Design-Based Research and Design-Based Implementation Research

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.


The original entry was based on an forum discussion on the topic and draws from the contributions of multiple people who participated in the forum.

Design-based research (DBR) and Design-Based Implementation Research (DBIR) are two approaches to bridging research and practice. The approaches, often identified as being part of the learning sciences, specify ways to design studies that go beyond traditional models of how basic research and educational application relate to each other. Education is a complex phenomenon and many research methods (especially experimental methods) require that researchers simplify and isolate variables so that they can be tested.  That simplification can obscure critical features of an educational 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 using problem solving, collaboration, or creativity).

DBR specifically addresses the issue of designing innovation in complex environments. DBR involves the iterative design of a learning experience, with ongoing data collection being used 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 a new learning experience, an equally important goal is the creation of new and potentially generalizable knowledge about learning.

Educational settings such as schools have their own organizational, social, and political contexts that fundamentally shape the uptake and spread of innovation. DBIR is a form of design research that focuses specifically on the implementation of sustainable educational change.

One might think about DBIR as an extension of DBR to include broader attention to organizations and systems. 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 their shared, local context. Teams commit to systematic, iterative, and collaborative testing of interventions. Teams develop a locally relevant and shared theory. The ultimate goal is to do work together in ways that increase capacity for sustaining change in organizations and systems.

Findings from Research and Evaluation 

Although DBR and DBIR have most often been used to study classrooms and school-based learning and teaching, they are now beginning to show up in informal learning settings and are specifically referenced in the solicitation for NSF’s Advancing Informal STEM Learning (AISL)  program.

There is a long tradition of iterative design in informal learning, especially around using formative evaluation to create audience-centric learning experiences [link to formative evaluation resources in the PI guide to evaluation]. This is not the same thing as DBR and DBIR. One difference is that DBR/DBIR focus on creating new knowledge, not just creating a new experience. The knowledge can be a new learning theory, new design principles or heuristics, descriptions of organizational processes, or perhaps evidence-based claims about what works, for whom, and in what conditions.

To accomplish rigorous knowledge building and sharing, DBR/DBIR projects use specific methods guide their exploration. Some examples from the AISL portfolio include:

  • A DBR-like research/practice collaboration at the Exploratorium, where the team used iterative quasi-experimental design to identify generalizable features of exhibits that can support active prolonged engagement by visitors.
  • The Oregon Museum of Science and Industry used an iterative theory of action framework for a DBR project that explored features that make for effective interactive exhibits in everyday settings such as transit stops.
  • Carnegie Mellon University is currently using Sandoval’s (2014) Conjecture Mapping approach in a DBR project that explore ways for technology to support observation and identification skills of citizen scientists.
  • Please consider adding your DBR/DBIR project to this list!

Directions for Future Research 

These would come from some of the comments on the forum where people dropped questions for the group – like the WGBH person’s, or Scott P’s.



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Cobb, Paul, Jere Confrey, Andrea diSessa, Richard Lehrer, and Leona Schauble. 2003. “Design Experiments in Educational Research.” Educational Researcher, 32(1): 9–13.

Barab, S. A. (2014). Design-based research: a methodological toolkit for engineering change. In K. Sawyer (ed.) Handbook of the Learning Sciences, Vol 2, (pp. 233-270), Cambridge, MA: Cambridge University Press.

Cobb, P., & Gravemeijer, K. (2008). Experimenting to support and understand the learning processes. In A. E. Kelly, R. A. Lesh, & J. Y. Baek (Eds.), Handbook of design research methods in education: innovations in science, technology, engineering, and mathematics learning and teaching (pp. 68–95). New York: Routledge.

Lehrer, R., Schauble, L., & Petrosino, A. J.* ( 2001). Reconsidering the role of experiment in science education. In K. Crowley, C. Schunn, & T. Okada (Eds.). Designing for science: Implications from everyday, classroom, and professional settings (pp. 251-277). Mahwah, NJ: Lawrence Erlbaum Associates.

Sandoval, W. (2014). Conjecture mapping: An approach to systematic educational design research. Journal of the Learning Sciences, 23, 18–36.

Penuel, W.R., Fishman, B.J., Cheng, B.H., & Sabelli, N. (2011). Organizing Research and Development at the Intersection of Learning, Implementation, and Design. Educational Researcher 40(7): 331-337.