design based research euler

Design-Based Research

Copy citation

Table of contents, author information.

Franz Steiner Verlag

ISBN 978-3-515-10838-6 (Print)

ISBN 978-3-515-10841-6 (eBook)

First published: 14.07.2014

https://doi.org/10.25162/9783515108416

Related content

Content (18 chapter), inhaltsverzeichnis, teil a: anker, design research – a paradigm under development, teil b: vertiefungen, wissenschafts-praxis-kooperation in designbasierter forschung: im spannungsfeld zwischen wissenschaftlicher gültigkeit und praktischer relevanz, entwicklungsfrage: welchen stellenwert hat die entwicklung im kontext von design researchwie wird entwicklung zu einem wissenschaftlichen akt, potenziale von design research aus der perspektive der innovationsforschung, design principles als kristallisationspunkt für praxisgestaltung und wissenschaftliche erkenntnisgewinnung, wissensgenese in design-based-research projekten, methods of evaluation and reflection in design research, teil c: forschungspraxis, design-based research in der ausbildung von lehrkräften an berufsschulen: entwicklung, erprobung und evaluation des konzepts„aufgabenorientiertes coaching zur planung wirtschaftsberuflicher lernumgebungen“, portraying the design research cycle: professional development in indian slums, design research zur individuellen förderung in der beruflichen rehabilitation, selbstreguliertes lernen gestalten darstellung eines design research-zyklus in der wirtschaftswissenschaftlichen hochschulbildung, design science research in business research – with special emphasis on information systems, autorinnen und autoren.

In order provide you with a better browsing experience, we use cookies.

Change Password

Your password must have 8 characters or more and contain 3 of the following:.

• a lower case character, 
• an upper case character, 
• a special character 

Password Changed Successfully

Your password has been changed

• Sign in / Register

Request Username

Can't sign in? Forgot your username?

Enter your email address below and we will send you your username

If the address matches an existing account you will receive an email with instructions to retrieve your username

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

Search for more papers by this author

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).

• Alonzo, A. C. ( 2011 ). Learning progressions that support formative assessment practices . Measurement , 9 (2/3), 124–129. Google Scholar
• Anderson, T., & Shattuck, J. ( 2012 ). Design-based research: A decade of progress in education research? Educational Researcher , 41 (1), 16–25. Google Scholar
• Andrews, T. M., Leonard, M. J., Colgrove, C. A., & Kalinowski, S. T. ( 2011 ). Active learning not associated with student learning in a random sample of college biology courses . CBE—Life Sciences Education , 10 (4), 394–405. Link ,  Google Scholar
• Badenhorst, E., Hartman, N., & Mamede, S. ( 2016 ). How biomedical misconceptions may arise and affect medical students’ learning: A review of theoretical perspectives and empirical evidence . Health Professions Education , 2 (1), 10–17. Google Scholar
• Barab, S. ( 2014 ). Design-based research: A methodological toolkit for engineering change . In The Cambridge handbook of the learning sciences (2nd ed., pp. 151–170). Cambridge University Press. https://doi.org/10.1017/CBO9781139519526.011 Google Scholar
• Barab, S., & Squire, K. ( 2004 ). Design-based research: Putting a stake in the ground . Journal of the Learning Sciences , 13 (1), 1–14. Google Scholar
• Bell, P. ( 2004 ). On the theoretical breadth of design-based research in education . Educational Psychologist , 39 (4), 243–253. Google Scholar
• Borrego, M., Cutler, S., Prince, M., Henderson, C., & Froyd, J. E. ( 2013 ). Fidelity of implementation of research-based instructional strategies (RBIS) in engineering science courses . Journal of Engineering Education , 102 (3), 394–425. Google Scholar
• Brown, A. L. ( 1992 ). Design experiments: Theoretical and methodological challenges in creating complex interventions in classroom settings . Journal of the Learning Sciences , 2 (2), 141–178. Google Scholar
• Chi, M. T. H., Roscoe, R. D., Slotta, J. D., Roy, M., & Chase, C. C. ( 2012 ). Misconceived causal explanations for emergent processes . Cognitive Science , 36 (1), 1–61. Medline ,  Google Scholar
• Cobb, P., Confrey, J., diSessa, A., Lehrer, R., & Schauble, L. ( 2003 ). Design experiments in educational research . Educational Researcher , 32 (1), 9–13. Google Scholar
• Coley, J. D., & Tanner, K. D. ( 2012 ). Common origins of diverse misconceptions: Cognitive principles and the development of biology thinking . CBE—Life Sciences Education , 11 (3), 209–215. Link ,  Google Scholar
• Coley, J. D., & Tanner, K. ( 2015 ). Relations between intuitive biological thinking and biological misconceptions in biology majors and nonmajors . CBE—Life Sciences Education , 14 (1). https://doi.org/10.1187/cbe.14-06-0094 Medline ,  Google Scholar
• Collins, A., Joseph, D., & Bielaczyc, K. ( 2004 ). Design research: Theoretical and methodological issues . Journal of the Learning Sciences , 13 (1), 15–42. Google Scholar
• Corcoran, T., Mosher, F. A., & Rogat, A. D. ( 2009 ). Learning progressions in science: An evidence-based approach to reform (CPRE Research Report No. RR-63) . Philadelphia, PA: Consortium for Policy Research in Education. Google Scholar
• Davidesco, I., & Milne, C. ( 2019 ). Implementing cognitive science and discipline-based education research in the undergraduate science classroom . CBE—Life Sciences Education , 18 (3), es4. Link ,  Google Scholar
• Design-Based Research Collective . ( 2003 ). Design-based research: An emerging paradigm for educational inquiry . Educational Researcher , 32 (1), 5–8. Google Scholar
• Dolan, E. L. ( 2015 ). Biology education research 2.0 . CBE—Life Sciences Education , 14 (4), ed1. Link ,  Google Scholar
• Duschl, R., Maeng, S., & Sezen, A. ( 2011 ). Learning progressions and teaching sequences: A review and analysis . Studies in Science Education , 47 (2), 123–182. Google Scholar
• Eddy, S. L., Converse, M., & Wenderoth, M. P. ( 2015 ). PORTAAL: A classroom observation tool assessing evidence-based teaching practices for active learning in large science, technology, engineering, and mathematics classes . CBE—Life Sciences Education , 14 (2), ar23. Link ,  Google Scholar
• Edelson, D. C. ( 2002 ). Design research: What we learn when we engage in design . Journal of the Learning Sciences , 11 (1), 105–121. Google Scholar
• Flynn, E., Pine, K., & Lewis, C. ( 2006 ). The microgenetic method—Time for change? The Psychologist , 19 (3), 152–155. Google Scholar
• Gouvea, J. S., & Simon, M. R. ( 2018 ). Challenging cognitive construals: A dynamic alternative to stable misconceptions . CBE—Life Sciences Education , 17 (2), ar34. Link ,  Google Scholar
• Gunckel, K. L., Mohan, L., Covitt, B. A., & Anderson, C. W. ( 2012 ). Addressing challenges in developing learning progressions for environmental science literacy . In Alonzo, A. C.Gotwals, A. W. (Eds.), Learning progressions in science: Current challenges and future directions (pp. 39–75). Rotterdam: SensePublishers. https://doi.org/10.1007/978-94-6091-824-7_4 Google Scholar
• Hake, R. R. ( 2007 ). Design-based research in physics education research: A review . In Kelly, A. E.Lesh, R. A.Baek, J. Y. (Eds.), Handbook of design research methods in mathematics, science, and technology education (p. 24). New York: Routledge. Google Scholar
• Hoadley, C. M. ( 2004 ). Methodological alignment in design-based research . Educational Psychologist , 39 (4), 203–212. Google Scholar
• Jackson, M., Tran, A., Wenderoth, M. P., & Doherty, J. H. ( 2018 ). Peer vs. self-grading of practice exams: Which is better? CBE—Life Sciences Education , 17 (3), es44. https://doi.org/10.1187/cbe.18-04-0052 Link ,  Google Scholar
• Jin, H., & Anderson, C. W. ( 2012 ). A learning progression for energy in socio-ecological systems . Journal of Research in Science Teaching , 49 (9), 1149–1180. Google Scholar
• Joseph, D. ( 2004 ). The practice of design-based research: Uncovering the interplay between design, research, and the real-world context . Educational Psychologist , 39 (4), 235–242. Google Scholar
• Kelly, A. E. ( 2014 ). Design-based research in engineering education . In Cambridge handbook of engineering education research (pp. 497–518). New York, NY: Cambridge University Press. https://doi.org/10.1017/CBO9781139013451.032 Google Scholar
• Lee, C. J., Toven-Lindsey, B., Shapiro, C., Soh, M., Mazrouee, S., Levis-Fitzgerald, M., & Sanders, E. R. ( 2018 ). Error-discovery learning boosts student engagement and performance, while reducing student attrition in a bioinformatics course . CBE—Life Sciences Education , 17 (3), ar40. https://doi.org/10.1187/cbe.17-04-0061 Link ,  Google Scholar
• Lo, S. M., Gardner, G. E., Reid, J., Napoleon-Fanis, V., Carroll, P., Smith, E., & Sato, B. K. ( 2019 ). Prevailing questions and methodologies in biology education research: A longitudinal analysis of research in CBE — life sciences education and at the society for the advancement of biology education research . CBE—Life Sciences Education , 18 (1), ar9. Link ,  Google Scholar
• Marbach-Ad, G., Briken, V., El-Sayed, N. M., Frauwirth, K., Fredericksen, B., Hutcheson, S., … & Smith, A. C. ( 2009 ). Assessing student understanding of host pathogen interactions using a concept inventory . Journal of Microbiology & Biology Education , 10 (1), 43–50. Medline ,  Google Scholar
• McFarland, J. L., Price, R. M., Wenderoth, M. P., Martinková, P., Cliff, W., Michael, J. , … & Wright, A. ( 2017 ). Development and validation of the homeostasis concept inventory . CBE—Life Sciences Education , 16 (2), ar35. Link ,  Google Scholar
• McKenney, S., & Reeves, T. C. ( 2013 ). Systematic review of design-based research progress: Is a little knowledge a dangerous thing? Educational Researcher , 42 (2), 97–100. Google Scholar
• Mestre, J. P., Cheville, A., & Herman, G. L. ( 2018 ). Promoting DBER-cognitive psychology collaborations in STEM education . Journal of Engineering Education , 107 (1), 5–10. Google Scholar
• Michael, J. A. ( 2007 ). What makes physiology hard for students to learn? Results of a faculty survey . AJP: Advances in Physiology Education , 31 (1), 34–40. Medline ,  Google Scholar
• Michael, J. A., Modell, H., McFarland, J., & Cliff, W. ( 2009 ). The “core principles” of physiology: What should students understand? Advances in Physiology Education , 33 (1), 10–16. Medline ,  Google Scholar
• Middleton, J., Gorard, S., Taylor, C., & Bannan-Ritland, B. ( 2008 ). The “compleat” design experiment: From soup to nuts . In Kelly, A. E.Lesh, R. A.Baek, J. Y. (Eds.), Handbook of design research methods in education: Innovations in science, technology, engineering, and mathematics learning and teaching (pp. 21–46). New York, NY: Routledge. Google Scholar
• Modell, H. I. ( 2000 ). How to help students understand physiology? Emphasize general models . Advances in Physiology Education , 23 (1), S101–S107. Medline ,  Google Scholar
• Mohan, L., Chen, J., & Anderson, C. W. ( 2009 ). Developing a multi-year learning progression for carbon cycling in socio-ecological systems . Journal of Research in Science Teaching , 46 (6), 675–698. Google Scholar
• National Academies of Sciences, Engineering, and Medicine . ( 2018 ). How People Learn II: Learners, Contexts, and Cultures . Washington, DC: National Academies Press. Retrieved June 24, 2019, from https://doi.org/10.17226/24783 Google Scholar
• National Research Council (NRC) . ( 2002 ). Scientific research in education . Washington, DC: National Academies Press. Retrieved January 31, 2019, from https://doi.org/10.17226/10236 Google Scholar
• NRC . ( 2007 ). Taking science to school: Learning and teaching science in grades K–8 . Washington, DC: National Academies Press. Retrieved March 22, 2019, from www.nap.edu/catalog/11625/taking-science-to-school-learning-and-teaching-science-in-grades . https://doi.org/10.17226/11625 Google Scholar
• NRC . ( 2012 ). Discipline-based education research: Understanding and improving learning in undergraduate science and engineering . Washington, DC: National Academies Press. Retrieved from www.nap.edu/catalog/13362/discipline-based-education-research-understanding-and-improving-learning-in-undergraduate . https://doi.org/10.17226/13362 Google Scholar
• NRC . ( 2018 ). How people learn II: Learners, contexts, and cultures . Washington, DC: National Academies Press. Retrieved from www.nap.edu/read/24783/chapter/7 . https://doi.org/10.17226/24783 Google Scholar
• O’Donnell, A. M. ( 2004 ). A commentary on design research . Educational Psychologist , 39 (4), 255–260. Google Scholar
• Offerdahl, E. G., McConnell, M., & Boyer, J. ( 2018 ). Can I have your recipe? Using a fidelity of implementation (FOI) framework to identify the key ingredients of formative assessment for learning . CBE—Life Sciences Education , 17 (4), es16. Link ,  Google Scholar
• Peffer, M., & Renken, M. ( 2016 ). Practical strategies for collaboration across discipline-based education research and the learning sciences . CBE—Life Sciences Education , 15 (4), es11. Link ,  Google Scholar
• Reiser, B. J., Smith, B. K., Tabak, I., Steinmuller, F., Sandoval, W. A., & Leone, A. J. ( 2001 ). BGuILE: Strategic and conceptual scaffolds for scientific inquiry in biology classrooms . In Carver, S. M.Klahr, D. (Eds.), Cognition and instruction: Twenty-five years of progress (pp. 263–305). Mahwah, NJ: Lawrence Erlbaum Associates, Inc. Google Scholar
• Sandoval, W. ( 2014 ). Conjecture mapping: An approach to systematic educational design research . Journal of the Learning Sciences , 23 (1), 18–36. Google Scholar
• Sandoval, W. A., & Bell, P. ( 2004 ). Design-based research methods for studying learning in context: Introduction . Educational Psychologist , 39 (4), 199–201. Google Scholar
• Schinske, J. N., Balke, V. L., Bangera, M. G., Bonney, K. M., Brownell, S. E., Carter, R. S. , … & Corwin, L. A. ( 2017 ). Broadening participation in biology education research: Engaging community college students and faculty . CBE—Life Sciences Education , 16 (2), mr1. Link ,  Google Scholar
• Scott, E., Anderson, C. W., Mashood, K. K., Matz, R. L., Underwood, S. M., & Sawtelle, V. ( 2018 ). Developing an analytical framework to characterize student reasoning about complex processes . CBE—Life Sciences Education , 17 (3), ar49. https://doi.org/10.1187/cbe.17-10-0225 Link ,  Google Scholar
• Scott, E., Wenderoth, M. P., & Doherty, J. H. ( 2019 ). Learning progressions: An empirically grounded, learner-centered framework to guide biology instruction . CBE—Life Sciences Education , 18 (4), es5. https://doi.org/10.1187/cbe.19-03-0059 Link ,  Google Scholar
• Sharma, M. D., & McShane, K. ( 2008 ). A methodological framework for understanding and describing discipline-based scholarship of teaching in higher education through design-based research . Higher Education Research & Development , 27 (3), 257–270. Google Scholar
• Shavelson, R. J., Phillips, D. C., Towne, L., & Feuer, M. J. ( 2003 ). On the science of education design studies . Educational Researcher , 32 (1), 25–28. Google Scholar
• Siegler, R. S. ( 2006 ). Microgenetic analyses of learning . In Damon, W.Lerner, R. M. (Eds.), Handbook of child psychology (pp. 464–510). Hoboken, NJ: John Wiley & Sons, Inc. https://doi.org/10.1002/9780470147658.chpsy0211 Google Scholar
• Stringer, E. T. ( 2013 ). Action research . Thousand Oaks, CA: Sage Publications, Inc. Google Scholar
• Subramaniam, M., Jean, B. S., Taylor, N. G., Kodama, C., Follman, R., & Casciotti, D. ( 2015 ). Bit by bit: Using design-based research to improve the health literacy of adolescents . JMIR Research Protocols , 4 (2), e62. Medline ,  Google Scholar
• Summers, M. M., Couch, B. A., Knight, J. K., Brownell, S. E., Crowe, A. J., Semsar, K. , … & Batzli, J. ( 2018 ). EcoEvo-MAPS: An ecology and evolution assessment for introductory through advanced undergraduates . CBE—Life Sciences Education , 17 (2), ar18. Link ,  Google Scholar
• Szteinberg, G., Balicki, S., Banks, G., Clinchot, M., Cullipher, S., Huie, R. , … & Sevian, H. ( 2014 ). Collaborative professional development in chemistry education research: Bridging the gap between research and practice . Journal of Chemical Education , 91 (9), 1401–1408. Google Scholar
• Talanquer, V. ( 2014 ). DBER and STEM education reform: Are we up to the challenge? Journal of Research in Science Teaching , 51 (6), 809–819. Google Scholar
• Wang, F., & Hannafin, M. J. ( 2005 ). Design-based research and technology-enhanced learning environments . Educational Technology Research and Development , 53 (4), 5–23. Google Scholar
• Wang, J.-R. ( 2004 ). Development and validation of a Two-tier instrument to examine understanding of internal transport in plants and the human circulatory system . International Journal of Science and Mathematics Education , 2 (2), 131–157. Google Scholar
• Warfa, A.-R. M. ( 2016 ). Mixed-methods design in biology education research: Approach and uses . CBE—Life Sciences Education , 15 (4), rm5. Link ,  Google Scholar
• Windschitl, M., Thompson, J., Braaten, M., & Stroupe, D. ( 2012 ). Proposing a core set of instructional practices and tools for teachers of science . Science Education , 96 (5), 878–903. Google Scholar
• Zagallo, P., Meddleton, S., & Bolger, M. S. ( 2016 ). Teaching real data interpretation with models (TRIM): Analysis of student dialogue in a large-enrollment cell and developmental biology course . CBE—Life Sciences Education , 15 (2), ar17. Link ,  Google Scholar
• Codéveloppement d’un programme d’autogestion de la douleur chronique en ligne: un projet de recherche basé sur la conception et axé sur l’engagement des patients 12 March 2024 | Canadian Journal of Pain, Vol. 8, No. 1
• Behavioral assessment of soft skill development in a highly structured pre-health biology course for undergraduates 29 Aug 2024 | Journal of Microbiology & Biology Education, Vol. 25, No. 2
• Developing and Validating the Preschool Nutrition Education Practices Survey 1 Aug 2024 | Journal of Nutrition Education and Behavior, Vol. 56, No. 8
• Immediate Versus Delayed Low-Stakes Questioning: Encouraging the Testing Effect Through Embedded Video Questions to Support Students’ Knowledge Outcomes, Self-Regulation, and Critical Thinking 30 July 2024 | Technology, Knowledge and Learning, Vol. 87
• Innovative strategies to strengthen teaching-researching skills in chemistry and biology education: a systematic literature review 10 July 2024 | Frontiers in Education, Vol. 9
• Enhancing undergraduates’ engagement in a learning community by including their voices in the technological and instructional design 1 Jun 2024 | Computers & Education, Vol. 214
• Practice-Based Teacher Education Benefits Graduate Trainees and Their Students Through Inclusive and Active Teaching Methods 16 October 2023 | Journal for STEM Education Research, Vol. 7, No. 1
• Designing and Evaluating Generative AI-Based Voice-Interaction Agents for Improving L2 Learners’ Oral Communication Competence 2 July 2024
• A Research-Led Contribution of Engineering Education for a Sustainable Future 1 June 2024
• Development of Inclusive Tools Through Digital Fabrication for Chemistry Learning in Students with and Without Visual Impairment 5 August 2024
• Leveraging learning experience design: digital media approaches to influence motivational traits that support student learning behaviors in undergraduate online courses 11 October 2022 | Journal of Computing in Higher Education, Vol. 35, No. 3
• Investigating an Assessment Design that Prevents Students from Using ChatGPT as the Sole Basis to Pass Assessment at the Tertiary Level 30 November 2023 | E-Journal of Humanities, Arts and Social Sciences
• Spatial Variations in Aquatic Insect Community Structure in the Winam Gulf of Lake Victoria, Kenya 8 Sep 2023 | International Journal of Ecology, Vol. 2023
• The Perceived Effectiveness of Various Forms of Feedback on the Acquisition of Technical Skills by Advanced Learners in Simulation-Based Health Professions Education 28 Aug 2023 | Cureus, Vol. 44
• Occupational therapists' acceptance of 3D printing 22 August 2023 | South African Journal of Occupational Therapy, Vol. 53, No. 2
• An app by students for students – the DPaCK-model for a digital collaborative teamwork project to identify butterflies 4 August 2023 | Frontiers in Education, Vol. 8
• Applying DBR to design protocols for synchronous online Chinese learning: An activity theoretic perspective 1 Aug 2023 | System, Vol. 116
• Defining the Nature of Augmented Feedback for Learning Intraosseous Access Skills in Simulation-Based Health Professions Education 14 Jul 2023 | Cureus, Vol. 86
• Practice-based 21st-century teacher education: Design principles for adaptive expertise 1 Jul 2023 | Teaching and Teacher Education, Vol. 128
• Undergraduate students’ neurophysiological reasoning: what we learn from the attractive distractors students select 1 Jun 2023 | Advances in Physiology Education, Vol. 47, No. 2
• Oaks to arteries: the Physiology Core Concept of flow down gradients supports transfer of student reasoning 1 Jun 2023 | Advances in Physiology Education, Vol. 47, No. 2
• Audrey Chen ,
• Kimberley A. Phillips ,
• Jennifer E. Schaefer , and
• Patrick M. Sonner
• Kyle Frantz,, Monitoring Editor
• Optimizing the Learner’s Role in Feedback: Development of a Feedback-Preparedness Online Application for Medical Students in the Clinical Setting 8 May 2023 | Cureus, Vol. 42
• History, Status, and Development of AI-Based Learning Science 8 April 2023 | SN Computer Science, Vol. 4, No. 3
• An Analytical Dashboard of Collaborative Activities for the Knowledge Building 4 March 2023 | Technology, Knowledge and Learning, Vol. 29
• The Application of a Design-Based Research Framework for Simulation-Based Education 22 Nov 2022 | Cureus, Vol. 22
• Erin Stanfield ,
• Corin D. Slown ,
• Quentin Sedlacek , and
• Suzanne E. Worcester
• James Hewlett, Monitoring Editor
• 2022 | , Vol. 511
• The effect of the e-mentoring-based education program on professional development of preschool teachers 3 July 2021 | Education and Information Technologies, Vol. 27, No. 1
• 2022 | Education Sciences, Vol. 12, No. 8
• Training Digital Competences of Educators in Continuing Education: A Three-Level Approach 27 October 2022
• Design-based research as a framework for developing and deploying augmented reality applications and scenarios for intercultural exchange 13 December 2021
• Repetition Is Important to Students and Their Understanding during Laboratory Courses That Include Research 10 Sep 2021 | Journal of Microbiology & Biology Education, Vol. 22, No. 2
• Another look at the core concepts of physiology: revisions and resources 1 Dec 2020 | Advances in Physiology Education, Vol. 44, No. 4

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).

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Design-based research in economic education

2018, EDeR Journal

This article describes the phases of a design-based research (DBR) process using the example of an economic education project. The project was carried out in the "urban economics" subject area for economic education in upper secondary schools (15-18 years). The research questions, research design, and central research results are described in detail. Finally, the experiences from the project are incorporated to reflect the DBR's specific performance characteristics.

Related Papers

EDeR Journal

Educational Design Research

In recent years, the quality of management education in general, and particularly of MBA and Executive MBA programs, has been called into question. There are serious doubts about universities’ ability to give students the competencies they need to deal with complex problems in modern society. One part of the discussion focuses on ethical issues and the process through which students develop values and attitudes. In line with the economic crisis, there has been increasing interest in the development of learners’ attitudes to responsibility. We report the results of a study that starts with an ambitious and yet ill-structured learning goal in a demanding educational practice area: How can pedagogical interventions in management education be designed to promote learners‘ attitudes to responsible leadership? As a starting point, there are neither consensual definitions of responsible leadership nor substantial theories available to design promising interventions. De-sign- based research (DBR) provides a structured process to deal with research problems, starting with innovative but imprecisely defined objectives and unknown ways to reach them. We introduce the DBR design and describe the research process and results from a project conducted at St.Gallen University’s Executive MBA program. In close collaboration with practitioners, interventions evolved through multiple cycles of development, testing and refinement with the pursuit of theory-building and practical innovation.

The use of video analysis in Design-Based Research (DBR) seems to be promising, because the quality of video data matches the reality of educational fields. Educational fields are multidimensional and complex. And more than other types of data, video may capture, for example, the simultaneity of verbal and non-verbal interactions. This seems to be valuable in the quest for new insights and better designs of educational interventions. However, to date there has been limited use of video data in researching their design. This paper aims at reflecting how the benefits of video-based analysis may be utilised in DBR. Experiences with the collection and analysis of video data in a project to design self-organised learning (SOL) at a vocational school in Germany will be used as a case study to illustrate the type of findings that may feed into the DBR process. In this case, the project school had already introduced a sophisticated SOL model but was experiencing various implementation difficulties. Resolving issues like this requires insights into how exactly a concept is realised and what happens in the field. Therefore, video data on classroom interactions was gathered and sub-sequently analysed using the documentary method. This led to the reconstruction of two different types of orientation that were guiding the students when they dealt with their self organised learning environment. In a subversive orientation, students playfully infiltrate the formal learning space with peer activities. In a confirming orientation, students stick to both, the (informal) rules of the (formal) learning arrangement and of the peer environment, thus expressing respect for the boundary between these two worlds. These findingshave been used to redesign the SOL intervention.

EDer Journal

Design-based research (DBR) is a programme where researchers co-operate with practitioners to work out new solutions. In DBR researchers interfere in daily life and participate in practitioners’ working processes. One open question is: What kind of knowledge can be generated in these projects? My starting point here is a DBR project in vocational education and training in Germany which is used for an investigation of the epistemological background of this kind of research enterprise. The characteristics of DBR are reflected on the basis of phenomenological and hermeneutical approaches. The basic assumptions of these concepts are introduced and applied to the DBR approach to show how DBR generally works and how, specially, features of DBR like participation in daily life, co-operation with practitioners, gathering knowledge in the field a. s. o. can be handled. The line of argumentation in this contribution is a radical switch between practical questions in daily work in DBR on one hand and theoretical re-assurance on the other hand. For researchers, DBR is an enterprise in a new world. The analytical paradigm does not prepare the voyagers for this journey. Therefore the non-analytic continental tradition of philosophy has to be re-discovered.

The transition from school to vocational education and training (VET) is becoming more difficult for an increasing number of adolescents. Despite the growing significance of this phase, there is hardly any research regarding interventions targeting stu-dents' resilience, especially with regard to their capacity to join the labour market. This paper aims at describing the research process of developing three different interventions in cooperation with a number of practitioners who teach in so-called interim solutions. The goal of the paper is, thus, to illustrate how design-based research (DBR) can be conducted in the context of vocational education. The comprehensive three-cycle development of three interventions with the aim of fostering students' conflict management competence, attribution and self-efficacy will be used as a single case study to illustrate a complex DBR project. Each step in the design-research process will be reflected , resulting in a discussion of the possibilities and obstacles of combining formative and summative evaluation in the DBR process.

Journal of Further and Higher Education

Jose Eos Trinidad

Student-centred learning (SCL) is often understood differently by people, and the differences are even more salient in higher education, where there are some very strong advocates and vocal critics. Theoretical research on SCL in higher education often highlights five key aspects but these have not been empirically scrutinised. Qualitative research with students and faculty from a large private university in the Philippines shows that there are particular facets of SCL that faculty and students eagerly subscribe to, particularly in terms of class engagement , skills building, and having motivated students. However, they fail to readily question how assessments and power relations between teachers and students are part of SCL. It suggests that SCL is viewed and reduced to effective classroom practices that have little to do with more foundational aspects of the teacher-student relationship and manifold possibilities for learning.

This research explored the implementation of a technology-enhanced instructional model for interdisciplinary learning. The model was developed in a previous phase of this research via DBR in the context of higher-education. Our aim in the current phase was to extend the applicability of the model and refine its underlying design principles based on their implementation in three secondary schools. For this purpose, a research-practice partnership was established, which included researchers, practitioners from an educational non-governmental organization , school principals, and teachers. Three practitioner-teams, facilitated by one of the researchers, collaboratively designed their own technology-enhanced interdisciplinary learning environments , in which they adapted the instructional model. This paper presents a new type of principled practical knowledge (PPK)-enhanced principled instructional model-which was obtained by comparison between the practitioners' designs and the original, higher-education context design. The PPK broadened the partnership's understanding of ways to promote interdisciplinary learning. Furthermore, it has raised new perspectives that were not considered during the development of the model, thereby allowing deeper understanding of the notion of interdisciplinary learning. Thus, this study illustrates how the establishment of productive research-practice partnerships can serve as a powerful strategy for implementing and scaling educational innovations beyond the original DBR context.

Educators in some Canadian schools are especially challenged in developing innovative pedagogical approaches that can intellectually engage their students in deep learning of core curriculum content, while equipping them with 21st century competencies. In this paper, we discuss some key ideas on how an intervention, the design and building of digital video games, was implemented and explored, to address this challenge, in four grade 6 social studies classes at a Calgary charter school, utilizing a design-based research framework. Findings revealed that: (i) to effectively implement this intervention in the classroom context, teachers needed to shift/modify their design of instructional activities compared to how they would normally design them in their social studies classes to teach the same chosen content; and (ii) the intervention, as implemented, seem to have the potential to be an effective innovative pedagogy for deep learning and one that promotes the intellectual engagement of students and their development and application of 21st century competencies. Some implications of these findings for the implementation of interventions in school, in terms of transforming the classroom environment, assessing the type of theoretical support needed, using design-based research as an effective framework to study how interventions and developing policy for the implementation of interventions are listed for K-12 educators, school jurisdictions and Alberta Education.

Distance Education

Shironica P . Karunanayaka

The adoption of open educational practices (OEP) requires a shift in our perceptions and practices about learning and teaching towards a more participatory, creative and a sharing culture. Such shifts are best achieved and sustained through carefully designed learning experiences that involve people in reflecting on authentic problems and situations. The impact of this kind of capacity building around OEP are difficult to ascertain because they are an outcome of a combination of factors involving the practitioners themselves, their educational context, as well as their views about intellectual property, its ownership and its role in educational development. Getting a clear estimate of the impacts of this kind of capacity building requires a multiplicity of evidence. This paper discusses impacts of innovative design strategies adopted in four MOOCs on shifting mindsets of practitioners towards OEP.

This editorial article introduces the overall conceptualisation of the journal EDeR-Educational Design Research and its review-and workflow model. In addition, it provides an overview of all contributions that made it into Issue 1 of EDeR, before it invites the wider Design-Based Research community to participate in a variety of roles and functions. EDeR is seeking contributions on Design-Based Research within the educational sciences. All kinds of educational contexts can be addressed, such as higher education, adult education & professional training, vocational education, school education, and so forth. We welcome contributions on Design-Based Research from pedagogical, psychological, sociological, economic, information-technological perspectives, and so forth, as long as they focus on educational problems and challenges. Furthermore, we also encourage contributions that focus on methodological and conceptual challenges and the further development of the Design-based Research approach within the educational sciences.

Rhia Moreno

The foreign language (FL) classroom can be an anxious environment where students feel uncomfortable having to communicate in a language in which they feel inadequate and have little practice. Low self-efficacy in skill-specific tasks is oftentimes the culprit. While there are a number of factors involved in successful language learning, this study examines how practice affects students’ sense of self-efficacy in the foreign language classroom. Using self-efficacy theory and design-based research, this qualitative study ‘flipped’ the classroom to focus on student input and output practice in class with grammar instruction video- recorded for homework. Data were recursively collected and analyzed from ten courses over three semesters. Classroom observations and reflection were triangulated with interviews and focus groups. Findings suggest that practice and self-efficacy in the FL classroom are indeed linked and that other factors such as peer familiarity and grading also play a role. The paper concludes with implications for language learning and teaching.

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

Australasian Journal of Educational Technology

Victoria I Marin

Hennie Esterhuizen , Seugnet Blignaut

Anne-Mette Nortvig

University of Hull

Adrian Hodge

PhD Thesis, Concordia University, Montreal, Canada

Alannah Fitzgerald

Thom Cochrane

Roshini Pillay

Computer-Supported Collaborative Learning at the Workplace

In M. Campbell, J. Willems, C. Adachi, D. Blake, I. Doherty, S. Krishnan, S. Macfarlane, L. Ngo, M. O’Donnell, S. Palmer, L. Riddell, I. Story, H. Suri & J. Tai (Eds.), Open Oceans: Learning without borders. Proceedings ASCILITE 2018 Geelong (pp. 423-428).

Georgina Binns

Intercultural Education

Pikir Review

Elena Railean

Group memory enhancing learning and knowledge practices in team meetings

Dr Amanda (Mandy) Lacy

European Journal of STEM Education

Lectito Journals

Journal of Formative Design in Learning

Michael K. Thomas

Research in Learning Technology

Henriikka Vartiainen

Shirin Vossoughi , Megan Bang

Stephen Petrina

Information and Learning Sciences

Rebecca Reynolds , Preben Hansen

Innovate Learning Review

Curt Bonk , Thomas C Reeves

Anthony Welch

Rafael Albuquerque

Edgar Andres Sosa Neira

Dr Mahmoud M S Abdallah

In T. Plomp, & N. Nieveen (Eds.), Educational design research-Part B: Illustrative cases (pp. 927-946). Enschede, the Netherlands: SLO. SLO • Netherlands institute for curriculum development Credits 2013 SLO (Netherlands institute for curriculum development), Enschede

National-Louis University Digital Commons

Darren Aitchison

Jennifer K . Shah

Kabugo David

Carl Winsløw

Fifth Annual International Conference on the …

Stefan Detchev

Mario Sánchez Aguilar

Subscription Prices and …

Piia Näykki

Rob Cuthbert

Joelle Fanghanel

Erik Bohemia

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

• About IJPBL

Home > Libraries > LIBRARIESPUBLISHING > PUPOAJ > IJPBL > Vol. 11 (2017) > Iss. 2

Problem-Based Assignments as a Trigger for Developing Ethical and Reflective Competencies

Dieter Euler , University of St. Gallen, Switzerland Follow Patrizia Kühner , University of St. Gallen, Switzerland Follow

The following research question serves as the starting point of this research and development project: How, in the context of a didactic design, can problem-based assignments trigger learning activities for the development of ethical and reflective competencies in students in economics courses? This paper focuses on the design of problem-based assignments as a trigger for purposeful learning activities, and, based on the literature, summarizes the principles of problem-based assignment design. A case study on the “city economics” topic outlines the implementation of these principles in a specific economics class context. In the context of a design-based research approach, the development and testing of the design are linked to the creation of design principles comprising many situations. In conclusion, the paper presents its preliminary research results.

Recommended Citation

Euler, D. , & Kühner, P. (2017). Problem-Based Assignments as a Trigger for Developing Ethical and Reflective Competencies. Interdisciplinary Journal of Problem-Based Learning, 11 (2). Available at: https://doi.org/10.7771/1541-5015.1668

Since May 11, 2017

Included in

Educational Assessment, Evaluation, and Research Commons

To view the content in your browser, please download Adobe Reader or, alternately, you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.

• Journal Home
• IJPBL Celebrates 10 Years: 2006-2016
• Most Popular Papers
• Receive Email Notices or RSS

Advanced Search

ISSN: 1541-5015

Published with the support of the Teaching Academy at Purdue University, the School of Education at Indiana University, and the Educational Technology program at the University of South Carolina.

Home | About | FAQ | My Account | Accessibility Statement

Privacy Copyright

Research on the Clustering of Emotional Elements in Art and Design Based on Visual Language Communication

New citation alert added.

This alert has been successfully added and will be sent to:

You will be notified whenever a record that you have chosen has been cited.

To manage your alert preferences, click on the button below.

New Citation Alert!

Please log in to your account

Information & Contributors

Bibliometrics & citations, view options, index terms.

Applied computing

Arts and humanities

Performing arts

Computing methodologies

Machine learning

Learning paradigms

Unsupervised learning

Cluster analysis

Information systems

Information retrieval

Information systems applications

Data mining

Theory of computation

Theory and algorithms for application domains

Machine learning theory

Unsupervised learning and clustering

Recommendations

A new hybrid method based on partitioning-based dbscan and ant clustering.

Clustering problem is an unsupervised learning problem. It is a procedure that partition data objects into matching clusters. The data objects in the same cluster are quite similar to each other and dissimilar in the other clusters. Density-based ...

Research on Emotional Design of Visual Interaction Based on Cognitive Psychology

Based on Norman's three-level theory of emotion-instinct, behavior, and reflection, the article quantifies and analyzes the emotional elements in visual interaction design. Cognitive psychology provides a foundation for studying user behavior and ...

A Case Study for Clustering Characteristics Evaluation and Clustering Dimension Selection by Using UCI News Data

The purpose of this study is to research and explore the clustering algorithm, and provide methods for clustering characteristics evaluation and clustering dimension selection, in order to help the user to understand the impact and meaning of clustering ...

Information

Published in.

United States

Publication History

Author tags.

• Information Extraction
• Artistic Design
• Analysis of Emotional Elements

Contributors

Other metrics, bibliometrics, article metrics.

• 0 Total Citations
• 0 Total Downloads
• Downloads (Last 12 months) 0
• Downloads (Last 6 weeks) 0

View options

Login options.

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Share this publication link.

Copying failed.

Share on social media

Affiliations, export citations.

• Please download or close your previous search result export first before starting a new bulk export. Preview is not available. By clicking download, a status dialog will open to start the export process. The process may take a few minutes but once it finishes a file will be downloadable from your browser. You may continue to browse the DL while the export process is in progress. Download
• Download citation
• Copy citation

We are preparing your search results for download ...

We will inform you here when the file is ready.

Your file of search results citations is now ready.

Your search export query has expired. Please try again.

EDeR. Educational Design Research

Announcements

December lectures: a new online series connecting the dbr community.

Starting in December 2024, the interdisciplinary DBR-Network in collaboration with EDeR are organizing the December Lectures series. This is an open series of online lectures by researchers who work on topcis relevant for Design-Based Research / Educational Design Research.

Details (Date, meeting link) will be shared her in due time.

If you are interested in offering a lecture yoursel, please contact the EDeR editorial office ( [email protected] )

The first December lecture ist held by Heather Lynn Johnson , University of Colorado Denver. 

Heather is a professor of mathematics education and will talk about How Graphs Work: A Design-Based Approach to Cultivate Community Among Introductory Undergraduate Mathematics Instructors

Current Issue

Design-based research in der deutschdidaktik. sprach-, literatur- und mediendidaktische forschungsprojekte design-based research in german didactics. research projects on language, literature and media didactics.

• PDF (Deutsch)

Academic Articles

Literary learning with digital media in inclusive settings insights and results from the dbr-project 'digili' focussing on students' cooperation, multiperspectivity in the context of design-based research theoretical-methodological considerations to reading literacy at the end of primary school, design-based research und inklusion das diamanten-modell zur fachdidaktischen entwicklungsforschung inklusiver unterrichtsarrangements im deutschunterricht, practice illustrations, überarbeitungsphasen als kristallisationspunkt eines design-research-projekts ein praxisbeispiel, sprach(en)bewusstheit im deutschunterricht gestaltung einer sprachreflexiven lernumgebung in einem designbasierten projekt, ein strukturorientiertes lrs-training in klasse 6/7 einblicke in ein iterativ-zyklisch entwickeltes material und dessen erprobung und weiterentwicklung, information.

• For Readers
• For Authors
• For Librarians

Developed By

Published by University of Paderborn Department for Management and Human Resources Education Warburger-Str. 100 33098 Paderborn

• Corpus ID: 115785020

Design Principles als Kristallisationspunkt für Praxisgestaltung und wissenschaftliche Erkenntnisgewinnung

• Dieter Euler
• Published 2014

11 Citations

Report on the development of a competence model for validation experts in close collaboration with science and practice.

• Highly Influenced
• 14 Excerpts

Entwicklung von Gestaltungsprinzipien zur Förderung interkultureller Lehrkompetenz

Educational design research.

• 12 Excerpts

How to Maximise Spatial Presence: Design Guidelines for a Virtual Learning Environment for School Use

Lern- und assistenzsysteme partizipativ integrieren – entwicklung einer systematik zur prozessgestaltung auf basis eines organisationspädagogischen ansatzes, körperbasierte zugangsweisen als form der inklusiv-medialen welterschliessung, qualitäten der mentor*innenqualifizierung m-v. eine design based research studie zu einem lernbegleitungsprogramm an der schnittstelle zwischen schule und hochschule, how does didactic knowledge develop: experiences from a design project, of reflections based on a grounded theory study praxistransfer in der tertiären bildungsforschung, responsible leadership in management education: a design-based re-search study., related papers.

Showing 1 through 3 of 0 Related Papers

Historical environmental Kuznets curve for the USA and the UK: cyclical environmental Kuznets curve evidence

• Published: 17 September 2024

Cite this article

• Tolga Omay   ORCID: orcid.org/0000-0003-0263-2258 1 ,
• Julide Yildirim   ORCID: orcid.org/0000-0002-4739-6028 2 &
• Nazmiye Balta-Ozkan   ORCID: orcid.org/0000-0002-2848-5535 3  

Human activities, including population growth, industrialization, and urbanization, have increasingly impacted the environment. Despite the benefits of economic growth to individual welfare, its negative environmental consequences necessitate a thorough assessment. The environmental Kuznets curve (EKC), positing an inverted U-shaped relationship between income per capita and environmental degradation, has been extensively studied since its proposition by Grossman and Krueger (Environmental impacts of a North American free trade agreement, National Bureau of Economic Research working paper, 1991. https://doi.org/10.3386/w3914 ). However, empirical evidence on the validity and shape of the EKC varies due to methodological differences, country-specific dynamics, and other factors. Examining the historical growth paths of individual countries helps explain the mixed findings in empirical EKC research. Long-term data allow researchers to determine the EKC's shape and turning points, aiding policymakers in devising appropriate environmental policies for each economic growth cycle within the framework of global environmental governance. Accordingly, this study contributes to the literature by taking a historical perspective on the EKC, focusing specifically on the United States and the United Kingdom. Drawing on data spanning from 1850, we employ advanced econometric techniques, including fractional frequency flexible Fourier form Dickey–Fuller-type unit root tests and structural breaks unit root tests, to overcome limitations of traditional linearized EKC estimations. Moreover, the classical polynomial regression approach is employed to model the long-term cycles based on the scatterplot inspection of per capita carbon dioxide (CO 2 ) and per capita GNP series. Contrary to conventional expectations, our empirical findings do not support the existence of a clear inverted U-shaped EKC relationship between CO 2 emissions and economic growth for either country. Instead, our analysis reveals the presence of multiple regimes, indicating a cyclical pattern where economic growth affects environmental quality with varying severity over time. Furthermore, we demonstrate proper modeling techniques for the EKC, highlighting the importance of identification and misspecification tests. Our study identifies cyclical EKC patterns for both the UK and the USA, with the UK exhibiting two cycles and the USA exhibiting three, shaped by varying economic, social, and technological contexts. By revealing the nuances of the economic growth-environmental degradation nexus for these early developer countries, our study provides valuable insights for policymakers seeking to devise evidence-based and environmentally sustainable growth policies within the framework of global environmental governance. These findings underscore the importance of considering historical context and structural changes when analyzing the EKC, providing valuable insights for policymakers aiming to design adaptive and sustainable economic growth strategies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

The technical details of these tests are given in supplementary material as technical annex.

We use the genetic algorithm in our estimation process of the smooth transition trend since it is shown to be the best performing algorithm in estimating LST types of equations. For details, see Omay et al. ( 2018 ).

Please see https://www.bankofengland.co.uk/statistics/research-datasets for detailed information about the historical sources of the data.

http://www.nber.org/data/abc/ .

http://cdiac.ornl.gov/ .

https://www.nber.org/cycles.html .

Please see Mittlefehldt ( 2018 ) for a review of development of renewable technologies in the USA since the energy crises of the 1970s.

https://www.carbonbrief.org/analysis-uk-carbon-emissions-in-2017-fell-to-levels-last-seen-in-1890 .

https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/272061/5761.pdf (Accessed on 20/02/2021).

https://www.iea.org/articles/global-co2-emissions-in-2019 .

Alford, B. W. E. (1981). New industries for old? British Industry between the wars. In R. Floud & D. McCloskey (Eds.), The economic history of Britain since 1700 (pp. 308–331). Cambridge University Press.

Google Scholar  

Apergis, N., & Ozturk, I. (2015). Testing environmental Kuznets curve hypothesis in Asian countries. Ecological Indicators, 52 , 16–22. https://doi.org/10.1016/j.ecolind.2014.11.026

Article   Google Scholar  

Arapostathis, S., Carlsson-Hyslop, A., Pearson, P. J. G., Thornton, J., Gradillas, M., Laczay, S., & Wallis, S. (2013). Governing transitions: Cases and Insights from two periods in the history of the UK gas industry. Energy Policy, 52 , 25–44. https://doi.org/10.1016/j.enpol.2012.08.016

Ashby, L., & Anderson, M. (1977). Studies in the politics of environmental protection: The Historical Roots of the British Clean Air Act, 1956: II. The appeal to public opinion over domestic smoke, 1880–1892. Interdisciplinary Science Reviews, 2 , 9–26.

Article   CAS   Google Scholar  

Balaguer, J., & Cantavella, M. (2016). Estimating the environmental kuznets curve for spain by considering fuel oil prices (1874–2011). Ecological Indicators, 60 , 853–859. https://doi.org/10.1016/j.ecolind.2015.08.006

Ball, M., & Wood, A. (1996). Trend growth in post-1850 British economic history: The Kalman filter and historical judgment. Journal of the Royal Statistical Society: Series D (The Statistician), 45 (2), 143–152.

Bell, M. L., Davis, D. L., & Fletcher, T. (2004). A Retrospective assessment of mortality from the london smog episode of 1952: The role of influenza and pollution. Environmental Health Perspectives, 112 , 6–8. https://doi.org/10.1289/ehp.6539

Berry, H., Kaul, A., & Lee, N. (2021). Follow the smoke: The pollution haven effect on global sourcing. Strategic Management Journal, 42 (13), 2420–2450.

Bhanumati, P., de Haan, M., & Tebrake, J. W. (2022). Greenhouse emissions rise to record, erasing drop during pandemic. IMF Blogs.

Brimblecombe, P. (1977). London air pollution, 1500–1900. Atmospheric Environment, 11 , 1157–1162.

Broadberry, S., & Howlett, P. (2005). The United Kingdom during World War I: Business as usual? In S. Broadberry, & M. Harrison (Ed.), The economics of world war I. (pp. 206–234) Cambridge University Press. https://doi.org/10.1017/CBO9780511497339.008

Campbell, B. C. (1999). Understanding economic change in the gilded age. OAH Magazine of History, 13 , 16–20. https://doi.org/10.1093/maghis/13.4.16

Climate Change Committee. (2020). The sixth carbon budget: The UK’s path to net zero.

Cheng, S. C., Wu, T. P., Lee, K. C., & Chang, T. (2014). Flexible fourier unit root test of unemployment for PIIGS countries. Economic Modelling, 36 , 142–148. https://doi.org/10.1016/j.econmod.2013.09.021

Churchill, S. A., Inekwe, J., Ivanovski, K., & Smyth, R. (2018). The environmental Kuznets curve in the OECD: 1870–2014. Energy Economics, 75 , 389–399. https://doi.org/10.1016/j.eneco.2018.09.004

Congregado, E., Feria-Gallardo, J., Golpe, A. A., & Iglesias, J. (2016). The environmental Kuznets curve and CO 2 emissions in the USA: Is the relationship between GDP and CO 2 emissions time varying? Evidence across economic sectors. Environmental Science and Pollution Research, 23 , 18407–18420. https://doi.org/10.1007/s11356-016-6982-9

Coombs, B. (2013). British tank production and the war economy, 1934–1945 . Bloomsburry Academic.

Destek, M. A., & Sinha, A. (2020). Renewable, Non-renewable energy consumption, economic growth, trade openness and ecological footprint: Evidence from organisation for economic co-operation and development countries. Journal of Cleaner Production . https://doi.org/10.1016/j.jclepro.2019.118537

Dinda, S. (2004). Environmental Kuznets curve hypothesis: A survey. Ecological Economics, 49 , 431–455. https://doi.org/10.1016/j.ecolecon.2004.02.011

Dogan, E., & Turkekul, B. (2016). CO 2 emissions, real output, energy consumption, trade, urbanization and financial development: Testing the EKC hypothesis for the USA. Environmental Science and Pollution Research, 23 , 1203–1213. https://doi.org/10.1007/s11356-015-5323-8

Doms, M., & Dunne, T. (1995). Energy intensity, electricity consumption, and advanced manufacturing-technology usage. Technological Forecasting and Social Change, 49 , 297–310. https://doi.org/10.1016/0040-1625(95)00055-F

Enders, W., & Lee, J. (2012). A unit root test using a fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74 , 574–599. https://doi.org/10.1111/j.1468-0084.2011.00662.x

Enders, W., & Li, J. (2015). Trend-cycle decomposition allowing for multiple smooth structural changes in the trend of US real GDP. Journal of Macroeconomics, 44 , 71–81. https://doi.org/10.1016/j.jmacro.2015.02.002

Esteve, V., & Tamarit, C. (2012a). Is there an environmental Kuznets curve for spain? Fresh evidence from old data. Economic Modelling, 29 , 2696–2703. https://doi.org/10.1016/j.econmod.2012.08.016

Esteve, V., & Tamarit, C. (2012b). Threshold cointegration and nonlinear adjustment between CO 2 and income: The environmental Kuznets curve in Spain, 1857–2007. Energy Economics, 34 , 2148–2156. https://doi.org/10.1016/j.eneco.2012.03.001

Fosten, J., Morley, B., & Taylor, T. (2012). Dynamic misspecification in the environmental Kuznets curve: Evidence from CO 2 and SO 2 emissions in the United Kingdom. Ecological Economics, 76 , 25–33. https://doi.org/10.1016/j.ecolecon.2012.01.023

Fouquet, R., & Pearson, P. J. G. (1998). A Thousand years of energy use in The United Kingdom. Energy Journal, 19 , 1–41. https://doi.org/10.5547/ISSN0195-6574-EJ-Vol19-No4-1

Gordon, R. J. (1986). The American business cycle: Continuity and change . University of Chicago Press.

Book   Google Scholar  

Grid, N. (2020). Energy.

Grossman, G., & Krueger, A. (1991). Environmental ımpacts of a North American free trade agreement, national bureau of economic research working paper. https://doi.org/10.3386/w3914

Hanif, I. (2017). Economics-energy-environment nexus in Latin America and the Caribbean. Energy, 141 , 170–178. https://doi.org/10.1016/j.energy.2017.09.054

Harrison, M. (1998). The economics of World War II: An overview. In M. Harrison (Ed.), The economics of world war II: six great powers in ınternational comparison. (pp. 1–42) Cambridge University Press. https://doi.org/10.1017/CBO9780511497339.002

Harrison, S., & Weder, M. (2009). Technological change and the roaring twenties: A neoclassical perspective. Journal of Macroeconomics, 31 , 363–375. https://doi.org/10.1016/j.jmacro.2009.05.003

He, J., & Richard, P. (2009). Environmental Kuznets curve for CO 2 in Canada. Ecological Economics, 11 (3), 1–11.

Holtz-Ekain, D., & Selden, T. M. (1995). Stoking the fires? CO 2 emissions and economic growth. Journal of Public Economics, 57 , 85–101. https://doi.org/10.1016/0047-2727(94)01449-X

Humphrey, W. S., & Stanislaw, J. (1979). Economic growth and energy consumption in the UK, 1700–1975. Energy Policy, 7 , 29–42.

Huntington, H. G. (2005). US carbon emissions, technological progress and economic growth since 1870. International Journal of Global Energy Issues, 23 , 292–306. https://doi.org/10.1504/IJGEI.2005.006948

Jayanthakumaran, K., Verma, R., & Liu, Y. (2012). CO 2 emissions, energy consumption, trade and income: A comparative analysis of China and India. Energy Policy, 42 , 450–460. https://doi.org/10.1016/j.enpol.2011.12.010

Kander, A., & Lindmark, M. (2004). Energy consumption pollutant emissions and growth in the long run: Sweden through 200 years. European Review of Economic History, 8 , 297–335. https://doi.org/10.1017/S1361491604001224

Kasman, A., & Duman, Y. S. (2015). CO 2 emissions, economic growth, energy consumption, trade and urbanization in new EU member and candidate countries: A panel data analysis. Economic Modelling, 44 , 97–103. https://doi.org/10.1016/j.econmod.2014.10.022

Klausen, J. (1998). War and welfare: Europe and the United States, 1945 to the Present . Martin’s Press.

Lanne, M., & Liski, M. (2004). Trends and breaks in per-capita carbon dioxide emissions, 1870–2028. Energy Journal, 25 (4), 41–65. https://doi.org/10.5547/ISSN0195-6574-EJ-Vol25-No4-3

Larson, E. D. (2012). Can US carbon emissions keep falling? Climate Central, 2 .

Leybourne, S., Newbold, P., & Vougas, D. (1998). Unit roots and smooth transitions. Journal of Time Series Analysis, 19 , 83–97. https://doi.org/10.1111/1467-9892.00078

Lindmark, M. (2002). An EKC-pattern in historical perspective: Carbon dioxide emissions, technology, fuel prices and growth in Sweden 1870–1997. Ecological Economics, 42 , 333–347. https://doi.org/10.1016/S0921-8009(02)00108-8

Lindmark, M., & Acar, S. (2014). The environmental Kuznets curve and the pasteur effect: Environmental costs in Sweden 1850–2000. European Review of Economic History, 18 , 306–323. https://doi.org/10.1093/ereh/heu011

Lipford, J. W., & Yandle, B. (2010). Environmental Kuznets curves, carbon emissions, and public choice. Environment and Development Economics, 15 (4), 417–438.

Liu, Z., Ciais, P., Deng, Z., Lei, R., Davis, S. J., Feng, S., & Schellnhuber, H. J. (2020). Near-real-time monitoring of global CO 2 emissions reveals the effects of the COVID-19 pandemic. Nature Communications, 11 (1), 5172. https://doi.org/10.1038/s41467-020-18922-7

Mikayilov, J. I., Hasanov, F. J., & Galeotti, M. (2018). Decoupling of CO 2 emissions and GDP: A time-varying cointegration approach. Ecological Indicators, 95 , 615–628. https://doi.org/10.1016/j.ecolind.2018.07.051

Mittlefehldt, S. (2018). From appropriate technology to the clean energy economy: Renewable energy and environmental politics since the 1970s. Journal of Environmental Studies and Sciences, 8 , 212–219. https://doi.org/10.1007/s13412-018-0471-z

Moomaw, W. R., & Unruh, G. C. (1997). Are environmental Kuznets curves misleading us? The case of CO 2 emissions. Environment and Development Economics, 2 , 451–463. https://doi.org/10.1017/S1355770X97000247

Morley, J. C., Nelson, C. R., & Zivot, E. (2003). Why are the beveridge-nelson and unobserved-components decompositions of GDP so different? Review of Economics and Statistics, 85 , 235–243.

Moutinho, V., Madaleno, M., & Elheddad, M. (2020). Determinants of the environmental kuznets curve considering economic activity sector diversification in the OPEC countries. Journal of Cleaner Production . https://doi.org/10.1016/j.jclepro.2020.122642

Mulatu, A. (2017). The structure of UK outbound FDI and environmental regulation. Environmental and Resource Economics, 68 , 65–96. https://doi.org/10.1007/s10640-017-0145-4

Nasir, M. A., Huynh, T. L. D., & Tram, H. T. X. (2019). Role of financial development, economic growth and foreign direct investment in driving climate change: A case of emerging ASEAN. Journal of Environmental Management, 242 , 131–141.

Nelson, H. T., von Hippel, D., Peterson, T., & Garagulagian, R. (2016). The great recession or progressive energy policies? Explaining the decline in US greenhouse gas emissions forecasts. Journal of Environmental Planning and Management, 59 , 480–500. https://doi.org/10.1080/09640568.2015.1017042

Omay, T. (2015). Fractional frequency flexible fourier form to approximate smooth breaks in unit root testing. Economic Letters, 134 , 123–126. https://doi.org/10.1016/j.econlet.2015.07.010

Omay, T., Emirmahmutoglu, F., & Hasanov, M. (2018). Structural break, nonlinearity and asymmetry: A re-examination of PPP proposition. Applied Economics, 50 , 1289–1308. https://doi.org/10.1080/00036846.2017.1361005

Omay, T., Gupta, R., & Bonaccolto, G. (2017). The US real GNP is trend-stationary after all. Applied Economics Letters, 24 , 510–514. https://doi.org/10.1080/13504851.2016.1205719

Ray, R. L., Singh, V. P., Singh, S. K., Acharya, B. S., & He, Y. (2022). What is the impact of COVID-19 pandemic on global carbon emissions? Science of the Total Environment, 816 , 151503. https://doi.org/10.1016/j.scitotenv.2021.151503

Sanyé-Mengual, E., Secchi, M., Corrado, S., Beylot, A., & Sala, S. (2019). Assessing the decoupling of economic growth from environmental impacts in the european union: A consumption-based approach. Journal of Cleaner Production, 236 , 117535. https://doi.org/10.1016/j.jclepro.2019.07.010

Schenk, C. R. (2005). Britain in the world economy. In P. Addison & H. Jones (Eds.), A companion to contemporary Britain 1939–2000 (pp. 463–481). Blackwell.

Şentürk, H., Omay, T., Yildirim, J., & Köse, N. (2020). Environmental Kuznets curve: Non-linear panel regression analysis. Environmental Modeling and Assessment, 25 , 633–651. https://doi.org/10.1007/s10666-020-09702-0

Sephton, P., & Mann, J. (2016). Compelling evidence of an environmental Kuznets curve in the United Kingdom. Environmental and Resource Economics, 64 , 301–315. https://doi.org/10.1007/s10640-014-9871-z

Shahbaz, M., Khraief, N., & Mahalik, M. K. (2020). Investigating the environmental Kuznets’s curve for Sweden: Evidence from multivariate adaptive regression splines (MARS). Empirical Economics, 59 , 1883–1902. https://doi.org/10.1007/s00181-019-01698-1

Shahbaz, M., Omay, T., & Roubaud, D. (2018). Sharp and smooth breaks in unit root. The Journal of Energy and Development, 44 , 5–40.

Shahbaz, M., Shafiullah, M., Papavassiliou, V. G., & Hammoudeh, S. (2017). The CO 2 –growth nexus revisited: A nonparametric analysis for the G7 economies over nearly two centuries. Energy Economics, 65 , 183–193. https://doi.org/10.1016/j.eneco.2017.05.007

Song, Y., Zhang, M., & Zhou, M. (2019). Study on the decoupling relationship between CO 2 emissions and economic development based on two-dimensional decoupling theory: A case between china and the United States. Ecological Indicators, 102 , 230–236. https://doi.org/10.1016/j.ecolind.2019.02.044

Stradling, D., & Thorsheim, P. (1999). The smoke of great cities: British and American efforts to control air pollution, 1860–1914. Environmental History, 4 (1), 6–31.

Tol, R. S. J., Pacala, S. W., & Socolow, R. H. (2009). Understanding long-term energy use and carbon dioxide emissions in the USA. Journal of Policy Modelling, 31 , 425–445. https://doi.org/10.1016/j.jpolmod.2008.12.002

Unruh, G. C., & Moomaw, W. R. (1998). An alternative analysis of apparent EKC-type transitions. Ecological Economics, 25 (2), 221–229. https://doi.org/10.1016/S0921-8009(97)00182-1

Wei, C. (2024). Historical trend and drivers of China’s CO 2 emissions from 2000 to 2020. Environment, Development and Sustainability, 26 (1), 2225–2244. https://doi.org/10.1007/s10668-022-02811-8

Wrigley, E. A. (2013). Energy and the English industria revolution. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371 , 1–272. https://doi.org/10.5860/choice.48-4603

Yang, J., Hao, Y., & Feng, C. (2021). A race between economic growth and carbon emissions: What play important roles towards global low-carbon development? Energy Economics, 100 , 105327. https://doi.org/10.1016/j.eneco.2021.105327

Yilanci, V., Gorus, M. S., & Andreoni, V. (2023). Reinvestigation of the validity of the EKC hypothesis extended with energy: A time-varying analysis for the United Kingdom. Journal of Cleaner Production, 428 , 139284.

Download references

This work was not supported by any institution.

Author information

Authors and affiliations.

Department of Economics, Atılım University, Ankara, Turkey

Department of Economics, TED University, Ankara, Turkey

Julide Yildirim

School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire, MK43 0AL, UK

Nazmiye Balta-Ozkan

You can also search for this author in PubMed   Google Scholar

Contributions

Tolga Omay, Julide Yildirim, Nazmiye Balta-Ozkan equally contribute to conceptualization, methodology, writing-review & editing, validation, and formal analysis. Data curation, and writing-original draft.

Corresponding author

Correspondence to Tolga Omay .

Ethics declarations

Conflict of interests.

The authors declare no competing interests.

Ethical approval

Not applicable.

Consent for publication

Additional information, publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Omay, T., Yildirim, J. & Balta-Ozkan, N. Historical environmental Kuznets curve for the USA and the UK: cyclical environmental Kuznets curve evidence. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-05320-y

Download citation

Received : 01 February 2022

Accepted : 17 August 2024

Published : 17 September 2024

DOI : https://doi.org/10.1007/s10668-024-05320-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Cyclical environmental Kuznets curve
• Environmental degradation
• Economic growth
• Fourier filter
• Smooth structural breaks unit root test

JEL Classification

• Find a journal
• Publish with us
• Track your research