Interaction networksin knowledge-building learning communities

Abstract

Network analysis of interactions may provide insights for teachers and researchers. Using network data, teachers and researchers can track and analyse the structure and mechanisms of a classroom or a course community. Teachers can use it to improve tasks, processes, groups, motivation, assessment or other aspects of learning. This paper reports on a network analysis of inquiry-based interactions that emerged during university courses between undergraduate students engaged in collaborative inquiry. The students’ task was to summarize, share and discuss their findings in a course blog during the semester. Interactions of three university courses were analysed on three levels. First, macrolevel whole network analysis was used to identify the main structural properties of the course communities and compare them. The whole network measures were network density, centralization, components, average path length, diameter and reciprocity. According to the analysis, the interaction networks were dense, well-connected and highly reciprocated. This suggests that the communities were cohesive and the information flow was rather smooth. Second, the subnetwork structure of the communities was investigated. Connectivity and fragmentation, groupings, components and the core/periphery structure were analysed. This showed the latent structure of potential alliances and collaboration between students. Finally, a microlevel centrality analysis was performed to measure the positions of individuals (students) inside their communities. The results demonstrated the varying centrality of the students based on the interactions initiated and received. The findings are discussed.