Social Network Analysis as a method to study the structure of contacts within teams of a school for secondary education

Abstract

Social Network Analyses (SNA) is, mostly, used to study contacts between people, for instance in the study of the networks during networked learning. In the current study, we used SNA to study the contacts between teachers of a secondary school (n = 117) that were clustered in teams ( n = 6), based on the learning relations they have within and between teacher teams. These learning relations were inquired via an instrument called the ‘Contactmap’. On this map, teacher could indicate with whom they had contacts (within and between teams), how frequent (daily, weakly, or monthly) and the topic of these contacts. Information on the nature of these contacts within and between teams gives an indication of the information exchange within and between teams and the kind of learning within the team. It appeared that SNA can be used to indicate the dominant mode of team learning and learning processes characterized by the structure and topic of the social contacts. SNA can also be used to determine the web of contacts for each teacher personally and it turns out that a teachers functions in a school organization influences the relative number of (learning) contacts. For schools, SNA on the number and nature of contacts is valuable information because it helps to understand and uncover which teachers are influential in information exchange, what subgroups exist, and which teachers are on the edge of these networks. In sum, it gives valuable information about informal social relationships that can be used to optimize the efficiency of communication structures that exist in schools as well as their potential for professional development and school innovation. This study is part of a larger long term research programme on the dynamics of teacher professional development networks funded by the Dutch government. The findings of this research will be used for improving tools and instruments to detect and visualise informal learning relationships and to further understand the kind of capital and value these networks produce.