Knowledge Transfer in a Two-Mode Network Between Higher Education Teachers and Their Innovative Teaching Projects

Abstract

Knowledge transfer (KT) and innovation diffusion are closely related to each other because it is knowledge regarding an innovation that gets adopted. Little research in learning analytics provides insight into KT processes in two-mode networks, especially in the context of educational innovations. It is unclear how such networks are structured and whether funding can create a network structure efficient for KT. We used a case-study approach to analyze a two-mode network of 208 university members (based on archival data) who worked together on 91 innovative teaching projects. Our results show that the two-mode network displays a decentralized structure and more clustering than can be assumed by chance, promoting KT and learning. To gain a deeper understanding of the kind of knowledge that is transferred in the network, we analyzed the effects of different educational innovation elements (e.g., game-based learning) as attributes of higher education teachers. Overall, our results suggest that funding and the creation of project structures in the context of educational innovation is a sustainable way to create KT, and therefore organizational change. Furthermore, the results imply that university practitioners need to implement networking interventions to create more connections between subgroups in teacher-related networks.