Design-Based Implementation Research (DBIR) was developed by education researchers in response to evidence that research-based innovations are often difficult to sustain or use at scale in real-world classrooms and schools, even when they proved effective in small-scale studies. DBIR emphasizes design and research specifically focused on the issues of broader implementation.
Each new environment into which an innovation is introduced has distinctive characteristics, constraints, and priorities. No single innovation works for all stakeholders in all settings. DBIR practitioners pioneered the use of collaborative design methodologies, particularly Design-Based Research (DBR), to address the challenges of implementing innovations in complex systems. DBIR practitioners collaborate with a broad group of stakeholders to create a shared understanding of the local context and use implementation evidence to iteratively design the innovation so it addresses the needs and conditions in that context. Four principles underlie DBIR:
- A focus on persistent problems of practice, as experienced from multiple stakeholders’ perspectives;
- A commitment to iterative, collaborative design in realistic contexts;
- A concern with developing theory and knowledge related to both classroom learning and implementation through systematic inquiry; and
- A concern with developing capacity for sustaining change in educational systems.
DBIR does not specify a particular method or analytic approach, recognizing that a range of different methods is appropriate in different circumstances and in different phases of the innovation research and development lifecycle.
Key lessons from cyberlearning research:
- Researchers engaged in DBIR not only investigate and build theory about an innovation’s impact on learning, but also about how and why an innovation is implemented differently in different settings.
- DBIR researchers use techniques such as program mapping to understand the organizational structures of the settings into which innovations are introduced.
- Researchers engaged in DBIR not only iteratively and collaboratively design programs with stakeholders, but they also iteratively and collaboratively design the implementation plan, the outcomes, and the measures.
- Scaling an innovation requires building capacity within systems to sustain that innovation and transfer ownership to local stakeholders, using techniques such as establishing professional routines and supporting communication networks.
- By looking at implementation variation across multiple contexts, DBIR projects can inform policymaking at the system level, which can enhance sustainability.
DBIR shares many practices with participatory design and design-based research but has a greater focus on identifying problems of practice from multiple stakeholders’ perspectives and developing strategies to sustain innovations.
DBIR shares aspects of its approach with Implementation Research, but with a greater focus on using results to inform design.
DBIR draws on multiple methods. DBIR requires selecting methods to best answer the specific research questions.
Examples of NSF Cyberlearning projects that overlap with topics discussed in this primer (see project tag map).
- NetStat: EAGER: A Representation and Communication Infrastructure for Classroom Collaboration in Data Modeling and Statistics
- EXP: Inclusive Design for Engaging All Learners (IDEAL): Designing Technology for Cultural Brokering
- CAREER: Designing a New Nexus: Examining the Social Construction of Electronics and Computing Toolkits to Broaden Participation and Deepen Learning
- EXP: Collaborative Research: Perception and Production in Second Language: The Roles of Voice Variability and Familiarity
- Towards Virtual Worlds that Afford Knowledge Integration Across Project Challenges and Disciplines
More posts: design-based-research
Design as Scholarship: Case Studies from the Learning Sciences, edited by Vanessa Svihla & Richard Reeve. For researchers in the Learning Sciences, there is sparse literature how we actually go about designing. Design as Scholarship: Case Studies from the Learning Sciences addresses this need by providing design stories of how researchers actually do their work—how they identified and met needs, how they collaborated across disciplinary boundaries, and how they took advantage of emergence or opportunism in their work. The book includes chapters on designing technologies for learning, community co-design, and more.
References and key readings documenting the thinking behind the concept, important milestones in the work, foundational examples to build from, and summaries along the way.
Fishman, B., Penuel, W. R., Allen, A., & Cheng, B. H., & Sabelli, N. (2013). Design-Based Implementation Research: An Emerging Model for Transforming the Relationship of Research and Practice. In Fishman, Penuel, Allen, & Cheng (Eds.), Design-based implementation research: Theories, methods, and exemplars. National Society for the Study of Education Yearbook, Vol. 112(2), pp. 136-156. New York: Teachers College Record.
Penuel, W., Fishman, B., Cheng, B., & Sabelli, N., (2011). Organizing research and development at the intersection of learning, implementation, and design. Educational Researcher, 40(7), 331–337.
Bryk, A. S., Gomez, L. M., & Grunow, A. (2011). Getting ideas into action: Building networked improvement communities in education. Stanford, CA: Carnegie Foundation for the Advancement of Teaching.
Publications from NSF-funded Cyberlearning Projects
Clarke, P. J., Pava, J., Davis, D., Hernandez, F., & King, T. M. (2012). Using WReSTT in SE courses: An empirical study. In Proceedings of the 43rd ACM Technical Symposium on Computer Science Education, (pp. 307-312). New York, New York: Special Interest Group in Computer Science Education.
Goswami, A., Walia, G. S., & Abufardeh, S. (2014). Using a Web-Based Testing Tool Repository in Programming Course: An Empirical Study. In Proceedings of the International Conference on Frontiers in Education: Computer Science and Computer Engineering (p. 1). Las Vegas, Nevada: Computer Engineering and Applied Computing.
Clarke, P., Davis, D., Lau, R., King, T. (2014). Student Learning and Use of Tools in an Undergraduate Software Testing Class. In Proceedings of the 121st ASEE Annual Conference & Exposition. Indianapolis, IN: American Society for Engineering Education.
Ding, M. (2016). Developing preservice elementary teachers’ specialized content knowledge: the case of associative property. International Journal of STEM Education, Vol. 3(1), pp. 1.
Johnson, R., Severance, S., Leary, H., Miller, S. (2014). Mathematical tasks as boundary objects in design-based implementation research. In Polman, J. L., Kyza, E. A., O’Neill, D. K., Tabak, I., Penuel, W. R., Jurow, A. S., O’Connor, K., Lee, T., and D’Amico, L. (Eds.), Learning and becoming in practice: The International Conference of the Learning Sciences (ICLS) 2014, Vol. 2 (pp. 1127-1131). Boulder, CO: International Society of the Learning Sciences.
Johnson, R., Leary, H., Severance, S., Penuel, W. R., Sumner, W., Devaul, H., & Dibie, O. (2014, November). Capacity for Customization: Algebra Teachers, Curriculum Design, and Common Core. Poster session presented to University of Colorado Academic Affairs, Boulder, CO.
Hellmann, J. D. (2015). DataSnap: Enabling Domain Experts and Introductory Programmers to Process Big Data in a Block-Based Programming Language (Doctoral dissertation). Retrieved from Virginta Tech Elextronic Theses and Dissertations. (16249). Retrieved from http://hdl.handle.net/10919/54544.
Primers are developed by small teams of volunteers and licensed under a Creative Commons Attribution 4.0 International License.
Fishman, B., Cheng, B., & Penuel, W. (2014). CIRCL Primer: Design-Based Implementation Research. In CIRCL Primer Series. Retrieved from http://circlcenter.org/dbir/
After citing this primer in your text, consider adding: “Used under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).”