Social network analysis and educational change: unravelling the role of innovative teaching staff in a higher education environment

Figures & data

Figure 1. Adopter categorization based on innovativeness (Rogers 1983).

Table 1. Network measures used in the study and the purpose of their use.

Network measure	Purpose
Network centralities	Identifying the most central or influential actors in a network
Degree centrality	Number of relationships an actor has in a network
Betweenness centrality	The extent to which an actor appears on the shortest path between other actors. Actors with high betweenness centrality are important brokers of information or resources.
Closeness centrality	The average distance between an actor and all other actors in a network. Actors with high closeness centrality are well-connected and have access to information from a large portion of the network.
Eigenvector centrality	Relative importance of an actor based on the centrality of the actors to which it is connected. Actors with high eigenvector centrality have many relationships to other central actors in the network.
Network density	The extent to which all actors in a network are connected
Network modularity	Measures the division of the network into smaller communities

Figure 2. Distribution of the results in the DOI survey and the subsequent categorization.

Figure 3. Network visualization of the overlap between algorithm-detected subgroups departmental research groups. The four research groups are labeled as A, B, C, D, and the four detected communities are also labeled as A, B, C, D. In cases where there is an overlap between the two, the nodes are gray, and in the four cases where there is no overlap, the nodes are red.

Figure 4. Comparison of the complete teaching network (338 ties) with the teaching network consisting of strong ties (62 ties).

Figure 5. Comparison of the influence on the network (i.e. eigenvector centrality) of different categories of innovativeness for both the full network and frequent conversations (i.e. strong ties).

Figure 6. Combined personal networks of most innovative teachers. Black nodes indicate Innovators and Early adopters, gray nodes indicate other teachers.

Figure 7. Graph densities of Innovators and Early adopters (1st), Early majority (2nd), Late majority (3rd), Late adopters (4th).

Rogers, Everett M. 1983. Diffusion of Innovations. 3rd ed. London, NY: Free Press; Collier Macmillan.

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Data availability statement

The datasets analyzed during the current study are not publicly available as part of data protection legislation, but the modified version based on anonymized and deidentified data is available from the corresponding author on reasonable request.