virtual communities of practice in academia: an automated discourse analysis

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



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

Knowledge domain

Participation Expert status (centrality)

Time in the CoP

Role in CoP Expertise



Educational technology acceptance •  Unified Theory of Acceptance and Use of Technology

(UTAUT; Venkatesh et al., 2003, 2012)


Technology use behavior

Performance expectancy

Facilitating conditions

Technology use intention

Effort expectancy

Social influence

Technology anxiety

3. Research model


Participation

Domain knowledge

Expert status (centrality)

Role in CoP

Expertise

Time in CoP




Effort expectancy

Social influence

Technology anxiety

CoP model

Acceptance model

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


4. Methodology

Automated content analysis


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


4. Findings


Technology use behavior




Effort expectancy

Social influence

Technology anxiety

.30***

.22**

.23**

R2 = .36 n.s.

n.s. -.28**

Partial verification of UTAUT model

R2 = .06

4. Findings


Successful verification of CoP model

Domain knowledge

Participation Expert status

Time in the CoP

Expertise

.99***

n.s.

.87***

R2 = .98 significant mediation effect

R2 = .76

n.s.

Role in CoP

5. Discussion

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


Participation Expertise Expert status

Technology anxiety

Role in CoP

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


Thank you for your attention! [email protected]


virtual communities of practice in academia: an automated discourse analysis

Education

mihail truanmatu

beate baltes

george smeaton

mihai dasclu

technology use

manual content analysis

cop role

acceptance models