Abstract
We present an in-depth case study of a learning network that aims to transform infrastructure and practice across the research enterprise to advance societal impacts. The theory of social morphogenesis guides our processual qualitative analysis of the network. We describe how different types of boundary work, both building and navigating across boundaries, operate in tension while contributing to transformative capacity. We conclude that learning networks can play a robust role in fostering transformation by drawing together and holding together forces which expand knowledge and authority over time iteratively and recursively. In addition to this theoretical contribution, we provide practical guidance for how network leaders can dynamically manage boundaries, shifting emphasis between strength and fluidity to support transformative change across sites and scales.
Highlights
Learning networks offer the academic community a way to address the critical challenge of broadening the impact of the research enterprise to meet societal needs and the increasingly complex requirements of funding agencies
The framework explains the critical role of boundary work and the growth of knowledge and authority as assets of a network as a learning network progresses over time
We examine transformative capacity cultivated through a learning network; we posit that such capacity manifests as a combination of a network’s robust knowledge resources and authority in the broader system
Summary
Used to improve higher education STEM instructional practices E. Goldstein et al 2016; Kezar 2014; Kezar and Gehrke 2015), networked approaches to transformation vary in structure and design but share a common purpose to work across institutional contexts, bringing together diverse capacities and points of view to enable learning and stimulate transformation of practice and norms in higher education. Our results, presented as a theoretical contribution, bridge core social theory about structure and agency with the study of networks to enable application in network practice. Our conclusions align with recent studies of transformative learning networks that show a flexible and light structure, with emphasis on dynamic and adaptive principles, enables transformative capacity by optimizing interactions across sites and scales (Goldstein et al 2017b)
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