Bodong Chen

Crisscross Landscapes

Notes: Nistor. (2014). Participation in virtual academic communities of practice



Citekey: @Nistor2014

Nistor, N., Baltes, B., Dascălu, M., Mihăilă, D., Smeaton, G., & Trăuşan-Matu, Ş. (2014). Participation in virtual academic communities of practice under the influence of technology acceptance and community factors. A learning analytics application. Computers in Human Behavior, 34, 339–344. doi:10.1016/j.chb.2013.10.051


Not a typical learning analytics research even though ‘learning analytics’ is featured in the title.


2.2. Educational technology acceptance (p. 340)

A prominent acceptance theory is Venkatesh’s Unified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh et al., 2003, 2012) that explains the use of educational technology under the influence of use intention, further determined by performance and effort expectancy, and social influence. Additionally, facilitating conditions and computer anxiety (Nistor, Lerche, Weinberger, Ceobanu, & Heymann, 2012) directly affect the use of educational technology. (p. 340)

A critical review of technology acceptance models including UTAUT was done by Bagozzi (2007) who observed the oversimplifying, unidimensional definition of acceptance (p. 340)

RQ1 (acceptance model verification): To what extent do acceptance factors (technology use intention, performance expectancy, effort expectancy, social influence, facilitating conditions and technology anxiety) predict participation in vCoP? RQ2 (CoP model verification): Does participation in vCoP significantly mediate the influence of expertise on expert status? (p. 340)

Participation in vCoP was operationalized as the number of interventions of each vCoP member. The quality of these interventions was considered as an indicator of expertise. Expert status was measured as betweenness centrality in the vCoP social network, i.e. the number of shortest paths connecting all vCoP members with each other and passing through that particular member. All three CoP variables were automatically determined. (p. 341)

What does ‘intervention’ mean in this paper?? Confusing. (p. 341)

Further, each forum discussion thread was represented as a graph with interventions as nodes and ‘‘reply to’’ relationships as links. The cohesion graph (Dasca ̆lu, Dessus, Tra ̆ußsan-Matu, Bianco, & Nardy, 2013), built using semantic distances in WordNet (Budanitsky & Hirst, 2006; Miller, 1995), Latent Semantic Analysis (Landauer & Dumais, 1997) and Latent Dirichlet Allocation (Blei, Ng, & Jordan, 2003), was used for determining the quality of the interventions. (p. 341)

While the members of this vCoP were the target population of the study, the examined sample consisted of N = 133 participants, whose gender, age and position are displayed in Table 1. (p. 341)

The acceptance variables technology use intention, performance and effort expectancy, social influence, facilitating conditions and technology anxiety were measured by questionnaire (p. 341)

The CoP model could be entirely confirmed in the vCoP setting. Participants’ expertise, i.e. their quality of interventions had a significant impact on their participation/use behavior (b = .99, p < .000), explaining the variance in participation almost entirely (R2 = .98), whereas participants’ time spent in the CoP had no significant effect. (p. 342)

To test the mediating effect of participation, in the first step a regression analysis was performed with participant’s number of interventions as predictor and participant’s expert status as criterion (b = .92, p < .001); the residual variance of participant’s expert status was saved. In the second step, another regression analysis was performed with the quality of interventions as a predictor and the residual variance calculated in the first step as a criterion. The effect measured by the latter regression analysis was non-significant, showing that the mediating effect of participation was significant. (p. 342)

The acceptance model could be only partially confirmed. Performance expectancy (b = .30, p < .01), effort expectancy (b = .19, p<.05), and social influence (b=.22, p<.05), significantly impacted use intention, explaining one third of its variance (R2 = .33). (p. 342)

This study aimed to apply learning analytics to verify the CoP model (Nistor & Fischer, 2012) and the acceptance model (UTAUT; Venkatesh et al., 2003, 2012) in vCoP setting. (p. 343)

The learning analytics (Siemens & Gasevic, 2012) application was successful, as far as data extracted from online discussions could be used to verify two major conceptual models of the Educational Sciences. (p. 344)

For educational practice, this study prepared the development of automated tools for monitoring and assessment of collaboration in vCoP platforms. Such tools may be employed, e.g. to improve mentoring of virtual faculty in its various forms. (p. 344)

learning analytics
This article features a title including ‘learning analytics’. However, its relationship with learning analytics only becomes clearer when discussing future possibilities of tools. The main body of the work is survey research, with discussion forum analysis part of the work. It’s more an analysis of online interactions in vCoP rather than analytics. (p. 344)