William A. Sandoval

Design-Based Research: An Emerging Paradigm for Educational Inquiry

The authors argue that design-based research, which blends empirical educational research with the theory-driven design of learning environments, is an important methodology for understanding how, when, and why educational innovations work in practice. Design-based researchers’ innovations embody specific theoretical claims about teaching and learning, and help us understand the relationships among educational theory, designed artifact, and practice. Design is central in efforts to foster learning, create usable knowledge, and advance theories of learning and teaching in complex settings. Design-based research also may contribute to the growth of human capacity for subsequent educational reform.

Design-Based Research Methods for Studying Learning in Context: Introduction

The field of psychology has a long history of interaction with education, and educational psychology has had a profound impact on how issues of learning have been framed and studied in educational contexts. Still, it has never been simple to translate theoretical insights into educational practice. Educational psychology has been criticized for not creating “usable knowledge” (Lagemann, 2002). Currently, educational researchers generally have been pushed to justify how their claims are “scientific” and “evidence-based” (National Research Council, 2002). There is a tension between the desire for locally usable knowledge on the one hand and scientifically sound, generalizable knowledge on the other. Lagemann, for example, argued that the traditional paradigm of psychology has striven for experimental control at the expense of fidelity to learning as it actually occurs. Thus, although such claims might be scientific in one sense, they do not adequately explain or predict the phenomena they purport to address. This critique extends the long-standing debate surrounding the ecological validity of well-defined psychological tasks and their relation to psychological phenomena as they come to occur in everyday settings (Brunswik, 1943; Lewin, 1943). As a field, we still lack an adequate methodological reconciliation that attends to issues of both experimental control and ecological validity. At the same time, there is considerable unease with the perceived “credibility gap” (Levin & O’Donnell, 1999) of much of educational research because it is not produced with what are considered to be scientific methods. From this perspective, the knowledge from educational research has limited usability because it is not trustworthy. An educational psychology that is both usable in a practical sense and scientifically trustworthy cannot proceed without directly studying the phenomena it hopes to explain in its inherent messiness. A little over a decade ago, Brown (1992) described her evolving approach to “design experimentation” as an effort to bridge laboratory studies of learning with studies of complex instructional interventions based on such insights. She showed how insights from the laboratory were inherently limited in their ability to explain or predict learning in the classroom. The challenge, as she saw it, was to develop a methodology of experimenting with intervention designs in situ to develop theories of learning (and teaching) that accounted for the multiple interactions of people acting in a complex social setting. At the same time, Collins (1992) was putting forth a notion of educational research as a “design science,” like aerospace engineering, that required a methodology to systematically test design variants for effectiveness. Achieving such a design science, however, requires a sufficient understanding of the underlying variables at all relevant layers of a complex social system (schooling)—an understanding that we do not yet have (Collins, Joseph, & Bielaczyc, 2004). The last 12 years have seen an increasing uptake of the design experimentation methodology, so much so that a recent handbook on research in math and science education is replete with examples and formulations of the approach (Kelly & Lesh, 2000). The general approach has been called by many names. We have settled on the term design-based research over the other commonly used phrases “design experimentation,” which connotes a specific form of controlled experimentation that does not capture the breadth of the approach, or “design research,” which is too easily confused with research design and other efforts in design fields that lack in situ research components. The approach to research described in this issue is design based in that it is theoretiEDUCATIONAL PSYCHOLOGIST, 39(4), 199–201 Copyright

Developing Learning Theory by Refining Conjectures Embodied in Educational Designs.

Designed learning environments embody conjectures about learning and instruction, and the empirical study of learning environments allows such conjectures to be refined over time. The construct of embodied conjecture is introduced as a way to demonstrate the theoretical nature of learning environment design and to frame methodological issues in studying such conjectures. An example of embodied conjecture and its history of empirical refinement are presented to provide a concrete example of how the effort to design instructional change can lead to a productive shift in view of the underlying learning issues at hand. This example is used to suggest some general features of embodied conjectures and to raise methodological issues for refining them.

Conjecture Mapping: An Approach to Systematic Educational Design Research.

Design research is strongly associated with the learning sciences community, and in the 2 decades since its conception it has become broadly accepted. Yet within and without the learning sciences there remains confusion about how to do design research, with most scholarship on the approach describing what it is rather than how to do it. This article describes a technique for mapping conjectures through a learning environment design, distinguishing conjectures about how the design should function from theoretical conjectures that explain how that function produces intended outcomes.

Developing Children's Early Competencies to Engage With Science

Science educators have long been concerned with how formal schooling contributes to learners’ capacities to engage with science after school. This article frames productive engagement as fundamentally about the coordination of claims with evidence, but such coordination requires a number of reasoning capabilities to evaluate the strength of evidence, critique methods, and other factors upon which evidence evaluation rests, evaluating sources and potential biases, and so on. Although the general discourse on education commonly suggests students are bad at such things, we review cognitive development research that demonstrates children display a variety of capabilities, even at early ages, that can be productively built upon by formal science instruction. We use this research to suggest some possibilities for formal schooling to develop childrens capacities for evaluating claims within the pursuit of personally meaningful goals. We conclude with observations of useful directions our analysis opens to research.

The Special Case of Scientific Data Sharing with Education

The seemingly simple task of reusing data for science education relies on the presence of scientific data, scientists willing to share, infrastructure to provide access, and mechanisms to share between the two disparate communities of scientists and science students. What makes sharing between scientists and science students a special case of data sharing, is that all of the implicit knowledge attending the data must pass along this same vector. Our work at the Center for Embedded Networked Sensing studying aspects of this data reuse problem has shown us a rough outline of how the future of this data sharing will look. Our approach is to start from the prospective of the scientists, looking for opportunities to support scientific research, and then leveraging the data for reuse by education. The investment needed to capture high quality scientific data necessitates the consideration of reuse by the general population as well as other interested scientific parties.

The Influence of Non-Epistemic Features of Settings on Epistemic Cognition

Situated theories of learning recognize the rules, tools, goals, and communities within which activities develop. Similarly, situated theories of epistemic cognition recognize that individuals’ ideas about knowledge are tentative and dependent on particular contexts. In this study, we bring these frameworks together and qualitatively examine how one high school student thinks about knowledge at the intersection between multiple settings while creating a documentary film about a socioscientific issue. We describe several non-epistemic features of settings that impact epistemic cognition, including time constraints, tool characteristics, and participation norms.RésuméLes théories de l’apprentissage contextuel reconnaissent les règles, les outils, les objectifs et les communautés où se déroulent les activités. De la même façon, les théories de l’épistémologie cognitive contextuelle reconnaissent que les idées des individus sur la connaissance sont souvent hésitantes et dépendent de contextes particuliers. Dans cette étude, nous unissons ces deux cadres théoriques et proposons une analyse qualitative portant sur la façon dont un étudiant de niveau secondaire conçoit la connaissance, au moment où il se trouve à une croisée des chemins entre de multiples contextes, pendant qu’il tourne un documentaire sur un sujet socio-scientifique. Nous décrivons plusieurs traits non épistémiques des contextes qui influencent la cognition épistémique, y compris les contraintes temporelles, les caractéristiques des outils utilisés et les norms de participation.