Virtual Communities of Practice in Academia: An Automated Discourse Analysis

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

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 – 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

Virtual Communities of Practice in Academia: An Automated Discourse Analysis

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