Science Education in Three-Part Harmony: Balancing
Conceptual, Epistemic, and Social Learning Goals
Rutgers, the State University of New Jersey
wo major reform efforts in K–12 science education have taken place during the
past 50 years. The first was the 1950–1970 curriculum reform efforts motivated
by the launching of Sputnik and sponsored by the newly formed National Science
Foundation (NSF) in the United States and by the Nuffield Foundation in the
United Kingdom. The signature goal for these reformed programs was to produce
courses of study that would get students to “think like scientists,” thus placing them
in a “pipeline” for science careers (Rudolph, 2002).
The second U.S. and U.K. reform effort in science education began in the 1980s
and continues to this day as part of the national standards movement. Referred to as
the “Science for All” movement in the United States and the “Public Understanding
of Science” in the United Kingdom, here the education goal was and is to develop a
scientifically literate populace that can participate in both the economic and democra-
tic agendas of our increasingly global market–focused science, technology, engineering,
and mathematics (STEM) societies. In addition to the economic and democratic
imperatives as a purpose for science education, more recent voices of science education
reform (Driver, Leach, Millar, & Scott, 1996; Millar, 1996; Millar & Hunt, 2001;
Osborne, Duschl, & Fairbrother, 2002) have advocated that the proper perspective for
science education in schools ought to be the cultural imperative. The cultural impera-
tive perspective sees STEM disciplines, knowledge, and practices as woven into the
very fabric of our nations and societies. What the cultural imperative provides that the
democratic and economic imperatives do not is recognition of important social and
epistemic dimensions that are embedded in the growth, evaluation, representation, and
communication of STEM knowledge and practices. New perspectives and under-
standings in the learning sciences about learning and learning environments, and in
science studies about knowing and inquiring, highlight the importance of science edu-
cation teaching and learning harmonizing conceptual, epistemological, and social
@2007 American Educational Research Association
2 Review of Research in Education, 32
Traditionally, science curriculum has focused on what one needs to know to do sci-
ence. Schwab (1962) called this the “rhetoric of conclusions” approach to science edu-
cation, and he advocated that science education be an “enquiry into enquiry.” Thirty
years later, Duschl (1990) commented on the problem of “final form science” instruc-
tion, a signal that little progress had been made toward shifting the focus of science
education from what we know to how we know and why we believe. The new per-
spective of science education focuses on what students need to do to learn science. The
notion of to do in science education has traditionally been associated with the manip-
ulation of objects and materials to engage learners with phenomena to teach what we
know. This is embodied in disconnected, modularized, hands-on and textbook
approaches that have been a hallmark of elementary and secondary science curricula
since the 1960s reform efforts. The dominant format in curriculum materials and
pedagogical practices is to reveal, demonstrate, and reinforce via typically short inves-
tigations and lessons either (a) “what we know” as identified in textbooks or by the
authority of the teacher or (b) the general processes of science without any meaning-
ful connections to relevant contexts or the development of conceptual knowledge.
What has been missing is a sense of to do that embodies the dialogic knowledge-building
processes that are at the core of science, namely, obtaining and using principles and
evidence to develop explanations and predictions that represent our best-reasoned
beliefs about the natural world. In other words, missing from the pedagogical con-
versation is how we know what we know and why we believe it.
Two recent National Research Council (NRC) reports—Raising Above the Gath-
ering Storm (RAGS; NRC, 2006) and Taking Science to School: Learning and Teaching
Science Kindergarten to Eighth Grade (TSTS; NRC, 2007)—serve as evidence, though,
that competing perspectives and agendas in science education persist. The RAGS
report is a response to STEM workforce issues, for example, shortages in attracting
and retaining students and teachers in science programs and careers. The TSTS report
reflects new research understandings about how children learn science and how to
design and implement effective science learning environments. The RAGS report
emphasizes the economic imperative of keeping the United States competitive in
STEM global markets. The RAGS focus is on the “pipeline,” the emergence of new
interdisciplinary sciences, the integration of sciences and technologies, and the need
for more Advanced Placement courses at the high school level.
The TSTS report puts emphasis on the cultural imperative and harmonizing
learning goals by advocating the development of four strands of scientific proficiency
for all students. Students who understand science
1. know, use, and interpret scientific explanations of the natural world;
2. generate and evaluate scientific evidence and explanations;
3. understand the nature and development of scientific knowledge; and
4. participate productively in scientific practices and discourse.
The four strands of scientific proficiency reflect an important change in focus for
science education, one that embraces a shift from teaching about what to teaching
about how and why. But as one of the TSTS research recommendations indicate,
more research knowledge exists for how children perform in Strands 1 and 2 than
exists for children’s performance in Strands 3 and 4.
The focus of this chapter is to examine research and development efforts on the
critical role epistemic understanding and scientific reasoning play in the development
of understanding science. The first section of the chapter presents an overview of
salient developments in two new scholarly domains—learning sciences and science
studies—that inform the framing of research on epistemic reasoning and learning
goals in science education. The second section examines specific programs of research
that seek to develop classrooms as epistemic communities. The third and final section
moves to a discussion of the design of science curriculum, instruction, and assessment
models. Issues are raised about what constitutes the appropriate “grain size” of ideas,
evidence, information, and explanations for K–12 science education that seeks to har-
monize across conceptual, epistemic, and social learning goals.
SHIFTING THE AGENDA IN SCIENCE EDUCATION
The agenda for science education has broadened in ways that demand a rethink-
ing of approaches to curriculum, instruction, and assessment. We live in a time when
there is rapid growth of scientific knowledge, scientific tools and technologies, and
scientific theories. Like the first science education reformers in the 1950s and 1960s,
we are today faced with the challenge of making important decisions about what and
how to teach. But unlike the 1960s reform effort, we now have a deeper understand-
ing of how and under what conditions learning occurs. We also have a richer under-
standing of the dynamics occurring in the growth of or advancements in scientific
knowledge. Essentially, we have learned about learning through advancements in two
scholarly domains that can help us in our thinking about how to reform K–12 science
1. Learning sciences: A group of disciplines focusing on learning and the design of
learning environments that draw from cognitive, developmental, and social psy-
chology; anthropology; linguistics; philosophy of mind; artificial intelligence; and
2. Science studies: A group of disciplines focusing on knowing and inquiring that draw
from history, philosophy, anthropology, and sociology of science as well as cognitive
psychology, computer science, science education, and artificial intelligence.
It is well beyond the scope of this chapter to provide a thorough review of devel-
opments in these two domains. An overview follows below. For comprehensive
reviews of developments in the learning sciences, interested readers are directed to
recent NRC (1999, 2001, 2007) reports and to The Cambridge Handbook of the
Duschl: Science Education in Three-Part Harmony 3
Learning Sciences (Sawyer, 2006). For overviews and commentary on the emergence
of science studies, refer to Godfrey-Smith (2003), Longino (2002), Kitcher (1993,
1998), Koertge (1998), and Zammito (2004).
What the learning sciences literature tells us is that the structure of knowledge and
the processes of knowing and learning are much more nuanced than initially
described by associative and behavioral learning theories. That is, context and content
matter. Thus, there is a general move away from an emphasis on domain-general rea-
soning and skill development to domain-specific reasoning and practices develop-
ment. The richer understanding of learning and reasoning domain-specific contexts
provide has significant implications for the design of pedagogical models and learn-
In a review article titled “A Short History of Psychological Theories of Learning,”
Bruner (2004) concludes, “It was the cognitive revolution that brought down [asso-
ciative and behavioral] learning theory” and “that it was the study of language and par-
ticularly of language acquisition that precipitated learning theory’s decline”
(p. 19). The cognitive, social, and cultural dynamics of learning are mutually support-
ive of one another and intertwined such that “you cannot strip learning of its content,
nor study it in a ‘neutral’ context. It is always situated, always related to some ongoing
enterprise” (Bruner, 2004, p. 20). In this sense, psychologists claim that learning has a
historical dynamic because learning is shaped by experiences, by the sequencing of
those experiences, and by the guiding hand of thoughtful mediation directed toward
learning goals (Lehrer & Schauble, 2006b; Rogoff, 1990).
One domain in particular from the learning sciences has helped us understand cog-
nitive development; it is research on infants’ and children’s learning (NRC, 2007).
This new field of scholarly work reveals how infants and young children are capable of
abstract reasoning in core knowledge domains of science and mathematics (e.g.,
change, form, and function; physical attributes and properties of objects; systems and
interactions; number sense; causal inference; distinguishing animate from inanimate).
Researchers are learning that young children are capable of complex reasoning, for
example, theory building. These and other forms of scientific reasoning are possible
when children are provided with multiple opportunities that sustain their engagement
with select scientific practices over time such as predicting, observing, testing, measur-
ing, counting, recording, collaborating, and communicating (Carey, 2004; Gelman &
Brenneman, 2004; Gopnik et al., 2004; Hapgood, Magnusson, & Palincsar, 2004;
Metz, 2004; Spelke, 2000).
Schauble (2007) reminds us, though, that although we certainly want to answer
the question, “Where does reasoning and learning come from?” we must also ask,
“Where is reasoning going?” and “What conditions support productive change?”
Answers to the first question help us better understand the foundation on which further development can
build. Answers to the second provide a sense of developmental trajectory, or more likely, trajectories.
4 Review of Research in Education, 32
What characteristic changes are coming up? What pathways of change are usually observed? And answers
to the third question focus on how those changes can get supported in a productive way. (p. 51)
The study of infants and child development is but one important element of the
learning sciences. Sawyer (2006), in the preface of The Cambridge Handbook of the
Learning Sciences, states that the “goal of the learning sciences is to better understand
the cognitive and social processes that result in the most effective learning” (p. xi).
The emergence of the learning sciences community in the past three decades has
shifted the educational and developmental research agenda to the redesign of class-
rooms and other out-of-school learning environments. The stakeholders in the design
of classrooms and learning environments are teachers, parents, administrators, poli-
cymakers, and professionals. The learning science constructs for such redesign include
(a) transition from novice to expert performance, (b) using prior knowledge, (c) scaf-
folding, (d) externalization and articulation, (e) reflection, and (f) building from con-
crete to abstract knowledge.
The learning sciences emerged from the earlier constructivist theories of learning
and from the pioneering research in the cognitive sciences. Our deeper understand-
ing of how children’s thinking is fundamentally different from that of adults, cou-
pled with richer understandings of expertise, representation, reflection, problem
solving, and thinking, provided a foundation for a major tenet of the learning sci-
ences: “Students learn deeper knowledge when they engage in activities that are sim-
ilar to the everyday activities of professionals who work in a discipline” (Sawyer,
2006, p. 4). Subsequent research on informal learning reveals the importance of par-
ticipation structures and the development of practices in culturally valued activities
(Cole, 1996). Focusing on scaffolding, apprenticeship, legitimate peripheral partici-
pation, and guided participation, informal learning researchers provided “broader
units of analysis...: these views move beyond the study of individuals alone to con-
sider how learning occurs within enduring social groups such as families and com-
munities” (Bransford et al., 2006, p. 24).
One element of the learning sciences and an important dynamic of relevance here is
the development of expertise within and among knowledge workers, for example, sci-
entists, engineers, mathematicians, medical doctors, and so on. Cognitive, historical,
sociological, and anthropological studies of knowledge workers revealed the importance
of practices that are central to the professional activities in these knowledge growth com-
munities. With respect to the scientific disciplines and, in particular, the study of epis-
temic cultures, cognitive models of science (cf. Giere, 1988; Goldman, 1986; Kitcher,
1993; Thagard, 1992) coupled with sociocultural models of science (cf. Knorr-Cetina,
1999; T. Kuhn, 1962/1996; Longino, 1990, 2002) have established the important role
that models, mechanisms, and peers have in the advancement and refinement of scien-
tific knowledge and the methods regarding the growth of scientific knowledge. Science
takes place in complex settings of cognitive, epistemic, and social practices.
The implication for science learning is that more and more contemporary science
is being done at the boundaries of disciplines. Thus, there is a connectedness in the
Duschl: Science Education in Three-Part Harmony 5
practices of science that are not typically found in school classroom environments. An
examination of school curriculum, for example, reveals disconnected and isolated
units of instruction the norm in K–8 science education (NRC, 2007). An examina-
tion of the growth of scientific knowledge as provided by science studies scholars can
provide some helpful insights on how to precede with the redesign agenda.
In very broad brushstrokes, 20th-century developments in science studies can be
divided into three periods. In the first, logical positivism, with its emphasis on mathe-
matical logic and the hypothetico-deductive method, was dominant. Some of the major
figures in the movement were Rudolf Carnap, Carl G. Hempel, Ernest Nagel, and Hans
Reichenbach. Logical positivism views of science held to several assumptions:
1. There is an epistemologically significant distinction between observation lan-
guage and theoretical language, and this distinction can be made in terms of syn-
tax or grammar.
2. Some form of inductive logic would be found that will provide a formal criterion
for theory evaluation.
3. There is an important dichotomy between contexts of discovery and contexts of
In the 1950s and ’60s, various writers questioned these and other fundamental
assumptions of logical positivism and argued for the relevance of historical and psy-
chological factors in understanding science. Thomas S. Kuhn is the best known of the
figures in this movement, but there were numerous others, including Paul Feyer-
abend, Norwood Russell Hanson, Mary Hesse, and Stephen Toulmin.
T. Kuhn (1962/1996) introduced the conception of paradigm shifts in the origi-
nal version of Structure of Scientific Revolutions and then revised it in the postscript to
the 1970 second edition, introducing the concept of a disciplinary matrix. One
important aspect of Kuhn’s work was the distinction between revolutionary and nor-
mal science. Revolutionary science involves significant conceptual changes, whereas
normal science consists of “puzzle solving,” of making nature fit into the boxes spec-
ified by the disciplinary matrix.
In this view of science, theories still played a central role, but they shared the stage
with other elements of science, including a social dimension. Although Kuhn saw
the scientific communities as essential elements in the cognitive functioning of sci-
ence, his early work did not present a detailed analysis. The most recent movements
in philosophy of science can be seen as filling in some of the gaps left by Kuhn’s
demolition of the basic tenets of logical positivism. This movement
1. emphasizes the role of models and data construction in the scientific practices of
6 Review of Research in Education, 32
2. sees the scientific community as an essential part of the scientific process, and
3. sees the cognitive scientific processes as a distributed system that includes instru-
ments, forms of representation, and agreed upon systems for communication and
Science is seen as having important social phenomena with unique norms for par-
ticipation in a community of peers. Perhaps the most important element Kuhn and
others added to our understanding of the nature of science is the recognition that
most of the theory change that occurs in science is not final theory acceptance but
improvement and refinement of a theory. Ninety-nine percent of what occurs in sci-
ence is neither the context of discovery nor the context of justification, as the logical
positivists proposed, but the context of theory development, of conceptual modifica-
tion. The dialogical processes of theory development and of dealing with anomalous
data occupy a great deal of scientists’ time and energy. The logical positivist’s context
of justification is a formal final point—the end of a journey; moreover, it is a desti-
nation few theories ever achieve, and so over emphasis on it entirely misses the impor-
tance of the journey. Importantly, the journey involved in the growth of scientific
knowledge reveals the ways in which scientists respond to new data, to new theories
that interpret data, or to both. Some people describe this feature of the scientific
process by saying that scientific claims are tentative; I prefer to say that science and
scientists are responsive, thus avoiding the connotation that tentative claims are
unsupported by evidence or scientific reasoning.
One of the important findings from the science studies literature is that not only
does scientific knowledge change with time, but so, too, do the methods of inquiry
and the criteria for the evaluation of knowledge change. The accretion growth model
of scientific knowledge is no longer tenable. Nor is a model of the growth of knowl-
edge that appeals to changes in theory commitments alone, for example, a conceptual
change model. Changes in research programs that drive the growth of scientific
knowledge also can be because of changes in methodological commitments or goal
commitments (Duschl, 1990). Science studies examining contemporary science prac-
tices recognize that both the conceptual frameworks and the methodological practices
of science have changed with time. Changes in methodology are a consequence of
new tools, new technologies, and new explanatory models and theories that, in turn,
have shaped and will continue to shape scientific knowledge and scientific practices.
As science has progressed as a way of knowing, yet another dichotomy has
emerged, and it is one that is critically important for a contemporary consideration of
the design of K–12 curriculum, instruction, and assessment. That dichotomy is the
blurring of boundaries between science and technology and between different
branches of the sciences themselves, yet another outcome of learning how to learn that
challenges our beliefs about what counts as data, evidence, and explanations. Acker-
man (1985) refers to such developments as the shifts in the “data texts” of science and
warns that the conversations among contemporary scientists about measurement,
observations, data, evidence, models, and explanations is of a kind that is quite
Duschl: Science Education in Three-Part Harmony 7
foreign from the conversations found in the general population. Consequently, under-
standing discipline-based epistemic frameworks, as opposed to or in addition to learn-
ing-based epistemic frameworks, is critically important for situating school science
learning, knowing, and inquiry (Kelly & Duschl, 2002; Hammer & Elby, 2003).
Pickering (1995) referred to this conflation when describing experiments in high-
energy physics as the “mangle of practice.” Zammito (2004) writes,
Pickering’s (1990) “practical realism” or interpretation of “science as practice” offers a robust apprecia-
tion for the complexity of science, its “rich plurality of elements of knowledge and practice,” which he has
come to call the “the mangle of practice.” Indeed, as Ian Hacking (1988) has noted, it is the “richness,
complexity and variety of scientific life” which has occasioned the widespread new emphasis on science as
practice. As against the “statics of knowledge,” the frame of existing theoretical ideas, Pickering (1990)
situates the essence of scientific life in the “dynamics of practice,” that is, “a complex process of recipro-
cal and interdependent tunings and refigurings of material procedures, interpretations and theories.”
For Pickering, scientific inquiry during its planning and implementation stages is a
patchy and fragmented set of processes mobilized around resources. Planning is the
contingent and creative designation of goals. Implementation for Pickering (1989) has
three elements: a “material procedure” which involves setting up, running and monitoring an apparatus;
an “instrumental model,” which conceives how the apparatus should function; and a “phenomenal
model,” which “endows experimental findings within meaning and significance... a conceptual under-
standing of whatever aspect of the phenomenal world is under investigation. The “hard work” of science
comes in trying to make all these work together. (Zammito, 2004, pp. 226–227)
The role of modeling practices in science and of model-based reasoning has led
Lehrer and Schauble (2006a), among others, to investigate ways to design classroom
learning environments that promote students’ modeling and model-based reasoning.
This research focus has, in turn, contributed to new views about the image of science
we present to students in school science. The TSTS report (NRC, 2007) interprets
these science studies perspectives by stating that science involves the following
important epistemic and social practices:
1. Building theories and models
2. Constructing arguments
3. Using specialized ways of talking, writing, and representing phenomena
The “pipeline” curriculum agenda is a pushdown curriculum driven by scientists’
perspectives of what one needs to know to do science. This orientation was criticized
right from the inception of early NSF curricula (Duschl, 1990; Easley, 1959; Rudolph,
2005). The science-for-scientists approach initially ignored research on teaching and
learning in the conceptualization and design of science curricula. What ensued, then,
8 Review of Research in Education, 32
was a content–process (CP) curriculum orientation in school science that typically sep-
arated one, content learning, from the other, process learning. A competing curricu-
lum orientation is the discovery–inquiry (DI) approach to teaching science introduced
during the NSF curriculum reform movement of the 1950s and 1960s, characterized
by Rudolph (2002) as the scientist-in-the-classroom period of U.S. science education.
Although the many initial ideas of Schwab (1958, 1962) to orient science learning to
an “enquiry of enquiry” and thereby avoid the “rhetoric of conclusions” conditions
found in classrooms still have cachet today, something got lost in the translation to cur-
riculum materials. What got lost in the design of inquiry curriculum materials was the
focus on the important roles that guiding conceptions, evidence, and explanations have
in framing the syntactic, semantic, and pragmatic structures of scientific inquiry,
namely, the epistemic criteria, the conceptual clusters, and the experimental and
knowledge-building practices used when doing science (Duschl & Grandy, 2007).
Since the first NSF-funded era of science education reform in the 1960s and
1970s, we see a shift in views about the nature of science from science as experimen-
tation to science as explanation and model building, from science inquiry as an indi-
vidualistic process to scientific inquiry as an individual and social process, and from
science teaching focusing on the management of learners’ behaviors and hands-on
materials to science teaching focusing on the management of learners’ ideas, access to
information, and interactions between learners. Some of the shifts have been moti-
vated by new technological development, but new theories about learning, as men-
tioned above, have contributed, too.
One important change that has significant implications for school science concerns
the realm of scientific observations and representations. In the past 100 years, new
technologies and new scientific theories have modified the nature of scientific obser-
vation from an enterprise dominated by sense perception, aided or unaided, to a the-
ory-driven enterprise (Duschl, Deak, Ellenbogen, & Holton, 1999). We now know
that what we see is influenced by what we know and by how we “look.” In this sense,
scientific theories are inextricably involved in the design and interpretation of exper-
imental methods and scientific instrumentation. The implication is that there are
additional important details for the development of learners’ scientific literacy, rea-
soning, and images about the nature of science.
Consider that the developments in scientific theory coupled with concomitant
advances in material sciences, engineering, and technologies have given rise to radi-
cally new ways of observing nature and engaging with phenomenon. At the beginning
of the 20th century, scientists were debating the existence of atoms and genes; by the
end of the century, they were manipulating individual atoms and engaging in genetic
engineering. These developments are representative of the disciplinary details (con-
ceptual, epistemic, and social) that are altering the nature of scientific inquiry and
have greatly complicated our images of what it means to engage in scientific inquiry.
Whereas once scientific inquiry was principally the domain of unaided sense percep-
tion, today scientific inquiry is guided by highly theoretical beliefs that determine the
very existence of observational events (e.g., neutrino capture experiments in the ice
Duschl: Science Education in Three-Part Harmony 9
fields of Antarctica). Whereas once scientific inquiry was practiced by individuals or
small groups with established patrons, today scientific inquiry involves large interna-
tional communities of university and industrial scientists guided by complimentary or
competing beliefs and goals, often fighting for limited governmental grants to enable
Scientific databases such as Geographical Information Systems make it possible to
engage in rich scientific inquiry without engaging in hands-on science involving the
collection of data. Instead, the data are provided and the inquiry begins with the selec-
tion of information for analysis. This is one example of how science education has
shifted from management of materials for collecting data to management of informa-
tion for scrutinizing databases. Such a shift has implications regarding the manner in
which interactions with phenomena are designed and included in science lessons for
all grade levels and the level of details we elect to include pursue. Information in the
guise of data, evidence, models, and explanations represents, in an important sense,
the new materials for school classrooms and laboratories.
Historically, scientific inquiry has often been motivated by practical concerns; for
example, improvements in astronomy were largely driven and financed by the quest
for a better calendar, and thermodynamics was primarily motivated by the desire for
more efficient steam engines. But today, scientific inquiry underpins the development
of vastly more powerful new technologies and addresses more pressing social prob-
lems, for example, finding clean renewable energy sources, feeding an exploding
world population through genetically modified food technologies, and stem cell
research. In such pragmatic problem-based contexts, new scientific knowledge is as
much a consequence of inquiry as the goal of inquiry. New tools, new theories, and
new technologies have contributed to advances in science such that the very founda-
tional acts of science, such as observation and measurement, have evolved to the point
that direct human interactions are no longer required. As mentioned above, entities
such as genes and atoms whose existence and precise nature were debated a mere two
generations ago are now being manipulated.
The findings from science studies and from the learning sciences suggest new con-
ceptions for school science and new designs for learning environments in terms of
models of curriculum, instruction, and assessment. A new generation of educational
researchers is turning attention to design research with a shared goal of sorting out the
proper trajectories, developmental pathways, or learning progressions that support the
growth of knowledge and the development of reasoning. Within this domain of
research, epistemic practices are among the salient topics of inquiry. Thus, there is
attention to the design of learning environments as epistemic communities of practice.
EPISTEMIC COMMUNITIES OF PRACTICE
When we synthesize the learning sciences research (NRC, 1999, 2001; Sawyer,
2006), the science studies research (cf. Giere, 1988, 1999; Longino, 2002; Nersess-
ian, 1992), and science education research (cf. Millar, Leach, & Osborne, 2000;
Minstrel & Van Zee, 2001; NRC, 2007) we learn the following:
10 Review of Research in Education, 32
1. The incorporation and assessment of science learning in educational contexts
should focus on three integrated domains:
• he conceptual structures and cognitive processes used when reasoning scientifically,
• the epistemic frameworks used when developing and evaluating scientific
• the social processes and contexts that shape how knowledge is communicated,
represented, argued, and debated.
2. The conditions for science learning and assessment improve through the estab-
• learning environments that promote active productive student learning,
• instructional sequences that promote integrating science learning across each of
the three domains in (1),
• activities and tasks that make students’ thinking visible in each of the three
• teacher-designed assessment practices that monitor learning and provide feed-
back on thinking and learning in each of the three domains.
Taken together, the recent developments in the learning sciences and science stud-
ies have implications for how we conceptualize the design and delivery of science cur-
riculum materials for purposes of supporting students’ learning as well as teachers’
assessments for promoting learning. However, existing curricula rarely provide these
kinds of experiences and learning opportunities (Duschl & Grandy, 2007; Ford,
2005; Hapgood et al., 2004; NRC, 2007).
Why is this the case? Well, one partial answer, the psychological component, is
because of a lack of research on how children learn and develop scientific knowledge
and inquiry practices over time when guided with competent instruction, Schauble’s
(2007) second and third questions above. A second partial answer, the philosophical
component, is the image of science that prevails in science education. What does it
mean to be doing science? Is it fundamentally about conducting experiments and test-
ing hypotheses? Is it fundamentally about building theories? Or is it fundamentally
about participating in a community of practice that uses and tests models that explain
the results of experiments and that inform the structure of theories? A third partial
answer, the pedagogical component, concerns the teaching and communication of
science. What is most worth knowing? Is it what we know? Or is it how we know and
why we believe it even in the face of plausible competing alternatives?
The focus and goals of precollege science education have shifted. In brief, the
almost exclusive emphases on conceptual goals of science learning are making room
for epistemic and social learning goals. In the rapidly changing world of STEM
activities, an understanding of criteria for evaluating knowledge claims, that is,
deciding what counts, is as important as an understanding of conceptual frameworks
for developing knowledge claims. The relation needs to be a symbiotic one; this is
not an either–or situation. Conceptual and epistemic learning should be concurrent
in science classrooms, situated within curriculum, instruction, and assessment models
Duschl: Science Education in Three-Part Harmony 11
that promote the development of each. Moreover, they should reinforce each other,
even mutually establish each other. To accomplish a redesign of science learning
environments, new perspectives regarding the role of CP and DI approaches to
science education are needed.
The history of science education since World War II shows numerous attempts
to move instruction away from textbooks and lectures to investigations and experi-
ments (Rudolph, 2002, 2005). Curriculum materials were developed to prepare the
next generation of scientists, and lessons were written to help students think like sci-
entists. The CP continuum is the dominant paradigm of science education: “Here is
what we know and this is how we go about getting the knowledge,” where getting
the knowledge is following the testing hypothesis scientific method. The persistence
of the CP continuum today seems to have more to do with the adherence to the old
view of scientific methods and to the way schools are run and organized and less to
do with what we understand about effective learning environments and children’s
learning (NRC, 2007).
The rival DI continuum was introduced during the time of the 1950s-to-1960s
curriculum and teacher-development interventions. The recent focus on science as
inquiry in the United States suggests that the DI approach has not made inroads on
the CP science education practices. The NRC’s (1996) National Science Education
Standards and Inquiry Addendum to the National Science Education Standards (NRC,
2000), along with the edited book Inquiring Into Inquiry Learning and Teaching in
Science (Minstrell & Van Zee, 2000), clearly signal dissatisfaction with school science
programs that continue to promote CP orientations.
Many of the extant K–8 science curriculum programs have been found wanting in
terms of the lean reasoning demands required of students (cf. Ford, 2005; Hapgood
et al., 2004; Metz, 1995; NRC, 2007). What the research shows is that curricula
addressing domain-general reasoning skills and surface-level knowledge dominate
over curricula addressing core knowledge and domain-specific reasoning opportuni-
ties that meaningfully integrate knowledge. This situation is partially because of a lack
of consensus about what is most worth learning, for example, the “big ideas” or core
knowledge of early science learning, and because of K–8 teachers’ knowledge of sci-
ence. The reasoning-lean curriculum approaches (a) tend to separate reasoning and
learning into discrete lessons, thus blurring and glossing over the salient themes and
big ideas of science, thus making American curricula “a mile wide and an inch deep”
(Schmidt, McNight, & Raizen, 1997); and (b) in the case of middle school textbooks,
tend to present science topics as unrelated items with little or no regard to relations
between them (Kesidou & Roseman, 2002).
An alternative to the CP and DI approaches is to consider dialectical discourse
frameworks based on an evidence–explanation (E-E) continuum that engage learners in
conversations of inquiry. Driven by a consideration of the growth of scientific knowl-
edge and coupled with analyses of the cognitive and social practices of scientists, the
E-E focus is on engaging learners in conversations examining “science-in-the-making”
practices (Kelly, Chen, & Crawford, 1998). During science-in-the-making episodes, the
12 Review of Research in Education, 32
detailed dialectical exchanges between observations and theory and the accompanying
data texts play out. The scientific knowledge we hold is put into practice and tested.
Importantly, here is how and when the important dialectical discourses about data rep-
resentations, data and conceptual models, evidence, explanatory theories, and methods
are incorporated into science learning environments. An important issue for school sci-
ence is deciding at what level of detail and in what sequence.
The E-E continuum (Duschl, 2003) has its roots in perspectives from science stud-
ies and connects to cognitive and psychological views of learning. The call for conver-
sations is recognition of the value and importance that representation, communication,
and evaluation play in science learning. I use conversation in a very broad sense to
include, among others ideas, argumentation, debate, modeling, drawing, writing, and
other genres of language. Such an expanded repertoire helps us to consider an impor-
tant domain of research in both formal and informal science learning settings, namely,
how to mediate the learning experiences.
The position advanced by Schauble, Leinhardt, and Martin (1997) and Pea
(1993), and adopted here, is that such learning mediations should focus on promot-
ing talk, activity structures, signs and symbol systems, or collectively what I will call
conversations. For science learning, the conversations should mediate the transitions
from evidence to explanations, or vice versa, and thereby unfold discovery and
inquiry. Adopting an image of science education that is guided by the development,
evaluation, and deployment of data texts is grounded in the idea that scientific inquiry
and scientific reasoning are both fundamentally decision-making activities mediated
by epistemological, cultural, and technological factors. The appeal to adopting the
E-E continuum as a framework for designing science education curriculum, instruction,
and assessment models is that it helps work out the details of the epistemic discourse
processes. The E-E continuum recognizes, whereas the CP and DI approaches do not,
how cognitive structures and social practices guide judgments about scientific data
texts. It does so by formatting into the instructional sequence select junctures of rea-
soning, for example, data texts transformations. At each of these junctures or transfor-
mations, instruction pauses to allow students to make and report judgments. Then
students are encouraged to engage in rhetoric–argument, representation–communication
and modeling–theorizing practices. The critical transformations or judgments in the
E-E continuum include
1. selecting or generating data to become evidence,
2. using evidence to ascertain patterns of evidence and models, and
3. employing the models and patterns to propose explanations.
Another important judgment is, of course, deciding what data to obtain and what
observations or measurements are needed (Lehrer & Schauble, 2006a, 2006b;
Petrosino, Lehrer, & Schauble, 2003). The development of measurement to launch
the E-E continuum is critically important. Such decisions and judgments are critical
entities for explicitly teaching students about the nature of science (Duschl, 2000;
Duschl: Science Education in Three-Part Harmony 13
Kenyon and Reiser, 2004; L. Kuhn & Reiser, 2004). How raw data are selected and
analyzed to be evidence, how evidence is selected and analyzed to generate patterns
and models, and how the patterns and models are used for scientific explanations are
important “transitional” practices in doing science. Each transition involves data texts
and making epistemic judgments about “what counts.” The complex relationship
between evidence and explanation in science warrants an examination of the tools we
teach children to use (e.g., Tinkerplots) and of changes or boundary adjustments in
three kinds of criteria children employ to relate evidence to explanation: (a) criteria
for assigning data to one of four categories: fact, artifact, irrelevant, or anomalous;
(b) criteria for identifying patterns or models in selected data; and (c) criteria for the-
ories or explanations created to account for the patterns or models (Duschl, 2000).
The preceding discussion sets out some of the challenges and attending scientific
inquiry details that face recommendations to redesign science learning environments.
Designing Epistemic Learning Environments
Recall from the introduction of the chapter that there is a need for more research
on the third and fourth strands of scientific proficiency, “Understand the nature and
development of scientific knowledge” and “Participate productively in scientific
practices and discourse.” There is, however, some good recent research contributing
to our understandings of learning environments that advance in tandem (e.g., har-
monize) epistemic, social, and conceptual learning.
Lehrer and Schauble (2006a) report on a 10-year program of research that exam-
ines model-based reasoning and instruction in science and mathematics. Critical to
the design of these learning environments is engagement in analogical mapping of
students’ representational systems and emergent models to the natural world. Impor-
tant instructional supports are coordinated around three forms of collective activity:
(a) finding ways to help students understand and appropriate the process of scientific
inquiry, (b) emphasizing the development and use of varying forms of representations
and inscriptions, and (c) capitalizing on the cyclical nature of modeling (p. 381).
Sandoval (2003) has explored how high school students’ epistemological ideas
interact with conceptual understandings. Written explanations in the domain of nat-
ural selection were used as the dependent measure. Analyses showed that students did
seek causal accounts of data and were sensitive to causal coherence, but they failed to
support key claims with explicit evidence critical to an explanation. Sandoval posits
that although students have productive epistemic resources to bring to inquiry, there
is a need to deepen the epistemic discourse on student-generated artifacts. The rec-
ommendation is to hold more frequent public classroom discourse focused on stu-
dents’ explanations. “Epistemically, such a discourse would focus on the coherence of
groups’ claims, and how any particular claim can be judged as warranted” (Sandoval,
2003, p. 46).
Sandoval (2005) argues that having a better understanding of how scientific
knowledge is constructed makes one better at doing and learning science. The goal
is to engage students in a set of practices that build models from patterns of evidence
14 Review of Research in Education, 32
(e.g., the E-E continuum transformations described above) and that examine how
what comes to count as evidence depends on careful observations and building of
arguments. Schauble, Glaser, Duschl, Shultz, and Johns (1995) found that students
participating in sequenced inquiry lessons with explicit epistemic goals (e.g., evalu-
ating causal explanations for the carrying capacity performance of designed boats)
showed improved learning compared to students who simply enacted the investiga-
tions. Understanding the purposes of experimentation made a difference. Other
reports of research that have found positive leaning LEARNING? effects of students’
working with and from evidence and seeing argumentation as a key feature of doing
science include Kelly and Crawford (1997); Sandoval and Reiser (2004); Toth,
Suthers, and Lesgold (2002); and Songer and Linn (1991).
Additional insights for the design of reflective classroom discourse environments
comes from research by Rosebery, Warren, and Conant (1992); Smith, Maclin,
Houghton, and Hennessey (2000); van Zee and Minstrell (1997); and Herrenkohl
and Guerra (1998). Rosebery, Warren, and Conant’s study spanned an entire school
year, whereas that of Smith, Maclin, Houghton, and Hennessey followed a cohort
of students for several years with the same teacher. Both studies used classroom prac-
tices that place a heavy emphasis on (a) requiring evidence for claims, (b) evaluating
the fit of new ideas to data, (c) justifications for specific claims, and (d) examining
methods for generating data. Engle and Conant (2002) refer to such classroom dis-
course as “productive disciplinary engagement” when it is grounded in the discipli-
nary norms for both social and cognitive activity.
The research by van Zee and Minstrell (1997) shows the positive gains in learn-
ing that come about when the authority for classroom conversation shifts from the
teacher to the students. Employing a technique they call the “reflective toss,” van Zee
and Minstrell found that students become more active in the classroom discourse,
with the positive consequence of making student thinking more visible to both the
teacher and the students themselves. Herrenkohl and Guerra (1998) examined the
effect on student engagement of guidelines for students who constituted the audi-
ence; that is, the scaffolding was on listening to others. The intellectual goals for stu-
dents were predicting and theorizing, summarizing results, and relating predictions,
theories, and results. The audience role assignments were designed to correspond
with the intellectual roles and required students to check and critique classmates’
work. Students were directed to develop a “question chart” that would support them
in their intellectual roles, that is, what questions they could ask when it was their job
to check summaries of results? Examples of students’ questions are What helped you
find your results? How did you get that? What were your results? What made that
happen? Did your group agree on the results? and Did you like what happened? Fol-
lowing the framework developed by Hatano and Inagaki (1991), Herrenhkohl and
Guerra used the audience-role procedures to engage students in (a) asking clarifica-
tion questions, (b) challenging others’ claims, and (c) coordinating bits of knowl-
edge. The focus on listening skills and audience roles helps to foster productive
community discourse on students’ “thinking in science.”
Duschl: Science Education in Three-Part Harmony 15
WHAT GRAIN SIZE KNOWLEDGE CLAIMS?
A critical aspect to the development of domain knowledge and reasoning is the appro-
priation of language in that domain (Gee, 1996; Lemke, 1990). The implication of
focusing on evidence, measurement, models, and other data texts (Ackerman, 1985) is
that the language of science is different from normal conventions or conceptions of lan-
guage. The language of science includes mathematical, stochastic, representational, and
epistemological elements as well as domain-specific descriptors and forms of evidence.
The challenge for learning sciences research that seeks to understand and promote dia-
logic processes is one of understanding how to mediate and coordinate language acquisi-
tion in these various forms of communicating and representing scientific claims. A
tension in science education has been deciding the right balance between domain-
general learning goals (e.g., control of variables reasoning) and domain-specific learning
goals (e.g., building and revising explanatory models). Another tension is deciding the
balance between generalized investigative process skills and situated scientific practices.
The thesis being developed in this chapter is to move science education away
from a dominant focus on conceptual learning toward a more balanced focus among
things conceptual, epistemic, and social. Such a shift has significant implications for
the design of curriculum, instruction, and assessment frameworks. For example, one
emerging issue in science education, posed as a recommendation from TSTS (NRC,
2007), is to develop learning progressions that function across grade bands, for
example, 2 to 5 or 4 to 8. To address the fragmented curriculum problem, one rec-
ommendation from the NRC report is to adopt curriculum sequences that facilitate
student learning; one of the TSTS report conclusions is to begin researching the
design of learning progressions. The conclusion states,
Sustained exploration of a focused set of core ideas in a discipline is a promising direction for organizing
science instruction and curriculum across grades K-8. A research and development program is needed to
identify and elaborate the progressions of learning and instruction that can support students’ under-
standing of these core ideas. The difficult issue is deciding what to emphasize and what to eliminate.
(NRC, 2007 p. x)
The learning-progression approach to the design of curriculum, instruction, and
assessment is grounded in domain-specific or core-knowledge theories of cognitive
development and learning as documented in recent NRC reports (NRC, 1999, 2001,
2007; Smith, Wiser, Anderson, & Krajcik, 2006). The emerging notion is for learn-
ing progressions at the K–8 grades to be built on the most generative and core ideas
that are central to the discipline of science and that support students’ science learn-
ing. Additionally, the core ideas should be accessible to students in kindergarten and
have the potential for sustained exploration across K–8 (NRC, 2007).
Learning progressions would be designed to also take up epistemic and social goals
of science through the teaching of scientific practices, such as measurement, argu-
mentation, explanation, model building, and debate and decision making. Critically
16 Review of Research in Education, 32
important for the children’s development of science learning, as discussed above, are
the appropriation of criteria for assessing and evaluating
• the status of knowledge claims,
• the status of investigative methods,
• the tools of measurement, and
• the status of representations and audience for communicating ideas and information.
This list represents a sample of elements in the “mangle of practice” for school sci-
ence. Recall that the TSTS’s third and fourth strands of scientific proficiency, respec-
tively, are “Understand the nature and development of scientific knowledge” and
“Participate productively in scientific practices and discourse.” In other words, the
development of epistemic discourse practices is central to learning within learning
progressions. But this raises yet again the important issue regarding details or the grain
size of information and ideas we ask children to consider. Clearly judging students’
ideas as right or wrong does not provide valuable feedback to learners. Formative
assessment strategies that seek to make thinking visible are most effective when the
appropriate level of details is designed into tasks such that knowledge deepens, rea-
soning develops, and learning progresses. Within science education, an important
issue is the level of detail needed to develop epistemic reasoning. Consider, for exam-
ple, the various frameworks used to guide and promote argumentation discourse in
classroom learning environments and computer-supported classroom learning.
The adoption and development of argumentation frameworks has gained in
importance in the past two decades. Jimenez-Aleixandre and Erduran (in press), in
the opening chapter of their edited volume on argumentation research in the science
classroom (Erduran and Jimenez-Aleixandre, in press), propose five potential contri-
butions the introduction of argumentation can have on science learning environ-
ments. First is supporting access to cognitive and metacognitive reasoning. Second is
supporting the development communication and critical thinking. Third is support-
ing the development of scientific literacy and enabling students to engage in the lan-
guage of science. Fourth is supporting participation in practices of scientific culture
and developing epistemic criteria to evaluate knowledge. Fifth is supporting the
growth of reasoning employing rational criteria. Employing argumentation practices
along any one of these five dimensions requires contexts and levels of detail that make
such outcomes of argumentation possible, let alone successful.
When looking across the various available options for argumentation frameworks,
one sees that there are issues regarding the grain size of information being sought and
used (Duschl & Osborne, 2002; Kelly, 2007; Sampson & Clark, 2006). Toulmin
(1958), for example, distinguished between field-dependent and field-independent
forms of argumentation, with the latter focusing on the general patterns of arguments
involving claims, warrants, backings, rebuttals, qualifiers, and conclusions. Perelman
and Olbrechts-Tyteca (1958/1969) maintain that argumentation is fundamentally
rhetorical in nature, focusing as it does on persuasion. Walton (1996) advocates that
Duschl: Science Education in Three-Part Harmony 17
argumentation be seen as a dialectical process guided by informal logic, because con-
siderations for goals, intents, values, and audiences creep into the process. Jimenez-
Aleixandre and Erduran (2008), using Darwin’s “one long argument” On the Origin
of Species as a context, describe several aspects of argumentation. Arguments provide
evidence for the justification of knowledge. Arguments bring about convergence of
lines of reasoning and theoretical frameworks. Arguments seek to convince audiences.
Arguments can be seen as debates between two parties or two competing theses. As
their edited volume demonstrates, there is a wide variety of frameworks employed in
science classrooms. The question asked by Sampson and Clark (2006) in a review of
five different frameworks for examining rhetorical argumentation is “How does any
framework inform us about the quality of students’ argumentation?”
Argumentation, although common among many cultures and communities,
when played out in science, has particular what counts rules for knowledge building.
Such knowledge-building rules represent the epistemic demands (Sampson & Clark,
2006), epistemic resources (Hammer & Elby, 2003), epistemic actions (Pontecorvo
& Girardet, 1993), and the practices of epistemic communities (Duschl & Grandy,
2007). Thus, as stated above, when thinking about argumentation discourse in class-
rooms, there is a need to have tools that can support and scaffold students’ partici-
pation in argumentation discourse and, importantly, teachers’ assessment of the
students’ argumentation to guide its development.
Sampson and Clark (2006) review five frameworks used for the assessment of
• Toulmin’s argument pattern in science education research (Jimenez-Aleixandre,
Rodriguez, & Duschl, 2000; Kelly, Druker, & Chen, 1998; Osborne, Erduran &
• Zohar and Nemet’s (2002) modification of Toulmin,
• Kelly and Takao’s (2002; Takao & Kelly, 2003) framework examining the epis-
temic status of propositions,
• Sandoval’s (2003; Sandoval & Millwood, 2005) framework for examining the
conceptual and epistemic quality of arguments, and
• Lawson’s (2003) framework for examining the hypothetic-deductive validity of
The focus of the review was “(a) illustrating the logic and assumptions that have
pervaded research in the field, (b) summarizing the constraints and affordances
of these different approaches, and (c) making recommendations for new directions”
(p. 655). The analyses were conducted with lenses examining the epistemological cri-
teria used by each of the five frameworks. What Sampson and Clark (2006) report
is that the extant frameworks do not get down to a precise level of epistemic criteria:
Unfortunately...the majority of the analytical methods that have been developed to assess and charac-
terize the nature of the rhetorical arguments...have provided very little information about how the
rhetorical arguments generated by students reflect these criteria. (p. 659)
18 Review of Research in Education, 32
Adoption of argumentation frameworks for use in classrooms does indeed have
potential to shape the epistemic and social practices of students. Kelly (2007) makes
an important cautionary point, though, about classroom discourse practices. He
argues that norms of interaction that permit close examination of evidence while pre-
serving pupils’ dignity is not well understood. Pointing to the social nature of sci-
ence epistemology, Kelly goes on to state that epistemic criteria are the accepted
norms for justifying and evaluating knowledge among a given community. Making
that community a K–12 classroom opens up a broad range of issues about social
engagements and the content of those engagements. Following Longino’s (2002)
social norms for social knowledge scheme, Kelly offers up some suggestions for mak-
ing science classrooms places where dialectical discourse interactions like argumen-
tation can occur:
• A need for venues and for public discussion and corrections among members.
• A need for uptake of criticism, tolerance for dissent and changing views, but such
levels of disagreement may pose problems if left unresolved.
• A need for public standards that would change with relevant criticism and as the
inquiring community changes goals and values.
• A need for intellectual authority; teachers’ authority needs to be tempered to sup-
port open discussions; students’ experiences with shared authority can lead to con-
fidence, responsibility, and understanding of cognitive goals of science.
Changing the nature of classroom discourse practices has implications for teachers
as well, naturally. A position taken by Osborne et al. (2004) and Erduran, Simon, and
Osborne (2004) is that teacher comfort is a justification for using a generic use-of-
rebuttal framework that can define levels of engagement and function across science
domains. Although there are merits in this position regarding teachers’ comfort with
the basics of managing a classroom that promotes scientific argumentation discourse,
there remains concerns about the quality of argumentation and reasoning that can
emerge if more refined epistemic criteria are not introduced to students.
Shifting the focus of learning from what to how and why requires new forms of
knowledge to be brought to the classroom conversations. Consider the proficiency
of “Understand the nature and development of scientific knowledge.” What is the
appropriate level of detail or grain size of information to consider? To begin address-
ing this important issue, let us revisit the idea that philosophers of science have tra-
ditionally drawn a distinction between (a) the context of generation and discovery
where new ideas, methods, and questions emerge and (b) the context of justification
where ideas, methods, and hypotheses are tested against the prevailing evidence and
tested for coherence with prevailing beliefs. Contemporary practices in science edu-
cation reflect this endpoint perspective on nature and development of science.
What we have learned in the science studies as well as in the learning sciences is
that a consideration only for the endpoints of generation and justification is not the
proper scientific game nor is it the appropriate game of science education. What
Duschl: Science Education in Three-Part Harmony 19
research suggests is the proper game for understanding the nature and development
of scientific knowledge is engagement with the ongoing pursuit and refinement of
methods, evidence, and explanations and the subsequent handing of anomalies that
are a critical component of proposing and evaluating scientific models and theories.
In other words, dialogical processes characterize science-in-the making approaches
and the epistemic and social dynamics that seek to fill in the details between the ini-
tial and important context of generation scientific activities and the concluding and
necessary context of justification activities.
The epistemic and social dynamics, though, bring new and important practices to
bear for learning environments. Key among them is the need for establishing dialogic
or dialectical learning environments that facilitate two important activities. One is
making students’ thinking visible and doing so within a given conceptually grounded
learning context that by design promotes the attainment of scientific reasoning and
the motivation to learn. The other is enabling dynamic assessments of learning that
provide feedback to learners on the conceptual, epistemic, and social dimensions of
engaging in science and science education. The focus needs to be on both inquiry
practices and on literacy practices. The inquiry practices address the middle ground
between the generation and justification endpoints and include such things as obtain-
ing and using measurements, data, evidence, models, anomalies, and explanations.
The literacy practices address the communication and representation activities of sci-
ence, activities that embrace, among other things, mathematics, reading and writing,
argumentation, modeling, measurement, and representation.
Once again, though, we are confronted with the issue of grain size and norms of
interaction. What is the appropriate level of detail needed in the middle-ground dis-
course between generation and justification? What is the appropriate level of detail to
assume for students’ science learning? Should the level of detail be fixed and static, or
should it be dynamic, deepening with students’ and teachers’ level of expertise and
experiences? These are but some of the important research questions that we face
regarding the coherent infusion of learning science and science studies into K–12 sci-
Ackerman, R. J. (1985). Data, instruments, and theory: A dialectical approach to understanding
science. Princeton, NJ: Princeton University Press.
Bransford, J., Barron , B., Pea, R., Meltzoff, A., Kuhl, P., Bell, P., et al. (2006). Foundations
and opportunities for an interdisciplinary science of learning. In K. Sawyer (Ed.), The Cam-
bridge handbook of the learning sciences (pp. 19–34). New York: Cambridge University Press.
Bruner, J. (2004). The psychology of learning: A short history. Daedalus, Winter, 13–20.
Carey, S. (2004). Bootstrapping and the origin of concepts. Daedalus, Winter, 59–68.
Cole, M. (1996). Cultural psychology: A once and future discipline. Cambridge, MA: Belknap.
Driver, R., Leach, J., Millar, R., & Scott. P. (1996). Young people’s images of science. Philadel-
phia: Open University Press.
Duschl, R. (2003). Assessment of inquiry. In J. M. Atkin & J. Coffey (Eds.), Everyday assess-
ment in science classrooms (pp. 41–59). Washington, DC: National Science Teachers Asso-
20 Review of Research in Education, 32
Duschl, R., & Grandy, R. (Eds.). (2007). Establishing a consensus agenda for K-12 science
inquiry. Rotterdam, Netherlands: Sense.
Duschl, R., & Osborne, J. (2002). Argumentation and discourse processes in science educa-
tion. Studies in Science Education, 38, 39–72.
Duschl R. A. (1990). Restructuring science education. The importance of theories and their devel-
opment. New York: Teachers’ College Press.
Duschl, R. A. (2000). Making the nature of science explicit. In R. Millar, J. Leach, &
J. Osborne (Eds.), Improving science education: The contribution of research (pp. 187–206).
Philadelphia: Open University Press.
Duschl, R. A., Deak, G. O., Ellenbogen, K. M., & Holton, D. L. (1999). Developmental and
educational perspectives on theory change: To have and hold, or to have and hone? Science
and Education, 8, 525–541.
Easley, J. (1959). The Physical Science Study Committee and educational theory. Harvard
Educational Review, 29(1), 4–11.
Engle, R., & Conant, F. (2002). Guiding principles for fostering productive disciplinary
engagement: Explaining an emergent argument in a community of learner’s classroom.
Cognition and Instruction, 20, 399–483.
Erduran, S., & Jimenez-Aleixandre, M. P. (Eds.). (in press). Argumentation in science educa-
tion: Perspectives from classroom-based research. Dordrecht, Netherlands: Springer.
Erduran, S., Simon, S., & Osborne, J. (2004). TAPing into argumentation: Developments in
the application of Toulmin’s argument pattern for studying science discourse. Science Edu-
cation, 88, 915–933.
Ford, D. (2005). The challenges of observing geologically: Third grades descriptions of rock
and mineral properties. Science Education, 89, 276–295.
Gee, J. (1996). Social linguistics and literacies: Ideology in discourses (2nd ed.). London: Taylor
Gelman, R., & Brenneman, K. (2004). Science learning pathways for young children. Early
Childhood Research Quarterly, 19, 150–158.
Giere, R. (1988). Explaining science: A cognitive approach. Chicago: University of Chicago Press.
Giere, R. (1999). Science without laws. Chicago: University of Chicago Press.
Godfrey-Smith, P. (2003). Theory and reality. Chicago: University of Chicago Press.
Goldman, A. (1986). Epistemology and cognition. Cambridge, MA: Harvard University Press.
Gopnick, A., Glymour, C., Sobel, D., Schulz, L., Kushnir, T., & Danks, D. (2004). A the-
ory of causal learning in children: Causal maps and Bayes nets. Psychological Review, 111(1),
Hammer, D., & Elby, A. (2003). Tapping epistemological resources from learning physics.
Journal of the Learning Sciences, 12, 53–91.
Hapgood, S., Magnusson, S. J., & Palinscar, A. S. (2004). Teacher, text, and experience: A
case of young children’s scientific inquiry. Journal of the Learning Sciences, 13, 455–505.
Hatano, G., & Inagaki, K. (1991). Sharing cognition through collective comprehension activity.
In L. B. Resnick, J. M. Levine, & S. D. Teasley (Eds.), Perspectives on socially shared cognition
(pp. 331–348). Washington, DC: American Psychological Association.
Herrenkohl, L., & Guerra, M. (1998). Participant structures, scientific discourse, and student
engagement in fourth grade. Cognition and Instruction, 16
Jimenez-Aleixandre, M. P., & Erduran, S. (in press). Argumentation in science education: An
overview. In S. Erduran & M. P. Jimenez-Aleixandre (Eds.), Argumentation in science edu-
cation: Perspectives from classroom-based research. Dordrecht, Netherlands: Springer.
Jimenez-Aleixandre, M., Rodriguez, A., & Duschl, R. (2000). “Doing the lesson” or “doing
science”: Argument in high school genetics. Science Education, 84, 757–792.
Kelly, G. (2007). Inquiry, activity, and epistemic practice. In R. Duschl &R. Grandy
Duschl: Science Education in Three-Part Harmony 21
(Eds.), Establishing a consensus agenda for K-12 science inquiry (pp. 99–117). Rotterdam,
Kelly, G. J., Chen, C., & Crawford, T. (1998). Methodological considerations for studying
science-in-the-making in educational settings. Research in Science Education, 28(1), 23–50.
Kelly, G. J., & Crawford, T. (1997). An ethnographic investigation of the discourse processes
of school science. Science Education, 81(5), 533–560.
Kelly, G. J., Druker, S., & Chen, C. (1998). Students’ reasoning about electricity: combining
performance assessments with argumentation analysis. International Journal of Science Edu-
cation, 20(7), 849–871.
Kelly, G. J., & Duschl, R. (2002, April). Toward a research agenda for epistemological studies
in science education. Paper presented at the annual meeting of the National Association for
Research in Science Teaching, New Orleans, LA.
Kelly, G. J., & Takao, A. (2002). Epistemic levels in argument: an analysis of university
oceanography students’ use of evidence in writing. Science Education, 86(3), 314–342.
Kenyon, L., & Reiser, B. (2004, April). Students’ epistemologies of science and their influence on
inquiry practices. Paper presented at the annual meeting of the National Association for
Research in Science Teaching, Dallas, TX.
Kesidou, S., & Roseman, J. (2002). How well do middle school science programs measure up?
Findings from Project 2061’s curriculum review. Journal of Research in Science Teaching,
Kitcher, P. (1993). The advancement of science: Science without legend, objectivity without illu-
sions. New York: Oxford University Press.
Kitcher, P. (1998). A plea for science studies. In N. Koertge (Ed.), A house built on sand:
Exposing postmodernist myths about science (pp. 32-56). New York: Oxford University Press.
Knorr-Cetina, K. (1999). Epistemic cultures: How science makes knowledge. Cambridge, MA:
Harvard University Press.
Koertge, N. (Ed). (1998). A house built on sand: Exposing postmodernist myths about science.
New York: Oxford University Press.
Kuhn, L., & Reiser, B. (2004, April). Students constructing and defending evidence-based scien-
tific explanations. Paper presented at the annual meeting of the National Association for
Research in Science Teaching, Dallas, TX.
Kuhn, T. (1996). The structure of scientific revolutions (4th ed.). Chicago: University of
Chicago Press. (Original work published 1962)
Lawson, A. (2003). The nature and development of hypothetico-deductive argumentation
with implications for science learning. International Journal of Science Education, 25(11),
Lemke, J. (1990). Talking science: Language, learning, and values. Norwood, NJ: Ablex.
Lehrer, R., & Schauble, L. (2006a). Cultivating model-based reasoning in science education.
In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 371–388). New
York: Cambridge University Press.
Lehrer, R., & Schauble, L. (2006b). Scientific thinking and science literacy. In W. Damon,
R. Lehrer, K. A. Renninger, and I. E. Sigel (Eds.), Handbook of child psychology: Vol. 4.
Child psychology in practice (6th ed., pp. 153–196). Hoboken, NJ: Wiley.
Longino, H. (1990). Science as social knowledge. Princeton, NJ: Princeton University Press.
Longino, H. (2002). The fate of knowledge. Princeton, NJ: Princeton University Press.
Metz, K. (1995). Reassessment of developmental constraints on children’s science instruction.
Review of Educational Research, 65, 93–127.
Metz, K. (2004). Children’s understanding of scientific inquiry: Their conceptualization of
uncertainty in investigations of their own design. Cognition and Instruction, 22, 219–290.
Millar, R. (1996). Towards a science curriculum for public understanding. School Science
Review, 77(280), 7–18.
22 Review of Research in Education, 32
Millar, R., & Hunt, A. (2001). Science for public understanding: A different way to teach and
learn science. School Science Review, 83(304), PAGE RANGE.
Millar, R., Leach, J., & Osborne, J. (Eds.). (2000). Improving science education: The contribu-
tion of research. Philadelphia: Open University Press.
Minstrell, J., & Van Zee, E. (Eds.). (2000). Teaching in the inquiry-based science classroom.
Washington, DC: American Association for the Advancement of Science.
National Research Council. (1996). National science education standards. Washington, DC:
National Academy Press.
National Research Council. (1999). How people learn. Washington, DC: National Academy
National Research Council. (2000). Inquiry and the national science education standards.
Washington, DC: National Academy Press.
National Research Council. (2001). Knowing what students know: The science and design of
educational assessment. Washington, DC: National Academy Press.
National Research Council. (2006). Rising above the gathering storm. Washington, DC:
National Academy Press.
National Research Council. (2007). Taking science to school: Learning and teaching science
kindergarten to eighth grade. Washington, DC: National Academy Press.
Nersessian, N. (1992). Constructing and instructing: The role of abstraction techniques in
creating and learning physics. In R. A. Duschl & R. J. Hamilton (Eds.), Philosophy of sci-
ence, cognitive psychology, and educational theory and practice (pp. 48–68). New York: State
University of New York Press.
Osborne, J. F., Duschl, R., & Fairbrother, R. (2002). Breaking the mould: Teaching science for
public understanding. London: Nuffield Foundation.
Osborne, J., Erduran, S., & Simon, S. (2004). Enhancing the quality of argumentation in sci-
ence classrooms. Journal of Research in Science Teaching, 41(10), 994–1020.
Pea, R. (1993). Learning scientific concepts through material and social activities: Conversa-
tional analysis meets conceptual change. Educational Psychologist, 28, 265–277.
Perelman, C., & Olbrechts-Tyteca, L. (1969). A new rhetoric: A treatise on argumentation.
South Bend, IN: University of Notre Dame Press. (Original work published 1958)
Petrosino, A., Lehrer, R., & Schauble, L. (2003). Structuring error and experimental variation
as distribution in the fourth grade. Mathematical Thinking and Learning, 5(2/3), 131–156.
Pickering, A. (1990). Knowledge, practice and mere construction. Social Studies of Science,
Pickering, A. (1989). Living in the material world: On realism and experimental practice. In
D. Gooding, T. Pinch, & S. Schaffer (Eds.), The uses of experiment: Studies in the natural
sciences (pp. PAGE RANGE). Cambridge, UK: Cambridge University Press.
Pickering, A. (1995). The mangle of practice: Time, agency and science. Chicago: University of
Pontecorvo, C., & Girardet, H. (1993). Arguing and reasoning in understanding historical
topics. Cognition and Instruction, 11(3/4), 365–395.
Rogoff, B. (1990). Apprenticeship in thinking: Cognitive development in a social context. New York:
Oxford University Press.
Rosebery, A. S., Warren, B., & Conant, F. (1992). Appropriating scientific discourse: Find-
ings from language minority classrooms. Journal of the Learning Sciences, 2(1), 61–94.
Rudolph, J. (2002). Scientists in the classroom: The Cold War reconstruction of American science
education. New York: Palgrave Macmillan.
Rudolph, J. (2005). Epistemology for the masses: The origins of the “scientific method” in
American schools. History of Education Quarterly, 45(2), 341–376.
Sampson, V., & Clark, D. (2006). Assessment of argument in science education: A critical
review of the literature. In S. A. Barab, K. E. Hay, & D. T. Hickey (Eds.), Proceedings of
Duschl: Science Education in Three-Part Harmony 23
the 7th International Conference of the Learning Sciences (pp. 655–661). Bloomington, IN:
International Society of the Learning Sciences.
Sandoval, W. A. (2003). Conceptual and epistemic aspects of students’ scientific explanations.
Journal of the Learning Sciences, 12(1), 5–51.
Sandoval, W. A. (2005) Understanding students’ practical epistemologies and their influence
on learning through inquiry. Science Education, 89(4), PAGE RANGE.
Sandoval, W. A., & Millwood, K. (2005). The quality of students’ use of evidence in written
scientific explanations. Cognition and Instruction, 23(1), 23–55.
Sandoval, W. A., & Reiser, B. J. (2004). Explanation-driven inquiry: Integrating conceptual
and epistemic scaffolds for scientific inquiry. Science Education, 88, 345–372.
Sawyer, R. K. (Ed.). (2006). The Cambridge handbook of the learning sciences. New York: Cam-
bridge University Press.
Schauble, L. (2007). Three questions about development. In R. Duschl & R. Grandy (Eds.),
Establishing a consensus agenda for K-12 science inquiry (pp. 50–56). Rotterdam, Nether-
Schauble, L., Glaser, R., Duschl, R., Schultz, S., & John, J. (1995). Students’ understanding
of the objectives and procedures of experimentation in the science classroom. Journal of the
Learning Sciences, 4(2) 131–166.
Schauble, L., Leinhardt, G., & Martin, L. M. (1997). A framework for organizing a cumulative
research agenda in informal learning contexts. Journal of Museum Education, 22(2/3), 3–8.
Schmidt, W. H., McKnight, C. C., & Raizen, S. A. (1997). A splintered vision: An investiga-
tion of US science and mathematics education. Boston: Kluwer Academic.
Schwab, J. (1962). The teaching of science as inquiry. In J. Schwab & P. Brandwein (Eds.),
The teaching of science (pp. 1–104). Cambridge, MA: Harvard University Press.
Schwab, J. (1958). The nature of enquiry. Bulletin of Atomic Scientists. PLS COMPLETE REF
Smith, C., Maclin, D., Houghton, C., & Hennessey, M. G. (2000). Sixth-grade students’ epis-
temologies of science: The impact of school science experience on epistemological develop-
ment. Cognition and Instruction, 18(3), 285–316.
Smith, C., Wiser, M., Anderson, C., & Krajcik, J. (2006). Implications of research on chil-
dren’s learning for assessment: Matter and atomic molecular theory. Measurement: Interdis-
ciplinary Research and Perspectives (Vol. 4, pp. 11–98). Mahwah, NJ: Lawrence Erlbaum.
Songer, N., & Linn, M. (1991). How do students’ views of the scientific enterprise influence
knowledge integration? Journal of Research in Science Teaching, 28(9), 761–784.
Spelke, E. (2000). Core knowledge. American Psychologist, 55, 1233–1243.
Takao, A., & Kelly, G. (2003). Assessment of evidence in university students’ scientific writ-
ing. Science and Education, 12(4), 341–363.
Thagard, P. (1992). Conceptual revolutions. Princeton, NJ: Princeton University Press.
Toth, E., Suthers, D., & Lesgold, A. (2002). “Mapping to know”: The effects of representa-
tional guidance and reflective assessment on scientific inquiry. Science Education, 86(2),
Toulmin, S. (1958). The uses of argument. Cambridge: Cambridge University Press.
Van Zee, E. & Minstrell, J. (1997). Using questioning to guide student thinking. Journal of
the Learning Sciences, 6, 227–269.
Walton, D. N. (1996). Argumentation schemes for presumptive reasoning. Mahwah, NJ:
Zammito, J. H. (2004). A nice derangement of epistemes: Post-positivism in the study of science
from Quine to Latour. Chicago: University of Chicago Press.
Zohar, A., & Nemet, F. (2002). Fostering students’ knowledge and argumentation skills
through dilemmas in human genetics. Journal of Research in Science Teaching, 39(1), 35–62.
24 Review of Research in Education, 32
Richard A. Duschl (PhD, 1983, University of Maryland-College Park) is a professor
of science education in the Graduate School of Education at Rutgers University and
executive member of the Rutgers Center for Cognitive Studies. Prior to joining
Rutgers, he held the chair of science education at King’s College London. He recently
served as chair of the National Research Council research synthesis report Taking
Science to School: Learning and Teaching Science in Grades K-8 (National Academies
Press, 2007). His research focuses on establishing epistemic learning environments and
on the role of students’ inquiry and argumentation processes. Richard has twice
received the JRST Award (1989, 2003) for outstanding research article published in
the Journal of Research in Science Teaching. He also served for more than a decade
as editor of the research journal Science Education and editor for TC Press’s Ways of
Knowing in Science and Math book series.
Duschl: Science Education in Three-Part Harmony 25