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.

Evaluating the content structure of intelligent tutor systems-a psychological network analysis

“…Importantly, other types of network analyses exist and have been applied to address research questions within the realm of learning analytics, such as social network analysis (e.g., Chen and Poquet, 2022;Dawson et al, 2014;Mallavarapu et al, 2022; EVALUATING THE CONTENT STRUCTURE OF INTELLIGENT TUTOR SYSTEMS 6 Norz et al, 2023;Saqr and López-Pernas, 2022;Stasewitsch et al, 2022), semantic network analysis (e.g., Chowdhury et al, 2024), or epistemic network analysis (e.g., Shaffer et al, 2016). The difference between these network analysis and psychological network analysis is that the edges within psychological network analysis are estimated (i.e., they represent the correlation between two variables) and are not directly obtained from the data (e.g., edges within social network analysis may represent the amount of communication between people and are thus directly obtained from the data).…”

A psychological network analysis to specify predictions of fraction subtopics on algebra subtopics in an intelligent tutoring system

“…Importantly, in psychological networks, the weights of the edges between nodes (representing correlation strength) are estimated and the accuracy of these estimates increases with increasing sample size . The fact that edges are estimated is the main di erence between psychological network analysis and other network analysis, such as social network analysis (e.g., (Chen & Poquet, 2022;Dawson et al, 2014;Mallavarapu et al, 2022;Norz et al, 2023;Saqr & López-Pernas, 2022;Stasewitsch et al, 2022)) or epistemic network analysis (e.g., (Sha er et al, 2016)) applied within the learning analytics community. Based on these estimated weights, other nodes' indices are then inferred to reflect each node's relevance within the network.…”

Investigating Cognitive Network Models of Learners’ Knowledge Representations

“…The social network then provides a snapshot of collaborative patterns in educational settings that can be used to predict academic success or model student engagement. The networks discussed in the special section in this issue depict such social networks (Saqr & López-Pernas, 2022;Mallavarapu et al, 2022;Stasewitsch et al, 2022).…”