              Technology
      influence                                  conditions                  anxiety




                  Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
3. Research model
                                                                               CoP model
                                      Role in CoP

      Domain
     knowledge
                                                                        Expert status
                                     Participation
                                                                         (centrality)
     Time in CoP

      Expertise



                           Technology             Facilitating           Technology
                          use intention           conditions               anxiety



      Performance             Effort                   Social
      expectancy           expectancy                influence
                                                                       Acceptance model

              Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
4. Methodology

 Design: Correlation study
 Sample: N = 129 members of academic vCoP at US
 American online university (20 full-time, 500 part-time staff)
 Setting: Asynchronous discussion forum
 Variables:
 •  Acceptance
 •  Expertise, as reflected in the quality of interventions
 •  Expert status/Centrality
 Methods:
 •  Acceptance: UTAUT questionnaire
 •  CoP: Automated content analysis
 •  Centrality: Social Network Analysis

                  Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
4. Methodology

 Automated content analysis




           Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
4. Methodology

 Automated content analysis




           Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
4. Methodology

 Automated content analysis




           Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
4. Methodology

 Automated content analysis




           Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
4. Methodology

 Automated content analysis – Validation
 •  Manual content analysis:
    Critical thinking framework
 •  Categories: initiation of discussion, exploration
    of the problem, solution, judgment, resolution
 •  Argumentation quality rating
 Ø Strong correlation (r = .79, p < .000) between
    automated and manual content analysis



             Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
4. Findings

 Partial verification of UTAUT model


    Performance
    expectancy        .30***
                                R2 = .36                        R2 = .06
       Effort  .22**           Technology           n.s.       Technology
    expectancy                use intention                   use behavior
                                                       n.s.                        -.28**
       Social         .23**                      Facilitating              Technology
     influence                                   conditions                  anxiety




                  Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
4. Findings

 Successful verification of CoP model

                                             Role in CoP
         Domain
        knowledge            .99***
                                                                 .87***
        n.s.                                 Participation                 Expert status

        Time in the
                              n.s.               R2 = .98                       R2 = .76
           CoP                          significant mediation effect


        Expertise




               Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
5. Discussion

 •  Automated content analysis is useful for assessing vCoP activity
 •  Technology acceptance develops use intention
 •  However, use behavior is influenced by CoP factors



                               Role in CoP



       Expertise              Participation                   Expert status


                               Technology
                                 anxiety



                   Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
6. Conclusions

 Consequences for educational research
 •  CoP model was confirmed
 •  Acceptance models need reconceptualization for
    complex educational environments

 Consequence for educational practice
 •  Development of assessment tools for
    collaboration in vCoP


            Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
Thank you for your attention!

nic.nistor@lmu.de




           Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

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Virtual Communities of Practice in Academia: An Automated Discourse Analysis

  • 1. Virtual Communities of Practice in Academia: An Automated Discourse Analysis Nicolae Nistor, Beate Baltes, George Smeaton, Mihai Dascălu, Dan Mihailă & Ștefan Trăușan-Matu LAK13 – DCLA13
  • 2. 1. Rationale •  Increasing use of virtual communities of practice (vCoPs) in academia •  Available technology acceptance and CoP models •  Models are methodologically limited and insufficiently tested in vCoPs •  Participation in vCoP = technology use? If so, the combined acceptance x CoP model should be valid Ø  Validation of automated discourse analysis Ø  Verification of the acceptance x CoP model in an academic vCoP Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
  • 3. 2. Theoretical background Communities of practice (Lave & Wenger, 1991; Wenger, 1998) •  Groups of people sharing goals, practice and knowledge over lengthy periods of time •  Environment for knowledge construction/creation •  Practice and knowledge are reflected in dialogue •  Main factors •  expertise •  participation •  expert status Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
  • 4. 2. Theoretical background Communities of practice Conceptual model (Nistor & Fischer, 2012) Knowledge domain Expert status Participation (centrality) Time in the CoP Role in CoP Expertise Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
  • 5. 2. Theoretical background Educational technology acceptance •  Unified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh et al., 2003, 2012) Performance expectancy Effort Technology Technology expectancy use intention use behavior Social Facilitating Technology influence conditions anxiety Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
  • 6. 3. Research model CoP model Role in CoP Domain knowledge Expert status Participation (centrality) Time in CoP Expertise Technology Facilitating Technology use intention conditions anxiety Performance Effort Social expectancy expectancy influence Acceptance model Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
  • 7. 4. Methodology Design: Correlation study Sample: N = 129 members of academic vCoP at US American online university (20 full-time, 500 part-time staff) Setting: Asynchronous discussion forum Variables: •  Acceptance •  Expertise, as reflected in the quality of interventions •  Expert status/Centrality Methods: •  Acceptance: UTAUT questionnaire •  CoP: Automated content analysis •  Centrality: Social Network Analysis Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
  • 8. 4. Methodology Automated content analysis Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
  • 9. 4. Methodology Automated content analysis Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
  • 10. 4. Methodology Automated content analysis Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
  • 11. 4. Methodology Automated content analysis Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
  • 12. 4. Methodology Automated content analysis – Validation •  Manual content analysis: Critical thinking framework •  Categories: initiation of discussion, exploration of the problem, solution, judgment, resolution •  Argumentation quality rating Ø Strong correlation (r = .79, p < .000) between automated and manual content analysis Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
  • 13. 4. Findings Partial verification of UTAUT model Performance expectancy .30*** R2 = .36 R2 = .06 Effort .22** Technology n.s. Technology expectancy use intention use behavior n.s. -.28** Social .23** Facilitating Technology influence conditions anxiety Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
  • 14. 4. Findings Successful verification of CoP model Role in CoP Domain knowledge .99*** .87*** n.s. Participation Expert status Time in the n.s. R2 = .98 R2 = .76 CoP significant mediation effect Expertise Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
  • 15. 5. Discussion •  Automated content analysis is useful for assessing vCoP activity •  Technology acceptance develops use intention •  However, use behavior is influenced by CoP factors Role in CoP Expertise Participation Expert status Technology anxiety Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
  • 16. 6. Conclusions Consequences for educational research •  CoP model was confirmed •  Acceptance models need reconceptualization for complex educational environments Consequence for educational practice •  Development of assessment tools for collaboration in vCoP Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
  • 17. Thank you for your attention! nic.nistor@lmu.de Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013