Development of computer-supported collaborative social networks in a distributed learning community

Abstract

This study examines the formation and change of collaborative learning social networks in a distributed learning community. A social network perspective is employed to understand how collaborative networks evolved over time when 31 distributed learners collaborated on a design project using a computer-mediated communication system during two semesters. Special attention was paid to how pre-existing friendship networks influenced the formation of macro-level collaborative learning networks and individual level social capital. We discovered that pre-existing friendship networks significantly influenced the formation of collaborative learning networks, but the effect was dependent on the developmental phase of community. Also, pre-existing networks generally acted as a social liability that constrained learners' ability to enhance their social networks and build social capital when they participated in a new learning environment. The results suggest that, in order to fully understand how to build effective collaborative learning and work environments, participants' social network structures need to be considered.

Acknowledgement

The authors gratefully acknowledge the support of NASA Langley Research Center, through Cooperative Agreement No. NCC-1-01004. Additional support was provided by the State of New York and the AT&T Foundation.

Notes

¹The two universities are relatively close, but a post-hoc interview revealed that students from the two universities seldom met with each other face-to-face. Only five students indicated that they had met their remote partner once or twice for the entire study year for their group projects.

²To test whether the deletion of these people made any significant changes in the results, additional analyses were conducted including those people in the model. The results were almost identical except for small changes in coefficient values. Overall, the model fits got lowered and degree centrality became a less significant predictor (but still significant at 0.01 level).