text:
Learning in
Interactive Environments: Prior Knowledge and New Experience
by Jeremy Roschelle
University of Massachusetts, Dartmouth
This article summarizes research on the roles of prior knowledge in learning. Educators often
focus on the ideas that they want their audience to have. But research has shown that a learner's prior knowledge
often confounds an educator's best efforts to deliver ideas accurately. A large body of findings shows that learning
proceeds primarily from prior knowledge, and only secondarily from the presented materials. Prior knowledge can be
at odds with the presented material, and consequently, learners will distort presented material. Neglect of prior
knowledge can result in the audience learning something opposed to the educator's intentions, no matter how well
those intentions are executed in an exhibit, book, or lecture.
Consider a hypothetical book on wool production in Australia. Australian ranchers raise sheep in
an extremely hot desert climate. The sheep are raised to have wool so thick that without yearly trimmings the sheep
would be unable to walk. To many children, these facts together are absurd. Children think wool is hot; if you put
a thermometer inside a wool sweater, the mercury would go up (Lewis, 1991). Wouldn't sheep grow more wool in cold
places where they need to stay warm? Is wool hot because the sheep absorb the desert warmth?
Alternatively, consider a hypothetical exhibit on fish schooling. Fish follow each other in
close formation that looks highly organized. But no single fish is the leader, and none of the fish know how to
command the others. Many people assume that any organized system is the result of a centralized planner who directs
the others. They think "there must be an older fish, who is smarter than the rest, and who leads the school. If
marine biologists believe otherwise, well I guess its true, but I'll never be a marine biologist!"
Then again, consider a hypothetical lecture on jazz. Upon a first listening, one might hear
the music as ugly, chaotic, and meaningless: "it's just a lot of notes." Many years later, same music provides a
rich and rewarding experience, and with more listening, yet more difficult music becomes accessible. How can you
learn jazz if all you understand is classical music or pop?
To help people make the most of a new experience, educators need to understand how prior
knowledge affects learning. To the child who does not yet understand heat and temperature, no quick explanation can
possibly resolve the contradiction between the hot desert and the warm wool; it takes weeks to years for this
understanding to emerge (Lewis, 1991). The adult who is unfamiliar with the possibilities of decentralized systems
can't quickly be convinced that schooling fish have no leader (Resnick, 1992) and instead they may be alienated from
the setting. There is no way to give the first-time jazz listener the epiphany available to more practiced ears.
Prior knowledge determines what we learn from experience.
Prior knowledge also forces a theoretical shift to viewing learning as "conceptual change."
(Strike & Posner, 1985; West & Pines,1985). Previously learning was considered a process of accumulating
information or experience. Prior knowledge is the bane of transmission-absorption models of learning. Mere
absorption cannot account for the revolutionary changes in thought that must occur. The child simply can't absorb
knowledge about wool, because prior knowledge about heat renders incoming ideas nonsensical. One can't assimilate
fish schooling to a centralized mindset; distinct concepts for understanding decentralized systems must be
developed. Jazz can't be translated into rock; one must cultivate ears for its unique organization.
On the other hand, it is impossible to learn without prior knowledge. Eliminating prior
undertanding of heat won't explain why that sweater is still so nice in the winter, or how thick-coated sheep can be
raised in the desert. The idea of decentralized systems must be built from some anchor in prior experience. It is
easiest to appreciate unfamiliar music by starting with "crossover" artists who populate the periphery between jazz
and rock or classical music.
The aspects of learning, prior knowledge and experience drawn out in these examples have a solid
basis in research on learning. There is widespread agreement that prior knowledge influences learning, and that
learners construct concepts from prior knowledge (Resnick, 1983; Glaserfeld, 1984). But there is much debate about
how to use this fact to improve learning.
This article presents a set of research findings, theories, and empirical methods that can help
the designer of interactive experiences work more effectively with the prior knowledge of their audience. It focuses
on the central tension that dominates the debate about prior knowledge. This tension is between celebrating
learners' constructive capabilities and bemoaning the inadequacy of their understanding. On one hand, educators
rally to the slogan of constructivism: "create experiences that engage students in actively making sense of concepts
for themselves." On the other hand, research tends to characterize prior knowledge as conflicting with the learning
process, and thus tries to suppress, eradicate, or overcome its influence.
The juxtaposition of these points of view creates a paradox: how can students' ideas be both
"fundamentally flawed" and "a means for constructing knowledge?" The question cuts to the heart of constructivism:
constructivism depends on continuity, because new knowledge is constructed from old. But how can students construct
knowledge from their existing concepts if their existing concepts are flawed? Prior knowledge appears to be
simultaneously necessary and problematic.This version of the learning paradox (Bereiter, 1985) is called the
"paradox of continuity" (Roschelle, 1991). Smith, diSessa, and Roschelle (1993) argue that educational reforms must
include strategies that might avoid, resolve, or overcome the paradox. Throughout the article, I endeavor to show
how designers can work with prior knowledge despite its apparent flaws and without succumbing to an irresolvable
contradiction. This requires careful consideration of assumptions about knowledge, experience, and learning.
The article is organized in three sections:
In the first section, I present findings both on how scientists learn, and on how students learn
science. Evidence on scientific conceptual change leads to a recommendation to view science learning as refinement
of everyday ideas, requiring a long time and in a rich social context. Consideration of how students learn science
leads to additional recommendations: we should study successful learning, avoid interpreting prior knowledge in
terms of dichotomies, see prior knowledge as providing flexible building blocks, and look for long-term
transformations in the structure and coordination of knowledge.
The second section presents several major theoretical perspectives on the process of conceptual
change. Piaget emphasizes changes in the structure of prior knowledge. His theory and methods suggest that designers
create tasks that engage learners and create tension between assimilation and accommodation. Engagement in physical
aspects of a challenging task can lead to reformulation of intellectual aspects of the task. Dewey emphasizes the
conditions under which inquiry can resolve problematic experience. He suggests that designers discover that which is
problematic for learners, and establish conditions that support the process of inquiry: time, talk, and tools.
Vygotsky emphasizes the role of social process in learning, suggesting that new concepts appear first socially, and
only gradually become psychological. He suggests that designers provide social models of appropriate activity,
enable groups of learners to do more complex activities than they could handle individually, and use signs to enable
people to negotiate the different meanings they find in social activity. Perspectives from information processing
and situated learning theories are also briefly discussed in this section.
The third discussion summarizes some useful empirical methods. Successful design of interactive
learning experiences builds on an understanding of how learners think. This requires using empirical methods to
uncover prior knowledge. Traditional tests are written from the experts' perspective, and label learners'
differences as "errors." More modern and sophisticated methods allow educators to discover and work with the logic
of learners' reasoning. These methods include clinical interviews, think-aloud problem solving, and video
interaction analysis.
Empirical Findings in Science and Mathematics Learning
Because prior knowledge is usually specific to a subject matter, it is difficult to state
general facts about prior knowledge across all areas of human interest. Therefore, this article focuses on one area,
science and mathematics learning, in order to provide a detailed example of prior knowledge at work. Prior knowledge
has been studied more extensively in science and mathematics than in other areas. While the specific forms of prior
knowledge in art or history may be different, we can expect that similar issues will arise.
Prior knowledge can be viewed from two perspectives, that of the accomplished scientist or, of
that of the learner. Let's start with the scientist.
Science as Refinement of Prior Knowledge
In this section we discuss the role that prior knowledge plays in thinking of accomplished scientists. I use the
term "scientific knowledge" broadly here, referring both to "concepts," and also scientists' modes of perception,
focus of attention, procedural skills, modes of reasoning, discourse practices, and beliefs about knowledge. It is
conventional to think that scientific knowledge is different from everyday knowledge, and must replace everyday
knowledge. But when we look more closely, it becomes apparent that scientists reuse metaphors and ideas drawn from
prior knowledge. Moreover we see that this transformation occurs very gradually, and depends on the social practices
of the scientific community. Only over long periods of time, and through extended conversations with their
colleagues, do scientists shape theories that are distinct from their commonsense roots.
The cartoonist presents the typical scientist as an Einstein scribbling mathematical formulae on
a blackboard. Study of the scientific process reveals, however, that science does not always begin with mathematical
abstractions nor empirical findings, but rather with ideas close to the surface of everyday knowledge. Einstein, for
example, roots his own intellectual development not in mathematics, but rather in everyday ideas of rigidity,
simultaneity, and measurement (Einstein, 1950; Wertheimer, 1982; Miller, 1986).
Einstein (1950) said that everyday knowledge provides a huge store of useful metaphors and
ideas. From these, the scientist makes a free selection of a set of axioms, and thereupon begins constructing a
theory. Einstein thought the origin of his theory might relate to a childlike exploration of space, and consulted
with Piaget on the possible similarities between his personal intellectual development and that of children (Miller,
1986). In analyzing the work of other scientists, philosophers (Black, 1962; Kuhn 1970; Toulmin, 1972) and
historians (Miller, 1986, Nercessian, 1988) emphasize that science is a constructive activity. Its materials are
drawn in part from the familiar images and metaphors of prior knowledge (Lightman, 1989, Miller, 1986).
If science draws upon everyday knowledge, why does scientific knowledge often appear so
different from everyday knowledge, both in its form and content? In traditional accounts, philosophers searched for
a Great Divide that separated scientific from everyday knowledge, much like the division between sacred and profane
knowledge. If such a divorce could be made, scientific learning could be cut free of the biases of prior knowledge.
These traditional accounts have not succeeded in establishing a firm divide between everyday and scientific
An alternative to the Great Divide account comes from the work of sociologists, historians, and
anthropologists who have studied scientific work (e.g. Latour, 1987; Knorr, 1981). From their inquiries, we learn
that the properties of scientific knowledge arise from the social practices enacted by specific scientific
communities. Discourse processes transform prior knowledge into refined concepts that can be applied consistently by
members of the scientific community. Scientific knowledge is not a type of knowledge, but rather a refined product,
for which prior knowledge supplied the raw materials and social interaction supplied the tools.
The preceding discussion illustrates the contrast between replacement and re-use. New knowledge
does not replace prior knowledge, rather new knowledge re-uses prior knowledge. Re-use is made possible by a process
in which prior knowledge is refined, and placed in a more encompassing structure. The more encompassing structure
comes in part from the social discourse norms that prevail within a community of practice.
The importance of time and social context become apparent when we consider how scientists
learn. Kuhn (1970) argues that scientific knowledge does not always progress smoothly, but calls for "paradigm
shifts" that involve large-scale conceptual change. To invent Relativistic physics, Einstein had to depart from the
very foundations of Newtonian science (Einstein, 1961). In paradigm shifts, the paradox of continuity again arises:
how can scientists formulate a better theory if all they have is a flawed prior theory?
Analyzing conceptual change, Toulmin (1972) argues that conceptual change is not the mere
replacement of one theory by another. Conceptual change occurs slowly, and involves a complex restructuring of prior
knowledge to encompass new ideas, findings, and requirements. Thus Einstein does not merely replace Newton, he
transforms Newtonian ideas and places them inside a new, encompassing analysis of space and time. Toulmin emphases
that conceptual change, like normal science, is continual and incremental. It is mediated by physical tools, and
regulated by social discourse. Only from the distant perspective of history does a paradigm shift appear as
replacement. From a close-up perspective, conceptual change looks like variation and selection in a interrelated
system of knowledge. Individual scientists vary the meaning of concepts and the use of methods. Given specific
social rules and a long time over which to operate, selection can result in large-scale changes in concepts.
From this analysis of the scientific process comes a series of important lesson for those who
study learning: knowledge begins with the selection of ideas from everyday experience. The construction of
scientific knowledge is a slow, continuous process of transformation taking place over a long period of time,
involving successive approximation, and only gradually and incompletely becoming "different" from everyday
In general, learning involves three different scales of changes. Most commonly, learners
assimilate additional experience to their current theories and practices. Somewhat less frequently, an experience
causes a small cognitive shock that leads the learner to put ideas together differently. Much more rarely, learners
undertake major transformations of thought that affect everything from fundamental assumptions to their ways of
seeing, conceiving, and talking about their experience. While rare, this third kind of change is most profound and
highly valued.
These lessons have three implications for designers of interactive experiences. First, designers
should seek to refine prior knowledge, and not attempt to replace learners' understanding with their own.
Second, designers must anticipate a long-term learning process, of which the short-term experience will form
an incremental part. Third, designers must remember that learning depends on social interaction; conversations
shape the form and content of the concepts that learners construct. Only part of specialized knowledge can exist
explicitly as information; the rest must come from engagement in the practice of discourse of the community.
We next move to the viewpoint of learner. This will stress similar points, but draw attention
to specific difficulties that arise in trying to interpret learners' prior knowledge. First, we review data that
shows the dominant paradox of continuity in science education: science learners need prior knowledge, but prior
knowledge seems to mislead them. Then we present a guidelines for resolving the paradox by reconsidering assumptions
about learning. These guidelines may help educators interpret prior knowledge both in science and other areas.
Studies of Science Learning: Deepening the Paradox
Studies of students' prior knowledge in science and mathematics began in the 1970s and have since produced a
voluminous literature (see reviews in Confrey, 1990; McDermott, 1984; Eylon & Linn, 1988). Interest in prior
knowledge began with the careful documentation of common errors made by students in solving physics and mathematics
problems. Analysis of interviews with these students reveals that the errors are not random slips, but rather derive
from underlying concepts.
For example, when students are asked to explain a toss of a ball straight up in the air, they
describe the motion in terms of an "initial upwards force" which slowly "dies out," until it is "balanced" by
gravity at the top of the trajectory. Physicists, in contrast, explain the ball toss in terms of a single constant
force, gravity, which gradually changes the momentum of the ball: On its way upwards, the momentum is positive and
decreasing; at the top, it is zero; and going down, the momentum is negative and increasing.
From analysis of students' thinking, researchers have determined that this "mistaken"
explanation is not peculiar to this problem. Students commonly give explanations in terms of "imparting force,"
"dying out," and "balancing"(diSessa, 1993). From these commonsense ideas, students can generate endless explanations
for different situations. In many cases, these explanations disagree with conventional Newtonian theory.
The text below examines the complex findings that have emerged from investigations of students'
concepts. Notice that research tends to deepen the paradox of continuity: as we learn more about students' prior
knowledge, the construction of scientific knowledge not only seems slow, but also seems increasingly improbable.
After they established the existence of prior concepts, researchers investigated the
consequences of those concepts for subsequent learning. Most studies have looked at the role of prior knowledge in a
conventional science course. The results depend on the nature of the task used to probe students' learning. If the
task is procedural calculation, students can often learn to get the right answer independent of their prior
knowledge. However, if the task requires students to make a prediction, give a qualitative explanation, or otherwise
express their understanding, studies show that their prior knowledge "interferes. diSessa (1982), for instance,
found students who were receiving an "A" grade in freshman physics at MIT, but could not explain the simple ball
toss problem correctly. Using their prior knowledge, students often construct idiosyncratic, nonconforming
understandings of the scientific concepts.
The prevalence of this effect has been widely documented. Halhoun and Hestenes (1985a &
1985b) found that 30% to 40% of physics students who pass freshman physics at various universities misunderstood the
concepts. This also has been found at the elementary and secondary school levels, across both Western and
non-Western cultures around the world. Indeed, some researchers suggest that 30% to 40% of physics teachers at the
secondary school level misunderstand physics concepts because of their prior knowledge.
The processes by which "misconceptions" arise from a combination of prior knowledge and
instructed subject matter are not unique to Newtonian mechanics. Children have concepts that differ from scientists'
in biology (Carey, 1985; Keil 1979), heat and temperature (Lewis, 1991; Wiser & Carey, 1983), electricity (Cohen,
Eylon & Ganiel, 1983; Gentner & Gentner, 1983), mathematics (Resnick & Ford, 1981; VanLehn, 1989),
probability (Shaughnessy, 1985), statistics (Tversky & Kahneman, 1983) and computer programming (Spohrer,
Soloway, & Pope, 1989), and encounter difficulties as they interpret the scientific theories of these subjects.
Furthermore, it's not just children who produce mistaken interpretations by combining prior knowledge with
instruction. Consider Tversky & Kahneman's (1982) findings about simple statistics. They have identified
erroneous prior concepts about statistical phenomena that are widespread among professional
psychologistsscientists who use statistics regularly. For example, both students and scientists suffer from
"confirmation bias" that distorts experience to fit prior theory.
Prior knowledge exists not only at the level of "concepts," but also at the levels of
perception, focus of attention, procedural skills, modes of reasoning, and beliefs about knowledge. Trowbridge
and McDermott (1980) studied perception of motion. Students perceive equal speed at the moment when two objects
pass, whereas scientists observe a faster object passing a slower one. Anzai and Yokohama (1984), Larkin (1983),
and Chi, Feltovich, and Glaser (1990) studied how students perceive physics problems and found they often notice
superficial physical features, such as the presence of a rope, whereas scientists perceive theoretically-relevant
features, such as the presence of a pivot point. Larkin, McDermott, Simon and Simon (1980) studied students'
solutions to standard physics problems and found that students often reason backwards from the goal towards the
known facts, whereas scientists often proceed forward from the given facts to the desired unknown. Similarly,
Kuhn (Kuhn, Amsel, & O'Loughlin, 1988) studied children's reasoning at many ages and found that children only
slowly develop the capability to coordinate evidence and theory in the way scientists do. Finally, Songer (1988)
and Hammer (1991) studied students beliefs about the nature of scientific knowledge. They found that students
sometimes have beliefs that foster attitudes antagonist to science learning.
In summary, prior knowledge comes in diverse forms. It affects how students interpret
instruction. While it may not prevent them from carrying out procedures correctly, it frequently leads to
unconventional and unacceptable explanations. Prior knowledge is active at levels ranging from perception to
conception to beliefs about learning itself. Moreover, its effects are widespread through lay and professional
population, from young children through to adults, and from low to high ability students.
Implications of Prior Knowledge: Learning as Conceptual Change
The overwhelming weight of the evidence has forced informed educators to fundamentally change the way science is
taught. Learners are more likely to construct an interpretation that agrees with prior knowledge, and consequently
disagrees with the viewpoint of the teacher. Thus, the effects of prior knowledge require a change from the view
that learning is absorption of transmitted knowledge, to the view that learning is conceptual change (Resnick, 1983;
Champagne, Gunstone, & Klopfer, 1985). Over time, learners need to accomplish the rarest form of change, a
paradigm shift in their basic assumptions about the natural world, and the accompanying ways they see, conceive, and
talk about the world. Conceptual change is a process of transition from ordinary ways of perceiving, directing
attention, conceptualizing, reasoning, and justifying. Slowly learners transform prior knowledge to accommodate new
scientific ideas (Posner, Strike, Hewson, & Gertzog, 1982).
Most of the data on science learning stresses differences between prior knowledge and scientific
knowledge, rather than commonalities (Smith, et al, 1993). This has had an unfortunate consequence: rather than
making education seem easier, it now appears to be impossible. Teachers get the impression that students need prior
knowledge to learn new concepts, but prior knowledge misleads students to unconventional interpretations of concepts.
Moreover, as the perception of a gap has increased, the metaphors used to describe the learning process have become
more adversarial: prior knowledge must be confronted, challenged, overcome, replaced, eradicated, or destroyed in
order for new knowledge to take its place. Educators celebrate students' constructive capabilities, and then roll
out the heavy artillery to destroy it. The weight of the evidence makes paradox of continuity appear as a gaping
voidthere seems to be no bridge from prior knowledge to desired knowledge, with many apparent pitfalls along
the way.
Undoing the Paradox of Continuity in Science Learning
Smith et al. (1993) recently investigated the paradox of continuity that arises in science education research. They
suggest a interpretative theoretical framework that accepts the flawed character of some prior knowledge, but still
gives it a positive role. The gist of their argument is that the paradox arises from implicit biases in theory and
method. To undo the paradox, one must reconsider the implicit assumptions in science learning research.
First, one must recognize a bias in the data set. Almost all the data begins from identifying
learning failureexamining a situation in which students make errors, and then identifying the concept that
causes the error. If we start, on the other hand, by identifying success, and then investigating the concepts that
enable success, we find an equally strong role for prior knowledge. Prior knowledge is properly understood not as a
causes of errors or success, but rather as the raw material that conditions all learning.
Second, biases in research methodology tend to produce "attributes" of prior knowledge, which
might be better understood as "attributes of the learning task." For example, prior knowledge is said to be
resistant to change by conventional instruction. Students might be resisting the learning experience, and not the
knowledge. For example, most conventional science courses focus on manipulating mathematical expressions that refer
to idealized situations, i.e. a frictionless plane. We should not expect such an abstract experiences to enable much
change in familiar concepts of motion. When learning experiences are more concrete, related to familiar situations
and interactive, "resistance" often disappears, and students construct new concepts quickly. Prior knowledge and
conventional instructed knowledge may not be in conflict, but rather may be ships passing in the night.
Likewise, research methods that compare expert and novice performance tend to characterize their
findings in dichotomies. For example, Larkin (1983) suggests that scientific knowledge is abstract, whereas prior
knowledge is concrete. Other popular dichotomies are general vs. superficial, theoretical vs. familiar, and
structural vs. superficial. A methodology based on dichotomies is well suited to sorting objects onto a bipolar
spectrum, but is not well suited to analysis of how emergent wholes integrate pre-existing parts. For example,
dichotomy-based methods mistakenly assert that science is abstract, and cannot identify how scientific knowledge
successfully coordinates both concrete and abstract elements. A bias to dichotomies obscures the continuing roles
prior knowledge plays in a more encompassing knowledge structures.
Third, one must be careful about the status that is attributed to prior knowledge. Researchers
have termed prior knowledge "preconceptions." "alternative conceptions," "naive conceptions," "misconceptions," as
well as "naive theories" and "alternative theories." Each term is loaded with theoretical connotations that may be
quite misleading and inaccurate, even if unintentionally so.
Terms that ascribe the status of a "theory" to prior knowledge are particularly misleading. For
example, some researchers have drawn analogies between students' ideas and historical theories, such as medieval
impetus theory (McCloskey, 1983). However, children are not "short scientists" nor are ordinary adults "medieval
scientists." All people, including scientists, build knowledge from a pool of familiar metaphors like "balancing"
and "dying out." This pool of metaphors is not structured like a theory; it is not necessarily consistent, complete,
or deductively sound. Rather it is a loose aggregate of useful ideas that can be flexibly
applied. Although children and ordinary adults sometimes produce
explanations that sound like medieval theory, they do necessarily
hold their knowledge in the same regard that a scientist holds
a theory.
Terms that focus on the mistaken or alternative
status of prior knowledge are also misleading. Prior knowledge
can produce mistakes, but it also can produce correct insights.
Sometimes the same element of prior knowledge can provide both
an incorrect alternative to one theory and be a component of a
correct theory in another topic area. For example, consider the
common idea of "force as a mover," which holds that an applied
force results in a proportional velocity (diSessa, 1983). This
is often misapplied to situations in which a constant force acts
on a frictionless object. Conventional electromagnetism texts,
however, assert that "an electron moves with a velocity proportional
to the applied electromotive force." Thus "force as a mover,"
can be either a misconception or a sanctioned modeling concept,
depending on the context of use. The consequence of such observations
is that educators should treat prior knowledge as a store of generative
metaphors, not a collection of wrong theories. Prior knowledge
is like a set of building blocks, and not like an enemy fortress.
Fourth, one must beware of a reductionist bias
in theorizing about prior knowledge. In general, research has
focused on identifying a very small number of knowledge elements
and attributing great power to each. Studies of science learning,
to the contrary, remind us that scientific thinking is comprised
of many diverse components. Learning can occur by recontextualizing,
re-prioritizing, or refining the parts. For example, many "misconceptions"
are correct elements of knowledge which have been over generalized.
By specifying a narrower range of situations, the concepts become
"correct." In mathematics, for instance, students often have the
misconception that the x-intercept of a line is equal to the inverse
of the "b" term in equations of the form "y = mx + b." This concept
is correct, but only in the case where the slope of the line is
1 (Moschkovich, 1992). One step in refining knowledge is adjusting
the context in which the knowledge is applicable.
Similarly, as students learn science, knowledge elements change in priority (diSessa, 1993).
For example, we ordinarily think of surfaces as rigid. To understand the normal force, however, we must lower
the priority of rigidity and raise the priority of springiness. In analyzing a book on a table, for instance,
the scientist sees a heavy object compressing the surface of the table slightly, giving rise to a restoring
force upwards. Thus the scientist, while understanding that books and tables are mostly rigid, gives a higher
priority to springiness. Both springiness and rigidity are commonsense concepts; to accommodate Newtonian
theory, only their relative priority shifts.
Likewise, Roschelle (1991) investigated how students develop a concept of vector addition
suitable for understanding acceleration. Relevant prior knowledge for vector addition includes a commonsensical
notion of addition, as well as concepts of pulling, guiding, and hinging. Through concrete experience over time,
students form a synthetic vector addition concept that draws upon these initial metaphors, but is considerably
more precise and specific. According to diSessa (1993), science learning involves many such shifts in generality,
priority, and refinement. The net result is the transformation of a loose aggregate of knowledge into a
crystalline structure of well-established priorities, tuned to the demands of conventional scientific theory.
In summary, we see that students quickly acquire
many different kinds of knowledge, but only slowly acquire the
ability to coordinate and integrate these different sources of
understanding. Students can learn to calculate from mathematical
formulas and can learn to give qualitative explanations, but it
takes a long time to acquire to the ability to coordinate qualitative
explanations with mathematical formulas that represent a theory.
In the previous section on scientists' use
of prior knowledge, it was emphasized that knowledge changes slowly
by restructuring, not replacement. This is equally true for science
students. Moreover, to overcome the paradox of continuity for
science learning, we should attend to several guidelines for interpreting
prior knowledge:
To the list discussed above, I would add that theories of prior knowledge tend to have an individualistic and psychological bias. This bias is partially reflecting in the selection of "concepts" as a focus of study. On every occasion of concept use, however, a learner is in a social and physical situation; these situations powerfully effect the learning that takes place (Roschelle & Clancey, 1992).
Educational experiments that work with prior
knowledge have realized considerable success in provoking and
supporting conceptual change. Clement, Brown & Zeitsman (1989)
have developed a science curriculum based on "anchoring analogies"everyday
concepts from which scientific concepts can grow. Similarly, Minstrel
(1989) has developed classroom techniques for gradually restructuring
students conceptions. White (1993) has developed a computer-based
curriculum called "ThinkerTools" which develops a scientific concept
of motion gradually over several months. White's curriculum include
explicit attention to differences between scientific discourse
and ordinary discourse (e.g. the meaning of "law"), and organizes
a social context that more closely resembles the collegial environment
of scientific work than the authoritarian classroom. Roschelle
(1991) studied students' learning from similar computer software
and concluded that students learn the scientific concept of acceleration
through a series of gradual transformations of their prior knowledge.
In reviewing teaching methods that work, Scott, Asoko, and Driver (1991) note two successful strategies, one based on explicitly working with conflicts, and the other based on building on correct prior knowledge. In any educational situation, there is likely to be some conflict, and some extensions to prior knowledge. Learners can succeed in conceptual change as long as appropriate care is taken in acknowledging students ideas, embedding them in an appropriate socially discourse, and providing ample support for the cognitive struggles that will occur.
In summarizing the broad sweep of research, perhaps the most important lessons are these. First, we must give up the notion of transmitting knowledge to absorbent minds; learning is a process of conceptual change. Second, conceptual change is a slow, transformative process. Rather than rejecting prior knowledge and accepting instructed knowledge, learners must gradually refine and restructure their prior knowledge. Third, to overcome the paradox of continuity, we should study success, avoid dichotomy-based empirical methods, see prior knowledge as providing building blocks, look for learning as long-term transformation knowledge into larger, more systematically coordinated wholes.
Prior Knowledge in Theories of Learning
Research in science and mathematics learning
has not yet produced a successful theory of learning, nor are
theories available in other subjects. The current state of the
art, as described above, merely suggests a set of framing assumptions
that dissolve the paradox of continuity sufficiently to allow
education to proceed.
But how does knowledge change and grow? To answer this question, we must turn to more general theories of learning. Philosophically, the issue of prior knowledge arises in Epistemology, the study of justified true belief (Edwards, 1967) Kant was concerned with identifying certain knowledge. He distinguished between "a prior" and "a posterior" knowledge. "A prior" schemata consist of basic structures that enable us to detect regularities in the environment. Space and time were Kant's primary candidates for "a priori" status. Most other knowledge comes from synthetic combination of schemata with experience.
Most theories of conceptual change stick with this framework of "a priori" structures combining synthetically with new experience, though they vary the notions of schemata, experience, and the construction process in which schemata and experience come together. They also differ in emphasis: Piaget emphasizes psychological changes to schemata, Dewey emphasizes the transformative possibilities in experience, and Vygotsky emphasizes the role of social interaction in reconstructing the relationship of structures to experience. In the few short pages available here present a quick tour of how these theories treat the issue of prior knowledge.
Piaget: Developmental Growth of Schemata
Piaget's theory (Inhelder & Piaget, 1958; Ginsburg & Opper 1979; Gruber & Voneche, 1979) concerns the development of schemata in relation to new experience. Children, like adults, combine prior schemata with experience. However, children's notions of space and time qualitatively differ from adults' (Piaget, 1970) 1. Piaget provides a theory of conceptual change that focuses on the development of schemata from childhood to maturity.
Piaget provides a characterization of children's knowledge at four stages of maturity, termed sensi-motor, preoperational, concrete operational, and formal operational (Corsini, 1994). At each successive stage, more encompassing structures become available to children to make sense of experience. For example, Piaget demonstrates that children cannot perform controlled experiments with variables, or reason with ratios, before the formal operational stage. Prior knowledge, in the form of structural schemata, thus play a determining role in how children make sense of interactive experience.
In Piaget's account of conceptual change, knowledge grows by reformulation. Piaget identifies a set of invariant change functions, which are innate, universal, and age independent. These are assimilation, accommodation, and equilibration. Assimilation increases knowledge while preserving of structure, by integrating information into existing schemata. Accommodation increases knowledge by modifying structure to account for new experience. For Piaget, the critical episodes in learning occur when a tension arises between assimilation and accommodation, and neither mechanism can succeed on its own. Equilibration coordinates assimilation and accommodation, allowing the learner to craft a new, more coherent balance between schemata and sensory evidence. Reformulation does not replace prior knowledge, but rather differentiates and integrates prior knowledge into a more coherent whole.
Piaget influences educators not only by his theory, but also by his method. He spent long hours coming to know children's modes of thinking (using the clinical interview, discussed later). After Piaget, we must assume that children will make sense of experience using their own schemata. Yet, we also must carefully interview children, seeking an understanding of their form of coherence. Most followers of Piaget are constructivists who cultivate a deep appreciation of children's sense-making, and design interactive experiences accordingly.
Piaget generated many innovative task-settings in which children become involved in active manipulation of physical objects. Trying to achieve a goal in physical task can promote conflict between assimilation and accommodation in the accompanying psychological task. Moreover, alternative physical actions can suggest different conceptual operations, and thus opportunities that arise in physical activity can inspire mental restructuring. Using these insights, Kuhn et al. (1988) shows that children can learn to coordinate theory and evidence in a period of several weeks if provided with engaging, playful, thought-provoking tasks. Harel & Papert (1991) extend this point by suggesting that the best tasks for constructing ideas are those in which children have to build something that works. While "construction" and "constructivism" are not necessarily linked, they go well together. Dewey's theory, discussed in the next section, also identifies designing, making, and tinkering real things as critical to conceptual change.
In summary, Piaget suggests that learners overcome the paradox of continuity with the help of slow, maturational processes that operate when doing a task provokes conflict between accommodation and assimilation, and support for equilibration between these. He suggests that designers of interactive experiences invest the empirical effort needed to appreciate learner's perspective. From an understanding of this perspective, one can design tasks that are likely both to attract learners, to provoke disequilibration, and to support the necessary but difficult work of knowledge reformulation. Tasks should be simple and direct, with individual concrete operations mapping closely to the conceptual operations at stake. Experience in which learners construct a working physical arrangement are often powerful for constructing knowledge; for example, the best way to progress past your prior understanding of a painting might be to try to paint one like it.
Dewey: The Conditions for Reflective Experience
Whereas Piaget develops a theory of the growth of structuring schemata, Dewey elaborates the experiential side of learning (Dewey, 1938b). Piaget exposes Kant's "a priori" structures as genetic variants, not fixed truths. Dewey exposes the problematic nature of experience, which is not "given" to us either, but rather is created in our transactions with nature and with each other, and thus is dependent on the prior knowledge that we bring to it.
In Dewey's account of learning (Dewey, 1916; Dewey, 1938a; McDermott 1981), problematic experience comes to the fore. For Dewey, primordial experience occurs in a physical and social situation. Moreover, learners are not "in" a situation like paint is "in" a bucket; rather experience is an active transaction that coordinates doing and undergoing (Dewey, 1938b). Even in watching a painting, we actively direct our gaze, and undergo a transformation of our field of vision. Experiential transactions have simple qualities that we can directly apprehend; e.g., they can be joyful, frightening, tasty, or harmonious.
In most of life, we proceed smoothly from one transaction to the next, using and enjoying the objects of our experience. But sometimes, experience has the quality of being problematic. By this Dewey means that we feel confused, uncertain, incoherent, unable to act. We are unable to coordinate prior knowledge and prior habit to cope with the exigencies of the moment. In the situation of problematic experience, we can engage a different mode of life from use and enjoying, which Dewey calls inquiry.
Inquiry (Dewey, 1938a), the reflective transformation
of perception, thought, and action, re-unifies experience into
a more satisfactory whole. The process of inquiry involves reflection
on experience; we apply tools like concepts, drawings, and gestures
to point to features of experience that are troublesome. At the
same time, we apply tools to project possible solutions. Through
experiment and reflection, both schemata and perception are slowly
transformed to bring coherence, coordination, and meaning to our
Inquiry involves psychological, physical, and
social interaction. Schön (1979) gives a good example. A
team of engineers was trying to design a synthetic paint brush,
but the paint would not go on smoothly. One engineer decided to
look very carefully at how a bristle brush works. As he slowly
painted, the others watched. Slowly they saw that the real brush
was not like a sponge. Metaphors of "pumping" and "channeling"
came into discussion to describe how paint flowed smoothly down
the bristles. Over time, the engineers transformed their notion
of painting from absorbing paint, to pumping and channeling paint.
This enabled them to design a successful synthetic brush.
In this example, we see how a problematic experience involves prior knowledge. Prior knowledge was invoked both in creating the original problematic (seeing a brush as releasing paint) and the new understanding (metaphors of pumping and channeling). The process of inquiry involves psychological, social, and physical interaction that gradually enabled the engineers to transform their puzzlement into a new understanding.
Dewey is often viewed as a child-centered educator, who emphasized growth of the child's interest and capabilities over the mandates of a curriculum. However, Dewey took pains to oppose any attempt to place a child's prior knowledge and a curriculum's desired knowledge in conflict or dichotomy (Dewey, 1938b). We should neither champion children's native desires over the hard-earned wisdom of disciplines, nor static views into children's minds. Dewey urged a view of children's knowledge as fluid, flexible, generative, and unformed. By designing appropriate experiences, an educator should be able to move from children's interest and capabilities towards the more stable, definite, and structured content of organized subject matters. Thus an educators responsibility is both to enable the child to engage in inquiry, and to guide inquiry so it leads towards broader participation in the culture the child is to enter.
Dewey's life work was concerned with understanding
the conditions that enable inquiry to proceed, and herein lies
the most salient inspiration for designers of interactive experiences.
The key lesson is this: Attend to that which is problematic in
an experiential transaction, from the point of view of the learner,
and allow time and space for inquiry to occur as an activity in
its own right. A secondary concern is provide tools that enable
inquiry to be effective. Inquiry occurs not in the head, but in
direct engagement with the world and with others. To succeed,
learners need ways to sketch and explore ideas and phenomena,
and to test alternatives experimentally. Moreover, language (which
Dewey calls the "tool of tools") can be an invaluable means for
re-describing, re-orienting, and restructuring experience. Attempts
to coordinate one person's understanding with another gradually
shifts idiosyncratic ideas towards a common ground. Thus educators
interested in working with children's prior knowledge, should
look for situations in which that prior knowledge becomes problematic,
and should create three conditions that enable inquiry to proceed
successfully: time, tools, and talk.
Dewey overcomes the paradox of continuity by
focusing on the nature of experience. Under the right conditions,
a learner engaged with a problematic experience can effect a transformation
of prior knowledge. This transformation restructures thought,
perception, and action elements into a more integrated, coherent
whole. Over a long time, with careful guidance, the net result
of many local transformations can be an overall set of ideas and
practices that approximates the central core of an organized subject
Vygotsky: Social Reconstruction of Prior Knowledge
Vygotsky developed his work partially in response to Piaget's neglect of social interaction. Whereas Piaget emphasizes the maturation of schemata within the individual, Vygotsky(1986) argued that advanced concepts appear first in social interaction, and only gradually become accessible to an individual. Thus Vygotsky primarily elaborated the role of social interaction in transformation of prior knowledge.
In one of his studies, Vygotsky (1986) specifically examined the role of prior knowledge is science learning. He argued that children have spontaneous concepts and scientific concepts, and that these are not in conflict, but rather are part of a unitary process. In this process, Vygotsky sees spontaneous concepts growing upwards in generality, preparing the ground for more systematic reasoning. Simultaneously scientific concepts, which are introduced by instruction, grow downwards to organize and utilize the spontaneous concepts. Upon achieving a through and systematic intertwining, the learner gains both the power of the abstract (maximum substitutability) and of the concrete (maximum applicability).
The restructuring process that intertwines
spontaneous and specialized concepts occurs in social interaction,
and is mediated by sign systems, such as language and drawing.
Whereas Piaget focuses on disequilibrium among schemata, and Dewey
focuses on problematic experiences, Vygotsky turns our attention
to the "Zone of Promixal Development (ZPD)" (Wertsch, 1985; Newman,
Griffith, & Cole, 1989). The ZPD is formed by the difference between
what a child can do without help and the capabilities of the child
in interaction with others. In this construction zone, the child
can participate in cultural practices slightly above his or her
own individual capability. Successful participation can lead to
internalization. In Vygotsky's account, the primary resources
for restructuring prior knowledge come from culture. Moreover,
the restructuring process itself occurs externally, in social
discourse. Children share, negotiate, and try out meanings in
social experience, and adults can shape those meanings by bringing
them into the framework of cultural practice.
Recent translations of Vygotsky have inspired designers of interactive experience in several ways. First, the concept of the ZPD suggests that designers provide "scaffolding" to enable learners to participate in a more complex discourse than they could handle on their own (Brown & Ferrara, 1985). This scaffolding can be in the form of social processes that manage some of the complexity of a task for learners, allowing them to participate while focusing only on one aspect. In addition, educators can engage in "cognitive modeling" whereby they act out and verbalize a reasoning process that usually occurs only in an expert's head (Palinscar & Brown, 1984). Thus learners can acquire reasoning practices by imitation and apprenticeship (Collins, Brown, & Newman, 1989; Rogoff, 1990). Finally, Vygotsky inspires designers to create "mediational means" that enable learners to negotiate the meaning of a concept verbally (Hickman, 1985). Meditional means can be a graphic notation system or a set of linguistic conventions that extend students ability to talk about and act upon the relation between their understanding and another person's understanding.
Like the other theorists, Vygotsky overcomes
the paradox of continuity by suggesting that learning coordinates
spontaneous and specialized concepts in a gradual transformative
process. Unlike Piaget's maturational account, Vygotsky sees structure
coming from culture and gradually expanding into individuals'
psychological repertoire through social interaction in the ZPD.
By scaffolding, modeling, and negotiating, experienced adults
are able to guide learning so as to bring the learner into a specialized
cultural community.
Information Processing and Situated Learning
Piaget, Dewey, and Vygotsky each developed their theories in the first half of the 20th century. In the second half of the century, information processing views have dominated, only recently to be challenged by a loosely coupled set of ideas called "situated cognition." We briefly survey the additional resources that these advances contribute to an understanding of prior knowledge.
Information processing psychology builds on the metaphor of mind as a computer of symbolic data (Newell & Simon, 1972; Posner, 1989). Successful information processing (IP) models utilize mechanisms similar to those described by Piaget: accommodation modifies a schema, or assimilation modifies data to fit an existing schema. However, IP modeling has worked best in areas where prior knowledge is weakestÑÊ in rule-dominated logic and gaming tasks. Modeling learning in areas were commonsense is rich has proven to be an immense task. Moreover, the analogy between minds and computers quickly breaks down where prior knowledge is concerned: you can reprogram a computer, completely replacing its existing program with a different one, whereas human minds must make new knowledge from old. Likewise, computer models have impoverished capabilities for experience and social interaction.
To those interested in prior knowledge and learning, the major contribution of IP is the production of innovative representational systems and sound scientific methodology for analyzing learning processes. The relevant methodological contributions of IP are briefly summarized later in this paper. The representations can help in two ways. First, they can make it easier to describe prior knowledge precisely. For example, VanLehn (1989) showed how the concepts underlying mistakes in addition problems could be given a precise description. From this specific diagnosis, a teacher could provide more focused instruction. Second, representations can be a tool that allows the learner to reflect. For example, children can use "semantic networks" to map the associations among ideas before, during and after learning. Likewise, tree diagrams can help students understand processes that are hierarchically composed rather than linearly composed, such as the generation of a geometric proof (Koedinger & Anderson, 1990). Providing a tool for representing prior knowledge can enable learners to reflect more systematically on prior knowledge.
Situated learning (Brown, Collins, & Duguid,
1989; Lave, 1988) has emerged in the last decade as a critique
of IP's focus on internal schemata and neglect of physical and
social context. Situated learning, like Deweyian theory, holds
that all learning occurs within experiential transactionsÑ coordinations
between personal agency and environmental structures. Like Vygotsky,
situated learning also emphasis the social construction of knowledge.
Most striking in relation to the IP accounts, is the overall conception
of learning as enculturation. In place of relations between schemata
and experience, situated accounts focus on learning in terms of
relations between people, physical materials, and cultural communities
(Lave & Wenger, 1989). Knowledge is developed, shared, and passed
on to the next generation by local communities that maintain a
particular discourse or work practice, such as a craft guild or
academic discipline. Growing ability to participate in a community-based
culture has precedence over the ability to know. In fact, situated
learning has relatively little to say about "prior knowledge"
as such, but focuses instead on how ordinary work and discourse
practices can become specialized, and how identities develop.
In its present (and quickly evolving) state,
situated learning offers a constructive critique of Kantian-derived
conceptions of learning. First, it reminds us that knowledge and
social identity are tightly intertwined. A person's prior knowledge
is part of his or her personal identity in society. Conceptual
change almost always involves a transformation of identitythe
specialization of concepts about motion not only enables a child
to think more like a scientist, but also allows a child to progress
towards becoming a scientist. Becoming a participant in a community
can be a stronger motivation the gaining knowledge This is a useful
corrective to educators who focus on the "right knowledge" and
forget to ask who a learner is becoming.
Lave and Wenger (1989) offer the notion of "legitimate peripheral participation" (LPP) to make this more precise. LPP suggests that "becoming " requires participation in the activities of a community. However, learners often cannot participate in the core activities of a specialized group, e.g. an ordinary person cannot join a scientific laboratory. Thus learning often occurs on the periphery of the community, in specialized places that have been legitimized as entry points. Museums, schools, and clubs (e.g. 4H) can serve this purpose. LPP guides us to develop interactive experiences that form part of a legitimate trajectory towards full membership in a specialized cultural community. Because transformation of identity and conceptual change both operate gradually over a long period of time, it is important to specify an overall trajectory that could enable a learner to move from the periphery to the core of a community.
At the cutting edge of current work on prior knowledge, we find researchers concerned with the mutual interaction of social discourse practices with constructive, participatory experiences.
How to Investigate Prior Knowledge
Due to the pervasive influence of prior knowledge
on learning, good designers of interactive experiences need to
cultivate a sensitivity to the different points of view that learners
will bring to an experience. This sensitivity is best gained by
first hand experience with other's points of view; no description
in the literature can fully convey the character and constitution
of a learners' prior knowledge. Fortunately, becoming sensitive
to prior knowledge is not hard to do. One must simply look and
listen closely as learners use your materials. When something
strange and incomprehensible occurs, don't give in to temptation
to brush it aside; take the occurrence as opportunity to learn.
Understanding prior knowledge is 90% perspiration
and 10% method. Standard tests are useless, because they are almost
always written from the perspective of the expert. Instead, it
is crucial to get learners to talk and then to pay careful attention
to what they say and do. Three specific methods from research
community can be helpful.
Piaget developed the clinical interview as a method for investigating children's sense-making. A clinical interview (Posner & Gertzog, 1982; White, 1985; ) usually involves a task in which the learner manipulates some physical materials. Good tasks are simple and focus tightly on the concept at stake. Thus, a strange set of actions in the task readily indicates a different sensibility. The interviewer then probes the learner's understanding by asking questions about things the learner has said or done and avoiding leading questions. As the interview progresses, it is often helpful to ask the learner to consider alternatives to see how stable a particular concept is. A transcript of the resulting interview provides a great deal of detail about prior knowledge.
Researchers in information processing theory have developed the technique of the think-aloud protocol (Ericsson & Simon, 1984; Simon & Kaplan, 1989), which collects information about a learner's problem solving process. The learner is trained to "think aloud" while they perform on a simple task, like addition. Thinking aloud means simply verbalizing the stream of consciousness, and not explaining or justifying actions to the interviewer. The interviewer does not ask questions, but merely prompts the learner to "say what you are thinking" whenever the learner stops talking. Then the learner is given the target problem-solving task, and recorded on audio tape. The resulting "protocol" can then be analyzed for evidence of the prior knowledge and differences in thinking processes (Robertson, 1990).
The situated learning community is developing techniques for using video recordings to study prior knowledge in full social and environmental context (Roschelle & Goldman, 1991; Suchman & Trigg, 1991; Jordan, in preparation). Typically, a small group of learners is recorded on video tape as they work on and discuss a common task. The camera is set to a constant, wide-angle shot and left unattended, so as to avoid intrusion. Care is taken to get good audio. When the video is finished it may be put to several uses. Learners may review the video with an interviewer, creating an opportunity to interpret their own behavior. In addition, it is often helpful to watch the video with a multidisciplinary panel of colleagues; surprisingly diverse interpretations will often emerge. Finally, the strongest benefit of video is that when a problematic event occurs, the investigator can review it repeatedly. With repeated viewing and conscious cultivation of multiple perspectives, an investigator will begin to sense each participant's prior knowledge and dispositions.
Conclusions: Prior Knowledge and Museum Assessment
Prior knowledge has diverse and pervasive effects
on the learning. Museum experiences cannot eliminate or disable
prior knowledge, but rather must work with it. Thus museums, like
all educational institutions, must come to grips with the paradox
of continuity: prior knowledge is both necessary and problematic.
Conceptual change must somehow resolve, overcome or avoid this
Prior knowledge is implicated in both failure and success; thus knowledge is best seen as raw material to be refined. Instead of assuming bipolar dichotomies where desired knowledge replaces prior knowledge, designers should expect learning to occur through a transformative, restructuring process that produces integrative wholes that coordinate pre-existing parts. Refinement and restructuring occurs incrementally and gradually; conceptual change is hard work and takes a long time.
Museums are potentially well-positioned as
sites for conceptual change. Museums provide the visitor with
opportunities to experience authentic objects directly. Cognitive
confrontations provoked by interaction with objects are at the
heart of Piaget's theory, as well as Dewey's. Museums allow visitors
to learn socially in small, voluntary groups. Social discourse
is the major means of conceptual change in Vygotsky's theory,
as well as the contemporary views of situated learning. Museums
can provide novel and challenging settings with opportunities
for interaction, contemplation, and inquiry. Dewey focuses attention
on the problematic nature of learning experiences, and the need
for educators to anticipate the resources that learners will need
to resolve the conceptual struggles that arise. Museums can provide
intellectual, physical, and social resources to aid in the resolution
of problematic experience.
But too often in my experience, museums do
not rise to this challenge; rather than acknowledging and working
from the learner's point of view, museums present an aggressively
professional point of view. Too often exhibits seem to assume
that a good presentation will make underlying concepts obvious,
and therefore provide few or no resources when I find the exhibit
problematicalien, awkward, confusing, frustrating, inaccessible,
incomprehensible, mysterious, offensive, opaque, strange, or just
too exotic. Too often museums neglect the social nature of visits,
and I find interaction difficult or uncomfortable.
Success, however, need not be hard to come
by. Success begins with cultivation of the ability to look, listen,
and understand the learner's viewpoint, and to discover the seeds
from which knowledge and identity can grow. Other institutions,
especially schools, do a downright awful job of supporting conceptual
change, as is well-documented throughout the literature. People
are naturally active, life-long learners. As Csikszentmihalyi
points out, museums need not do much more than provide a high
quality experience that engages prior knowledge in an achievable
intellectual challenge, and help visitors assemble the physical,
intellectual and social resources they will need to succeed. Unlike
schools, museums don't have to make visitors learn on a particular
schedule; museums can focus on catalyzing a spontaneous reaction
involving prior knowledge, authentic objects, social interaction,
and resources for inquiry.
Assessing long-term success is a more difficult
matter. As became clear during the Annapolis Conference, museums
have goals beyond subject matter content: encouraging curiosity,
caring, and exploration; providing a positive, memorable experience;
supporting constructivist learning processes; and developing a
sense of personal, cultural, and community identity. An excessive
focus on knowledge can work to the detriment of these other goals,
and miss the importance of museum learning entirely. Throughout
this chapter, I have argued that dramatic conceptual change is
a slow, unpredictable, difficult process. It is thus inappropriate
to expect deep conceptual change to predictably occur in a single
or short series of visits. Conversely, when deep conceptual change
does occur, it will almost certainly involve resources beyond
the museum's control such as books, videos, science kits, classes,
clubs, etc. Assigning partial credit for long-term learning accomplishments
is a dubious business at best. Finally, narrowing the museum's
focus to changes in conceptual content may harm other, equally
worthy goals. For example, curiosity and exploration may fall
by the wayside in an attempt to focus on subject matter, and personal
and cultural identity may become defined primarily in relation
to the community that owns the subject matter, rather than opening
to diverse modes of participation.
Prior knowledge nonetheless is implicated in
all the museum's goals. Curiosity, caring, and exploration begin
with what you know now. A memorable experience unites prior knowledge,
present experience, and future purposes in a coherent way. Constructivist
learning requires attention to the continuity of knowledge. Knowledge
and identity are bound togetherwe choose personal futures
based on what we know and understand today. Thus in assessing
museum learning, we can neither overemphasize nor ignore prior
This suggests that long-term museum assessment
should focus on how museums activate visitors' prior knowledge,
opening new and effective roads for long-term learning. Do museums
raise visitors' awareness of alternative perspectives? Do visitors
formulate personally relevant questions? Do visitors realize how
they can tap their current knowledge to enter a new field of inquiry?
Do museums provide models of constructive learning processes with
which visitors can go on learning? Do visitors become aware of
books, videos, and other resources that start from what they know
already? Are museums a place where visitors can use prior knowledge
to help their friends and family learn? Do museums provide a setting
for integrating diverse views that make a rich understanding?
The many powerful and poignant stories related at the conference suggest that museums do activate prior knowledge in these and other remarkably powerful ways. While assessment won't prove that museums cause long-term conceptual change, a variety of methods could bring to light the diverse ways in which museums can start with access points close to what a visitor knows already and can open the gate to those modes of inquiry, participation, and experience which our society values most highly.
Anzai, Y. & Yokohama, T. (1984). Internal models in physics problem
solving. Cognition and Instruction, 1, 397-450.
Berieter, C. (1985). Towards a solution of the learning paradox.
Review of Educational Research, 13, 233-341.
Black, M. (1962). Models and metaphors. Ithaca, NY: Cornell
University Press.
Boyd, R. (1986). Metaphor and theory change: What is "metaphor"
a metaphor for? In A. Ortony (Ed.), Metaphor and thought.
Cambridge: Cambridge University Press.
Brown, A.L.. & Ferrara, R.A..(1985). Diagnosing zones of proximal
development. In J.V. Wertsch (Ed.), Culture, communication,
and cognition. Cambridge: Cambridge University Press.
Brown, J.S., Collins, A., & Duguid, P. (1989). Situated cognition
and the culture of learning. Educational Researcher, 18,
Carey, S. Conceptual change in childhood. Cambridge, MA:
MIT Press.
Champagne, A.B., Gunstone, R.F., & Klopfer, L.E. (1985). Consequences
of knowledge about physical phenomena. In L.H.T. West and A.L.
Pines (Eds.), Cognitive Structure and Conceptual Change. New
York: Academic Press.
Chi, M.T.H., Feltovich, P.J., & Glaser, R. (1980). Categorization
and representation of physics problems by novices and experts.
Cognitive Science, 5, 121-152.
Clement, J., Brown, D.E., & Zietsman, A. (1989). Not all preconceptions
are misconceptions: Finding "anchoring conceptions" for grounding
instruction on students' intuitions. Paper presented at the annual
meeting of the American Educational Research Association, San
Francisco, CA.
Cohen, R., Eylon, B., & Ganeil, U. (1983). Potential differences
and current in simple electric circuits: A study of students'
concepts. American Journal of Physics, 51, 407-412.
Collins, A., Brown, J.S, & Newman, S. (1989). Cognitive apprenticeship:
Teaching the craft of reading, writing, and mathematics. In L.B.
Resnick (Ed.), Knowing, learning, and instruction: Essays in
honor of Robert Glaser. Hillsdale, NJ: Lawrence Erlbaum.
Confrey, J. (1990). A review of the research on student conceptions
in mathematics, science, and programming. Review of Research
in Education.
Corsini, R.J. (1994), Encyclopedia of Psychology, 2nd Edition,
New York: John Wiley, p 86-89.
Dewey, J. (1938a). The logic of inquiry. New York: Henry
Dewey, J. (1938b). Experience and education. New York:
Macmillan Company.
Dewey, J. (1916). Democracy and education. New York: Macmillan
diSessa, A.A. (1993). Towards an epistemology of physics. Cognition
and Instruction, 10(2 & 3), 105-225.
diSessa, A.A. (1983). Phenomenology and the evolution of intuition.
In D. Gentner & A.L. Stevens (Eds.), Mental models. Hillsdale,
NJ: Earlbaum.
diSessa, A.A. (1982). Unlearning Aristotelian physics: A study
of knowledge-based learning. Cognitive Science, 6, 37-75.
Edwards, P. (1967). The encyclopedia of philosophy. New
York: Macmillan.
Einstein, A. (1961). Relativity: The special and general theory.
New York: Crown Publishers.
Einstein, A. (1950). Out of my later years. New York: Philosophical
Ericsson, K.A. & Simon, H.A. (1984). Protocol Analysis.
Cambridge, MA: MIT Press.
Eylon, B. & Linn, M.C. (1988). Learning and instruction: An examination
of four research perspectives in science education. Review
of Educational Research, 58(3), 251-301.
Gentner, D. & Gentner, D.R., (1983). Flowing waters or teeming
crowds: Mental models of electricity. In D. Gentner & A.L. Stevens
(Eds.), Mental models. Hillsdale, NJ: Earlbaum.
Ginsburg, H. & Opper, S. (1979). Piaget's theory of intellectual
development. Englewood Cliffs, NJ: Prentice-Hall.
Glaserfeld, E.V. (1984). An introduction to radical constructivism.
In P. Watlawick (Ed.), The invented reality. New York:
W.W. Norton.
Gruber, H.E. & Voneche, J.J. (Eds.) (1977). The essential Piaget.
New York: Basic Books.
Halhoun, I.A., & Hestenes, D. (1985a). The initial knowledge state
of college physics students. American Journal of Physics,
53, 1043-1055.
Halhoun, I.A., & Hestenes, D. (1985b). Common sense concepts about
motion. American Journal of Physics, 53, 1056-1065.
Hammer, D.M. (1991). Defying commonsense: Epistemological beliefs
in an introductory physics course. Unpublished doctoral dissertation,
University of California, Berkeley.
Harel, I. & Papert, S. (Eds.) (1991). Constructionism. Norwood,
NJ: Ablex.
Hickman, M. (1985). The implications of discourse skills in Vygotsky's
developmental theory. Brown, A.L.. & Ferrara, R.A. (1985). Diagnosing
zones of proximal development. In J.V.Wertsch (Ed.), Culture,
communication, and cognition. Cambridge: Cambridge University
Inhelder, B. & Piaget, J. (1958). The growth of logical thinking
from childhood to adolescence: An essay on the construction of
formal operational structures. London: Routledge.
Jordan, B. (In preparation). Interaction analysis: Foundations
and theory.
Keil, F.C. (1979). Semantic and conceptual development. An
ontological perspective. Cambridge, MA: Harvard University
Knorr, Karin. (1981). The manufacture of knowledge: An essay
on the constructivist and contextual nature of science. Oxford:
Pergammon Press.
Koedinger, K.R. & Anderson, J.R. (1990). Abstract planning and
perceptual chunks: Elements of expertise in geometry. Cognitive
Science, 114(4), 511-550.
Kuhn, D., Amsel, E., & O'Loughlin, M. (1988). The development
of scientific thinking skills. San Diego, CA: Academic Press.
Kuhn, T. (1970). The structure of scientific revolutions.
Chicago: University of Chicago.
Latour, B. (1987). Science in action. Cambridge, MA: Harvard
University Press.
Larkin, J.H. (1983). The role of problem representation in physics.
In D. Gentner & A.L. Stevens (Eds.), Mental models. Hillsdale,
NJ: Earlbaum.
Larkin, J.H., McDermott, J., Simon, D.P., & Simon, H. (1980).
Expert and novice performance in solving physics problems. Science,
208, 1335-1342.
Lave, J. (1988). Cognition in Practice. Cambridge, UK: Cambridge
University Press.
Lave, J. & Wenger, E. (1989). Situated learning: Legitimate
peripheral participation. Cambridge, UK: Cambridge University
Lewis, E.L. (1991). The process of scientific knowledge acquisition
of middle school students learning thermodynamics. Unpublished
doctoral dissertation. University of California, Berkeley.
Lightman, A.P. (1989). Magic on the mind: Physicists' use of metaphor.
The American Scholar, Winter issue, 97-101.
McCloskey, M. (1983). Naive theories of motion. In D. Gentner
& A.L. Stevens (Eds.), Mental models. Hillsdale, NJ: Earlbaum.
McDermott, J.J. (1981). The philosophy of John Dewey. Chicago:
University of Chicago Press.
McDermott, L.C. (1984). Research on conceptual understanding in
mechanics. Physics Today, 37, 24-32.
Minstrell, J. (1989). Teaching science for understanding. In L.B.
Resnick & L. Klopfer (Eds.) Towards the thinking curriculum
(133-149). Alexandria, VA: Association of Supervision and Curriculum
Moschkovich, J.N. (1992). Making sense of linear equations and
graphs : an analysis of students' conceptions and language use.
Unpublished doctoral dissertation. University of California, Berkeley.
Newell, A. & Simon, H.A. (1972). Human problem solving.
Englewood Cliffs, NJ: Prentice-Hall.
Newman, D., Griffith, P., & Cole, M. (1989). The construction
zone: working for cognitive change in school. Cambridge, UK:
Cambridge University Press.
Miller, A.I. (1986). Imagery and scientific thought. Cambridge,
MA: MIT Press.
Nercessian, N.J. (1988). Reasoning from imagery and analogy in
scientific concept formation. PSA, 1, 41-47.
Palinscar, A.S. & Brown, A.L. (1984). Reciprocal teaching of comprehension-fostering
and monitoring activities. Cognition and Instruction, Hillsdale,
NJ: Lawrence Earlbaum.
Piaget. J. (1970). The child's conception of movement and speed.
New York: Basic Books.
Posner, G.J. & Gertzog, W.A. (1982). The clinical interview and
the measurement of conceptual change, Science Education,
66, 195-209.
Posner, G.J, Strike, K.A., Hewson, P.W., & Gertzog, W.A. (1982).
Accommodation of a scientific conception: Towards a theory of
conceptual change. Science Education, 66(2), 211-227.
Posner, M.I. (Ed.) (1989). Foundations of cognitive science.
Cambridge, MA: MIT Press.
Resnick, L.B. (1983). Mathematics and science learning: A new
conception. Science, 220, 477-478.
Resnick, M. (1992). Beyond the centralized mindset: Explorations
in massively parallel microworlds. Unpublished doctoral dissertation.
Massachusetts Institute of Technology.
Robertson, W.C. (1990). Detection of cognitive structure with
protocol data: Predicting performance on physics transfer problems.
Cognitive Science, 14, 253-280.
Rogoff, B. (1990). Apprenticeship in thinking: Cognitive development
in social context. Oxford: Oxford University Press.
Roschelle, J. (May 1994). Collaborative Inquiry: Reflections on
Dewey and Learning Technology. The Computing Teacher. 3-9.
Roschelle, J. (1991). Students' construction of qualitative physics
knowledge: Learning about velocity and acceleration in a computer
microworld. Unpublished doctoral dissertation, University of California,
Roschelle, J. and Clancey, W.J. (1992). Learning as social and
neural. Educational Psychologist, 27, 435-453.
Roschelle, J. & Goldman, S. (1991). VideoNoter: A productivity
tool for video data analysis. Behavior Research Methods, Instruments,
and Computers, 23, 219-224.
Schön, D. (1979). Generative metaphor. A perspective on problem-setting
in social policy. In A. Ortony (Ed.), Metaphor and thought.
Cambridge: Cambridge University Press.
Scott, P.H., Asoko, H.M., Driver, R.H. (1991). Teaching for conceptual
change: A review of strategies. In R. Duit, F. Goldberg, & H.
Niedderer, Research in Physics Learning: Theoretical issues
and empirical studies. Kiel, Germany: IPN.
Simon, H.A. & Kaplan, C.A. (1989). Foundations of Cognitive Science.
In M.I. Posner (Ed.), Foundations of Cognitive Science.
Cambridge, MA: MIT Press.
Smith, J.P., diSessa, A.A., Roschelle, J. (1993). Misconceptions
reconceived: A constructivist analysis of knowledge in transition.
Journal of the Learning Sciences, 3(2), 115-163.
Songer, N.B. (1989). Promoting integration of instructed and natural
world knowledge in thermodynamics. Unpublished Doctoral Dissertation.
University of California, Berkeley.
Spohrer, J.C., Soloway, E., & Pope, E. (1989). A goal/plan analysis
of buggy Pascal programs. In E. Soloway & J.C. Spohrer (Eds.)
Studying the novice programmer (pp. 355-399). Hillsdale,
NJ: Lawrence Erlbaum Associates.
Strike, K.A. & Posner, G.J. (1985). A conceptual change view of
learning and understanding. In L.H.T. West and A.L. Pines (Eds.),
Cognitive Structure and Conceptual Change. New York: Academic
Suchman, L. & Trigg, R. (1991). Understanding practice: Video
as a medium for reflection and design. In J. Greenbaum & M Kyng
(Eds.), Designing by doing: A tool box approach to collaborative
system design. Hillsdale, NJ: Earlbaum.
Toulman, S. (1972). Human Understanding. Princeton, NJ:
Princeton University Press.
Trowbridge, D.E. & McDermott, L.C. (1980). Investigation of student
understanding of acceleration in one dimension. American Journal
of Physics, 50, 242-253.
Tversky, A., & Kahneman, D. (1982). Judgment under uncertainty:
Heuristics and biases. In D. Kahneman, P. Slovic, & A. Tversky
(Eds.) Judgment under uncertainty: Heuristics and biases.
Cambridge: Cambridge University Press.
VanLehn, K. (1989). Mind bugs: The origins of procedural misconceptions.
Cambridge, MA: MIT Press.
Vygotsky, L. (1986). Thought and Language. Cambridge, MA:
MIT Press.
Wertheimer, M. (1982). Productive Thinking. Chicago: University
of Chicago Press.
Wertsch, J.T. (1985). Vygotsky and the social formation of
mind. Cambridge, MA: Harvard.
West,L.H.T. & Pines, A.L. (Eds.) (1985). Cognitive Structure
and Conceptual Change. New York: Academic Press.
White, B.Y. (1993). ThinkerTools: Causal models, conceptual change,
and science education. Cognition and Instruction, 10(1),
White, R.T. (1985). Interview protocols and dimensions of cognitive
structure. In L.H.T. West and A.L. Pines (Eds.), Cognitive
Structure and Conceptual Change. New York: Academic Press.
Wiser, M. & Carey, S. (1983). When heat and temperature were one.
In D. Gentner & A.L. Stevens (Eds.), Mental models. Hillsdale,
NJ: Earlbaum.
1 Piaget thus undercut Kant's position that one couldn't reason without certain prior concepts of space and time. Children have different concepts that Kant assumed, but can reason nonetheless. Piaget showed that very fundamental elements of reasoning develop as children mature. Einstein similarly showed that fundamental concepts of space and time also must change for physics to mature. Piaget and Einstein thus substantially extend the consideration of what changes in conceptual change; children and scientists alter the very foundations of what they know. Of course, such broad changes occur very slowly.
Reproduced with permission, from Public Institutions for Personal Learning: Establishing a Research Agenda. Copyright 1995, the American Association of Museums. All rights reserved.


text:
Eriksson invited to take Ukraine job
Ukraine's football federation is holding talks with former England manager Sven-Goran Eriksson about running the former Soviet republic's national team, the federation said on Wednesday.
"An offer has been made, negotiations are under way," a spokeswoman for the federation said.
Swede Eriksson, 64, has served as technical director of Thai club BEC Tero Sasana since September.
He has also managed AS Roma, Manchester City and Benfica.
Ukraine's previous boss Oleg Blokhin resigned in late September after failing to lead the team out of the group stages in the European Championship, which Ukraine co-hosted this year, and accepting the job of head coach at Dynamo Kiev.
Ukraine had earlier approached English club manager Harry Redknapp with a similar offer. Redknapp subsequently took over as manager of Queens Park Rangers.

subset: commoncrawl


text:
STD Test Express (est. 2009)
[39.716581, -74.969754] std, test, express, pathologist, hospit, physician, tissu, infecti, diseas, forens, medicin, doctor, procedur, treatment, laboratori, diagnost, herp, chlamydia, syphili, center, clinic, hiv, dna, antibodi, herpeselect, hepat, c, b, gonorrhea, specialist, physiciansurgeon
Fast, private, & embarrassment free STD Testing starts at just $79
(856) 300-0535 524 Williamstown Rd, Ste A, Sicklerville, NJ 08081 Hours Vary, Please Call Or Visit Website To Register For Testing. Walk Ins Cannot Be Accomodated. Official site
Categories: Pathologists, Std Testing, Hospitals
The Details
We speak English and Espanol Visa, Master Card, Discover, American Express, and Debit gladly accepted


text:
March 21, 2013 14:21 ET
MONTRÉAL, QUÉBEC--(Marketwire - March 21, 2013) - The Board of Directors of Logistec Corporation (TSX:LGT.A)(TSX:LGT.B) today announced that it has declared quarterly dividends of $0.09 per share on all outstanding Class A Common Shares and $0.099 per share on all outstanding Class B Subordinate Voting Shares.
The dividends will be paid on April 12, 2013 to all Logistec Corporation shareholders of record at the close of business on April 2, 2013. This dividend is an eligible dividend for Canada Revenue Agency purposes.
Dividends paid during 2012 totalled $0.3525 per Class A Common Share and $0.38775 per Class B Subordinate Voting Share.
About Logistec
Logistec Corporation is based in Montréal (QC) and provides specialized services to the marine community and industrial companies in the areas of bulk, break-bulk and container cargo handling in 23 ports in Eastern Canada, the Great Lakes and the U.S. East Coast. Logistec also offers marine transportation services geared primarily to the Arctic coastal trade, short-line rail transportation services, as well as agency services to foreign shipowners and operators serving the Canadian market. Furthermore, the Company operates in the environmental sector where it provides services to industrial companies and municipalities for the trenchless structural rehabilitation of underground water mains, PCB management, site remediation, risk assessment, and woven-hose manufacturing.
The Company has been profitable and has paid regular dividends since becoming public and payments have grown steadily over the years. A public company since 1969, Logistec's shares are listed on the Toronto Stock Exchange under the ticker symbols LGT.A and LGT.B. More information can be obtained at the Company's website at www.logistec.com.
Jean-Claude Dugas CAVice-President, FinanceLogistec firstname.lastname@example.com(514) 985-2345
See all RSS Newsfeeds


text: Warning: preg_replace(): The /e modifier is deprecated, use preg_replace_callback instead in ..../includes/class_bootstrap.php(430) : eval()'d code on line 106Warning: Declaration of vBForum_Item_SocialGroupMessage::getLoadQuery() should be compatible with vB_Model::getLoadQuery($required_query = '', $force_rebuild = false) in ..../packages/vbforum/item/socialgroupmessage.php on line 261Warning: Declaration of vBForum_Item_SocialGroupDiscussion::getLoadQuery() should be compatible with vB_Model::getLoadQuery($required_query = '', $force_rebuild = false) in ..../packages/vbforum/item/socialgroupdiscussion.php on line 337 Advanced Search - Bow hunting ohio | Ohio hunting forum Register Help Remember Me? Most Recent Posts ! Activity Stream New Posts New Events Mark Forums Read Advanced Search Advanced Search Search Single Content Type Search Multiple Content Types Search In Search Types: All Types Forums Posts Groups Group Messages Visitor Messages Search For Keyword(s): Search Entire Posts Search Titles Only User Name: Exact name Tag: Additional Options Find Posts Any Date Your Last Visit Yesterday A Week Ago 2 Weeks Ago A Month Ago 3 Months Ago 6 Months Ago A Year Ago and Newer and Older Sort Results by Title Date User Name Relevance in Descending Order in Ascending Order Search Tag Cloud These are the 70 most-searched-for thread tags antler sheds archery archery equipment australia bow bow and arrow broadhead bucks buying a new bow certificates climbing sticks compound bows corn crocodile crossbow crossbow ... crossbow for sale crown city wildlife area deer deer hunter killed deer hunting deer hunting knife driver license farm and fleet field staff ford from new york gps green cards grinder gun warranty hippie camo hippie chris hoyt hunt hunting gear hunting knife idaho ignitor january hunting looking for advice magnum meat seeker new hunter new to forum processing public land public land hunters release residence permit re string help scents. estrus scrapes smart scouter state parks tattle tail outdoors tracking tracking wounded deer tractor supply trade for compound bow trail treerider tree stand tree stand tree stands trophy hunting trophy ridge visas whitetail whitetail ranch working mans hunter Contact Us HOME Archive Privacy Statement Terms of Service Top All times are GMT -4. The time now is 06:51 AM. Powered by vBulletin® Version 4.2.2 Copyright © 2019 vBulletin Solutions, Inc. All rights reserved. Bowhunting Ohio


text:
Rhythm is the most important part of sight reading. If you have correct rhythm but incorrect pitch, at least you will be in the correct place. If you play the correct notes but the wrong rhythm you will quickly become lost.
To create a random set of rhythm:
Start Practicing Here
Once you have mastered rhythm, move on to melodies. Choose from four different clefs, multiple time signatures, note ranges and intervals. Change the tempo and even add a metronome to the background.
Sign up to:
Practice Melodies
Want to practice just triplets or sixteenth notes? Custom levels are available for both rhythm and melody practice and allow you to practice only the rhythms you want to work on.
Or perhaps you want to work on sight singing? Set the custom level to quarter notes and adjust the interval setting to change the difficulty.


text:
|Send News. Want a reply? Read this. More in the FAQ. News Forum - All Forums - Mobile - PDA - RSS Headlines Twitter
Wired.com has put out the Call for Submissions for their 12th annual Vaporware awards, the yearly celebration (in that derisive sort of way) of broken tech promises (thanks Mike Martinez). They explain that annual cover-boy Duke Nukem Forever is no longer eligible, saying: "The company developing the DNF first-person shooter, 3-D Realms, officially closed up shop in May, and Take-Two Interactive, the company that owns the rights to Duke, hasn't commented on the game's future. He'll be missed." This is actually not accurate, as while most of the DNF development team was laid off in May of this year, 3D Realms has maintained they are not closed, and that development of DNF "continues," so the game seems like a fine candidate for a write-in campaign.
Copyright © 1996-2019 Stephen Heaslip. All rights reserved.
All trademarks are properties of their respective owners.
Privacy Policy. Manage Cookie Settings.
News CGI copyright © 1999-2019 James "furn" Furness &
Blue's News.
All rights reserved.
Chatbear v1.4.0/blue++: Page generated 23 March 2019, 09:09.
Chatbear Announcements.