There’s usually an origin story for why people find themselves doing the work they do. For us, Emma and Cami, this origin is tied to integrity; a desire to see social sector organizations “walk the talk” of what they’re trying to do in the world. In different ways, we both started our careers supporting educators through significant transformation efforts. At the National Board for Professional Teaching Standards, Emma worked with expert teachers as they came together to write national standards for their discipline; at the Mexican Department of Rural Education, Cami participated in the implementation of an alternative dialogue-based model across the country. In our own ways, we both puzzled at the disconnect between the kind of learning that these educators so passionately believed in and the institutional dynamics that so often surrounded them. Somehow, efforts to create more connected, creative, and exploratory classrooms could feel, well, anything but.
When we learned about networked improvement communities, we were drawn to an approach to systems change that is anchored in co-learning and co-creation. We each resonated with the idea that we can only solve education’s most complex problems if we draw on the knowledge and skills of folks from across the system (teachers, leaders, academics, etc). We were also attracted to the fact that, rather than presuming any one person holds the solution, embedded in the design of an improvement cycle is an assumption of collective incompleteness. That is, community members begin the process by building a shared theory…and then spend the rest of their time together learning about its limitations.
This learning in practice is best exemplified in one of the keystone practices of improvement networks: the Plan-Do-Study-Act cycle. Running these cycles tasks folks with iterating their practices and mental models over and over again in the service of student outcomes (why didn’t this idea work when we thought it would? what did we miss? what can we try next?). At its best, this process is one of active inquiry, creativity, and dialogue. As such, it held, for us, the potential to not only address a specific problem, but to do so in a way that builds collective capacity for ongoing learning.
Of course, a significant amount of knowledge brokering is required in these communities, and much of our training in improvement highlighted the technical mechanisms through which knowledge can be made legible, transferable, and applicable across the system. This entailed, for example, becoming adept at translating different forms of information, such as moving from scholarly research findings into testable practices or consolidating individual reflections into network-level data. It also involved onboarding folks to the practice of improvement itself, hoping that its tools and processes could act as a lingua franca, allowing folks to have a shared language with which to talk about their collective efforts.
This is hard work. During our time supporting various improvement networks, we gained a deep understanding of the difficulty of developing a system of measures that is both practical and rigorous, as well as the challenges surrounding the design, application, and consolidation of diverse (and sometimes unruly) data. However, the biggest challenges we faced had little to do with these technical intricacies. Instead, the struggles we faced then are the same struggles we’ve seen in other forms of collaboration that try to build coalitions across silos. Regardless of the organizing approach, we’ve seen folks get frustrated and stuck when they move from passive information sharing (what improvers call a “sharing community”) and into active knowledge co-creation (a true learning community).
Because our education systems are so complex and disjointed, we lack a familiar script for how to do these knowledge collaborations well. In the absence of intentional design, we default to the dynamics, relationships, and narratives that are familiar within our own corners of the system. For example, system leaders may resort to a top-down implementation that makes little room for learning from failure, teachers may avoid the documentation practices that undergird collective learning, and research experts may fail to engage with data that does not conform to academic standards of evidence. In doing so, each person is leaning into what’s valued and standard practice in their role – even if this comes at the cost of the collective endeavor. And each, in turn, feels frustrated with how others’ actions limit their shared effectiveness.
Real knowledge co-creation is disruptive. It asks folks to step (ever-so-slightly, then ever-more-starkly) away from their usual ways of working. Whether it’s our timelines, our data use, or our priorities, we will find points of tension and conflict when we move from acting in isolation to acting as a collective. We need to negotiate; we need to give something up. There is no way around it, and there is no easy way out; no simple formula, process, or tool that can help a disparate group of folks figure out how they want to be and learn together.
In our experience, this messiness is both unavoidable and under-discussed. When we are in the messy middle of collaboration, we tend to turn, instead, to the safety of technical rigor (improvement tools, for instance), looking away from how doing so may be exacerbating the problem by adding yet another layer of complexity to an already fraught landscape. And, because we are so rarely explicit about how co-learning will force individuals and institutions to make hard decisions together, we’re unprepared when conflict arises.
These human and political elements flow beneath the surface in collaborative communities, and navigating them can feel daunting. In our own work, doing so raises a host of questions that lack a quick resolution: How do we build a container for authentic collaboration? How do we build trust and address conflict? How do we support equitable negotiation against existing power dynamics? We’re starting to explore these tensions and run our own tests on how to address them in our facilitation. We’d love to find fellow travelers interested in doing the same. We’ll be diving into these questions at our upcoming Knowledge Cafe, and we hope to see you there!
Cami Velasquez and Emma Parkerson work at Systems Design Lab, where they help groups to organize and make decisions toward a collective outcome. With training in continuous improvement, applied anthropology, and organization development, their sweet spot is in co-creating the conditions for meaningful collaboration across difference. They’re running an experiment with learning in public over on Substack; check it out to see some of their latest wonderings and learnings.