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Analysing the structure of a social network can help us understand the key factors influencing interaction and collaboration in a virtual learning community (VLC). Here, we describe the mechanisms used in social network analysis (SNA) to analyse the social network structure of a VLC for teachers and discuss the relationship between face‐to‐face and online collaborations. In contrast to previous research applying SNA to analyse measuring indexes alone, we emphasise the mechanisms combining SNA, questionnaires, content analysis and focus group interviews—the key methodology to analyse complex interaction in a VLC. On this basis, we present an analysis model for teachers' VLC and apply it to a teachers' VLC known as ‘IRIS’. The study participants comprised 172 K12 teachers aged between 25 and 55 years. This study collected collaboration data from 2006 to 2012 and analysed the social network structure using sociograms, centrality, cohesive subgroups, clique phenomenon, and matrix correlation of SNA. These findings suggest that face‐to‐face and online collaborations are both indispensable in teaching and in research and continuously supplement and remedy each other in professional development. Moreover, the model succeeded in accessing, describing and analysing the social network structure of a VLC.

Close links between students' conceptions of and approaches to learning were established in the past research. However, only a few quantitative studies investigated this relationship particularly with regard to mobile learning (m‐learning). The correlation between learners' conceptions and approaches to m‐learning was analysed using a partial least squares analysis applied to data obtained from a sample of 971 undergraduate students in China. The results indicated that students' conceptions of m‐learning could be classified into reproductive, transitional, and constructive levels. Students may hold multiple m‐learning applications than a predominant one; hence, examining m‐learning as one monolithic entity may provide limited information. Latent profile analysis identified four learning profiles based on students' preferred m‐learning applications: passive, mixed, surface‐supportive, and high‐engagement.. Moreover, a general trend was observed, whereby students with reproductive and surface‐supportive learning profiles showed a tendency to adopt surface approaches, whereas those expressing constructive and mixed learning profiles were more inclined to adopt deep approaches. Interestingly, students with transitional conceptions and high‐engagement learning profiles tended to take both surface and deep approaches.

With the increasing importance of adult and continuing education, the present study aimed to examine the factors that influence continuing web-based learning at work. Three questionnaires were utilised to investigate the association of the job characteristics from Karasek et al.'s (1998) job demand-control-support model and the self-regulated learning with web-based continuing learning; exploratory factor analyses indicate their adequate reliability and validity. A sample of 203 employees of an airline company completed three questionnaires. The path analysis reveals that job demands did not have any significant correlation with any other variables. However, job control, social support and self-regulated learning constituted significant predictors of attitudes towards web-based continuing learning. Furthermore, self-regulated learning mediated job characteristics and attitudes. In conclusion, this is one of the few studies to consider perceptions of both personal online learning (i.e., self-regulated learning and attitudes towards web-based continuing learning) and work-related variables (i.e., job characteristics). The study advances interdisciplinary perspectives on education, information and communications technology and psychology, which has implications for continuing adult education and successful implementation of online workplace learning.

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