Network Analysis of a Virtual Community of Learning of Economics Educators.
Year: 2015
Type: article
Abstract: Introduction. This paper aims at understanding virtual communities of learning in terms of dynamics, types of knowledge shared by participants, and network characteristics such as size, relationships, density, and centrality of participants. It looks at the relationships between these aspects and the evolution of communities of learning. It focuses specifically on a virtual community of learning concerned with Economics education (CoL01). Method. The methods employed include descriptive statistics, content and network analysis. Analysis. We examined in detail the case of a virtual community of Economics educators using the digital archive of exchanged messages during 15 years, and focusing on: community of learning characteristics and dynamics, its density, the centrality of participants and type of knowledge shared. We compared it with three other virtual communities in the same disciplinary domain. Results. The community of learning under analysis revealed a cyclical behaviour in its development. However, the behaviour of participants (i.e. ratio of answers per topic of discussion) remains stable. Leadership is not the responsibility of a single individual. Instead it is shared by a nucleus of persons inside the community of learning. Knowledge sharing is student and teaching-oriented. Conclusions. Contrary to the literature that emphasises the importance of the role of a moderator for community interaction, leadership in debate in the studied community is not centralised in a single person or a very small group. Instead, there is a strong nucleus of members in the community of learning which is more active during the whole period under analysis and may play a brokering role that is key to maintaining the dynamics of participation. Large digital public archives proved to be useful for a deep analysis of the characteristics and dynamics of communities of learning.
Source: Information Research
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W2228628810