Collaborative Approaches for Learning Ecosystems

January 1st, 2016

This article was migrated from a previous version of the Knowledge Base. The date stamp does not reflect the original publication date.

Overview 

Collaboration is practically required of projects hoping to develop and research learning innovations or improvements, but how exactly it is done is neither straightforward nor completely described. Collaboration and networked efforts are particularly useful when considering learning ecosystems–cross-contextual learning that happens organically or through an organized effort to coordinate a learning ecosystem. Here are described four approaches that can leverage the affordances of networks or collaborative relationships in and/or across learning contexts (adapted from Allen & Crowley, under review). These approaches can be used as specific frameworks to structure collaborative efforts among practitioners, organizational leaders, researchers, and designers; and they can be drawn from more generally to inspire collaborators and suggest best practices for projects hoping to harness the power of networked organizations and collaboration. These approaches have particular utility for researchers and practitioners hoping to operate at the level of the learning ecosystem, as opposed to more singular foci. The four approaches outlined here are: Communities of Practice (e.g. Lave & Wenger, 1991), Networked Improvement Communities (e.g. Bryk, Gomez, Grunow & LeMahieu, 2015), Collective Impact (e.g. Kania & Kramer, 2013), and Collaborative Rationality (e.g. Innes & Booher, 2010).

 

Communities of practice bring educators or other practitioners together to learn from those among them who are most expert, creating a tight-knit network who collectively know and know how to access one another’s strengths and resources (e.g. Lave & Wenger, 1991). Networked improvement communities approach complex challenges using improvement science research and development techniques to identify innovations, test, and implement them across different contexts in a structured network of researchers, designers, and practitioners (e.g. Bryk, Gomez, Grunow & LeMahieu, 2015). The collective impact approach, very popular among nonprofit organizations, uses networks of organizational leaders to align resources and activities around a shared, measurable goal (e.g. Kania & Kramer, 2013). Collaborative rationality is a policy-making approach to addressing wicked problems, which uses bottom-up communication techniques in a diverse, interdependent network to come up with the solutions that fit best for all stakeholders (e.g. Innes & Booher, 2010). Communities of practice and networked improvement communities are both learning communities that have been described in professions, industries, and formal education systems (e.g. Bryk, Gomez & Grunow, 2011; Horn, 2010). Networked improvement communities, collective impact, and collaborative rationality are all specific, prescribed strategies for engaging networks in processes of collective change toward addressing complex problems in various contexts, including formal education systems and complex public policy challenges (Bryk et al., 2015; Innes & Booher, 2010; Kania & Kramer, 2013; Russell et al., in press).

 

Findings from Research and Evaluation 

Communities of Practice

At its core, a community of practice is a connected group of people that includes a spectrum of learners from expert to novice, who learn from one another and from the experiences they bring to and have with the community. Communities of practice can be (but are certainly not limited to) professional communities, such as teachers (e.g. Horn, 2007, 2010), or learning communities, such as students in classrooms (e.g. Engle, 2006; Engle & Conant, 2002). Communities of practice were first described by Lave and Wenger with a focus on the phenomenon of ‚Äėlegitimate peripheral participation‚Äô, wherein practitioners (particularly tradespeople or office workers) in a specific field work in proximity to one another and novices, at the periphery, participate in the community‚Äôs practice and gradually develop into more expert members of the community, moving from the periphery toward the center (1991). Later, Wenger (1998) elaborated other important dimensions of communities of practice: mutual engagement, joint enterprise, and shared repertoire. Communities of practice have been applied in many contexts, including as a strategy for management, a way to improve teacher practice, and as an online engagement strategy (e.g. Horn, 2010; Wenger, McDermott & Snyder, 2002; Herrington, Herrington, Kervin & Ferry, 2006). The broad applicability of the concept of a community of practice is part of its appeal to these diverse contexts, and what makes it useful for informal science practitioners and researchers when considering the diversity of learning contexts and educational practitioners that comprise learning ecosystems.

Networked Improvement Communities (NICs)

Multiple organizational fields have implemented NICs, including the semiconductor industry and large-scale educational improvement initiatives (Bryk et al., 2011; Bryk et al., 2015; Russell et al., in press). NICs have begun to successfully leverage improvement science techniques to generate large-scale improvements in complex formal education systems (Bryk et al., 2015; Russell et al., in press). A systematic method for testing, documenting, and implementing changes across systems, with a strong emphasis on expertise and evidence-driven decisions, improvement science has demonstrated utility in measurably improving outcomes complex systems in healthcare and manufacturing industries (Perla, Provost & Parry, 2013). NICs make research and design expertise available to practitioners, and provide a structure for iteratively testing innovations and disseminating the results throughout the network for further testing in varied contexts (Russell et al., in press). The main features of a NIC are: 1) specific and measurable improvement goals; 2) a deep understanding of the systemic problem and its hypothesized solutions; 3) a shared and systematic inquiry cycle; and 4) an organizing hub (Bryk et al., 2011; Bryk et al., 2015; Russell et al., in press). This specific and highly structured approach to network-based projects holds potential for projects with a learning ecosystem focus, by providing a structure that has been tested among large networks of organizations and complex systems, and highlighting the importance of shared goals and understandings for those engaging in collaborative efforts.

Collective Impact

Collective impact is a specific structure or strategy intended to harness the resources and connections of a cross-sector network in order to effect large-scale social change. Networks using the collective impact approach unite organizational leaders from different sectors (e.g. nonprofit organizations, public agencies, and private companies) around a common agenda in order to influence a specific issue, such as public safety or high school graduation rates. Collective impact is designed to address complex problems, and is based on the notion that complex problems cannot be addressed by expertise in a single area (Kania & Kramer, 2011, 2013). Structurally, collective impact networks and networked improvement communities are similar: they both require a shared understanding and clear definition of the problem they are addressing, a system of shared measurement, an organizing hub, and ongoing evaluation (Kania & Kramer, 2011, 2013; Russell et al., in press; Turner, Merchant, Kania & Martin, 2012). In contrast to the emphasis on connecting researchers, designers, and practitioners in networked improvement communities; collective impact networks are comprised of organizational leaders from across different sectors, focusing on coordinating mutually reinforcing activities across those sectors. This diversity in expertise and organizational missions is important for the large-scale changes collective impact initiatives target. The notion of collective impact, and the strategies of focusing on coordinating organizational leadership and mutually reinforcing activities (that is to say, organizational activities that build up and support the activities of other organizations, as opposed to competing initiatives among organizations) have promise for researchers, designers, and informal science practitioners seeking to generate impact at the level of learning ecosystems.

Collaborative Rationality

Collaborative rationality is a facilitation approach used for engaging communities and other interdependent stakeholders to address wicked public policy problems. This approach is an alternative to the expertise-driven, linear, top-down model that tends to dominate public policy decision-making. Collaborative rationality has been used to develop policy solutions where there is conflict among stakeholders, such as water use and conservation in California. For communities facing wicked problems, policies driven by outside expertise with a ‚Äėonce and for all‚Äô outlook on the issue are not a viable option (Innes & Booher, 2010). Since collaborative rationality is a technique for communication among a group of interdependent stakeholders who in many cases have been at odds with one another, such as environmentalists, developers, and water authorities, the foci of this approach are different from the other three approaches described above. However, the theoretical conditions for successful collaborative rationality to address wicked problems can be useful to our understanding collaborative efforts in a more general sense. The three important conditions for collaborative rationality are abbreviated ‚ÄúDIAD‚ÄĚ: Diversity, Interdependence, and Authentic Dialogue. Diversity in organizational function and philosophical viewpoints is necessary for collaborative rationality because if a people or organizations are all alike in their needs from a resource, problems are unlikely to be complex. The necessity of interdependent stakeholders for collaborative rationality is based on Habermas‚Äô (1981; cited in Innes & Booher, 2010) theory of communicative rationality‚ÄĒthe idea that a group of people with interdependent needs will ultimately arrive at a solution that is most rational for all if they are able to engage in authentic dialogue. Authentic dialogue is the last necessary feature of a collaboratively rational process: it requires that all participants in the conversation must be fully informed, with mutual assurance of the legitimacy, comprehensibility, accuracy, and sincerity of what is brought forward by all (Innes & Booher, 2010).

 

Examples of Collaborative and Networked Projects

 

Directions for Future Research 

Understanding how collaboration works and how to design collaborative systems and relationships to maximize success is a large-scale research endeavor. Interventions in learning ecosystems by way of networks or collaborative entities is a relatively new idea, and have not been documented thoroughly in most cases (see Allen & Crowley, under review for one example). The benefits of collaboration are often assumed to come ‚Äúfor free‚ÄĚ when new projects are designed, but truly successful collaborative projects and relationships require energy, resources, and commitment from all collaborating parties. Identifying best practices in collaboration, situations where collaborative relationships (and the specific dynamics of those relationships) are recommended or not, describing successful and challenging examples of collaborative projects and relationships, and more rigorous quasi-experimental examinations of collaboration strategies are all potential areas for future research.

References 

Allen, L. B. & Crowley, K. (under review). How Heterogeneous Networks Can Change Learning Ecologies: City-Scale Intervention for Climate Change Education. The Journal of the Learning Sciences.

Bryk, A. S., Gomez, L. M. & Grunow, A. (2011). Getting ideas into action: Building networked improvement communities in education. Frontiers in sociology of education (pp. 127-162). Netherlands: Springer.

Bryk, A. S., Gomez, L. M., Grunow, A. & LeMahieu, P. G. (2015). Learning to improve: How America’s schools can get better at getting better. Cambridge, MA: Harvard Education Press.

Engle, R. A. (2006). Framing Interactions to Foster Generative Learning: A Situative Explanation of Transfer in a Community of Learners Classroom. Journal of the Learning Sciences, 15(4), 451-498. doi:10.1207/s15327809jls1504_2

Engle, R. A. & Conant, F. R. (2002). Guiding principles for fostering productive disciplinary engagement: Explaining an emergent argument in a community of learners classroom. Cognition and Instruction, 20(4), 399-483. doi:10.1207/S1532690XCI2004_1

Habermas, J. (1981). The Theory of Communicative Action: Reason and the Rationalization of Society. Boston, MA: Beacon Press.

Herrington, A., Herrington, J., Kervin, L. & Ferry, B. (2006). The design of an online community of practice for beginning teachers. Contemporary issues in technology and teacher education, 6(1), 120-132.

Horn, I. S. (2007). Fast kids, slow kids, lazy kids: Framing the mismatch problem in mathematics teachers’ conversations. The Journal of the Learning Sciences, 16(1), 37-79. doi:10.1080/10508400709336942

Horn, I. S. (2010). Teaching replays, rehearsals and revisions. Teachers College Record, 112(1), 225-259.

Innes, J. E. & Booher, D. E. (2010). Planning with complexity: An introduction to collaborative rationality for public policy. New York: Routledge.

Kania, J. & Kramer, M. (2011). Collective Impact. Stanford Social Innovation Review(Winter 2011), 36-41.

Kania, J. & Kramer, M. (2013). Embracing emergence: How collective impact addresses complexity.  Retrieved fromhttp://www.ssireview.org/blog/entry/embracing_emergence_how_collective_impact_addresses_complexity

Lave, J. & Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation: Cambridge University Press.

Perla, R. J., Provost, L. P. & Parry, G. J. (2013). Seven Propositions of the Science of Improvement: Exploring Foundations. Quality Management in Health Care, 22(3), 170-186. doi:10.1097/QMH.0b013e31829a6a15

Russell, J. L., Bryk, A. S., Dolle, J., Gomez, L. M., LeMahieu, P. & Grunow, A. (in press). A framework for initation of networked improvement communities. Teachers College Record.

Turner, S., Merchant, K., Kania, J. & Martin, E. (2012). Understanding the Value of Backbone Organizations in Collective Impact. Stanford Social Innovation Review.

Wenger, E. (1998). Communities of practice: Learning, meaning, and identity. Cambridge: Cambridge University Press.

Wenger, E., McDermott, R. & Snyder, W. (2002). Cultivating communities of practice: A guide to managing knowledge. Boston, MA: Harvard Business School Press.