An Introduction to Design-Based Research with an Example From Statistics Education SpringerLink
Table Of Content
Educational researchers—as a source of potential expertise—are necessarily implicated in this complexity, linked to the communities and institutions through their very presence in spaces of learning, poised to contribute with possible solutions, yet often positioned as separate from the activities they observe, creating dilemmas of responsibility and engagement. These two types of design research, both DBR and IDR, share a common genesis among the design revolution of the 1960s, where designers, researchers, and scholars sought to elevate design from mere practice to an independent scholarly discipline, with its own research and distinct theoretical and methodological underpinnings. A scholarly focus on design methods, they argued, would foster the development of design theories, which would in turn improve the quality of design and design practice (Margolin, 2010). Research on design methods, termed design research, would be the foundation of this new discipline.
Appendix: Structure of a DBR Project with Illustrations
"Reinforcement learning is an ideal approach because you just need to know how quickly the bacteria are growing, which is relatively easy to determine," explains Dr. Weaver. "There's also room for human variations and errors. You don't need to measure the growth rates perfectly down to the exact millisecond every time." Study co-author Jeff Maltas, PhD, a postdoctoral fellow at Cleveland Clinic, uses computer models to predict how a bacterium's resistance to one antibiotic will make it weaker to another. He teamed up with Dr. Weaver to see if data-driven models could predict drug cycling regimens that minimize antibiotic resistance and maximize antibiotic susceptibility, despite the random nature of how bacteria evolve. One strategy physicians are using to modernize the way we treat bacterial infections is antibiotic cycling.
Contents
One way design-based researchers address this concern is by “specifying theoretically salient features of a learning environment design and mapping out how they are predicted to work together to produce desired outcomes” (Sandoval, 2014, p. 19). Through this process, researchers explicitly show before they begin the work how their theory of learning is embodied in the instructional tools to be tested, the specific data the tools will produce for analysis, and what outcomes will be taken as evidence for success. Consequently, modifying instructional tools midstream to account for these unanticipated factors can ensure they retain their methodological alignment with the underlying theory and predicted learning outcomes so that inferences drawn from the design experiment accurately reflect what was being tested (Edelson, 2002; Hoadley, 2004). Indeed, Barab (2014) states, “the messiness of real-world practice must be recognized, understood, and integrated as part of the theoretical claims if the claims are to have real-world explanatory value” (p. 153). Given that the nature of DBR is to design and implement some form of educational innovation, the DBR researcher will in some way be engaging with an individual or community, becoming part of a situated learning ecology, complete with a sociopolitical and cultural history. As with any research that involves providing an intervention or support, the question of what happens when the research ends is as much an ethical as a methodological one.
Role of DBR Researcher
This response stems from Messick (1992) and Schoenfeld’s (1992) arguments early on in the development of DBR that the consequentialness and validity of DBR efforts as potentially generalizable research depend on the “usefulness” of the theories and designs that emerge. Effectively, because DBR is the examination of situated theory, a design must be able to show pragmatic impact—it must succeed at showing the theory to be useful. If there is evidence of usefulness to both the context in which it takes place, and the field of educational research more broadly, then the DBR researcher can stake some broader knowledge claims that might be generalizable. As a result, the DBR paradigm tends to “treat changes in [local] contexts as necessary evidence for the viability of a theory” (Barab & Squire, 2004, p. 6). A design that fails or struggles can provide important information and knowledge to the field. Ultimately, though, DBR tends to privilege work that proves the usefulness of designs, whose pragmatic or theoretical findings can then be generalized within the learning science and education research fields.
Researcher as theorist
Consequently, the design principles must balance specificity with adaptability so they can be used broadly to inform instruction (Collins et al., 2004; Barab, 2014). Much like general processes of design, I think that DBR draws part of its strength from its adaptability. Researchers employing DBR usually remain adaptive to emerging needs and insights from the research context and/or changes in their own theoretical framing of their work. This is, in fact, part of the reason that researchers in the early 1990’s began to justify the need for a change of research paradigms in education that has lead to popular understandings of DBR. For instance, researchers such as Brown (1992) and Collins (1992) began to present the case for what they called “design experiments”.
Melissa: Should DBR Even Exist?
Typically, IDR scholars have focused on the relationship between design and research, as well as the underlying purpose, to define the approach. This section identifies three defining conceptualizations of IDR—the prepositional approach trinity, Cross’s -ologies, and Buchanan’s strategies of productive science—and discusses possible implications for DBR. Literature also appears to agree that a DBR process does not consist of a linear design process, but rather multiple cycles of design, testing, and revision (Anderson & Shattuck, 2012; Barab & Squire, 2004; Brown, 1992; Design-Based Research Collective, 2003; Shavelson et al., 2003). These iterations must also represent systematic adjustment of the design, with each adjustment and subsequent testing serving as a miniature experiment (Barab & Squire, 2004; Collins, 1992). Reflection on how the study was conducted allowed the researchers to properly place their experiences within the context of existing research.
Ethnographic study
Moreover, while studies involving these traditional methodologies often concluded by pointing toward implications—insights subsequent studies would need to take up—DBR allowed researchers to address implications iteratively and directly. No subsequent research was necessary, as emerging implications could be reflexively explored in the context of the initial design, offering considerable insight into how research is translated into theory and practice. Finally, and importantly, experimental approaches and design-based research differ in the kinds of conclusions they draw from their data. Experimental research can “identify that something meaningful happened; but [it is] not able to articulate what about the intervention caused that story to unfold” (Barab, 2014, p. 162).
Foundations of Learning and Instructional Design Technology
Course design process in a technology-enhanced learning environment - ScienceDirect.com
Course design process in a technology-enhanced learning environment.
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This chapter will provide a brief overview of the origin, paradigms, outcomes, and processes of design-based research (DBR). In these sections we explain that (a) DBR originated because some researchers believed that traditional research methods failed to improve classroom practices, (b) DBR places researchers as agents of change and research subjects as collaborators, (c) DBR produces both new designs and theories, and (d) DBR consists of an iterative process of design and evaluation to develop knowledge. The last “epistemic commitment” Sandoval (2014) articulated was that design-based research be an iterative process with an eye toward continually refining the instructional tools, based on evidence of student learning, to produce more robust learning environments. By viewing educational inquiry as formative research, design-based researchers recognize the difficulty in accounting for all variables that could impact student learning, or the implementation of the instructional tools, a priori (Collins et al., 2004).
A resulting recommendation, then, is that, in published works, scholars begin articulating which of these approaches they are using in that particular study. This can simplify the requirements on DBR researchers, because instead of feeling the necessity of doing all three in every paper, they can emphasize one. Jonas (2007) identified research into design as the most prevalent—and straightforward—form of IDR.
This analysis helps to provide an understanding of the context within which to execute an intervention. It is important to recognize that DBR is not only concerned with improving practice but also aims to advance theory and understanding (Collins et al., 2004). DBR’s emphasis on the importance of context enhances the knowledge claims of the research. “Researchers investigate cognition in context...with the broad goal of developing evidence-based claims derived from both laboratory-based and naturalistic investigations that result in knowledge about how people learn” (Barab & Squire, 2004, p.1).
This approach aligns with theoretical work in the learning sciences that indicates that providing students with conceptual frameworks improves their ability to integrate and retrieve knowledge (National Academies of Sciences, Engineering, and Medicine, 2018). Many BER studies employ experimental approaches that align with traditional scientific methods of experimentation, such as using treatment versus control groups, randomly assigning treatments to different groups, replicating interventions across multiple spatial or temporal periods, and using statistical methods to guide the kinds of inferences that arise from an experiment. While design-based research can similarly employ these strategies for educational inquiry, there are also some notable differences in its approach to experimentation (Collins et al., 2004; Hoadley, 2004). In this section, we contrast the differences between design-based research and what we call “experimental approaches,” although both paradigms represent a form of experimentation.
This review presents a comprehensive overview of recent advancements in the understanding of the mechanisms of water oxidation using transition metal-based heterogeneous electrocatalysts, including Mn, Fe, Co, Ni, and Cu-based catalysts. It highlights the catalytic mechanisms of different transition metals and emphasizes the importance of monitoring of key intermediates to explore the reaction pathway. In addition, advanced techniques for physical characterization of water oxidation intermediates are also introduced, for the purpose of providing information for establishing reliable methodologies in water oxidation research. The study of transition metal-based water oxidation electrocatalysts is instrumental in providing novel insights into understanding both natural and artificial energy conversion processes. In some cases, these types coincide with quantitative and qualitative research designs respectively,[6] though this need not be the case. In fixed designs, the design of the study is fixed before the main stage of data collection takes place.
Attending to these challenges is an important part of forming the design team and identifying the different roles researchers and instructors will play in the research. One recommendation is that DBR scholars adopt these as the characteristics of their work that they will make explicit in every published paper so that DBR articles can be recognized by readers and better aggregated together to show the value of DBR over time. One suggestion is that DBR scholars in their methodology sections could adopt these characteristics as subheadings. Also in the concluding sections, in addition to discussing research results, scholars would discuss Applications to Theory (perhaps dividing into Local Theory and Outcomes and Transferable Theory and Findings) and Applications for Practice.
One way of understanding the focus of design-based research is in terms of Pasteur's Quadrant (Stokes, 1997; see Figure 10.1). In this quadrant model for characterizing scientific research, the upper-left-hand cell consists of basic research for the sole purpose of understanding without an eye toward practical use. An example flux assessment question about ion flows given in a pre-unit/post-unit formative assessment in the neurophysiology unit. The researchers also introduce an exceptionally precise Spectre-style poisoning attack, enabling attackers to induce intricate patterns of branch mispredictions within victim code. “This manipulation leads the victim to execute unintended code paths, inadvertently exposing its confidential data,” said UC San Diego computer science Professor Dean Tullsen. To prepare for this discussion, Melissa and Luis each provide their perspective on design-based research and implications for research in design and education.
The DBR paradigm generally, and critical and CHAT iterations particularly, can fill an important need for participatory, theory-developing research in these contexts that simultaneously creates lived impacts. Related to this question of sustainability are internal concerns regarding the nature and ethics of participation in DBR, whether partners in a design are being adequately invited to engage in the design and modification processes that will unfold in their situated contexts and lived communities (Bang et al., 2016; Engeström, 2011). DBR has actively sought to examine multiple planes of analysis in learning that might be occurring in a learning ecology but has rarely attended to the subject-subject dynamics (Bang et al., 2016), or “relational equity” (DiGiacomo & Gutiérrez, 2015) that exists between researchers and participants as a point of focus.
We used our ion flux scoring rubrics to create a preliminary five-level learning progression framework (Table 1). The framework describes how students’ ideas about flux often start with teleological-driven accounts at the lowest level (i.e., level 1), shift to focusing on driving forces (e.g., concentration gradients, electrical gradients) in the middle levels, and arrive at complex ideas that integrate multiple interacting forces at the higher levels. However, our flux conceptual framework was largely based on student responses to our ion flux assessment items. Therefore, to further validate our learning progression framework, we needed a greater diversity of flux assessment items that investigated student thinking more broadly (i.e., about bulk flow, water movement) across physiological systems. This chapter has provided a brief overview of the origin, paradigms, outcomes, and processes of Design-Based Research (DBR). We explained that (a) DBR originated because some researchers believed that traditional research methods failed to improve classroom practices, (b) DBR places researchers as agents of change and research subjects as collaborators, (c) DBR produces both new designs and theories, and (d) DBR consists of an iterative process of design and evaluation to develop knowledge.
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