Transformative Learning Networks: Guidelines and Insights for Netweavers
By Bruce Goldstein, Associate Professor, University of Colorado Boulder
NSEC was created as a learning network, an inter-organizational voluntary collaborative that nurtures professional expertise. Learning networks are often attempted when deeply rooted obstacles to institutional change have proven resistant to both top-down or bottom-up change strategies. Effective learning networks have a loose, light structure that can amplify the potential for transformative change by combining site-based innovation with community-spanning interaction and exchange. However, many of the features that provide learning networks with transformative potential also make them difficult to organize and maintain. Learning networks require a high level of engagement and commitment in order to identify deep-rooted problems and coordinate disparate actors to implement solutions that are both site-specific and network-wide.
To address this challenge, NSEC commissioned researchers at the University of Colorado Boulder and Oregon State (see bios at: www.brugo.org) to prepare four case studies to identify the opportunities and challenges of a learning network approach, with the purpose of informing NSEC's design. In addition to myself, the University of Colorado Boulder project team includes Claire Chase, Lee Frankel-Goldwater, Jeremiah Osborne-Gowey, and Sarah Schweizer. In addition, the team includes Julie Risien at Oregon State University, who is Associate Director of the Center for Research on Lifelong STEM Learning and herself a member of NSEC. Our team assembled the case studies using interviews with netweavers, document analysis, and literature review. The four learning networks that our project team examined, along with their transformation challenges, are:
- NABI (National Alliance for Broader Impacts): Connecting the university-based research enterprise to societal impacts and addressing the cultural divide between academy and public;
- 100 Resilient Cities Network: Fostering resilience in response to the inability of city governments to address challenges to sustainability;
- Fire Adapted Community Learning Network: Creating fire adapted communities after 100 years of failed wildfire management policy; and
- START (Global Change SysTem for Analysis, Research & Training): Addressing the capacity deficit to address global change impacts in the developing world.
We draw on these four cases to explore how networks can foster new collaborative relationships, shared learning about practice, and collective capacity to effect transformative change. The loose and light structure of these networks holds the potential for co-learning without prescriptive actions and free from the constraints of institutions so that members can make collective progress towards addressing fundamental barriers to transformation.
Each case describes the network's origin, design and approach to collaborative learning. We then more deeply examine issues of practice including organizational learning, facilitation and "netweaving", integration across scales, collective action, sustainability and network health all in the context of elucidating transformative capacities.
Our main takeaways are:
- Learning networks rely on deliberate design and ongoing netweaving to function effectively. Netweavers initiate activities that build community by forming relationships, circulating ideas and practices through the network, and promoting a shared identity that provides the foundation for common practice and purpose. Netweaving requires an ability to operate flexibly within and across participating sites when relationships are pre-determined and subordinated to a chain of command, tensions open up between local and network-wide identity and objectives.
- A commitment to organizational learning is essential to ongoing network adaptation. The network must have mechanisms to recognize evolving needs and perceptions of membership and critically question its policies, objectives, and embedded values to continuously transform its structure and procedures. Three features associated with network learning were 1) multiple opportunities for feedback between netweavers and members, 2) encouragement to experiment with different approaches to network interaction, and 3) whole-network meetings where governance is explicitly addressed.
- Transformative capacity emerges from a productive tension within and between network sites. Such capacity is neither the sum of similar efforts at different sites and scales nor the least common denominator between them. A well-designed learning network not only supports heterogeneity across sites and scales, it mediates the relationship between sites, supporting expression and adoption of a new professional identity that can promote higher-order coherence as well as community autonomy.
Overall, we conclude that good netweaving employs a soft touch by mediating between different ideas about transformation and ways of knowing, being, and organizing without collapsing them into one perspective. This facilitates an open culture of inquiry and trust that can foster collective identity and ongoing commitment among network participants. This is especially important since transformative change may be either slow moving or punctuated, occurring only during rare windows of opportunity.
While these cases have provided specific ideas to inform NSEC's design, ultimately we believe that a collaborative network design only constitutes a starting point and initial hypothesis for an effective STEM network, and that developmental assessment and continuous improvement are necessary for network success. Accordingly, the next step in our group's contribution to NSEC is to share and collect the strategies and practices of netweavers, which we discussed at an BSEC-sponsored invitational workshop on Building and Sustaining Networks in STEM Education held before the annual NSEC meeting during the summer of 2017 in New Orleans.
A review of selected papers on learning networks that focuses on 1) developmental evaluation of a learning network 2) value creation framework to bring stories of values and metrics together, 3) the use of social network analysis for evaluation.