Nicolae Nistor, Beate Baltes, George Smeaton, Mihai Dascalu, Dan Mihaila, & Stefan Trausan-Matu.. Talk at DCLA13 Leuven 2013, co-located with LAK13

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