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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.

Zusammenfassung. Der Inno-Lehre wurde angelehnt an den Center-of-Excellence-Ansatz und an den aktuellen Diskurs über innovative Hochschullehre entwickelt, um das Innovationsklima in der Hochschullehre zu messen. 162 Lehrende und 585 Studierende verschiedener deutschsprachiger Hochschulen haben den Inno-Lehre ausgefüllt. Mit Hilfe einer explorativen Faktorenanalyse und Analyse der Itemkennwerte wurde eine 12-faktorielle Struktur des Inno-Lehre festgelegt. Die faktorielle Struktur des Inno-Lehre konnte durch eine konfirmatorische Faktorenanalyse bestätigt werden. Der Inno-Lehre korreliert positiv mit Skalen zur Studien- und Leistungszufriedenheit von Studierenden und gibt so Hinweise auf die konvergente Validität. Zwischen dem Inno-Lehre und Persönlichkeitsmerkmalen der Organisationsangehörigen zeigen sich keine bzw. geringe Korrelationen, was im Sinne der diskriminanten Validität interpretiert werden kann. Zusammenfassend ermöglicht der Inno-Lehre die standardisierte und ökonomische Erfassung des lehrbezogenen Innovationsklimas.

Innovation in higher education teaching is essential to respond to global challenges and actively improve teaching (e.g. through new technologies), necessitating the implementation of educational reform programmes that fund educational innovations. Although currently deployed strategies frequently promote networks between innovators to diffuse educational innovations, little is known about the efficiency of these networks or whether they promote innovation diffusion. This study investigates a network comprising 88 higher education teachers who received funding for their educational innovations in a German university. We collected longitudinal data by asking higher education teachers from whom they adopted innovative teaching ideas and requesting self-reports on innovative teaching climate. Our findings show that the teachers’ social network had a smaller path length and more clustering than might be expected by chance. This observation might indicate that the examined educational innovation network exhibits a small-world property and allows efficient exchange of ideas among the teachers. In line with our hypotheses, the network’s initial tendency toward hierarchy and homophily decreased over time in response to strategies and network interventions. In summary, this study provides initial empirical support that educational reform programmes can create efficient educational innovation networks, facilitating innovation diffusion and promoting change in higher education teaching.

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