There is growing recognition of the need for international development organizations to support localization through a catalytic, yet indirect, role. Results for Development (R4D) and others have transitioned from being ‘doers’ to assuming intermediary or facilitator roles. One of R4D’s signature approaches — Collaborative Learning — is a powerful tool for systems strengthening and locally led development. But the intangible nature of Collaborative Learning makes it difficult to measure its contribution to impact. How do we effectively assess the contributions of global development efforts to long-term systems change? Many in our field are asking similar questions about how to measure the hard to measure. Below are answers to frequently asked questions about Collaborative Learning, locally led development and how to measure impact.
What is Collaborative Learning?
Collaborative Learning is a peer-to-peer learning approach in which individuals work as a group to discuss challenges, jointly solve problems and co-create knowledge products. This approach is used in many fields including education and business. Results for Development (R4D) is applying Collaborative Learning to the field of international development as an important part of how we support locally- led development that leads to lasting systems-level change.
R4D’s Collaborative Learning Networks convene a set of change agents (government officials, sector experts, civil society actors, development partners) from different countries or regions around a shared purpose to learn from each other and co-create knowledge and tools to address challenges of common interest. The network supports members to adapt and translate the knowledge and tools to their specific contexts for use in governance, decision-making and implementation.
What are the benefits of Collaborative Learning?
Collaborative Learning is beneficial when there is a recognized common challenge and a common understanding of what needs to be done to address the challenge, but uncertainty about how to implement the required action. Countries may have access to global guidance and evidence but often express that they do not have access to the necessary practical “how-to” implementation knowledge that comes from the experiences of other countries.
Collaborative Learning complements traditional modes of learning (such as expert-driven Technical Assistance or one-off participatory learning events) in that it deliberately takes a collaborative approach to knowledge creation and learning that is participant-led and enables peers to help each other on an ongoing basis. It involves collaborative action- or implementation-oriented learning across geographies or sectors over a longer period of time (often organized in two-year cycles). The co-development of strategies, tools and knowledge products leads to increased ownership and uptake of the knowledge and best practices by those who were part of the process. And since networks work directly with relevant in-country stakeholders and institutions, they help develop the supportive ecosystems needed to bring about long-term systems reform. Collaborative Learning Networks contribute to the global evidence base by documenting country experience and lessons learned to benefit others far beyond the network. Collaborative Learning is also an important way for funders to connect with countries that they may not be able to otherwise — and through a relatively low-cost approach. These connections are a critical way for funders to center the perspectives and the priorities of local actors, ensuring the development agenda is driven by locally defined needs and goals.
How does Collaborative Learning support locally led development?
Collaborative Learning — unlike traditional approaches to technical assistance — centers the expertise of local change agents and captures the valuable tacit knowledge of practitioners to advance systems change. Traditional technical assistance models have a history of being one-directional, positioning experts from higher income countries as advisors to leaders in low- and middle-income countries. In contrast, Collaborative Learning Networks are country-led. They provide a structured process in which change agents determine priorities, set the learning agenda, identify and co-develop strategies and promising practices, and shape the future direction of the Collaborative Learning community.
What is the Collaborative Learning Theory of Change?
In the international development context, a ‘Theory of Change’ is a tool that describes an organization’s hypothesis, or theory, about how a program will contribute to change in a given context or system. Thus, the Theory of Change describes what an organization believes will happen or the results they expect to see from a given program. Since these theories are made up of beliefs and assumptions, it’s important to test those assumptions throughout the lifecycle of the program. In that way, the Theory of Change can serve as a roadmap, guiding teams through the suite of learning questions they will need to answer to ensure their program is progressing along the desired pathway to change.
The Collaborative Learning Theory of Change is a visual diagram that describes our hypothesis for how Collaborative Learning Networks contribute to shorter-term outputs and outcomes, as well as longer-term systems-level outcomes and impacts. The Collaborative Learning Network Theory of Change traces these impacts at the ecosystem level, country level and global level.
In the context of international development, the Collaborative Learning Theory of Change hypothesizes that Collaborative Learning Networks lead to:
- Stronger leadership and capacity of network members
- Stronger ecosystems of ongoing support for change agents
- New knowledge and evidence being generated, adapted and implemented at the country level while also increasing global knowledge and awareness
- Long term systems improvements for healthier and better educated people
Why assess the impact of Collaborative Learning?
Since our founding in 2008, R4D has launched or strengthened over 20 Collaborative Learning Networks. As R4D’s portfolio of networks has expanded, so has the need to measure their effectiveness. R4D has developed the Collaborative Learning Measurement & Learning Framework to guide network managers and partners through a systematic assessment of the contributory impact of Collaborative Learning Networks. Doing so allows them to:
- Generate robust evidence of network performance and contribution to impact
- Objectively learn what is and isn’t working well and adapt to improve outcomes
- Show donors and funders that Collaborative Learning contributes to significant global and local impacts in order to help attract more and new funders
- Generate persuasive and evidence-based communications to increase awareness of and buy-in for Collaborative Learning
- Demonstrate the added value of Collaborative Learning to global policymakers and contribute to a global body of knowledge that promotes and strengthens capacity for the approach
What are the challenges of assessing Collaborative Learning’s impact?
Collaborative Learning Networks are complex and measuring their impact is inherently challenging. Prior to R4D’s Measurement & Learning Framework, assessment has often included monitoring outputs for the sake of reporting to funders and gathering anecdotal feedback from network members on their successes without always being able to link that success back to the networks.
Producing good evidence of contributions to impact requires rich data collection and analysis that goes beyond anecdotal evidence. But it is challenging to do so in programs that aim for systemic change (with many interactive and often unpredictable influences) like Collaborative Learning Networks. Assessing contributions to impact for a Collaborative Learning Network requires a different approach than for programs that aim for linear change with clear and direct relationships between cause and effect.
How do you measure impact?
The impacts of Collaborative Learning Networks are often the result of what members do outside the network – the initiatives they are inspired to lead, the connections they choose to collaborate with, and how they adapt and implement lessons learned into their ongoing work. In short, the catalytic work of Collaborative Learning Networks contributes indirectly to many results seen at the systems level. Therefore, to measure impact, it is necessary to look beyond the network itself to see how it has contributed to wider systems strengthening.
Also, measuring impact is not just about numbers. The complexity and non-linear nature of systems change work means that it cannot be summed up in a neat indicator. This is why complexity-aware methods that draw on a combination of qualitative and quantitative data are most useful for unpacking a Collaborative Learning Network’s lengthy chain of results. Choosing the correct measurement and learning methodologies is essential to effectively measure impact.
It is also important that judgements about impact center the views and perspectives of all stakeholders, particularly those change agents who are living/working in the system of focus. There are several ways to prioritize and collect inclusive evidence, many of which apply participatory, group-based dialogue and analysis tools that help to create a more in-depth understanding of systems change.
To learn more visit the Collaborative Learning Networks Measurement & Learning Framework.
