Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Virtual Communities of Practice in Academia: An Automated Discourse Analysis

2,491 views

Published on

Nicolae Nistor, Beate Baltes, George Smeaton, Mihai Dascalu, Dan Mihaila, & Stefan Trausan-Matu..

Talk at DCLA13 Leuven 2013, co-located with LAK13

Published in: Education, Technology
  • Be the first to comment

  • Be the first to like this

Virtual Communities of Practice in Academia: An Automated Discourse Analysis

  1. 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. 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. 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. 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. 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. 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. 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. 8. 4. Methodology Automated content analysis Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
  9. 9. 4. Methodology Automated content analysis Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
  10. 10. 4. Methodology Automated content analysis Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
  11. 11. 4. Methodology Automated content analysis Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013
  12. 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. 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. 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. 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. 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. 17. Thank you for your attention!nic.nistor@lmu.de Nistor, Baltes, Smeaton, Dascălu, Mihailă & Trăușan-Matu, 2013

×