design based action research

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Design-Based Research: A Methodology to Extend and Enrich Biology Education Research

• Emily E. Scott
• Mary Pat Wenderoth
• Jennifer H. Doherty

*Address correspondence to: Emily E. Scott ( E-mail Address: [email protected] ).

Department of Biology, University of Washington, Seattle, WA 98195

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Recent calls in biology education research (BER) have recommended that researchers leverage learning theories and methodologies from other disciplines to investigate the mechanisms by which students to develop sophisticated ideas. We suggest design-based research from the learning sciences is a compelling methodology for achieving this aim. Design-based research investigates the “learning ecologies” that move student thinking toward mastery. These “learning ecologies” are grounded in theories of learning, produce measurable changes in student learning, generate design principles that guide the development of instructional tools, and are enacted using extended, iterative teaching experiments. In this essay, we introduce readers to the key elements of design-based research, using our own research into student learning in undergraduate physiology as an example of design-based research in BER. Then, we discuss how design-based research can extend work already done in BER and foster interdisciplinary collaborations among cognitive and learning scientists, biology education researchers, and instructors. We also explore some of the challenges associated with this methodological approach.

INTRODUCTION

There have been recent calls for biology education researchers to look toward other fields of educational inquiry for theories and methodologies to advance, and expand, our understanding of what helps students learn to think like biologists ( Coley and Tanner, 2012 ; Dolan, 2015 ; Peffer and Renken, 2016 ; Lo et al. , 2019 ). These calls include the recommendations that biology education researchers ground their work in learning theories from the cognitive and learning sciences ( Coley and Tanner, 2012 ) and begin investigating the underlying mechanisms by which students to develop sophisticated biology ideas ( Dolan, 2015 ; Lo et al. , 2019 ). Design-based research from the learning sciences is one methodology that seeks to do both by using theories of learning to investigate how “learning ecologies”—that is, complex systems of interactions among instructors, students, and environmental components—support the process of student learning ( Brown, 1992 ; Cobb et al. , 2003 ; Collins et al. , 2004 ; Peffer and Renken, 2016 ).

The purpose of this essay is twofold. First, we want to introduce readers to the key elements of design-based research, using our research into student learning in undergraduate physiology as an example of design-based research in biology education research (BER). Second, we will discuss how design-based research can extend work already done in BER and explore some of the challenges of its implementation. For a more in-depth review of design-based research, we direct readers to the following references: Brown (1992) , Barab and Squire (2004) , and Collins et al. (2004) , as well as commentaries by Anderson and Shattuck (2012) and McKenney and Reeves (2013) .

WHAT IS DESIGN-BASED RESEARCH?

Design-based research is a methodological approach that aligns with research methods from the fields of engineering or applied physics, where products are designed for specific purposes ( Brown, 1992 ; Joseph, 2004 ; Middleton et al. , 2008 ; Kelly, 2014 ). Consequently, investigators using design-based research approach educational inquiry much as an engineer develops a new product: First, the researchers identify a problem that needs to be addressed (e.g., a particular learning challenge that students face). Next, they design a potential “solution” to the problem in the form of instructional tools (e.g., reasoning strategies, worksheets; e.g., Reiser et al. , 2001 ) that theory and previous research suggest will address the problem. Then, the researchers test the instructional tools in a real-world setting (i.e., the classroom) to see if the tools positively impact student learning. As testing proceeds, researchers evaluate the instructional tools with emerging evidence of their effectiveness (or lack thereof) and progressively revise the tools— in real time —as necessary ( Collins et al. , 2004 ). Finally, the researchers reflect on the outcomes of the experiment, identifying the features of the instructional tools that were successful at addressing the initial learning problem, revising those aspects that were not helpful to learning, and determining how the research informed the theory underlying the experiment. This leads to another research cycle of designing, testing, evaluating, and reflecting to refine the instructional tools in support of student learning. We have characterized this iterative process in Figure 1 after Sandoval (2014) . Though we have portrayed four discrete phases to design-based research, there is often overlap of the phases as the research progresses (e.g., testing and evaluating can occur simultaneously).

FIGURE 1. The four phases of design-based research experienced in an iterative cycle (A). We also highlight the main features of each phase of our design-based research project investigating students’ use of flux in physiology (B).

Design-based research has no specific requirements for the form that instructional tools must take or the manner in which the tools are evaluated ( Bell, 2004 ; Anderson and Shattuck, 2012 ). Instead, design-based research has what Sandoval (2014) calls “epistemic commitments” 1 that inform the major goals of a design-based research project as well as how it is implemented. These epistemic commitments are: 1) Design based research should be grounded in theories of learning (e.g., constructivism, knowledge-in-pieces, conceptual change) that both inform the design of the instructional tools and are improved upon by the research ( Cobb et al. , 2003 ; Barab and Squire, 2004 ). This makes design-based research more than a method for testing whether or not an instructional tool works; it also investigates why the design worked and how it can be generalized to other learning environments ( Cobb et al. , 2003 ). 2) Design-based research should aim to produce measurable changes in student learning in classrooms around a particular learning problem ( Anderson and Shattuck, 2012 ; McKenney and Reeves, 2013 ). This requirement ensures that theoretical research into student learning is directly applicable, and impactful, to students and instructors in classroom settings ( Hoadley, 2004 ). 3) Design-based research should generate design principles that guide the development and implementation of future instructional tools ( Edelson, 2002 ). This commitment makes the research findings broadly applicable for use in a variety of classroom environments. 4) Design-based research should be enacted using extended, iterative teaching experiments in classrooms. By observing student learning over an extended period of time (e.g., throughout an entire term or across terms), researchers are more likely to observe the full effects of how the instructional tools impact student learning compared with short-term experiments ( Brown, 1992 ; Barab and Squire, 2004 ; Sandoval and Bell, 2004 ).

HOW IS DESIGN-BASED RESEARCH DIFFERENT FROM AN EXPERIMENTAL APPROACH?

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.

The first difference between an experimental approach and design-based research regards the role participants play in the experiment. In an experimental approach, the researcher is responsible for making all the decisions about how the experiment will be implemented and analyzed, while the instructor facilitates the experimental treatments. In design-based research, both researchers and instructors are engaged in all stages of the research from conception to reflection ( Collins et al. , 2004 ). In BER, a third condition frequently arises wherein the researcher is also the instructor. In this case, if the research questions being investigated produce generalizable results that have the potential to impact teaching broadly, then this is consistent with a design-based research approach ( Cobb et al. , 2003 ). However, when the research questions are self-reflective about how a researcher/instructor can improve his or her own classroom practices, this aligns more closely with “action research,” which is another methodology used in education research (see Stringer, 2013 ).

A second difference between experimental research and design-based research is the form that hypotheses take and the manner in which they are investigated ( Collins et al. , 2004 ; Sandoval, 2014 ). In experimental approaches, researchers develop a hypothesis about how a specific instructional intervention will impact student learning. The intervention is then tested in the classroom(s) while controlling for other variables that are not part of the study in order to isolate the effects of the intervention. Sometimes, researchers designate a “control” situation that serves as a comparison group that does not experience the intervention. For example, Jackson et al. (2018) were interested in comparing peer- and self-grading of weekly practice exams to if they were equally effective forms of deliberate practice for students in a large-enrollment class. To test this, the authors (including authors of this essay J.H.D., M.P.W.) designed an experiment in which lab sections of students in a large lecture course were randomly assigned to either a peer-grading or self-grading treatment so they could isolate the effects of each intervention. In design-based research, a hypothesis is conceptualized as the “design solution” rather than a specific intervention; that is, design-based researchers hypothesize that the designed instructional tools, when implemented in the classroom, will create a learning ecology that improves student learning around the identified learning problem ( Edelson, 2002 ; Bell, 2004 ). For example, Zagallo et al. (2016) developed a laboratory curriculum (i.e., the hypothesized “design solution”) for molecular and cellular biology majors to address the learning problem that students often struggle to connect scientific models and empirical data. This curriculum entailed: focusing instruction around a set of target biological models; developing small-group activities in which students interacted with the models by analyzing data from scientific papers; using formative assessment tools for student feedback; and providing students with a set of learning objectives they could use as study tools. They tested their curriculum in a novel, large-enrollment course of upper-division students over several years, making iterative changes to the curriculum as the study progressed.

By framing the research approach as an iterative endeavor of progressive refinement rather than a test of a particular intervention when all other variables are controlled, design-based researchers recognize that: 1) classrooms, and classroom experiences, are unique at any given time, making it difficult to truly “control” the environment in which an intervention occurs or establish a “control group” that differs only in the features of an intervention; and 2) many aspects of a classroom experience may influence the effectiveness of an intervention, often in unanticipated ways, which should be included in the research team’s analysis of an intervention’s success. Consequently, the research team is less concerned with controlling the research conditions—as in an experimental approach—and instead focuses on characterizing the learning environment ( Barab and Squire, 2004 ). This involves collecting data from multiple sources as the research progresses, including how the instructional tools were implemented, aspects of the implementation process that failed to go as planned, and how the instructional tools or implementation process was modified. These characterizations can provide important insights into what specific features of the instructional tools, or the learning environment, were most impactful to learning ( DBR Collective, 2003 ).

A third difference between experimental approaches and design-based research is when the instructional interventions can be modified. In experimental research, the intervention is fixed throughout the experimental period, with any revisions occurring only after the experiment has concluded. This is critical for ensuring that the results of the study provide evidence of the efficacy of a specific intervention. By contrast, design-based research takes a more flexible approach that allows instructional tools to be modified in situ as they are being implemented ( Hoadley, 2004 ; Barab, 2014 ). This flexibility allows the research team to modify instructional tools or strategies that prove inadequate for collecting the evidence necessary to evaluate the underlying theory and ensures a tight connection between interventions and a specific learning problem ( Collins et al. , 2004 ; Hoadley, 2004 ).

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). In other words, experimental methods are robust for identifying where differences in learning occur, such as between groups of students experiencing peer- or self-grading of practice exams ( Jackson et al. , 2018 ) or receiving different curricula (e.g., Chi et al. , 2012 ). However, these methods are not able to characterize the underlying learning process or mechanism involved in the different learning outcomes. By contrast, design-based research has the potential to uncover mechanisms of learning, because it investigates how the nature of student thinking changes as students experience instructional interventions ( Shavelson et al. , 2003 ; Barab, 2014 ). According to Sandoval (2014) , “Design research, as a means of uncovering causal processes, is oriented not to finding effects but to finding functions , to understanding how desired (and undesired) effects arise through interactions in a designed environment” (p. 30). In Zagallo et al. (2016) , the authors found that their curriculum supported students’ data-interpretation skills, because it stimulated students’ spontaneous use of argumentation during which group members coconstructed evidence-based claims from the data provided. Students also worked collaboratively to decode figures and identify data patterns. These strategies were identified from the researchers’ qualitative data analysis of in-class recordings of small-group discussions, which allowed them to observe what students were doing to support their learning. Because design-based research is focused on characterizing how learning occurs in classrooms, it can begin to answer the kinds of mechanistic questions others have identified as central to advancing BER ( National Research Council [NRC], 2012 ; Dolan, 2015 ; Lo et al. , 2019 ).

DESIGN-BASED RESEARCH IN ACTION: AN EXAMPLE FROM UNDERGRADUATE PHYSIOLOGY

To illustrate how design-based research could be employed in BER, we draw on our own research that investigates how students learn physiology. We will characterize one iteration of our design-based research cycle ( Figure 1 ), emphasizing how our project uses Sandoval’s four epistemic commitments (i.e., theory driven, practically applied, generating design principles, implemented in an iterative manner) to guide our implementation.

Identifying the Learning Problem

Understanding physiological phenomena is challenging for students, given the wide variety of contexts (e.g., cardiovascular, neuromuscular, respiratory; animal vs. plant) and scales involved (e.g., using molecular-level interactions to explain organism functioning; Wang, 2004 ; Michael, 2007 ; Badenhorst et al. , 2016 ). To address these learning challenges, Modell (2000) identified seven “general models” that undergird most physiology phenomena (i.e., control systems, conservation of mass, mass and heat flow, elastic properties of tissues, transport across membranes, cell-to-cell communication, molecular interactions). Instructors can use these models as a “conceptual framework” to help students build intellectual coherence across phenomena and develop a deeper understanding of physiology ( Modell, 2000 ; Michael et al. , 2009 ). 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 ).

Before the start of our design-based project, we had been using Modell’s (2000) general models to guide our instruction. In this essay, we will focus on how we used the general models of mass and heat flow and transport across membranes in our instruction. These two models together describe how materials flow down gradients (e.g., pressure gradients, electrochemical gradients) against sources of resistance (e.g., tube diameter, channel frequency). We call this flux reasoning. We emphasized the fundamental nature and broad utility of flux reasoning in lecture and lab and frequently highlighted when it could be applied to explain a phenomenon. We also developed a conceptual scaffold (the Flux Reasoning Tool) that students could use to reason about physiological processes involving flux.

Although these instructional approaches had improved students’ understanding of flux phenomena, we found that students often demonstrated little commitment to using flux broadly across physiological contexts. Instead, they considered flux to be just another fact to memorize and applied it to narrow circumstances (e.g., they would use flux to reason about ions flowing across membranes—the context where flux was first introduced—but not the bulk flow of blood in a vessel). Students also struggled to integrate the various components of flux (e.g., balancing chemical and electrical gradients, accounting for variable resistance). We saw these issues reflected in students’ lower than hoped for exam scores on the cumulative final of the course. From these experiences, and from conversations with other physiology instructors, we identified a learning problem to address through design-based research: How do students learn to use flux reasoning to explain material flows in multiple physiology contexts?

The process of identifying a learning problem usually emerges from a researcher’s own experiences (in or outside a classroom) or from previous research that has been described in the literature ( Cobb et al. , 2003 ). To remain true to Sandoval’s first epistemic commitment, a learning problem must advance a theory of learning ( Edelson, 2002 ; McKenney and Reeves, 2013 ). In our work, we investigated how conceptual frameworks based on fundamental scientific concepts (i.e., Modell’s general models) could help students reason productively about physiology phenomena (National Academies of Sciences, Engineering, and Medicine, 2018; Modell, 2000 ). Our specific theoretical question was: Can we characterize how students’ conceptual frameworks around flux change as they work toward robust ideas? Sandoval’s second epistemic commitment stated that a learning problem must aim to improve student learning outcomes. The practical significance of our learning problem was: Does using the concept of flux as a foundational idea for instructional tools increase students’ learning of physiological phenomena?

We investigated our learning problem in an introductory biology course at a large R1 institution. The introductory course is the third in a biology sequence that focuses on plant and animal physiology. The course typically serves between 250 and 600 students in their sophomore or junior years each term. Classes have the following average demographics: 68% male, 21% from lower-income situations, 12% from an underrepresented minority, and 26% first-generation college students.

Design-Based Research Cycle 1, Phase 1: Designing Instructional Tools

The first phase of design-based research involves developing instructional tools that address both the theoretical and practical concerns of the learning problem ( Edelson, 2002 ; Wang and Hannafin, 2005 ). These instructional tools can take many forms, such as specific instructional strategies, classroom worksheets and practices, or technological software, as long as they embody the underlying learning theory being investigated. They must also produce classroom experiences or materials that can be evaluated to determine whether learning outcomes were met ( Sandoval, 2014 ). Indeed, this alignment between theory, the nature of the instructional tools, and the ways students are assessed is central to ensuring rigorous design-based research ( Hoadley, 2004 ; Sandoval, 2014 ). Taken together, the instructional tools instantiate a hypothesized learning environment that will advance both the theoretical and practical questions driving the research ( Barab, 2014 ).

In our work, the theoretical claim that instruction based on fundamental scientific concepts would support students’ flux reasoning was embodied in our instructional approach by being the central focus of all instructional materials, which included: a revised version of the Flux Reasoning Tool ( Figure 2 ); case study–based units in lecture that explicitly emphasized flux phenomena in real-world contexts ( Windschitl et al. , 2012 ; Scott et al. , 2018 ; Figure 3 ); classroom activities in which students practiced using flux to address physiological scenarios; links to online videos describing key flux-related concepts; constructed-response assessment items that cued students to use flux reasoning in their thinking; and pretest/posttest formative assessment questions that tracked student learning ( Figure 4 ).

FIGURE 2. The Flux Reasoning Tool given to students at the beginning of the quarter.

FIGURE 3. An example flux case study that is presented to students at the beginning of the neurophysiology unit. Throughout the unit, students learn how ion flows into and out of cells, as mediated by chemical and electrical gradients and various ion/molecular channels, sends signals throughout the body. They use this information to better understand why Jaime experiences persistent neuropathy. Images from: uz.wikipedia.org/wiki/Fayl:Blausen_0822_SpinalCord.png and commons.wikimedia.org/wiki/File:Figure_38_01_07.jpg.

FIGURE 4. An example flux assessment question about ion flows given in a pre-unit/post-unit formative assessment in the neurophysiology unit.

Phase 2: Testing the Instructional Tools

In the second phase of design-based research, the instructional tools are tested by implementing them in classrooms. During this phase, the instructional tools are placed “in harm’s way … in order to expose the details of the process to scrutiny” ( Cobb et al. , 2003 , p. 10). In this way, researchers and instructors test how the tools perform in real-world settings, which may differ considerably from the design team’s initial expectations ( Hoadley, 2004 ). During this phase, if necessary, the design team may make adjustments to the tools as they are being used to account for these unanticipated conditions ( Collins et al. , 2004 ).

We implemented the instructional tools during the Autumn and Spring quarters of the 2016–2017 academic year. Students were taught to use the Flux Reasoning Tool at the beginning of the term in the context of the first case study unit focused on neurophysiology. Each physiology unit throughout the term was associated with a new concept-based case study (usually about flux) that framed the context of the teaching. Embedded within the daily lectures were classroom activities in which students could practice using flux. Students were also assigned readings from the textbook and videos related to flux to watch during each unit. Throughout the term, students took five exams that each contained some flux questions as well as some pre- and post-unit formative assessment questions. During Winter quarter, we conducted clinical interviews with students who would take our course in the Spring term (i.e., “pre” data) as well as students who had just completed our course in Autumn (i.e., “post” data).

Phase 3: Evaluating the Instructional Tools

The third phase of a design-based research cycle involves evaluating the effectiveness of instructional tools using evidence of student learning ( Barab and Squire, 2004 ; Anderson and Shattuck, 2012 ). This can be done using products produced by students (e.g., homework, lab reports), attitudinal gains measured with surveys, participation rates in activities, interview testimonials, classroom discourse practices, and formative assessment or exam data (e.g., Reiser et al. , 2001 ; Cobb et al. , 2003 ; Barab and Squire, 2004 ; Mohan et al. , 2009 ). Regardless of the source, evidence must be in a form that supports a systematic analysis that could be scrutinized by other researchers ( Cobb et al. , 2003 ; Barab, 2014 ). Also, because design-based research often involves multiple data streams, researchers may need to use both quantitative and qualitative analytical methods to produce a rich picture of how the instructional tools affected student learning ( Collins et al. , 2004 ; Anderson and Shattuck, 2012 ).

In our work, we used the quality of students’ written responses on exams and formative assessment questions to determine whether students improved their understanding of physiological phenomena involving flux. For each assessment question, we analyzed a subset of student’s pretest answers to identify overarching patterns in students’ reasoning about flux, characterized these overarching patterns, then ordinated the patterns into different levels of sophistication. These became our scoring rubrics, which identified five different levels of student reasoning about flux. We used the rubrics to code the remainder of students’ responses, with a code designating the level of student reasoning associated with a particular reasoning pattern. We used this ordinal rubric format because it would later inform our theoretical understanding of how students build flux conceptual frameworks (see phase 4). This also allowed us to both characterize the ideas students held about flux phenomena and identify the frequency distribution of those ideas in a class.

By analyzing changes in the frequency distributions of students’ ideas across the rubric levels at different time points in the term (e.g., pre-unit vs. post-unit), we could track both the number of students who gained more sophisticated ideas about flux as the term progressed and the quality of those ideas. If the frequency of students reasoning at higher levels increased from pre-unit to post-unit assessments, we could conclude that our instructional tools as a whole were supporting students’ development of sophisticated flux ideas. For example, on one neuromuscular ion flux assessment question in the Spring of 2017, we found that relatively more students were reasoning at the highest levels of our rubric (i.e., levels 4 and 5) on the post-unit test compared with the pre-unit test. This meant that more students were beginning to integrate sophisticated ideas about flux (i.e., they were balancing concentration and electrical gradients) in their reasoning about ion movement.

To help validate this finding, we drew on three additional data streams: 1) from in-class group recordings of students working with flux items, we noted that students increasingly incorporated ideas about gradients and resistance when constructing their explanations as the term progressed; 2) from plant assessment items in the latter part of the term, we began to see students using flux ideas unprompted; and 3) from interviews, we observed that students who had already taken the course used flux ideas in their reasoning.

Through these analyses, we also noticed an interesting pattern in the pre-unit test data for Spring 2017 when compared with the frequency distribution of students’ responses with a previous term (Autumn 2016). In Spring 2017, 42% of students reasoned at level 4 or 5 on the pre-unit test, indicating these students already had sophisticated ideas about ion flux before they took the pre-unit assessment. This was surprising, considering only 2% of students reasoned at these levels for this item on the Autumn 2016 pre-unit test.

Phase 4: Reflecting on the Instructional Tools and Their Implementation

The final phase of a design-based research cycle involves a retrospective analysis that addresses the epistemic commitments of this methodology: How was the theory underpinning the research advanced by the research endeavor (theoretical outcome)? Did the instructional tools support student learning about the learning problem (practical outcome)? What were the critical features of the design solution that supported student learning (design principles)? ( Cobb et al. , 2003 ; Barab and Squire, 2004 ).

Theoretical Outcome (Epistemic Commitment 1).

Reflecting on how a design-based research experiment advances theory is critical to our understanding of how students learn in educational settings ( Barab and Squire, 2004 ; Mohan et al. , 2009 ). In our work, we aimed to characterize how students’ conceptual frameworks around flux change as they work toward robust ideas. To do this, we drew on learning progression research as our theoretical framing ( NRC, 2007 ; Corcoran et al. , 2009 ; Duschl et al. , 2011 ; Scott et al. , 2019 ). Learning progression frameworks describe empirically derived patterns in student thinking that are ordered into levels representing cognitive shifts in the ways students conceive a topic as they work toward mastery ( Gunckel et al. , 2012 ). 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. We further validated these reasoning patterns with our student interviews. 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.

Practical Outcome (Epistemic Commitment 2).

In design-based research, learning theories must “do real work” by improving student learning in real-world settings ( DBR Collective, 2003 ). Therefore, design-based researchers must reflect on whether or not the data they collected show evidence that the instructional tools improved student learning ( Cobb et al. , 2003 ; Sharma and McShane, 2008 ). We determined whether our flux-based instructional approach aided student learning by analyzing the kinds of answers students provided to our assessment questions. Specifically, we considered students who reasoned at level 4 or above as demonstrating productive flux reasoning. Because almost half of students were reasoning at level 4 or 5 on the post-unit assessment after experiencing the instructional tools in the neurophysiology unit (in Spring 2017), we concluded that our tools supported student learning in physiology. Additionally, we noticed that students used language in their explanations that directly tied to the Flux Reasoning Tool ( Figure 2 ), which instructed them to use arrows to indicate the magnitude and direction of gradient-driving forces. For example, in a posttest response to our ion flux item ( Figure 4 ), one student wrote:

Ion movement is a function of concentration and electrical gradients . Which arrow is stronger determines the movement of K+. We can make the electrical arrow bigger and pointing in by making the membrane potential more negative than Ek [i.e., potassium’s equilibrium potential]. We can make the concentration arrow bigger and pointing in by making a very strong concentration gradient pointing in.

Given that almost half of students reasoned at level 4 or above, and that students used language from the Flux Reasoning Tool, we concluded that using fundamental concepts was a productive instructional approach for improving student learning in physiology and that our instructional tools aided student learning. However, some students in the 2016–2017 academic year continued to apply flux ideas more narrowly than intended (i.e., for ion and simple diffusion cases, but not water flux or bulk flow). This suggested that students had developed nascent flux conceptual frameworks after experiencing the instructional tools but could use more support to realize the broad applicability of this principle. Also, although our cross-sectional interview approach demonstrated how students’ ideas, overall, could change after experiencing the instructional tools, it did not provide information about how a student developed flux reasoning.

Reflecting on practical outcomes also means interpreting any learning gains in the context of the learning ecology. This reflection allowed us to identify whether there were particular aspects of the instructional tools that were better at supporting learning than others ( DBR Collective, 2003 ). Indeed, this was critical for our understanding why 42% of students scored at level 3 and above on the pre-unit ion assessment in the Spring of 2017, while only 2% of students scored level 3 and above in Autumn of 2016. When we reviewed notes of the Spring 2017 implementation scheme, we saw that the pretest was due at the end of the first day of class after students had been exposed to ion flux ideas in class and in a reading/video assignment about ion flow, which may be one reason for the students’ high performance on the pretest. Consequently, we could not tell whether students’ initial high performance was due to their learning from the activities in the first day of class or for other reasons we did not measure. It also indicated we needed to close pretests before the first day of class for a more accurate measure of students’ incoming ideas and the effectiveness of the instructional tools employed at the beginning of the unit.

Design Principles (Epistemic Commitment 3).

Although design-based research is enacted in local contexts (i.e., a particular classroom), its purpose is to inform learning ecologies that have broad applications to improve learning and teaching ( Edelson, 2002 ; Cobb et al. , 2003 ). Therefore, design-based research should produce design principles that describe characteristics of learning environments that researchers and instructors can use to develop instructional tools specific to their local contexts (e.g., Edelson, 2002 ; Subramaniam et al. , 2015 ). Consequently, the design principles must balance specificity with adaptability so they can be used broadly to inform instruction ( Collins et al. , 2004 ; Barab, 2014 ).

From our first cycle of design-based research, we developed the following design principles: 1) Key scientific concepts should provide an overarching framework for course organization. This way, the individual components that make up a course, like instructional units, activities, practice problems, and assessments, all reinforce the centrality of the key concept. 2) Instructional tools should explicitly articulate the principle of interest, with specific guidance on how that principle is applied in context. This stresses the applied nature of the principle and that it is more than a fact to be memorized. 3) Instructional tools need to show specific instances of how the principle is applied in multiple contexts to combat students’ narrow application of the principle to a limited number of contexts.

Design-Based Research Cycle 2, Phase 1: Redesign and Refine the Experiment

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 ). Robust instructional designs are the products of trial and error, which are strengthened by a systematic analysis of how they perform in real-world settings.

To continue to advance our work investigating student thinking using the principle of flux, we began a second cycle of design-based research that continued to address the learning problem of helping students reason with fundamental scientific concepts. In this cycle, we largely focused on broadening the number of physiological systems that had accompanying formative assessment questions (i.e., beyond ion flux), collecting student reasoning from a more diverse population of students (e.g., upper division, allied heath, community college), and refining and validating the flux learning progression with both written and interview data in a student through time. We developed a suite of constructed-response flux assessment questions that spanned neuromuscular, cardiovascular, respiratory, renal, and plant physiological contexts and asked students about several kinds of flux: ion movement, diffusion, water movement, and bulk flow (29 total questions; available at beyondmultiplechoice.org). This would provide us with rich qualitative data that we could use to refine the learning progression. We decided to administer written assessments and conduct interviews in a pretest/posttest manner at the beginning and end of each unit both as a way to increase our data about student reasoning and to provide students with additional practice using flux reasoning across contexts.

From this second round of designing instructional tools (i.e., broader range of assessment items), testing them in the classroom (i.e., administering the assessment items to diverse student populations), evaluating the tools (i.e., developing learning progression–aligned rubrics across phenomena from student data, tracking changes in the frequency distribution of students across levels through time), and reflecting on the tools’ success, we would develop a more thorough and robust characterization of how students use flux across systems that could better inform our creation of new instructional tools to support student learning.

HOW CAN DESIGN-BASED RESEARCH EXTEND AND ENRICH BER?

While design-based research has primarily been used in educational inquiry at the K–12 level (see Reiser et al. , 2001 ; Mohan et al. , 2009 ; Jin and Anderson, 2012 ), other science disciplines at undergraduate institutions have begun to employ this methodology to create robust instructional approaches (e.g., Szteinberg et al. , 2014 in chemistry; Hake, 2007 , and Sharma and McShane, 2008 , in physics; Kelly, 2014 , in engineering). Our own work, as well as that by Zagallo et al. (2016) , provides two examples of how design-based research could be implemented in BER. Below, we articulate some of the ways incorporating design-based research into BER could extend and enrich this field of educational inquiry.

Design-Based Research Connects Theory with Practice

One critique of BER is that it does not draw heavily enough on learning theories from other disciplines like cognitive psychology or the learning sciences to inform its research ( Coley and Tanner, 2012 ; Dolan, 2015 ; Peffer and Renken, 2016 ; Davidesco and Milne, 2019 ). For example, there has been considerable work in BER developing concept inventories as formative assessment tools that identify concepts students often struggle to learn (e.g., Marbach-Ad et al. , 2009 ; McFarland et al. , 2017 ; Summers et al. , 2018 ). However, much of this work is detached from a theoretical understanding of why students hold misconceptions in the first place, what the nature of their thinking is, and the learning mechanisms that would move students to a more productive understanding of domain ideas ( Alonzo, 2011 ). Using design-based research to understand the basis of students’ misconceptions would ground these practical learning problems in a theoretical understanding of the nature of student thinking (e.g., see Coley and Tanner, 2012 , 2015 ; Gouvea and Simon, 2018 ) and the kinds of instructional tools that would best support the learning process.

Design-Based Research Fosters Collaborations across Disciplines

Recently, there have been multiple calls across science, technology, engineering, and mathematics education fields to increase collaborations between BER and other disciplines so as to increase the robustness of science education research at the collegiate level ( Coley and Tanner, 2012 ; NRC, 2012 ; Talanquer, 2014 ; Dolan, 2015 ; Peffer and Renken, 2016 ; Mestre et al. , 2018 ; Davidesco and Milne, 2019 ). Engaging in design-based research provides both a mechanism and a motivation for fostering interdisciplinary collaborations, as it requires the design team to have theoretical knowledge of how students learn, domain knowledge of practical learning problems, and instructional knowledge for how to implement instructional tools in the classroom ( Edelson, 2002 ; Hoadley, 2004 ; Wang and Hannafin, 2005 ; Anderson and Shattuck, 2012 ). For example, in our current work, our research team consists of two discipline-based education learning scientists from an R1 institution, two physiology education researchers/instructors (one from an R1 institution the other from a community college), several physiology disciplinary experts/instructors, and a K–12 science education expert.

Design-based research collaborations have several distinct benefits for BER: first, learning or cognitive scientists could provide theoretical and methodological expertise that may be unfamiliar to biology education researchers with traditional science backgrounds ( Lo et al. , 2019 ). This would both improve the rigor of the research project and provide biology education researchers with the opportunity to explore ideas and methods from other disciplines. Second, collaborations between researchers and instructors could help increase the implementation of evidence-based teaching practices by instructors/faculty who are not education researchers and would benefit from support while shifting their instructional approaches ( Eddy et al. , 2015 ). This may be especially true for community college and primarily undergraduate institution faculty who often do not have access to the same kinds of resources that researchers and instructors at research-intensive institutions do ( Schinske et al. , 2017 ). Third, making instructors an integral part of a design-based research project ensures they are well versed in the theory and learning objectives underlying the instructional tools they are implementing in the classroom. This can improve the fidelity of implementation of the instructional tools, because the instructors understand the tools’ theoretical and practical purposes, which has been cited as one reason there have been mixed results on the impact of active learning across biology classes ( Andrews et al. , 2011 ; Borrego et al. , 2013 ; Lee et al. , 2018 ; Offerdahl et al. , 2018 ). It also gives instructors agency to make informed adjustments to the instructional tools during implementation that improve their practical applications while remaining true to the goals of the research ( Hoadley, 2004 ).

Design-Based Research Invites Using Mixed Methods to Analyze Data

The diverse nature of the data that are often collected in design-based research can require both qualitative and quantitative methodologies to produce a rich picture of how the instructional tools and their implementation influenced student learning ( Anderson and Shattuck, 2012 ). Using mixed methods may be less familiar to biology education researchers who were primarily trained in quantitative methods as biologists ( Lo et al. , 2019 ). However, according to Warfa (2016 , p. 2), “Integration of research findings from quantitative and qualitative inquiries in the same study or across studies maximizes the affordances of each approach and can provide better understanding of biology teaching and learning than either approach alone.” Although the number of BER studies using mixed methods has increased over the past decade ( Lo et al. , 2019 ), engaging in design-based research could further this trend through its collaborative nature of bringing social scientists together with biology education researchers to share research methodologies from different fields. By leveraging qualitative and quantitative methods, design-based researchers unpack “mechanism and process” by characterizing the nature of student thinking rather than “simply reporting that differences did or did not occur” ( Barab, 2014 , p. 158), which is important for continuing to advance our understanding of student learning in BER ( Dolan, 2015 ; Lo et al. , 2019 ).

CHALLENGES TO IMPLEMENTING DESIGN-BASED RESEARCH IN BER

As with any methodological approach, there can be challenges to implementing design-based research. Here, we highlight three that may be relevant to BER.

Collaborations Can Be Difficult to Maintain

While collaborations between researchers and instructors offer many affordances (as discussed earlier), the reality of connecting researchers across departments and institutions can be challenging. For example, Peffer and Renken (2016) noted that different traditions of scholarship can present barriers to collaboration where there is not mutual respect for the methods and ideas that are part and parcel to each discipline. Additionally, Schinske et al. (2017) identified several constraints that community college faculty face for engaging in BER, such as limited time or support (e.g., infrastructural, administrative, and peer support), which could also impact their ability to form the kinds of collaborations inherent in design-based research. Moreover, the iterative nature of design-based research requires these collaborations to persist for an extended period of time. 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.

Design-Based Research Experiments Are Resource Intensive

The focus of design-based research on studying learning ecologies to uncover mechanisms of learning requires that researchers collect multiple data streams through time, which often necessitates significant temporal and financial resources ( Collins et al., 2004 ; O’Donnell, 2004 ). Consequently, researchers must weigh both practical as well as methodological considerations when formulating their experimental design. For example, investigating learning mechanisms requires that researchers collect data at a frequency that will capture changes in student thinking ( Siegler, 2006 ). However, researchers may be constrained in the number of data-collection events they can anticipate depending on: the instructor’s ability to facilitate in-class collection events or solicit student participation in extracurricular activities (e.g., interviews); the cost of technological devices to record student conversations; the time and logistical considerations needed to schedule and conduct student interviews; the financial resources available to compensate student participants; the financial and temporal costs associated with analyzing large amounts of data.

Identifying learning mechanisms also requires in-depth analyses of qualitative data as students experience various instructional tools (e.g., microgenetic methods; Flynn et al. , 2006 ; Siegler, 2006 ). The high intensity of these in-depth analyses often limits the number of students who can be evaluated in this way, which must be balanced with the kinds of generalizations researchers wish to make about the effectiveness of the instructional tools ( O’Donnell, 2004 ). Because of the large variety of data streams that could be collected in a design-based research experiment—and the resources required to collect and analyze them—it is critical that the research team identify a priori how specific data streams, and the methods of their analysis, will provide the evidence necessary to address the theoretical and practical objectives of the research (see the following section on experimental rigor; Sandoval, 2014 ). These are critical management decisions because of the need for a transparent, systematic analysis of the data that others can scrutinize to evaluate the validity of the claims being made ( Cobb et al. , 2003 ).

Concerns with Experimental Rigor

The nature of design-based research, with its use of narrative to characterize versus control experimental environments, has drawn concerns about the rigor of this methodological approach. Some have challenged its ability to produce evidence-based warrants to support its claims of learning that can be replicated and critiqued by others ( Shavelson et al. , 2003 ; Hoadley, 2004 ). This is a valid concern that design-based researchers, and indeed all education researchers, must address to ensure their research meets established standards for education research ( NRC, 2002 ).

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. Moreover, by allowing instructional tools to be modified during the testing phase as needed, design-based researchers acknowledge that it is impossible to anticipate all aspects of the classroom environment that might impact the implementation of instructional tools, “as dozens (if not millions) of factors interact to produce the measureable outcomes related to learning” ( Hoadley, 2004 , p. 204; DBR Collective, 2003 ). 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).

CONCLUSIONS

providing a methodology that integrates theories of learning with practical experiences in classrooms,

using a range of analytical approaches that allow for researchers to uncover the underlying mechanisms of student thinking and learning,

fostering interdisciplinary collaborations among researchers and instructors, and

characterizing learning ecologies that account for the complexity involved in student learning

By employing this methodology from the learning sciences, biology education researchers can enrich our current understanding of what is required to help biology students achieve their personal and professional aims during their college experience. It can also stimulate new ideas for biology education that can be discussed and debated in our research community as we continue to explore and refine how best to serve the students who pass through our classroom doors.

1 “Epistemic commitment” is defined as engaging in certain practices that generate knowledge in an agreed-upon way.

ACKNOWLEDGMENTS

We thank the UW Biology Education Research Group’s (BERG) feedback on drafts of this essay as well as Dr. L. Jescovich for last-minute analyses. This work was supported by a National Science Foundation award (NSF DUE 1661263/1660643). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF. All procedures were conducted in accordance with approval from the Institutional Review Board at the University of Washington (52146) and the New England Independent Review Board (120160152).

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Submitted: 18 November 2019 Revised: 3 March 2020 Accepted: 25 March 2020

© 2020 E. E. Scott et al. CBE—Life Sciences Education © 2020 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

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Action design research: integration of method support

International Journal of Managing Projects in Business

ISSN : 1753-8378

Article publication date: 10 May 2022

Issue publication date: 19 December 2022

Action design research (ADR) has become widely accepted as a prominent research method within information systems when managing design-oriented research projects. One purpose of the ADR method is to provide methodological guidance for the building of IT artefacts. However, several scholars have reported a lack of guidance of method support at the micro level. This article aims to complement the macro level of the ADR method by integrating prescriptive method support at the micro level.

Design/methodology/approach

A qualitative approach including direct content analysis. An empirical ADR project was analysed in order to identify method support that could be integrated into the ADR method.

Method support at the micro level was identified for all the stages of the ADR method. The method support consists of procedural support, guiding concepts, and various techniques for the documentation of project tasks stated in the ADR method.

Research limitations/implications

The contribution to theory consists of aspects concerning the integration of macro and micro levels: relationships between normative and prescriptive support, continuous focus shifts, and method completeness.

Practical implications

The contribution to practice consists of explicit suggestions for method support that could be integrated into the ADR method.

Originality/value

This study extends previously provided knowledge by offering empirical evidence concerning theoretical constructions consisting of explicit relationships between ADR tasks and integrated method support, and elaboration on the integration of macro and micro levels.

• Action design research
• Project management
• ADR projects
• Design science research
• Design methods

Cronholm, S. and Göbel, H. (2022), "Action design research: integration of method support", International Journal of Managing Projects in Business , Vol. 15 No. 8, pp. 19-47. https://doi.org/10.1108/IJMPB-07-2021-0196

Emerald Publishing Limited

Copyright © 2022, Stefan Cronholm and Hannes Göbel

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Design science research (DSR) has become established as a widely accepted research paradigm within the field of Information Systems (IS) (e.g. Gregor and Hevner, 2013 ; Vaishnavi and Kuechler, 2015 ; Baskerville, 2018 ) and in other fields such as data science ( Mullarkey et al. , 2019 ) and business administration ( Dresch et al. , 2015 ). One specific characteristic of DSR is the interest in socio-technical artefact development and artefact theorising (e.g. Hanseth and Lyytinen, 2016 ; Gregor and Hevner, 2013 ; Baskerville et al. , 2018 ). Hanseth and Lyytinen (2016 , p. 2) state that DSR is socio-technical “ […] because its design domain involves both technical and social elements and their relationships”. One purpose of DSR is to respond to the dual mission of making theoretical contributions and assisting in solving the problems of practitioners (e.g. Rosemann and Vessey, 2008 ; Sein et al. , 2011 ).

The recognition of the DSR paradigm has created a need for useful DSR methods. According to the number of citations, one of the most popular DSR methods is action design research (ADR) ( Sein et al. , 2011 ). Sein et al. (2011 , p. 40) state that “ADR is a research method for generating prescriptive design knowledge through building and evaluating ensemble IT artifacts in an organizational setting”. Furthermore, Sein et al. (2011 , p. 53) state that the ADR method “[…] provides methodological guidance for IS researchers who study the design of ensemble artifacts”. Although several researchers report positive experiences from applying the ADR method ( Gregor et al. , 2014 ; Schuppan and Koehl, 2017 ; Cheng et al. , 2018 ), there is room for improvements concerning guidance on the micro level. Keizer-Broers and de Reuver (2016a) assert that the ADR method leaves a lot of freedom to the researcher concerning which design methods to use. Collatto et al. (2017) state that the ADR method only stresses macro steps. Mullarkey and Hevner (2019) argue that there is a need to give better support to method users for how to structure the key decisions and activities required for a rigorous application of the ADR method. Sein and Rossi (2019 , p. 21) respond to this statement by stating that “[…] we agree with some of their elaborations, such as unpacking the specific stages of ADR to make them more transparent and accessible”. In addition, Cronholm and Göbel (2019) report adequate support at the macro level but suggest the ADR method needs more detailed support for operationalisation in practice (i.e. the micro level).

Several studies assert that the combination of macro and micro levels is essential. Buckley et al. (2011) state there often exists a chasm between micro and macro and there is need to narrow this divide. Cronholm and Göbel (2019) state that method support on both the macro and micro levels is needed when translating the overall method strategy into operational actions. Several scholars have presented general arguments for combining macro and micro levels. Dopfer et al. (2004) emphasise the importance of focus on both macro and micro aspects during analysis. Moreover, they state that “The sum of micro is macro, and the decomposition of macro is micro” (p. 264). Uhrmacher et al. (2007 , p. 871) state that specific " […] attributes and dynamics are described at micro level, whereas the macro level holds aggregated variables and functions that describe high level dynamics”. Furthermore, Bassin et al. (2013) state that when both macro and micro plans are coordinated, projects have the tools to meet quality goals, test budget targets, as well as schedule and time-to-market demands.

Based on these arguments, the problem addressed in this study reads: there is a lack of prescriptive ADR method support at the micro level. A balanced macro and micro-level support is essential because it can streamline methods with the purpose of being experienced as helpful. However, identifying method support at the micro level is often time consuming and usually requires a lot of effort from the project teams. Against this background, the aim of our paper is to complement the macro level of the ADR method by integrating prescriptive method support at the micro level. The objective of our paper is not to develop new method support. Instead, the purpose is to support efficient use of the ADR method by identifying existing forms of method support at the micro level, which could be integrated into the ADR method.

Our research question reads: What method support needs to be integrated in order to gain a better balance between macro and micro levels? This means that we are interested in adding prescriptive method support (how to do something) to the ADR method. In this study, prescriptive method support at the micro level is defined as procedural support, guiding concepts, or techniques for project documentation.

In order to respond to the research question, we have analysed an empirical ADR project. The ADR project was conducted within the field of IS. The objective of the ADR project was to develop socio-technical resources to support organisations seeking to improve services offered to customers. During the ADR project, method support at the micro level was integrated into the ADR method to support the objectives of the project. The ADR method regards the process of artefact development as a collaborative effort involving both practitioners and researchers ( Sein et al. , 2011 ). The practice in this study consists of ADR projects. This means that the contribution to practice addresses ADR projects involving both researchers and practitioners. The contribution to practice consists of concrete suggestions for method support at the micro level that could be integrated into the ADR method. We are, in particular, targeting novice users. The theoretical contribution consists of method knowledge concerning the need for balancing method support at the macro and micro levels in order to improve efficiency and usefulness in future ADR projects.

The ensuing section briefly presents the ADR method. After that, we discuss the concept of method followed by a description of the ADR project. Next, we will introduce the research method. Then, we provide a literature review concerning previous integrations of method support in ADR projects. After that, we present the analysis of the empirical ADR project. Next, we discuss of the findings in relation to the literature review. Then, we elaborate on the implications for practice and research. Finally, our conclusions will be drawn.

2. The ADR method

One purpose of the ADR method is to support research projects with the development of IT artefacts shaped by organisational contexts. Another purpose is to support the development of design principles. The ADR method draws on design research (DR) and action research (AR) ( Sein et al. , 2011 ). The underlying assumption is that and DR and AR will not suffice on their own. Sein et al. (2011 , p. 39) state that current DR “ … are strong in their support of abstraction and invention, they consider organizational intervention to be secondary”. On the other hand, AR combines theory generation with researcher intervention to solve immediate organisational problems. Based on these characteristics, ADR has emerged as a cross-fertilisation between DR and AR.

The ADR method comprises four stages which are: Problem Formulation (identifying and conceptualising a research opportunity based on existing theories and technologies); Building, Intervention and Evaluation (realising the design of the artefact and articulating the design principles); Reflection and Learning (moves conceptually from building a solution for a particular instance to applying that learning to a broader class of problems); and Formalisation of Learning (the situated learning from an ADR project should be further developed into general solution concepts). Each stage involves principles and tasks (see Figure 1 ), which are further described in Section 7 (see also Sein et al. (2011) for a detailed description).

3. Method theory

In Section 1 , we argued that the ADR method lacks method support on the micro level. The purpose of this section is to further strengthen our argument by comparing the ADR method to a model developed by Goldkuhl et al. (1997) , which describes and explains fundamental method concepts. Goldkuhl et al. (1997) state that method theory involves several related method concepts (see Figure 2 ). One concept is perspective , which is defined as the conceptual and value bases of a method. A perspective involves values, principles and categories. Moreover, a perspective can be explicitly articulated in methods, or it can be inexplicit.

Another concept is framework , which is defined as a phase structure of the method. A framework provides possible method components to choose from. A framework can be regarded as an ordered phase structure of method components that can give information about why and what to do.

A method component is defined as a meaningful unit that links procedure, notation and concepts. Method components are often integrated with other method components but can be used separately and independently ( Goldkuhl et al. , 1997 ; Cronholm and Ågerfalk, 1999 ). Examples of method components are root cause analysis, data modelling, process modelling.

A procedure is defined as prescriptions for what actions to take and what questions to ask. A notation is defined as prescriptions for how answers to questions should be documented (e.g. diagrams, tables and text). Usually, the procedure and notation are tightly coupled to each other. The procedure involves some meta-concepts such as process, activity, information and object. Such general concepts are used when asking the questions, meaning they are parts of the prescribed procedure. They are also parts of the semantics of the notation. The concepts are the cement between procedure and notation; the overlapping parts of procedure and notation.

C o-operation forms are defined as the interaction of different people in research projects. This has to do with roles, responsibilities and division of work. It describes how people collaborate when performing method-guided work. Moreover, it describes who is asking and answering questions. Finally, collection forms describe how data will be gathered.

Based on these definitions and explanations of different method concepts, a comparison with the ADR method indicates that the resemblance is high. The ADR method explicitly denotes the socio-technical perspective as an overarching perspective. Moreover, the ADR method involves a framework that consists of an ordered phase structure, which involves four stages (problem formulation; building, intervention and evaluation; reflection and learning and formalisation of learning). Furthermore, the ADR method encompasses co-operation forms describing the collaboration between researchers and practitioners. This implies that the ADR method involves the perspectives of both practitioners and researchers.

The lower part of Figure 2 consists of method components. We interpret the concept of method component as similar to the concept of task which is used in the ADR method. The ADR method describes several tasks that should be conducted in different stages. These tasks and principles excellently describe what should be done and why something should be done. However, they do not provide explicit method support on the micro level that informs about how to do (procedure) and how to document something (notation) .

One interpretation of the purpose of omitting support at the micro level is that the authors of the ADR method make it possible for ADR projects to choose method support on an individual basis. This freedom can be regarded as positive, but it can also be perceived as inefficient and constitutes a barrier for novice IS researchers or researchers unfamiliar with IS methods in general. In this study, method support at the micro level is defined as something that provides procedural support. This definition is similar to what Goldkuhl et al. (1997) define as method component.

4. The ADR project

This section aims to describe the ADR project analysed in order to identify method support integrated into the ADR method. The purpose of the ADR project was to (1) develop socio-technical resources (e.g. methods, models, algorithms and digital tools) that could help organisations to utilise data in order to develop their service offerings and (2) create design knowledge (design principles) for socio-technical resources supporting service development.

The ADR project followed the four stages of the ADR method over a period of three years. Moreover, it involved four researchers and nine organisations. The organisations consisted of service providers and their customers. The researchers consisted of two professors and two PhD students from the field of information systems. The line of business the organisations represented was the car industry, telecom, and IT. The roles of the participating practitioners were IT Quality Managers, Head of Architecture and Solutions, IT consultants, Manager of Consumer Services, Business Manager, CEO, IT Process Framework Manager and Manager Consumer Sales. There were frequent interactions between researchers and practitioners in the ADR project. Following the ADR method, the ADR project can also be characterised as iterative.

The ADR method provided excellent support at the macro level. However, the ADR project identified a need to integrate method support at the micro level for all the four stages of the ADR method when developing socio-technical resources. Identifying appropriate method support was experienced as time-consuming and required a lot of effort from the project team.

The ADR project comprised an exceptional opportunity to analyse the need for method support at the micro level. This extended research interest corresponds to the purpose of this paper. In this paper, the role of the ADR project was to serve as a case in order to generate knowledge concerning the integration of the macro and micro levels based on empirical evidence.

5. Research method

In order to identify prescriptive method support at the micro level, we have conducted a qualitative study. Qualitative research can be defined as the type of research that finds out about experiences and help us to understand what is important to people ( Silverman, 2020 ). The reasons for conducting a qualitative study were that it supported: (1) the possibility to conduct an in-depth study ( DiCicco-Bloom and Crabtree, 2006 ), (2) the analysis of complex situations ( Silverman, 2020 ), and (3) the focus on qualitative questions such as “why”, “what” and “how” ( Patton, 1990 ). The qualitative study included a single case as our analysis unit. Yin (2011) states that a case study is an empirical inquiry, which examines a contemporary phenomenon in its real-life context. Moreover, our qualitative study also included using the method qualitative content analysis (see Section 5.2 ).

The design of our research method was also based on the fact that the two of researchers acted in multiple roles: (1) participated in the ADR project in order to develop socio-technical resources that could help organisations to utilise data in order to develop their service offerings and (2) analysed the ADR project in order to create knowledge about method support on the micro level that could be integrated into the ADR method. The two researchers are also the authors of this paper and they participated in the ADR project from start to end. Moreover, the analysis of method support on the micro level commenced already at the beginning of the ADR project. This research design, which is not unusual in qualitative studies ( Susman and Evered, 1978 ; Avison et al. , 2001 ), meant that we analysed the ADR project simultaneously as we participated in the development of social-technical resources (see Figure 3 ).

The overarching research method involved three phases: (1) a literature review of existing attempts to integrate method support into the ADR method, (2) an analysis of an empirical ADR project where there was a need to integrate method support into the ADR method, and (3) a comparison of the literature review and the results from the analysis of the empirical ADR project. Each phase will be presented in detail below.

5.1 Phase 1 literature review

In order to identify existing knowledge concerning the integration of method support into the ADR method, we needed to apply a literature review strategy. We were inspired by the literature review method presented by Webster and Watson (2002) . In the first step, “identifying the relevant literature”, we applied different keywords in order to find relevant articles. A search in the Scopus database using the simple search string “Action Design Research” resulted in more than 1 000 hits, which were not manageable to review. A review of leading journals included in the eight top IS journals using the same search string resulted in the opposite. We received less than 3o hits, and most of them were irrelevant with regard to the purpose of our study. The reviewed journals were European Journal of Information Systems (EJIS), Information Systems Journal (ISJ), Information Systems Research (ISR), Journal of Association of Information Systems (JAIS), Journal of Information Technology (JIT), Journal of Management Information Systems (JMIS), Journal of Strategic Information Systems (JSIS) and, Management Information Systems Quarterly (MISQ) . Based on these unsatisfactory results, we decided to use backward reference searching (i.e. snowball sampling, e.g. Naderifar et al. , 2017 ) by reviewing relevant papers cited in the identified articles in the leading IS Journals. In total, we analysed 48 papers. Out of these 48 papers, 14 were relevant to the purpose of this paper.

In the second step of the literature review, Webster and Watson (2002) recommend researchers to develop a concept-centric matrix. The purpose of the concept-centric matrix is to synthesise the literature. The concepts used to organise the matrix were the different types of method support identified in previously reported ADR projects.

5.2 Phase 2 analysis of an empirical ADR project

Step 1 Code selection

Step 2 Material collection

The purpose of the step “material collection” was to explore method support used on the micro level. We used the ADR tasks (the codes) as a lens to identify method support integrated into the ADR project. The base for identifying method support was project documentation involving design documentation, notes taken from workshops, meeting protocols, and videotapes. These materials were continuously produced during the three years the ADR project lasted.

Step 3 Material evaluation

In order to determine whether the integrated method support was successful or not, we evaluated the relationships between the ADR tasks and the integrated method support. Eisenhardt and Graebner (2007) state that theoretical constructions (the relationships between the ADR tasks and the integrated method support) should be evaluated by providing empirical evidence. In order to evaluate the relationships between the ADR tasks and the integrated method support, we searched in project documentation for empirical evidence concerning the value of the integrated method support (e.g. strengths and weaknesses). The predefined codes (the ADR tasks) were used as a lens to identify empirical statements. Furthermore, Eisenhardt and Graebner (2007) argue that a “[…] a separate table that summarises the evidence for each theoretical construct is a particularly effective way to present the case evidence”. We followed this advice when we developed and evaluated the theoretical constructions.

5.3 Phase 3 comparison of the literature review and the empirical ADR project

In order to identify how our study contributed with extended knowledge concerning method support at the micro level, we compared similarities and differences between the results of the analysis of the ADR project and the literature review. We used simple Venn diagrams (e.g. Chen and Boutros, 2011 ), which support set comparisons and visualises how two or more sets are overlapping. In our comparison, the first set consisted of method support identified in the ADR project, and the second set consisted of method support identified in the literature review (see Figure 4 ). The comparisons made it possible to identify (1) new method support used in the ADR project and not identified in the literature review (complement A), (2) method support used in both the ADR project and the literature review (intersection), and (3) method support found in the literature review but not used in the ADR project (complement B).

6. Phase 1 literature review

As mentioned in Section 5.1 , this section follows the literature review method presented by Webster and Watson (2002) . First, we present the identified literature related to the purpose of this paper. Second, we describe how we have structured the identified literature according to a concept-centric matrix. Finally, we summarise the literature review.

6.1 Identified relevant literature

The purpose of this section is to present existing knowledge concerning the integration of method support into the ADR method. Mullarkey and Hevner (2019) propose an elaborated process model for applying ADR by integrating concepts from the framework developed by Peffers et al. (2007) . More specifically, Mullarkey and Hevner (2019 , p. 6) suggest improvements that “better support users to structure the key decisions and activities necessary to rigorously apply ADR”. They have identified four distinct types of ADR cycles for diagnosis, design, implementation, and evolution of the growing artefact solution. The suggested process model also supports multiple entry points based on the current state of the problem environment and the goals of the ADR project.

Bub (2018) has developed a process model that has been integrated into the ADR method. One purpose of the process model is to ensure both practice-driven innovation as well as design science research. Bub (2018 , p. 337) states that the proposed process model “ … presents a combination of Design Science Research processes with innovation processes that are characterised by stage-gate-orientation”. On the other hand, Sein et al. (2011) highlights the importance of concurrent building and evaluation and state that a critical characteristic of the ADR method is that “… evaluation is not a separate stage of the research process that follows building. In this, ADR differs from the stage-gate models proposed in prior work” (p. 43).

Haj-Bolouri et al. (2016) propose participation action design research (PADRE), which includes adopting principles and philosophy from participatory action research and participatory design. They argue that the ADR method can benefit from incorporating learning within and across all stages. Furthermore, the ADR method should include a learning nexus, which is a repository of knowledge that accumulates when the ADR stages are conducted. This means that the results from reflection and learning are documented iteratively into the learning nexus.

Keijzer-Broers and de Reuver (2016) have illustrated how agile methods could be integrated into the ADR method. The reason is that “… researchers often face severe constraints in terms of budget and time within the practical setting” (p. 68). Keijzer-Broers and de Reuver (2016) argue that agile methods will be efficient and stimulate a quick start of the design process when combined with UX design methods ( Sy, 2007 ). Moreover, Keijzer-Broers et al. (2016) have used personas ( Long, 2009 ), user stories ( Cohn, 2004 ) and scenarios in order to evaluate the prototypes that are developed in the project.

Lüftenegger et al. (2017) have adopted the ADR method to guide close collaboration with the industry. The purpose of their study is to conceptualise a strategy based on service-dominant logic (SDL) ( Vargo and Lusch, 2004 ) and to develop a tool that can support the translation of abstract SDL into actionable insights for practitioners. In order to fulfil this purpose, they have used a simplified version of the Delphi method. The Delphi method provides detailed principles for making design choices during the process that ensure a valid study ( Okoli and Pawlowski, 2004 ). Furthermore, brainstorming sessions (e.g. Wilson, 2013 ) involving both researchers and practitioners were used in order to obtain consensus concerning the mapping of SDL concepts onto concepts familiar to the practitioners.

Schacht et al. (2015) have applied the ADR method to design a knowledge management system “ … that integrates the social and technical perspective by expressing and evaluating design principles according [to] the design science research approach” (p. 5). Method support used to complement the ADR method consisted of an exploratory interview study, a general inductive approach for analysing evaluation data ( Thomas, 2006 ), and focus groups that facilitated discussions and enabled the gathering of participants' attitudes, opinions, and beliefs ( Myers, 2009 ).

Venable et al. (2016 , p. 77) state that “ … extant DSR literature provides insufficient guidance on evaluation to enable Design Science Researchers to effectively design and incorporate evaluation activities into a DSR project that can achieve DSR goals and objectives”. This general statement includes the ADR method. The purpose of their study is to propose a framework for the evaluation of DSR (FEDS). In particular, the purpose of the framework is to address the questions: “What strategies and methods should be used for evaluation in a particular DSR project?” and “How should such evaluations be designed and conducted as part of a DSR project?”

Ebel et al. (2016) have applied the ADR method in order to present a framework for developing tool support for the design and management of new business models. The literature review was conducted by following a multistep process ( Zott et al. , 2011 ) and by means of qualitative content analysis ( Forman and Damschroder, 2008 ). In order to generate data, semi-structured expert interviews were conducted ( DiCicco-Bloom and Crabtree, 2006 ). Moreover, they have used Intra-Class-Correlation (ICC) coefficients ( Amabile, 1996 ) to check the inter-rater reliability of the analysed interviews. In order to evaluate the artefact's usability, the authors used the Questionnaire for User Interaction Satisfaction (QUIS) ( Chin et al. , 1988 ). Finally, the utility of the framework was confirmed by using exploratory focus groups ( Hevner and Chatterjee, 2010 ).

Spagnoletti et al. (2015) have presented results from an ADR project aimed to generate and orchestrate personalised elderly care interventions from a geriatric unit. They have used the ADR method as the overarching methodological framework. Moreover, the authors state that “The ADR framework allows experimenting with localised methods for building an ensemble artefact, including situated interventions and the evaluation of outcomes through multiple iterations” (p. 132). Data were generated from direct observations and focus groups involving domain experts, researchers and potential users. A questionnaire was used to assess the elderly participants' degree of social integration.

Göbel and Cronholm (2016) present intermediate results from evaluating a digital service platform and nascent design principles enabling researchers and practitioners to leverage other instances of digital service platforms. In order to validate the platform, semi-structured interviews and group interviews were conducted ( Patton, 1990 ). In order to justify theory generation, four grounding strategies were applied: value grounding (a reference to an addressed goal), conceptual grounding (talking about the world/defining categories), explanatory grounding (justification for statements), and empirical grounding (in terms of instantiation and evaluation) ( Goldkuhl, 2004 ).

Giessmann and Legner (2016) present design principles for the development of business models concerning cloud platforms. The ADR method was used for generating prescriptive design knowledge through building and evaluating IT artefacts in an organisational setting. The business model canvas ( Osterwalder and Pigneur, 2010 ) was used to analyse current business models and develop a business model for cloud platforms. Moreover, an ideation workshop was used to generate ideas for improving the business model.

Mettler (2018) state that professional social networks (PSN) is not widely developed in complex domains such as health care. They have used the ADR method in order to describe practical design propositions and possible tension along the contextualisation of PSN. The ADR method was complemented with focus groups, workshops and surveys.

Gregor et al. (2014) report from an ADR project that analysed the limited adoption of e-Government in Bangladesh. The method support integrated into the ADR method is related to the seven principles of the ADR method (see Figure 1 ). Unfortunately, the method support is mainly described in general terms and lacks references to scientific articles. One exception is the integration support for theorising and generalisation. The method support incorporated during the ADR stage Formalisation of Learning was a theorising framework suggested by Lee et al. (2011) , which provided an overarching guide in support of generalisation.

Henriques and O'Neill (2021) supplies a systematic approach to integrating focus groups into the ADR method. One important insight is that rigorous and committed stakeholder engagement is a critical success factor in complex projects. Other insights are that the usage of focus groups: (1) provide an efficient way to study artefacts, (2) propose improvements in its design, and (3) acknowledge the utility of those artefacts in real field use.

6.2 Structuring the review

The purpose of this section is to structure the results of the literature review through the use of a concept-centric matrix (see Table 1 ). The concept-centric matrix shows that there is a variety in the method support integrated into the ADR method.

6.3 Summarising the review

Not evaluated. (The integration of method support was not the primary objective of the articles reviewed, and therefore there was no discussion about the successfulness of the integration).

Fragmented.

Often described in general terms or in passing.

Not related to specific ADR tasks.

Moreover, the literature review revealed no suggestions concerning method support for the ADR stage Reflection and Learning and only little method support for the ADR stage Formalisation of Learning. We can conclude that the literature review strengthened our belief that there is a need for increased method knowledge concerning the balance of method support at the macro and micro levels.

7. Phase 2 analysis of the empirical ADR project

The purpose of Section 7.1 is to (1) present the tasks that guided the development of the digital tool and the design principles developed in the ADR project, and (2) describe the method support integrated into the ADR project. The purpose of Section 7.2 is to evaluate the integrated method support. Section 7.2 can also be regarded as a summary of the integrated method support.

7.1 Integration of method support in the ADR project

This section is organised according to the four ADR stages (see Figure 1 ). The focus is set on the tasks for each stage since they guided the progress of the ADR project. Below, we describe how the ADR project approached the tasks. Due to lack of space, we have (1) omitted descriptions of integration of method support that was also identified in the literature review, (2) excluded tasks when no integration was made in the ADR project, and (3) excluded descriptions of the most common and well-known method support such as interviews and workshops. This omission resulted in that not every task is described below.

7.1.1 ADR stage 1: problem formulation

7.1.1.1 task: identify and conceptualise the research opportunity.

The research opportunity should be identified at the intersection of technological and organisational domains ( Sein et al. , 2011 ). In order to identify a research opportunity concerning service assessment and service innovation, the ADR project conducted traditional semi-structured interviews and workshops with the participating organisations. This resulted in several research opportunities consisting of problems and needs concerning service assessment and service innovation, such as digital tools, structured processes for service assessment, and support for collaboration between service providers and their customers. The ADR project considered this strength since it illuminated different aspects of the problems identified, which contributed to a deeper understanding concerning the problems the organisations are facing.

To identify and visualise relationships in terms of cause and effect between the identified problems, the ADR project applied root-cause analysis ( Wilson et al. , 1993 ). The main problem was formulated as the lack of a digital tool supporting collaborative service assessment and service innovation. Based on the problem formulation, the ADR project decided to develop a digital tool that could support collaboration between service providers and their customers.

7.1.1.2 Task: formulate initial research questions

The ADR method does not provide much guidance on the formulation of initial research questions. In order to learn more about formulations of research questions concerning design-oriented projects, the ADR project consulted Gregor and Hevner (2013) , who state that research questions in DSR studies are always descriptive and prescriptive. This usually means that the knowledge contribution is to inform about what to do and how to do something , whilst research questions formulated in other scientific fields often lack the prescriptive dimension.

Gregor and Hevner (2013 , p. 343) argue that “Research questions typically center on how to increase some measure of operational utility vis-à-vis new or improved design artifacts”. The support for formulating the research question consists of two analytical enquiries: “What do we know already?” and “From what existing knowledge can we draw?” (ibid.).

In the ADR project, the research question was continuously revised based on emerging empirical observations and theoretical insights. To gradually refine the research question also meant that the researchers in the ADR project kept an open mind during the research process. Furthermore, the ADR project also investigated whether other similar artefacts that have been used to solve the same or similar research problems in the past were in existence. This means that existing appropriate descriptive and propositional knowledge informed us when formulating the research question.

7.1.1.3 Task: cast the problem as an instance of a class of problems

Sein et al . (2011 , p. 40) state that “ … the action design researcher should generate knowledge that can be applied to the class of problems that the specific problem exemplifies”. Moreover, Sein et al. (2011) describe this task as a conceptual move from the specific-and-unique to generic-and-abstract.

In the description of a class of problems, the ADR project was inspired by UML class diagrams ( Object Management Group, 2020 ). A class is defined as a blueprint that is used to create an object. An object is referred to as an instance of a class. Another purpose of a class diagram is to support graphical descriptions of the relationship between a class of problems and the instances ( Berardi et al. , 2005 ). The identified problems in the ADR project were regarded as problem instances. These instances formed a basis for the identification of a class of problems. The class diagram was elaborated on in workshops that involved both researchers and practitioners.

7.1.1.4 Task: identify contributing theoretical bases and prior technology advances

Sein et al. (2011 , p. 41) state that “The action design researcher should inscribe theoretical elements in the ensemble artifact”. In order to identify relevant theories, the ADR project followed the literature review method suggested by Webster and Watson (2002) . This method included the steps: identifying relevant literature, structuring the review, and theoretical development. In order to synthesise the literature, the ADR project created a concept-centric matrix. The relevant theories that had a crucial impact on the design of digital tool were service-dominant logic ( Vargo and Lusch, 2004 ), collaboration theory ( Mathiassen, 2002 ), and digital innovation ( Nambisan et al. , 2017 ).

In order to identify prior technological advances such as similar digital tools, the ADR project conducted an empirical multidimensional market analysis ( Day, 1981 ). This included procedural support such as identification of strengths and weaknesses relative to the competition, and exploration of and comparison with existing technologies.

7.1.2 ADR stage 2: building, intervention and evaluation

7.1.2.1 task: discover initial knowledge-creation target.

Sein et al. (2011) state that the shaping of the artefact requires interaction between technological and contextual dimensions and that the interaction is manifested in knowledge-creation targets.

In order to decide the overall knowledge-creation target, the ADR project was inspired by the DSR contributions types suggested by Gregor and Hevner (2013) . These types involve (1) situated implementation of artefact (level 1), which includes software products or implemented processes, (2) nascent design theory (level 2), which includes constructs, methods, models, design principles, technology and rules, and (3) well-developed design theory (level 3), which could be mid-range or grand theories. As mentioned in Section 4 , the ADR project developed a digital tool concerning support for service assessment (level 1) and design principles that support the development of digital tools of this type (level 2). The ADR project also formulated specific goals such as: facilitate feasible and viable service assessment and service innovation, support an improved dialogue between service providers and customers, and embed a modern service innovation and value co-creation culture.

7.1.2.2 Task: execute building-intervention-evaluation (BIE) cycle(s)

Sein et al. (2011 , p. 42) state that “The outcome of the BIE stage is the realised design of the artifact”. Furthermore, the BIE cycle involves formulating general design principles that can be applied outside the studied context. Moreover, Sein et al. (2011) state that IT artefacts are ensembles shaped by the organisational context.

In order to find support for identifying contextual factors, the ADR project used the context framework presented by Rosemann et al. (2008) . The framework holds procedural support enabling users to derive relevant contextual information presented as an onion model. The purpose of the onion model was to identify, classify, understand and integrate relevant contextual factors. Examples of contextual factors found in the ADR project were service orientation, resources and service ecosystems, which were essential to consider during the design of the digital tool. The building of the digital tool followed agile development ( Schwaber, 1997 ). The ADR project implemented several inspection points to ensure that the artefact was designed according to the goals.

7.1.2.3 Task: assess need for additional cycles, repeat

Sein et al. (2011 , p. 43) state that the ADR method conceptualises the research process as" … interwoven activities of building the IT artifact, intervening in the organisation, and evaluating it concurrently” (p. 37). Moreover, the ADR method includes the naturalistic and formative evaluation of ensemble IT artefacts in a specific context whilst searching for new design knowledge (ibid.).

The ADR project was inspired by naturalistic evaluation described in the framework for evaluating design-oriented projects (FEDS) ( Venable et al. , 2016 ). This meant that the digital tool was empirically evaluated within the environments of the organisations', and feedback was collected from empirical use. One specific method support integrated into the ADR project was that of evaluation episodes. An evaluation episode is defined as “specific evaluation activities of specific evaluands using a specific evaluation method” ( Venable et al. , 2016 , p. 81).

A typical evaluation episode lasted for 2 h. Each episode included 1–2 service providers, 1–2 customers and 1–2 researchers. The process of the evaluation episode consisted of the following steps: (a) the service provider individually assessed different aspects of the digital tool without the involvement of the customer, (b) the customer individually assessed various aspects of the digital tool without the involvement of the service provider, (c) the service provider and the customer collaboratively analysed the individual assessments, and (d) the service provider and the customer collaboratively suggested improvements. Steps (a) and (b) were conducted in parallel. This evaluation process meant that the emergent design of the digital tool was heavily based on intervention, including the collection of contextual requirements from the participating organisations.

The ADR project carried out three iterations of the BIE cycle, which means that approximately 25 evaluation episodes were conducted with nine organisations. Each iteration resulted in a large amount of feedback. In order to structure the qualitative feedback, the ADR project used Grounded Theory ( Strauss and Corbin, 1998 ) to group the feedback into categories consisting of similar requirements that constituted a basis for a redesign.

7.1.3 ADR stage 3: reflection and learning

7.1.3.1 task: reflect on the design and redesign during the project, 7.1.3.1.1 task: evaluate adherence to principles.

The reason for presenting the two tasks, “ Reflect on the design and redesign during the project ” and “ Evaluate adherence to principles” together is that, in parallel, the ADR project developed the digital tool and design principles concerning the development of digital tools supporting service assessment and service innovation. There is also a tight coupling between these tasks. This meant that identified method support was used to assist both tasks.

The development of the digital tool was guided by the design principles that emerged during the BIE cycles. That is, the advances of the design principles were used to shape the digital tool.

The development of the design principles was guided by empirical feedback from the use of the digital tool. That is, the digital tool provided a platform for the evaluation of the design principles.

Reflection on the design principles: Were the design principles sufficiently articulated to provide the necessary support for designing digital tools supporting service assessment and service innovation?

Reflection on the digital tool: Was the feedback on the digital tool sufficient to provide adequate guidance on the development of the design principles?

In order to find method support concerning reflection, the ADR project consulted Daudelin (1996) , who suggests a structured reflection process consisting of four distinctive stages: (1) articulation of a problem (i.e. lack of digital support concerning service assessment and service innovation), (2) analysis of that problem (i.e. analysis of feedback concerning the usage of the digital tool in relation to problem formulation and goal formulation), (3) formulation and testing of a tentative theory to explain the problem (i.e. feedback from usage of the digital tool guided search for theoretical support), and (4) action (or deciding whether to act) (i.e. decision to redesign the digital tool according to new theoretical insights and empirical feedback).

7.1.4 ADR stage 4: formalisation of learning

7.1.4.1 task: articulate outcomes as design principles.

Sein et al. (2011) state that generalisation is challenging because of the highly situated nature of ADR outcomes that include organisational change along with the implementation of an IT artefact. However, the ADR method explicitly defines three levels of generalisation: the problem instance, the solution instance, and the derivation of design principles. In Section 7.1.1 , we have described the process for generalisation of the problem instance. In the same section, we also presented the method support multidimensional market analysis. This analysis helped us to identify a solution class, which were called digital tools supporting service assessment and service innovation. In the ADR project, the task “articulate outcomes as design principles” was divided into two sub-tasks: generalisation of design principles and formulation of design principles.

7.1.4.2 Sub-task generalisation of design principles

In order to generalise design principles, the ADR project was inspired by the well-cited article written by Lee and Baskerville (2003) , which make a distinction between what the researcher is generalising from and to . Based on this distinction, the authors suggest four types of generalisation: generalising from data to description (type EE), generalising from description to theory (type ET), generalising from theory to description (type TE), and generalising from concepts to theory (type TT). With regard to generalisation of design principles, the ADR method involves generalisation from description (empirical evidence identified in organisational contexts) and further to theory and generalising from concepts to theory (theoretical concepts identified in kernel theories were included as elements of general design principles).

In order to generalise the design principles, the ADR project utilised the fact that there were nine organisations participating. This meant that the emergence of the design principles was based on input from nine organisational contexts, which could be regarded as nine instances or cases. Although, these organisations shared an interest in developing a digital tool supporting service assessment and service innovation, their input varied concerning: problem formulations, goal descriptions, requirement specifications, and feedback concerning the design of the digital tool. Yin (1994) states that at high degree of variation will serve to support generalisation.

Moreover, the ADR project utilised the concept of analytical generalisation, which has received attention and approval from a prominent interpretive IS researcher ( Lee and Baskerville, 2003 ). Yin (1994) argues that the goal of analytical generalisation is to expand theories beyond their current domain. Analytical generalisation involves a reasoned judgement about the extent to which the findings from one study can be used as a guide to what might occur in another situation ( Kvale, 2007 ). Halkier (2011) claims that analytical generalisation involves generalisation on the basis of qualitative data, which correspond to the type ET in Lee and Baskerville (2003) and, consequently, also corresponds with the ADR method.

7.1.4.3 Sub-task: formulation of design principles

Sein et al. (2011) state that the design principles identified through the stage Reflection and Learning “ … are fully formulated and articulated during this stage of formalising learning” (p. 45). The ADR method does not provide any guidelines on the formulation of design principles.

The ADR project identified that several scholars criticise existing design principles for variances in how they are formulated (e.g. Chandra Kruse and Seidel, 2017 ). Consequently, the ADR project searched for support concerning formulation of design principles and identified several suggestions (meta-design principles) such as: Walls et al. (1992) , Van den Akker (1999) , Van Aken (2004) , and Chandra Kruse et al . (2016) . In order to support reusability, design principles within the same set should be uniformed in terms of structure, content, and level of abstraction ( Cronholm and Göbel, 2018 ).

The ADR project decided to follow the generic guideline suggested by Van den Akker (1999) . This guideline reads: “If you want to design intervention X [for the purpose/function Y in context Z], then you are best advised to give that intervention the characteristics A, B, and C [substantive emphasis], and to do that via procedures K, L, and M [procedural emphasis], because of arguments P, Q, and R.”. The reason for choosing this guideline is that it provides support for how to formulate and structure design principles.

7.2 Evaluation of integrated method support

The output from Section 7.1 consisted of descriptions of the integrated method support and its relationship to the ADR tasks, which can be regarded as theoretical constructions ( Eisenhardt and Graebner, 2007 ). The purpose of this section is to evaluate the theoretical construction. Column three in Tables 2–5 consists of empirical evidence concerning the value of the integrated method support. As mentioned in Section 5 , the empirical evidence was identified in the project documentation.

In the ADR project documentation, we have identified several notes and statements that show that the integrated method support in the ADR project was valuable. Furthermore, the evaluation confirms that the integrated method support improved the utility of the ADR tasks, which in turn, contributed to the fulfilment of project goals in the ADR project. Based on these observations, we claim that the theoretical constructs consisting of the ADR task and the integrated method support form a whole that responds to the questions: why to, what to, when to, and how to do something.

8. Phase 3 comparison of the ADR project and the literature review

The purpose of this section is to illuminate the knowledge contribution of our study by comparing the literature review (see Section 6 ) and the method support integrated into the ADR project (see Section 7 ).

As mentioned in Section 6.3 , the 14 articles included in our literature review were not explicit about what ADR tasks the integrated methods are supposed to support. In these cases, we interpreted general descriptions of the situations in which the method support was used. Furthermore, explicit references to the used method support were omitted in some articles. In these cases, we have added references that serve as a pointer to the method support used. In interpretive approaches such as text-based analysis of literature reviews, the analyst makes various decisions about how to comprehend the data ( Walsham, 1995 ). Risks concerning biased interpretation can be reduced by involving two or more researchers when searching for and analysing data. Therefore, two researchers authored this paper: (1) individually analysed the articles and used the predefined codes (tasks) to interpret the relationship between the task and the identified method support and (2) in a following step, the output from the individual analyses was jointly compared and reconciled. This meant that we had two comparable outputs; the output from the interpretation of the articles included in the literature review and the output from the analysis of the ADR project.

To illustrate the comparison between the method support integrated into the ADR project and method support identified in the literature review, we have used Venn diagrams (see Figures 5–14 ).

Additional method support. We identified additional method support in the ADR project that was not identified in the literature review.

Coverage of the ADR stages. We identified method support in the ADR project represented in all four ADR stages. The literature mainly identified method support for the first two ADR stages.

Inclusion of method support for artefact design and development of design principles. The ADR project involved method support for building and evaluating the IT artefact and developing design principles. Method support identified in the literature review is mainly oriented towards the building and evaluating of the IT artefact.

Clarity. The ADR project is explicit about which method support had been integrated into specific ADR tasks. The studies in the literature review sometimes mention what method support was used in passing. Probably, this is because the studies identified in the literature had other objectives than suggesting method support that could be integrated into the ADR method.

Provision of explicit references. The ADR project provides explicit references to the method support as integrated. The studies identified in the literature review sometimes omit explicit references to the method support.

9. Implications for practice and research

This section aims to discuss implications for practice ( Section 9.1 ) and research ( Section 9.2 ). As mentioned in Section 1 , the practice in our study consists of ADR projects which usually involve both researchers and practitioners ( Sein et al. , 2011 ).

9.1 Implications for practice

Based on the evaluation of the empirical ADR project, we highlight three significant implications for ADR project managers: operationalisation, understanding, and quality. First, we claim that the pinpointed micro support contributes to project managers regarding the operationalisation of ADR projects. For example, in the ADR project, the efficiency related to tasks such as “assessing the need for additional cycles” and “generalization of design principles” significantly improved when the micro support had been identified, described and refined in the subsequent project iterations. Consequently, the resources (e.g. time and money) needed for the project managers to plan and implement operative tasks were reduced.

Second, we state that the combination of macro and micro support helps project managers to foster a better understanding of the ADR method for participating organisations and novice researchers. For instance, the associated references to micro support helped the ADR project managers to argue for the necessity of conducting ADR tasks that are not common in traditional business projects (e.g. generalisation of design principles). Moreover, the combination of macro and micro support helped the ADR project managers to describe the ADR method as a whole to novice project members efficiently.

Third, we argue that the combination of macro and micro support helps project managers to leverage a higher quality of the results of ADR projects. For example, in the ADR project, the design principles were considered structured, generalised and easy to communicate due to the added micro support. Another example concerns micro support related to evaluation. The added micro support enabled the ADR project to deliver validated socio-technical resources to support organisations.

9.2 Implications for research

Based on the evaluation of the theoretical constructions created in Section 7.2 , we argue that it is essential to integrate method support at macro and micro levels. Our argumentation involves three statements.

Identified relationships between normative and prescriptive method support. Gregor (2006) states that methods informing about what to do are normative, whilst methods informing how to do something are prescriptive. We have created explicit theoretical constructions that balance the need for both normative and explicit method support. As mentioned in Section 1 , there was a need to unpack the specific stages of the ADR method to make them more transparent and accessible. In the ADR project, the translation of the macro level to the micro level was necessary in order to find support for operational tasks.

Supported a continuous shift of focus between the whole and its parts. Our analysis of the ADR project identified similarities with the hermeneutic perspective, which focuses on the relationships between the whole ( the ADR method ) and its parts (e.g. the integrated method support ), and argues that the whole and the parts need to harmonise ( Gadamer, 1975 ). This harmonisation requires a dialectic process that shifts between the whole and the parts and back to the whole (ibid.). In the ADR project, a constant focus shift generated an enhanced understanding of (1) the problem analysed, (2) the design of digital tool supporting service assessment and service innovation, (3) the development of the design principles, and d) the relationship between the development of the digital tool and design principles. A focus that is too one-sided on the macro level could mean that necessary details were overlooked, disregarded or omitted (e.g. what should be included in this function in the digital tool?). Vice versa, a too narrow-minded focus on the micro level could imply that the presence of overarching issues at the macro level was lost (e.g. is this functionality needed at all?).

Ensured the completeness of the ADR method in action. The existing macro level in the ADR method supported general and high-level issues often related to what to, when to, and why to do something. The added micro level in this study supported, in particular, issues related to how to do something. Strauss and Corbin (1998) call these generic questions ‘analytical’ and state that they should preferably be asked within empirical and qualitative research projects. The added method support on the micro level corresponds to the method component element presented by Goldkuhl et al. (1997) , (see Figure 2 ). In this respect, the integrated method support on the micro level has contributed to the completeness of the ADR method in action.

In the ADR project, these three statements have supported knowledge acquisition, efficiency and usefulness of the ADR method in action. This is valid for both problem formulations and suggested solutions regarding the digital tool and developed design principles. In addition, they have also supported the management of the ADR project in terms of project planning, project realisation, and project evaluation.

10. Conclusion

This paper aims to complement the macro level of the ADR method by suggesting integrated method support at the micro level. We can conclude that the suggested method support on the micro level integrated into the ADR project contributed with added value to all the ADR stages. This conclusion is based on the fact that we have identified method support that contributed to the success of the ADR project in all the ADR stages. We can also conclude that the interplay between the macro level in the ADR method and the suggested micro level in the ADR project harmonised and contributed to the fulfilment of the project goals. Therefore, we regard the suggested method support as a supplement to the ADR method.

The contribution to practice consists of concrete suggestions for integrating method support into the ADR method (see Section 7 ) and a discussion about implications (see Section 9.1 ). In order to support practical use, we have created explicit links between recommended tasks in the ADR method and the supplementary method support on the micro level. In particular, this supplement targets novice users, and hopefully, it will be considered efficient and effective in future ADR projects. The theoretical contribution consists of method knowledge concerning the need for balanced method support between the macro and micro levels, strengthening the ADR method concerning knowledge acquisition, efficiency and usefulness. The contribution consists of three statements concerning a combined macro and micro perspective: (1) the identification of relationships between normative and prescriptive method support, (2) a continuous shift of focus between the whole and its parts, and (3) the completeness of the ADR method in action (see Section 9.2 ).

The contributions to theory and practice are based on a review of existing literature resulting in a compilation of fragmented available knowledge and analysis of an empirical ADR project that developed new insights. Consequently, the knowledge created in this study is theoretically informed and empirically justified. We can conclude that our study extends existing knowledge concerning integrated method support at the micro level. The findings from this project are based on an analysis of one ADR project. Findings valid to a single case context are not necessarily valid to other contexts. Because single case studies do not allow for statistical generalisation, analytical generalisation is common in qualitative studies ( Lee and Baskerville, 2003 ; Wieringa, 2014 ). The purpose of analytical generalisation is to support the transfer of findings from one context to other contexts with similar characteristics (ibid.). In order to provide support for reuse of the findings, we have: (1) formulated abstractions of the findings on a generic level (see Section 9.2 ) and (2) provided transparent support for method integration on the specific level (see Section 7 ).

The fact that the ADR project was conducted within the field of IS, the primary scope of our findings is IS. However, we cannot foresee any barriers against implementing the suggested method support in future ADR projects carried out within other fields such as data science or business administration, or other research projects aiming at building artefacts with methods lacking micro levels. The method support can be regarded as a toolbox, and selections can be made in accordance with contextual project needs. To further validate the integrated method support, we recommend forthcoming ADR projects to implement and evaluate its usefulness.

ADR method: stages, principles and tasks

Method notion

Relationship between this study and the ADR project

Comparison of identified method support

Task: identify and conceptualise the research opportunity

Task: formulate initial research questions

Task: cast the problem as an instance of a class of problems

Task: identify contributing theoretical bases and prior technology advances

Task: discover initial knowledge-creation target

Task: execute building-intervention-evaluation (BIE) cycle(s)

Task: assess need for additional cycles, repeat

Tasks: reflect on the design and redesign during the project, and evaluate adherence to principles

Task: generalisation of design principles

Task: formulation of design principles

Concept-centric matrix concerning the identified method support

Theoretical constructions for the stage Problem Formulation

Theoretical constructions for the stage Building, Intervention and Evaluation

Theoretical constructions for the stage Reflection and Learning

Theoretical constructions for the stage Formalisation of Learning

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Action Research

What is action research.

Action research is a methodology that emphasizes collaboration between researchers and participants to identify problems, develop solutions and implement changes. Designers plan, act, observe and reflect, and aim to drive positive change in a specific context. Action research prioritizes practical solutions and improvement of practice, unlike knowledge generation, which is the priority of traditional methods.  

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Why is Action Research Important in UX Design?

Action research stands out as a unique approach in user experience design (UX design), among other types of research methodologies and fields. It has a hands-on, practical focus, so UX designers and researchers who engage in it devise and execute research that not only gathers data but also leads to actionable insights and solid real-world solutions. 

The concept of action research dates back to the 1940s, with its roots in the work of social psychologist Kurt Lewin. Lewin emphasized the importance of action in understanding and improving human systems. The approach rapidly gained popularity across various fields, including education, healthcare, social work and community development.  

Kurt Lewin, the Founder of social psychology.

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In UX design, the incorporation of action research appeared with the rise of human-centered design principles. As UX design started to focus more on users' needs and experiences, the participatory and problem-solving nature of action research became increasingly significant. Action research bridges the gap between theory and practice in UX design. It enables designers to move beyond hypothetical assumptions and base their design decisions on concrete, real-world data. This not only enhances the effectiveness of the design but also boosts its credibility and acceptance among users—vital bonuses for product designers and service designers. 

At its core, action research is a systematic, participatory and collaborative approach to research . It emphasizes direct engagement with specific issues or problems and aims to bring about positive change within a particular context. Traditional research methodologies tend to focus solely on the generation of theoretical knowledge. Meanwhile, action research aims to solve real-world problems and generate knowledge simultaneously .  

Action research helps designers and design teams gather first-hand insights so they can deeply understand their users' needs, preferences and behaviors. With it, they can devise solutions that genuinely address their users’ problems—and so design products or services that will resonate with their target audiences. As designers actively involve users in the research process, they can gather authentic insights and co-create solutions that are both effective and user-centric.  

Moreover, the iterative nature of action research aligns perfectly with the UX design process. It allows designers to continuously learn from users' feedback, adapt their designs accordingly, and test their effectiveness in real-world contexts. This iterative loop of planning, acting, observing and reflecting ensures that the final design solution is user-centric. However, it also ensures that actual user behavior and feedback validates the solution that a design team produces, which helps to make action research studies particularly rewarding for some brands. 

Designers can continuously learn from users’ feedback in action research and iterate accordingly.

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What is The Action Research Process?

Action research in UX design involves several stages. Each stage contributes to the ultimate goal: to create effective and user-centric design solutions. Here is a step-by-step breakdown of the process:  

1. Identify the Problem

This could be a particular pain point users are facing, a gap in the current UX design, or an opportunity for improvement.  

2. Plan the Action

Designers might need to devise new design features, modify existing ones or implement new user interaction strategies.  

3. Implement the Action

Designers put their planned actions into practice. They might prototype the new design, implement the new features or test the new user interaction strategies.  

4. Observe and Collect Data

As designers implement the action they’ve decided upon, it's crucial to observe its effects and collect data. This could mean that designers track user behaviors, collect user feedback, conduct usability tests or use other data collection methods.  

5. Reflect on the Results

From the collected data, designers reflect on the results, analyze the effectiveness of the action and draw insights. If the action has led to positive outcomes, they can further refine it and integrate it into the final design. If not, they can go back to plan new actions and repeat the process.  

An action research example could be where designers do the following: 

Identification : Designers observe a high abandonment rate during a checkout process for an e-commerce website. 

Planning : They analyze the checkout flow to identify potential friction points.  

Action : They isolate these points, streamline the checkout process, introduce guest checkout and optimize form fields.  

Observation : They monitor changes in abandonment rates and collect user feedback.  

Reflection : They assess the effectiveness of the changes as these reduce checkout abandonment.  

Outcome : The design team notices a significant decrease in checkout abandonment, which leads to higher conversion rates as more users successfully purchase goods.  

What Types of Action Research are there?

Action research splits into three main types: technical, collaborative and critical reflection.  

1. Technical Action Research

Technical action research focuses on improving the efficiency and effectiveness of a system or process. Designers often use it in organizational contexts to address specific issues or enhance operations. This could be where designers improve the usability of a website, optimize the load time of an application or enhance the accessibility of a digital product.  

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2. Collaborative Action Research

Collaborative action research emphasizes the active participation of stakeholders in the research process. It's about working together to identify issues, co-create solutions and implement changes. In the context of UX design, this could mean that designers collaborate with users to co-design a new feature, work with developers to optimize a process, or partner with business stakeholders to align the UX strategy with business goals.  

3. Critical Reflection Action Research

Critical reflection action research aims to challenge dominant power structures and social injustices within a particular context. It emphasizes the importance of where designers and design teams reflect on the underlying assumptions and values that drive research and decision-making processes. In UX design, this could be where designers question the design biases, challenge the stereotypes, and promote inclusivity and diversity in design decisions.  

What are the Benefits and Challenges of Action Research?

Like any UX research method or approach, action research comes with its own set of benefits and challenges.  

Benefits of Action Research

Real-world solutions.

Action research focuses on solving real-world problems. This quality makes it highly relevant and practical. It allows UX designers to create solutions that are not just theoretically sound but also valid in real-world contexts.  

User Involvement

Action research involves users in the research process, which lets designers gather first-hand insights into users' needs, preferences and behaviors. This not only enhances the accuracy and reliability of the research but also fosters user engagement and ownership long before user testing of high-fidelity prototypes.  

Continuous Learning

The iterative nature of action research promotes continuous learning and improvement. It enables designers to adapt their designs based on users' feedback and learn from their successes and failures. They can fine-tune better tools and deliverables, such as more accurate user personas, from their findings.

Author and Human-Computer Interaction Expert, Professor Alan Dix explains personas and why they are important: 

Challenges of Action Research

Time- and resource-intensive.

Action research involves multiple iterations of planning, acting, observing and reflecting, which can be time- and resource-intensive. 

Complexity of Real-world Contexts

It can be difficult to implement changes and observe their effects in real-world contexts. This is due to the complexity and unpredictability of real-world situations.  

Risk of Subjectivity

Since action research involves close collaboration with stakeholders, there's a risk of subjectivity and bias influencing the research outcomes. It's crucial for designers to maintain objectivity and integrity throughout the research process. 

Ethical Considerations

It can be a challenge to ensure all participants understand the nature of the research and agree to participate willingly. Also, it’s vital to safeguard the privacy of participants and sensitive data.  

Scope Creep

The iterative nature of action research might lead to expanding goals, and make the project unwieldy.  

Generalizability

The contextual focus of action research may limit the extent to which designers can generalize findings from field studies to other settings.  

Best Practices and Tips for Successful Action Research

1. define clear objectives.

To begin, designers should define clear objectives. They should ask the following: 

What is the problem to try to solve? 

What change is desirable as an outcome?  

To have clear objectives will guide their research process and help them stay focused.  

2. Involve Users

It’s vital to involve users in the research process. Designers should collaborate with them to identify issues, co-create solutions and implement changes in real time. This will not only enhance the relevance of the research but also foster user engagement and ownership.  

3. Use a Variety of Data Collection Methods

To conduct action research means to observe the effects of changes in real-world contexts. This requires a variety of data collection methods. Designers should use methods like surveys, user interviews, observations and usability tests to gather diverse and comprehensive data. 

UX Strategist and Consultant, William Hudson explains the value of usability testing in this video: 

4. Reflect and Learn

Action research is all about learning from action. Designers should reflect on the outcomes of their actions, analyze the effectiveness of their solutions and draw insights. They can use these insights to inform their future actions and continuously improve the design.  

5. Communicate and Share Findings

Lastly, designers should communicate and share their findings with all stakeholders. This not only fosters transparency and trust but also facilitates collective learning and improvement.  

What are Other Considerations to Bear in Mind with Action Research?

Quantitative data.

Action research involves both qualitative and quantitative data, but it's important to remember to place emphasis on qualitative data. While quantitative data can provide useful insights, designers who rely too heavily on it may find a less holistic view of the user experience. 

Professor Alan Dix explains the difference between quantitative and qualitative data in this video: 

User Needs and Preferences

Designers should focus action research on understanding user needs and preferences. If they ignore these in favor of more technical considerations, the resulting design solutions may not meet users' expectations or provide them with a satisfactory experience.  

User Feedback

It's important to seek user feedback at each stage of the action research process. Without this feedback, designers may not optimize design solutions for user needs. For example, they may find the information architecture confusing. Additionally, without user feedback, it can be difficult to identify any unexpected problems that may arise during the research process.  

Time Allocation

Action research requires time and effort to ensure successful outcomes. If designers or design teams don’t permit enough time for the research process, it can lead to rushed decisions and sloppy results. It's crucial to plan ahead and set aside enough time for each stage of the action research process—and ensure that stakeholders understand the time-consuming nature of research and digesting research findings, and don’t push for premature results. 

Contextual Factors

Contextual factors such as culture, environment and demographics play an important role in UX design. If designers ignore these factors, it can lead to ineffective design solutions that don't properly address users' needs and preferences or consider their context.  

Professor Alan Dix explains the need to consider users’ culture in design, in this video: 

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Overall, in the ever-evolving field of UX design, this is one methodology that can serve as a powerful research tool for driving positive change and promoting continuous learning. Since to do action research means to actively involve users in the research process and research projects, and focus on real-world problem-solving, it allows designers to create more user-centered designs. These digital solutions and services will be more likely to resonate with the target users and deliver exceptional user experiences.  

Despite its challenges, the benefits of action research far outweigh the risks. Action research is therefore a valuable approach for UX designers who are keen on creating a wide range of impactful and sustainable design solutions. The biggest lesson with action research is to ensure that user needs and preferences are at the center of the research process. 

Learn More about Action Research  

Take our User Research: Methods and Best Practices course.  

Take our Master Class Radical Participatory Design: Insights From NASA’s Service Design Lead with Victor Udoewa, Service Design Lead, NASA SBIR/STTR Program. 

Read more in-depth information in 3 things design thinking can learn from action research by Amin Mojtahedi, PhD . 

Find additional insights in What Technical Communicators and UX Designers Can Learn From Participatory Action Research by Guiseppe . 

Discover more insights and tips in Action Research: Steps, Benefits, and Tips by Lauren Stewart .

Questions related to Action Research

Action research and design thinking are both methodologies to solve problems and implement changes, but they have different approaches and emphases. Here's how they differ:  

Objectives  

Action research aims to solve specific problems within a community or organization through a cycle of planning, action, observation and reflection. It focuses on iterative learning and solving real-world problems through direct intervention.  

Design thinking focuses on addressing complex problems by understanding the user's needs, re-framing the problem in human-centric ways, creating many ideas in brainstorming sessions, and adopting a hands-on approach in prototyping and testing. It emphasizes innovation and the creation of solutions that are desirable, feasible and viable.  

Process  

Action research involves a cyclic process that includes:  

- Identify a problem.  

- Plan an action.  

- Implement the action.  

- Observe and evaluate the outcomes.  

- Reflect on the findings and plan the next cycle. 

Design thinking follows a non-linear, iterative process that typically includes five phases:  

- Empathize: Understand the needs of those you're designing for.  

- Define: Clearly articulate the problem you want to solve.  

- Ideate: Brainstorm a range of creative solutions.  

- Prototype: Build a representation of one or more of your ideas.  

- Test: Return to your original user group and test your idea for feedback.  

User Involvement  

Action research actively involves participants in the research process. The participants are co-researchers and have a direct stake in the problem at hand.  

Design thinking prioritizes empathy with users and stakeholders to ensure that the solutions are truly user-centered. While users are involved, especially in the empathy and testing phases, they may not be as deeply engaged in the entire process as they are in action research.  

Outcome  

Action research typically aims for practical outcomes that directly improve practices or address issues within the specific context studied. Its success is measurable by the extent of problem resolution or improvement.  

Design thinking seeks to generate innovative solutions that may not only solve the identified problem but also provide a basis for new products, services or ways of thinking. The success is often measurable in terms of innovation, user satisfaction and feasibility of implementation.  

In summary, while both action research and design thinking are valuable in addressing problems, action research is more about participatory problem-solving within specific contexts, and design thinking is about innovative solution-finding with a strong emphasis on user needs. 

Take our Design Thinking: The Ultimate Guide course. 

To define the research question in an action research project, start by identifying a specific problem or area of interest in your practice or work setting. Reflect on this issue deeply to understand its nuances and implications. Then, narrow your focus to a question that is both actionable and researchable. This question should aim to explore ways to improve, change or understand the problem better. Ensure the question is clear, concise and aligned with the goals of your project. It must invite inquiry and suggest a path towards finding practical solutions or gaining deeper insights. 

For instance, if you notice a decline in user engagement with a product, your research question could be, "How can we modify the user interface of our product to enhance user engagement?" This question clearly targets an improvement, focuses on a specific aspect (the user interface) and implies actionable outcomes (modifications to enhance engagement). 

Take our Master Class Radical Participatory Design: Insights From NASA’s Service Design Lead with Victor Udoewa, Service Design Lead, NASA SBIR/STTR Program.  

Designers use several tools and methods in action research to explore problems and implement solutions. Surveys allow them to gather feedback from a broad audience quickly. Interviews offer deep insights through personal conversations, focusing on users' experiences and needs. Observations help designers understand how people interact with products or services in real environments. Prototyping enables the testing of ideas and concepts through tangible models, and allows for immediate feedback and iteration. Finally, case studies provide detailed analysis of specific instances and offer valuable lessons and insights. 

These tools and methods empower designers to collect data, analyze findings and make informed decisions. When designers employ a combination of these approaches, they ensure a comprehensive understanding of the issues at hand and develop effective solutions. 

CEO of Experience Dynamics, Frank Spillers explains the need to be clear about the problem that designers should address: 

To engage stakeholders in an action research project, first identify all individuals or groups with an interest in the project's outcome. These might include users, team members, clients or community representatives. Clearly communicate the goals, benefits and expected outcomes of the project to them. Use presentations, reports, or informal meetings to share your vision and how their involvement adds value. 

Involve stakeholders early and often by soliciting their feedback through surveys, interviews or workshops. This inclusion not only provides valuable insights but also fosters a sense of ownership and commitment to the project. Establish regular update meetings or newsletters to keep stakeholders informed about progress, challenges and successes. Finally, ensure there are clear channels for stakeholders to share their input and concerns throughout the project. 

This approach creates a collaborative environment where stakeholders feel valued and engaged, leading to more meaningful and impactful outcomes. 

To measure the impact of an action research project, start by defining clear, measurable objectives at the beginning. These objectives should align with the goals of your project and provide a baseline against which you can measure progress. Use quantitative metrics such as increased user engagement, sales growth or improved performance scores for a tangible assessment of impact. Incorporate qualitative data as well, such as user feedback and case studies, to understand the subjective experiences and insights gained through the project. 

Conduct surveys or interviews before and after the project to compare results and identify changes. Analyze this data to assess how well the project met its objectives and what effect it had on the target issue or audience. Document lessons learned and unexpected outcomes to provide a comprehensive view of the project's impact. This approach ensures a holistic evaluation, combining numerical data and personal insights to gauge the success and influence of your action research project effectively. 

Take our Master Class Design KPIs: From Insights to Impact with Vitaly Friedman, Senior UX consultant, European Parliament, and Creative Lead, Smashing Magazine. 

When unexpected results or obstacles emerge during action research, first, take a step back and assess the situation. Identify the nature of the unexpected outcome or obstacle and analyze its potential impact on your project. This step is crucial for understanding the issue at hand. 

Next, communicate with your team and stakeholders about the situation. Open communication ensures everyone understands the issue and can contribute to finding a solution. 

Then, consider adjusting your research plan or design strategy to accommodate the new findings or to overcome the obstacles. This might involve revisiting your research questions, methods or even the design problem you are addressing. 

Always document these changes and the reasons behind them. This documentation will be valuable for understanding the project's evolution and for future reference. 

Finally, view these challenges as learning opportunities. Unexpected results can lead to new insights and innovations that strengthen your project in the long run. 

By remaining flexible, communicating effectively, and being willing to adjust your approach, you can navigate the uncertainties of action research and continue making progress towards your goals. 

Professor Alan Dix explains externalization, a creative process that can help designers to adapt to unexpected roadblocks and find a good way forward: 

Action research can significantly contribute to inclusive and accessible design by directly involving users with diverse needs in the research and design process. When designers engage individuals from various backgrounds, abilities and experiences, they can gain a deeper understanding of the wide range of user requirements and preferences. This approach ensures that the products or services they develop cater to a broader audience, including those with disabilities. 

Furthermore, action research allows for iterative testing and feedback loops with users. This quality enables designers to identify and address accessibility challenges early in the design process. The continuous engagement helps in refining designs to be more user-friendly and inclusive. 

Additionally, action research fosters a culture of empathy and understanding within design teams, as it emphasizes the importance of seeing the world from the users' perspectives. This empathetic approach leads to more thoughtful and inclusive design decisions, ultimately resulting in products and services that are accessible to everyone. 

By prioritizing inclusivity and accessibility through action research, designers can create more equitable and accessible solutions that enhance the user experience for all. 

Take our Master Class How to Design for Neurodiversity: Inclusive Content and UX with Katrin Suetterlin, UX Content Strategist, Architect and Consultant. 

To ensure the reliability and validity of data in action research, follow these steps: 

Define clear research questions: Start with specific, clear research questions to guide your data collection. This clarity helps in gathering relevant and focused data. 

Use multiple data sources: Collect data from various sources to cross-verify information. This triangulation strengthens the reliability of your findings. 

Apply consistent methods: Use consistent data collection methods throughout your research. If conducting surveys or interviews, keep questions consistent across participants to ensure comparability. 

Engage in peer review: Have peers or experts review your research design and data analysis. Feedback can help identify biases or errors, and enhance the validity of your findings. 

Document the process: Keep detailed records of your research process, including how you collected and analyzed data. Documentation allows others to understand and validate your research methodology. 

Test and refine instruments: If you’re using surveys or assessment tools, test them for reliability and validity before using them extensively. Pilot testing helps refine these instruments, and ensures they accurately measure what they intend to. 

When you adhere to these principles, you can enhance the reliability and validity of your action research data, leading to more trustworthy and impactful outcomes. 

Take our Data-Driven Design: Quantitative Research for UX course.  

To analyze data collected during an action research project, follow these steps: 

Organize the data: Begin by organizing your data, categorizing information based on types, sources or research questions. This organization makes the data manageable and prepares you for in-depth analysis. 

Identify patterns and themes: Look for patterns, trends and themes within your data. This might mean to code qualitative data or use statistical tools for quantitative data to uncover recurring elements or significant findings. 

Compare findings to objectives: Match your findings against the research objectives. Assess how the data answers your research questions or addresses the issues you set out to explore. 

Use software tools: Consider using data analysis software, especially for complex or large data sets. Tools like NVivo for qualitative data or SPSS for quantitative data can simplify analysis and help in identifying insights. 

Draw conclusions: Based on your analysis, draw conclusions about what the data reveals. Look for insights that answer your research questions or offer solutions to the problem you are investigating. 

Reflect and act: Reflect on the implications of your findings. Consider how they impact your understanding of the research problem and what actions they suggest for improvement or further investigation. 

This approach to data analysis ensures a thorough understanding of the collected data, allowing you to draw meaningful conclusions and make informed decisions based on your action research project. 

Professor Ann Blandford, Professor of Human-Computer Interaction, UCL explains valuable aspects of data collection in this video: 

Baskerville, R. L., & Wood-Harper, A. T. (1996). A critical perspective on action research as a method for information systems research . Journal of Information Technology, 11(3), 235-246.   

This influential paper examines the philosophical underpinnings of action research and its application in information systems research, which is closely related to UX design. It highlights the strengths of action research in addressing complex, real-world problems, as well as the challenges in maintaining rigor and achieving generalizability. The paper helped establish action research as a valuable methodology in the information systems and UX design fields.  

Di Mascio, T., & Tarantino, L. (2015). New Design Techniques for New Users: An Action Research-Based Approach . In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct (pp. 83-96). ACM. 

This paper describes an action research project that aimed to develop a novel data gathering technique for understanding the context of use of a technology-enhanced learning system for children. The authors argue that traditional laboratory experiments struggle to maintain relevance to the real world, and that action research, with its focus on solving practical problems, is better suited to addressing the needs of new ICT products and their users. The paper provides insights into the action research process and reflects on its value in defining new methods for solving complex, real-world problems. The work is influential in demonstrating the applicability of action research in the field of user experience design, particularly for designing for new and underserved user groups. 

Villari, B. (2014). Action research approach in design research . In Proceedings of the 5th STS Italia Conference A Matter of Design: Making Society through Science and Technology (pp. 306-316). STS Italia Publishing.  

This paper explores the application of action research in the field of design research. The author argues that design is a complex practice that requires interdisciplinary skills and the ability to engage with diverse communities. Action research is presented as a research strategy that can effectively merge theory and practice, linking the reflective dimension to practical activities. The key features of action research highlighted in the paper are its context-dependent nature, the close relationship between researchers and the communities involved, and the iterative process of examining one's own practice and using research insights to inform future actions. The paper is influential in demonstrating the value of action research in addressing the challenges of design research, particularly in terms of bridging the gap between theory and practice and fostering collaborative, user-centered approaches to design.  

Brandt, E. (2004). Action research in user-centred product development . AI & Society, 18(2), 113-133.  

This paper reports on the use of action research to introduce new user-centered work practices in two commercial product development projects. The author argues that the growing complexity of products and the increasing importance of quality, usability, and customization demand new collaborative approaches that involve customers and users directly in the development process. The paper highlights the value of using action research to support these new ways of working, particularly in terms of creating and reifying design insights in representations that can foster collaboration and continuity throughout the project. The work is influential in demonstrating the applicability of action research in the context of user-centered product development, where the need to bridge theory and practice and engage diverse stakeholders is paramount. The paper provides valuable insights into the practical challenges and benefits of adopting action research in this domain. 

1. Reason, P., & Bradbury, H. (Eds.). (2001). Handbook of action research: Participative inquiry and practice . SAGE Publications.  

This comprehensive handbook is considered a seminal work in the field of action research. It provides a thorough overview of the history, philosophical foundations, and diverse approaches to action research. The book features contributions from leading scholars and practitioners, covering topics such as participatory inquiry, critical action research, and the role of action research in organizational change and community development. It has been highly influential in establishing action research as a rigorous and impactful research methodology across various disciplines. 

 2. Stringer, E. T. (2013). Action Research (4th ed.) . SAGE Publications.  

This book by Ernest T. Stringer is a widely recognized and accessible guide to conducting action research. It provides clear, step-by-step instructions on the action research process, including gathering information, interpreting and explaining findings, and taking action to address practical problems. The book is particularly valuable for novice researchers and practitioners in fields such as education, social work, and community development, where action research is commonly applied. Its practical approach and real-life examples have made it a go-to resource for those seeking to engage in collaborative, solution-oriented research. 

3. McNiff, J. (2017). Action Research: All You Need to Know (1st ed.) . SAGE Publications.   

This book by Jean McNiff provides a comprehensive guide to conducting action research projects. It covers the key steps of the action research process, including identifying a problem, developing an action plan, implementing changes, and reflecting on the outcomes. The book is influential in the field of action research as it offers practical advice and strategies for practitioners across various disciplines, such as education, healthcare, and organizational development. It emphasizes the importance of critical reflection, collaboration, and the integration of theory and practice, making it a valuable resource for those seeking to engage in rigorous, transformative research. 

Answer a Short Quiz to Earn a Gift

What is a primary characteristic of action research in UX design?

• It drives practical changes through iterative cycles.
• It focuses solely on theoretical knowledge.
• It relies on external consultants to dictate changes.

Which type of action research improves system efficiency and effectiveness?

• Collaborative Action Research
• Critical Reflection Action Research
• Technical Action Research

What role do stakeholders play in collaborative action research?

• They participate actively in co-creating solutions.
• They provide financial support only.
• They review and approve final designs.

How do users in action research benefit the design process?

• They help make sure designs meet actual user needs and preferences.
• They help speed up the design process significantly.
• They limit the scope of design innovations.

What is the purpose of the reflection stage in the action research process?

• To document the research process for publication only
• To evaluate the effectiveness of actions and plan further improvements
• To finalize the product design without further changes

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Literature on Action Research

Here’s the entire UX literature on Action Research by the Interaction Design Foundation, collated in one place:

Learn more about Action Research

Take a deep dive into Action Research with our course User Research – Methods and Best Practices .

How do you plan to design a product or service that your users will love , if you don't know what they want in the first place? As a user experience designer, you shouldn't leave it to chance to design something outstanding; you should make the effort to understand your users and build on that knowledge from the outset. User research is the way to do this, and it can therefore be thought of as the largest part of user experience design .

In fact, user research is often the first step of a UX design process—after all, you cannot begin to design a product or service without first understanding what your users want! As you gain the skills required, and learn about the best practices in user research, you’ll get first-hand knowledge of your users and be able to design the optimal product—one that’s truly relevant for your users and, subsequently, outperforms your competitors’ .

This course will give you insights into the most essential qualitative research methods around and will teach you how to put them into practice in your design work. You’ll also have the opportunity to embark on three practical projects where you can apply what you’ve learned to carry out user research in the real world . You’ll learn details about how to plan user research projects and fit them into your own work processes in a way that maximizes the impact your research can have on your designs. On top of that, you’ll gain practice with different methods that will help you analyze the results of your research and communicate your findings to your clients and stakeholders—workshops, user journeys and personas, just to name a few!

By the end of the course, you’ll have not only a Course Certificate but also three case studies to add to your portfolio. And remember, a portfolio with engaging case studies is invaluable if you are looking to break into a career in UX design or user research!

We believe you should learn from the best, so we’ve gathered a team of experts to help teach this course alongside our own course instructors. That means you’ll meet a new instructor in each of the lessons on research methods who is an expert in their field—we hope you enjoy what they have in store for you!

All open-source articles on Action Research

An Introduction to Action Research

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Cognitive Maps in UX | IxDF

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3 Design-Based Research and Interventions

Design-Based Research (DBR) is a research methodology used by researchers in the learning sciences. DBR is a concentrated, collaborative and participatory approach to educational inquiry. The basic process of DBR involves developing solutions or interventions to problems (Anderson & Shattuck, 2012). An “Intervention” is any interference that would modify a process or situation. Interventions are thus intentionally implemented change strategies (Sundell & Olsson, 2017). Data analysis takes the form of iterative comparisons. The purpose of this research perspective is to generate new theories and frameworks for conceptualising learning and instruction.

One positive aspect of DBR is that it can be employed to bring researchers and practitioners together to design context-based solutions to educational problems, which have deep-rooted meaning for practitioners about the relationship between educational theory and practice. DBR assumes a timeframe which allows for several rounds of review and iteration. It might be seen as a long-term and intensive approach to educational inquiry which is not really suitable for doctoral work, but increasingly there are examples of this approach being used (Goff & Getenet, 2017).

DBR provides a significant methodological approach for understanding and addressing problems of practice, particularly in the educational context, where a long criticism of educational research is that it is often divorced from the reality of the everyday (Design-Based Research Collective, 2003). DBR is about balancing practice and theory, meaning the researcher must act both as a practitioner and a researcher. DBR allows the collection of data in multiple ways and encourages the development of meaningful relationships with the data and the participants. DBR can also be used as a practical way to engage with real-life issues in education.

DBR & Interventions: GO-GN Insights

Roberts (2019) used a design-based research (DBR) approach to examine how secondary students expanded their learning from formal to informal learning environments using the open learning design intervention (OLDI) framework to support the development of open educational practices (OEP).

“We took some methods and research classes in my EdD program. I took Design-based research (DBR) and found it confusing and overwhelming. As such, I decided to take an extra course on case study research because it seemed to speak to me the most. In my mind I thought I could compare and contrast a variety of secondary school teachers integrating open ed practices. Through my initial exploration, I discovered that in my school district (30,000 + students), there are many teachers using OEP, but they were not interested in working “with” me, they wanted me to watch and observe them teach – then write about it. I began to understand that not only did I want to consider focusing my research on an emerging pedagogy (OEP) I also realized that I wanted to consider newer participatory methods. I did notmthink of DBR in this context when I took the initial course. “I knew I wanted to work with a teacher and complete some kind of intervention in order to support them in thinking about and actually integrating OEP. DBR was suggested to me multiple times, but I kept pushing it away. At the same time many of my supervisory committee and my peers did not think I should even consider DBR. I discovered that many researchers don’t know about it and are fearful of it. As I learned, when you do choose DBR, it is kind of like being an open learner in that you believe in the philosophy behind the DBR process. You just “are” a DBR researcher and educator. “It took many hours of reflection, reading about different examples of DBR, going to workshops and webinars about DBR in order to really see the possible benefits of DBR (collaborative, iterative, responsive, flexibility, balance between theory/ practice and relationships based) to get me to take the plunge…” (Verena Roberts)

Useful references for Design-Based Research: Anderson & Shattuck (2012);Design-Based Research Collective (2003); Goff & Getenet (2017); Sundell & Olsson(2017)

Research Methods Handbook Copyright © 2020 by Rob Farrow; Francisco Iniesto; Martin Weller; and Rebecca Pitt is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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• Knowledge Base

Methodology

• What Is Action Research? | Definition & Examples

What Is Action Research? | Definition & Examples

Published on January 27, 2023 by Tegan George . Revised on January 12, 2024.

Table of contents

Types of action research, action research models, examples of action research, action research vs. traditional research, advantages and disadvantages of action research, other interesting articles, frequently asked questions about action research.

There are 2 common types of action research: participatory action research and practical action research.

• Participatory action research emphasizes that participants should be members of the community being studied, empowering those directly affected by outcomes of said research. In this method, participants are effectively co-researchers, with their lived experiences considered formative to the research process.
• Practical action research focuses more on how research is conducted and is designed to address and solve specific issues.

Both types of action research are more focused on increasing the capacity and ability of future practitioners than contributing to a theoretical body of knowledge.

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Action research is often reflected in 3 action research models: operational (sometimes called technical), collaboration, and critical reflection.

• Operational (or technical) action research is usually visualized like a spiral following a series of steps, such as “planning → acting → observing → reflecting.”
• Collaboration action research is more community-based, focused on building a network of similar individuals (e.g., college professors in a given geographic area) and compiling learnings from iterated feedback cycles.
• Critical reflection action research serves to contextualize systemic processes that are already ongoing (e.g., working retroactively to analyze existing school systems by questioning why certain practices were put into place and developed the way they did).

Action research is often used in fields like education because of its iterative and flexible style.

After the information was collected, the students were asked where they thought ramps or other accessibility measures would be best utilized, and the suggestions were sent to school administrators. Example: Practical action research Science teachers at your city’s high school have been witnessing a year-over-year decline in standardized test scores in chemistry. In seeking the source of this issue, they studied how concepts are taught in depth, focusing on the methods, tools, and approaches used by each teacher.

Action research differs sharply from other types of research in that it seeks to produce actionable processes over the course of the research rather than contributing to existing knowledge or drawing conclusions from datasets. In this way, action research is formative , not summative , and is conducted in an ongoing, iterative way.

As such, action research is different in purpose, context, and significance and is a good fit for those seeking to implement systemic change.

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Action research comes with advantages and disadvantages.

• Action research is highly adaptable , allowing researchers to mold their analysis to their individual needs and implement practical individual-level changes.
• Action research provides an immediate and actionable path forward for solving entrenched issues, rather than suggesting complicated, longer-term solutions rooted in complex data.
• Done correctly, action research can be very empowering , informing social change and allowing participants to effect that change in ways meaningful to their communities.

Disadvantages

• Due to their flexibility, action research studies are plagued by very limited generalizability  and are very difficult to replicate . They are often not considered theoretically rigorous due to the power the researcher holds in drawing conclusions.
• Action research can be complicated to structure in an ethical manner . Participants may feel pressured to participate or to participate in a certain way.
• Action research is at high risk for research biases such as selection bias , social desirability bias , or other types of cognitive biases .

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

Sources in this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

George, T. (2024, January 12). What Is Action Research? | Definition & Examples. Scribbr. Retrieved September 14, 2024, from https://www.scribbr.com/methodology/action-research/

Cohen, L., Manion, L., & Morrison, K. (2017). Research methods in education (8th edition). Routledge.

Naughton, G. M. (2001).  Action research (1st edition). Routledge.

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A Critical Comparison of Design-Based Research and Action Research

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• > The Cambridge Handbook of the Learning Sciences
• > Design-Based Research

Book contents

• The Cambridge Handbook of the Learning Sciences
• Copyright page
• Contributors
• 1 An Introduction to the Learning Sciences
• Part I Foundations
• Part II Methodologies
• 9 Design-Based Research
• 10 Analyzing Collaboration
• 11 Microgenetic Methods
• 12 A Learning Sciences Perspective on the Design and Use of Assessment in Education
• 13 Learning Analytics and Educational Data Mining
• Part III Grounding Technology in the Learning Sciences
• Part IV Learning Together
• Part V Learning Disciplinary Knowledge
• Part VI Moving Learning Sciences Research into the Classroom

9 - Design-Based Research

A Methodological Toolkit for Engineering Change

from Part II - Methodologies

Published online by Cambridge University Press:  14 March 2022

Design-based research (DBR) is a methodology used to study learning in environments that are designed and systematically changed by the researcher. The goal of DBR is to engage the close study of learning as it unfolds within a particular context that contains one or more theoretically inspired innovations and then to develop new theories, artifacts, and practices that can be used to inform research and learning in other related contexts beyond the one classroom being studied. In DBR, research improves practice at the same time as it results in fundamental research findings that can be generalized. The widespread use of DBR by learning scientists demonstrates the field’s commitment to impacting classroom practice, and is consistent with a focus on complex learning environments that involve many people in situated social practices.

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• Design-Based Research
• By Sasha Barab
• Edited by R. Keith Sawyer , University of North Carolina, Chapel Hill
• Book: The Cambridge Handbook of the Learning Sciences
• Online publication: 14 March 2022
• Chapter DOI: https://doi.org/10.1017/9781108888295.012

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Research Methods and Design

• Action Research
• Case Study Design
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Action research

A type of applied research designed to find the most effective way to bring about a desired social change or to solve a practical problem, usually in collaboration with those being researched.

SAGE Research Methods Videos

How do you define action research.

Professor David Coghlan explains action research as an approach that crosses many academic disciplines yet has a shared focus on taking action to address a problem. He describes the difference between this approach and empirical scientific approaches, particularly highlighting the challenge of getting action research to be taken seriously by academic journals

Dr. Nataliya Ivankova defines action research as using systematic research principles to address an issue in everyday life. She delineates the six steps of action research, and illustrates the concept using an anti-diabetes project in an urban area.

This is just one segment in a whole series about action research. You can find the rest of the series in our SAGE database, Research Methods:

Videos covering research methods and statistics

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9 Research Design: Action Research

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Action research is an exciting research design that leads to social and systems change. In this chapter, the authors describe action research, including how it differs from other approaches, along with a detailed explanation of the action research process, including planning, implementing, observing, reflecting, and sharing results. The authors highlight a case study delineating the iterative process within the school counseling field to provide readers with knowledge of the action research process. Additionally, authors share examples of current school counseling research to bring action research to life. School counselors and SCEs will understand how to utilize action research across the educational ecosystems to ignite change.

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• Action Research Professor Eileen Piggot-Irvine explains action research as research that doesn't conclude with data collection, but continues through an action in response to the research findings. She discusses how action research can be utilized best and where it is going in the future. Piggot-Irvine also discusses some of the drawbacks to using action research.
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• David Coghlan Discusses Action Research Professor David Coghlan explains action research as an approach that crosses many academic disciplines yet has a shared focus on taking action to address a problem. He describes the difference between this approach and empirical scientific approaches, particularly highlighting the challenge of getting action research to be taken seriously by academic journals.
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• Researching Racism in Schools Using Participatory Action Research Dr. Meagan Call-Cummings presents a participatory action research project that had three aims: to uncover and understand racism in schools, to empower marginalized students, and to determine the effectiveness of participatory action research as a means to effect social change.
• Researching Substance Abuse Using Community-Based Participatory Research Methods Professor Liliane Windsor discusses her research on substance abuse using community-based participatory research methods. In community-based participatory research, members of the community partner with academics to conduct the research. Windsor discusses community preparation and brainstorming, her research findings, and community collaborative boards
• Task-Based Language Learning and Teaching: An Action Research Study Megan Calvert, an ESL teacher, describes how she designed, tested, and adjusted a task-based learning exercise for a mixed-level English language course.

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Dust arrestment in subways: analysis and technique design

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• I. Lugin   ORCID: orcid.org/0000-0002-5287-3589 1 ,
• L. Kiyanitsa   ORCID: orcid.org/0000-0001-6436-1997 1 ,
• A. Krasyuk   ORCID: orcid.org/0000-0001-7579-3015 1 &
• T. Irgibayev   ORCID: orcid.org/0000-0003-2948-2683 2  

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The research is devoted to solve the problem of elevated dust levels in subway air through the implementation of a proposed dust collection system. A comprehensive experiment to determine the fractional and chemical compositions, as well as dust density, in the existing metro systems of Almaty (Kazakhstan) and Novosibirsk (Russian Federation) was conducted. The experiment results led to hypotheses about the sources of dust emission in subways. An innovative method for de-dusting tunnel air has been developed. The method is based on the use of air flows generated by the piston action of trains and the installation of labyrinth filters in the ventilation joints of stations. The parameters of the computational model of a subway line on the basis of decomposition approach to mathematical modeling of aerodynamic processes methods of computational aerodynamics by transition from a full model of a subway line to an open-ended periodic one have been substantiated. The research also justifies the geometric parameters of the labyrinth filters, determining their effectiveness based on air velocity and the number of filter element rows. Additionally, potential energy savings achievable with the proposed system were assessed. The scope of application of the results of the presented study of air distribution from the piston effect in subway structures and the effectiveness of the proposed air filtration system are limited to subways with single-track tunnels and open-type stations equipped with ventilation joints.

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Study on temporal and spatial evolution law for dust pollution in double roadway ventilation system of short wall continuous mining face

Acknowledgements

The study was carried out within the framework of the Project of Fundamental Scientific Research of the Russian Federation (state registration number is 121052500147-6) and was supported by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan, Grant No. AP09260842.

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Chinakal Institute of Mining, Siberian Branch, Russian Academy of Sciences, Krasny Prospect, Novosibirsk, Russia, 630091

I. Lugin, L. Kiyanitsa & A. Krasyuk

Satbayev University, Almaty, Republic of Kazakhstan

T. Irgibayev

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Lugin, I., Kiyanitsa, L., Krasyuk, A. et al. Dust arrestment in subways: analysis and technique design. Int. J. Environ. Sci. Technol. (2024). https://doi.org/10.1007/s13762-024-05970-5

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Received : 26 June 2023

Revised : 25 April 2024

Accepted : 13 August 2024

Published : 10 September 2024

DOI : https://doi.org/10.1007/s13762-024-05970-5

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