What’s the Difference between Design-Based Research and Design-Based Implementation Research?
You may have experienced occasional confusion between the terms Design-Based Research (DBR) and Design-Based Implementation Research (DBIR). These two approaches, springing from the field of the learning sciences, share a common goal and common history. But there are key distinctions.
The common goal of DBR and DBIR is to guide education beyond traditional models of how basic research is related to its application. Traditional research methods require one to simplify and isolate variables so that they can be “rigorously” tested. Isolating variables can feel neat and clean, but the simplification that’s required can obscure critical features of a learning environment (such as social or contextual features) and can also focus researchers on what can be easily measured (such as recall of knowledge) rather than what really matters (such as problem solving, collaboration, or creativity). Thus, we need an approach that can capture the complexity of education and learning.
DBR specifically addresses the issue of designing innovation in complex environments. DBR involves the iterative design of a learning experience, using ongoing data collection to help refine the design and to understand why certain features are working while others are not. While one goal of DBR is to create an innovative learning experience, an equally important goal is to create new and potentially generalizable knowledge and theory about learning. That knowledge often takes the form of design principles that can be used to guide new innovation.
DBIR grew out of the DBR tradition and is specifically concerned with how innovations get implemented and taken up in educational systems. Educational settings in and out of school have their own organizational, social, and political contexts that fundamentally shape the uptake and spread of innovation. In DBIR, teams of researchers and practitioners work in equal-status partnerships to address persistent problems of practice. The partnership identifies focal problems together, paying close attention to their shared, local context. Teams commit to systematic, iterative, and collaborative testing of interventions and often develop a locally relevant and shared theory. The ultimate goal is to work together in ways that increase capacity for sustaining change in organizations and systems.
Guidance for NSF AISL Proposers
If you are developing a proposal that involves DBR or DBIR to submit to the National Science Foundation’s (NSF’s) Advancing Informal STEM Learning (AISL) program, consider the following:
Since Research in Service to Practice or Innovations in Development project types are earlier stages of the development cycle, and the primary outcomes are often building the research knowledge base and developing promising new interventions and educational approaches, consider a DBR approach for these projects.
In contrast, DBIR is most appropriate for Broad Implementation projects. The routines and processes of strong DBIR work focus on guiding implementation and innovation. They are most appropriate when innovative approaches for designing STEM experiences need to consider the complexity of real-world situations, or when practices that are proven to work in a particular setting or under a particular set of circumstances are intended to be expanded to other settings and circumstances. Good DBIR work will still provide the opportunity to build knowledge, but what we learn most from DBIR approaches is how to spread and scale educational improvement in ways that make it relevant, adaptable, and sustainable.
The Common Guidelines for Education Research and Development, developed by the NSF and the Department of Education’s Institute for Education Services, is a helpful document for thinking about the different stages of research for your project.
Disclaimer: I do not represent NSF, nor am I speaking on behalf of AISL program officers. This guidance is my opinion based on my professional experience in education and learning sciences research. Please also note that DBR and DBIR are two of many types of educational and research methods.
Key Takeaways for Everyone
- Iteration is key to both DBR and DBIR. Strong proposals include plans for how iterative design/implementation will be guided and informed by data.
- DBR and DBIR are best rooted in persistent problems of practice. Strong DBIR proposals outline how practitioners, researchers, and/or evaluators will work together with complementary and equal roles throughout the whole project. Strong DBR proposals often do the same, or at least make a convincing case that the project is focusing on authentic, persistent problems of practice in informal STEM learning.
Although DBR and DBIR proposals may not be driven by traditional “research questions,” they are still guided by inquiry, adhere to specific research-based expectations, and maintain rigorous processes of iteration and collaboration. There are many examples in the DBR and DBIR literature to help guide proposers in using concepts and tools that are appropriate to one of these approaches. Here’s a few examples to get you started:
- A 2017 study, Researching the Value of Educator Actions for Learning (REVEAL), used DBR to iteratively refine a model of staff facilitation to support family learning at interactive math exhibits.
- An 2019 project, ChemAttitudes, used DBR to develop hands-on educational chemistry activities, based on a theoretical framework about affecting attitudes about science related to interest, relevance, and self-efficacy, while also testing and modifying the framework itself.
- A recently awarded study, Head Start on Engineering, is using DBIR to refine and improve an early learning program that develops and supports family-level Interest in engineering.
- Another recently awarded project is using DBIR to develop facilitation tools that can support learning in informal contexts.
- PEEP Family Science, which recently ended, used DBIR to study a family learning toolkit with digital and hands-on science learning resources.
Pictured: The PEEP Family Science project poster from the 2016 NSF AISL Principal Investigators Meeting.