Douglas N. Gordin
Roy D. Pea
School of Education and Social Policy
2115 North Campus Drive
Northwestern University
Evanston, IL 60208
(847) 467-2826
gordin@unix.sri.com
pea@unix.sri.com
Running Head: PROSPECTS FOR SCIENTIFIC VISUALIZATION
*We are grateful for research support of the CoVis Project by the National Science Foundation Grant
#MDR-9253462, by Apple Computer, Inc., External Research, by Sun Microsystems, and by our industrial
partners Ameritech and Bellcore. We would also like to thank our colleagues from the CoVis Project and
community of users for extended discussions of these issues, and continual useful feedback on design,
rationale, and pedagogical issues. Special thanks to climatology Professor Raymond T. Pierrehumbert for
his initiation and support of our entries into the wide world of climate data sets and tools. In
addition, we are indebted to our two reviewers at the Journal of the Learning Sciences. For reprint
requests please contact the first author.
Abstract
Scientific visualization has the potential to make science education more accessible and to provide a
means for authentic scientific inquiry. The role of scientific visualization within science is
explicated through examples of its use and by presenting a sociological account of science that portrays
scientific visualization as an important new inscriptional system. Three examples are provided of using
scientific visualization within education: the ChemViz Project at NCSA, the Image Processing for
Teaching project at the University of Arizona, and an undergraduate climatology course at the
University of Chicago. Potential problems of integrating scientific visualization within secondary
education are described, including students' need to learn basic scientific practices,
incompatibilities between the format of the traditional high school classroom and scientific inquiry,
and the need for additional infrastructure within schools. Finally, possible solutions to these
problems are described, culminating in a view of scientific visualization as a means to provide
education with an important new inscriptional system for exploratory, inquiry-driven learning and to
link the aims of education to the practices of science.
Keywords:Scientific Visualization, Science Education, Educational Technology, Project-Enhanced Science Learning, Graphical Media.
1.0 Introduction
Scientific Visualization (SciV) stands for diverse scientific and social enterprises, including: a new
type of graphic representation; the creation of dramatic scientific images and their animation; an
emerging academic field that combines elements of science, computing, semiotics, and the visual arts;
and the coordination of a suite of advanced technologies to collect, store, process and image large
data sets. The rallying cry of SciV has acted as a catalyst to generate new lines of scientific inquiry
and to push beyond the frontiers of computer graphics, thus producing new methods of representation. In
this paper, we consider how to place the considerable momentum of SciV at the service of science
education.
Compelling reasons exist to bring SciV into education. Scientists increasingly rely on the techniques of SciV in pursuing their lines of inquiry and in communicating results to the scientific community. For example, within medicine, three-dimensional reconstructions of the human body are being formed from magnetic resonance images (MRI), atmospheric science has developed movies showing the development of tornadoes and the emergence of a hole in the ozone layer above the Earth's atmosphere, physicists are portraying the sub-atomic microstructure of material, and astronomers are animating the processes presumed to underlie the emergence of galaxies and black holes. These techniques have been widely accepted by scientists and are already fundamental to the production of scientific results in widely varying areas of concentration. Numerous scientific meetings on scientific visualization now take place each year. The amount of usage of SciV by the scientific community in itself compels the attention of the education community, since a major function of education is to prepare students with the skills and tools that professional fields employ. In addition, the compelling visual attraction of these methods and their ability to provide concrete images for abstract conceptions often portrayed clumsily in textual or diagrammatic descriptions also attract educators, since here might lie a valuable ally in the attempt to demonstrate the connection of science to its phenomena.
These incentives are challenged by the difficulties of transferring the methodologies of science and the technologies of the broader community into the classroom. Past crashes of optimistic expectations in this area are well documented (Cohen, 1988; Cuban, 1986; Griffin & Cole, 1987). Using these analyses as sobering lessons has broadened the perspective taken here. In particular, we acknowledge the situated nature of the tools and methodologies that we wish to introduce to the classroom. Hence, SciV cannot be extracted from the fields that have defined it and deposited, like a load of freshly minted textbooks, on the doorstep of the schoolhouse. Instead, scientists and science educators must lead students to acquire the practices of the tools they use to pursue their craft of finding and forging descriptive patterns that reveal (or can come to signify) order amidst the seeming chaos of phenomena.
Our thesis is that SciV has the potential to make science education more accessible and to provide a means for authentic scientific inquiry. This is argued by presenting SciV as it is used within the scientific community and then describing how SciV can aid learning. The description of SciV within the science community occurs in Section 2.0 where the role of SciV within science is explicated through examples of its use and through a sociological account of science that emphasizes the role of inscriptions in the production and negotiation of knowledge. Section 3.0 presents the potential of SciV for education by enumerating the ways that SciV can aid learning and by describing three examples of its use within educational settings, namely, the ChemViz Project, the Image Processing for Teaching project, and an undergraduate climatology course at the University of Chicago. In addition, potential problems in using SciV within education are enumerated along with suggestions for their solution.
2.0 Scientific Visualization
This section presents SciV within science by considering it from several points of view, ranging from
substantive to sociological. First, representational characteristics of SciV are examined in order to
provide a rough overview of its distinctive characteristics. Second, an example is provided that uses
SciVs to explore basic patterns of climate, thus demonstrating how SciVs can be used to view and reason
about fundamental scientific forces. These two views are intended to provide a substantive view of
SciV, thus detailing how a scientist might describe SciV and its utility for reasoning about the world.
Third, the practices surrounding the use of SciV within the scientific community are described,
especially the standardization and distribution of large data sets. Fourth, a sociological perspective
is adopted that views SciVs as inscriptions that scientists use to produce knowledge, to persuade their
colleagues and to compete for scarce resources. These latter views show SciV within a broad social
context. In Sections 3.0 and 4.0, these varying views will be used to provide the basis for
understanding how SciV can be integrated with education, including its potential benefits and
problems.
2.1 Description of Scientific Visualization
The field of SciV is newly formed and thus resists clear definition. It is as much a social
construction as a cleanly defined area of inquiry. A landmark report from the National Science
Foundation (NSF) (McCormick, DeFanti, & Brown, 1987) established the field of SciV by linking
disparate elements from the disciplines of science, computer science, and the visual arts. Because the
defining characteristics of this emerging area are still being sought, the images it has produced still
provide the most compelling description of this new discipline. Overall, the significance of these
images are highly specific to the domains that produced them, since they portray the phenomena studied
by that each scientific field. However, they can be distinguished by the following characteristics:
The basic methodology of SciV can be explained through an extended comparison with digital photography.
A digital photograph is made up of many pieces, called pixels, each of which has a shade. The values of
these pixels are determined when the photograph is shot so that the more light received at a particular
area the higher the value recorded. When the digital photograph is graphically rendered each pixel is
mapped to a shade of gray indicated by these numbers. A SciV image is similar to these digital
photographs in that it too is stored as a set of values that can be rendered by mapping each number to
a particular shade. However, the values composing a SciV image are typically not formed from the
intensities of visible light; rather, the numbers may represent abstract qualities. An example data set
is global surface temperatures (see Figure 1).
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These temperatures are then viewed as a digital image, where each number is mapped to a specific shade.
This "temperature portrait" allows one to discern patterns and contrasts much as figure and
ground are seen in a photograph (e.g., in Figure 1 the high desert temperature of Australia's outback
shows up as red against the yellows and greens of the cooler coastal land and ocean). As seen in the
Figures, the values need not be mapped to shades of gray. In contrast, the extensive use of color is an
hallmark of SciV. Colorful palettes can be used, such as a full rainbow of colors or the color spectrum
of molten metals. Different palettes serve to bring out distinctive features of an image, thus
encouraging an association with the phenomena under study. SciV also uses video to show a sequence of
images that provides the appearance of change over time, for example, the annual temperature cycle
could be illustrated by animating a sequence of SciVs that shows daily temperature over the course of a
year. Another way of showing change over time is through three dimensional renderings. There, pixels
are rendered as cubes (or voxels) instead of squares and multiple data sets are piled on top of one
another (Elvins, 1992). This three dimensional space can then be explored by reducing it to selected
slices or planes or rendering some set of values transparent, allowing one to peer into the internals
of the assembled data. A crucial point is that the visualization algorithms usually attempt to
represent spatial phenomena from a sampled set of data points. Thus, the resulting visualization
portrays the original phenomena by making local interpolations. In contrast, graphs often attempt to
find a mathematical formula that characterizes a set of data points. This formal simplicity is not
required in SciV, since our visual perceptions are well suited to discriminating complex patterns and
anomalies. The above description provides a relatively complete account of how SciV uses raster or
pixel based images to render data sets.
Other representations are used as well, including contour lines and arrows (Brodie, et al., 1992). Contour lines are placed around areas that have roughly the same values. The most common uses of contour lines are in topographic maps to show elevation. The rate of change can by observed from the spacing of lines: when lines are bunched together the rate of change is higher than widely separated lines. Contour lines can be appropriately used on many types of data, for example, a contour map of temperature can be used to indicate temperature values and rates of change.
Arrows are used differently than contour lines or raster images in that each arrow encodes two values, typically a direction and a magnitude. Arrows are commonly used for mapping winds, indicating the wind's direction by the arrow's angle and the wind's strength by the length of the arrow. However, any data set made up of pairs can use arrows. For example, the stress on an airplane's wing is often shown using arrows. The above description of visual representations surveys some of the representations commonly used for SciVs of climate, which is the domain of the example below. Other scientific domains, such as biology and chemistry, rely much more extensively on three dimensional modeling. However, a description of these methods or a full survey of representations used in SciV is beyond the scope of this paper.
2.2 Example of Scientific Visualization
In order to go beyond the abstract description provided above, this section examines some SciVs in
order to show how they can be used to understand scientific phenomena. These examples will continue to
look at the temperature of the earth, through presenting factors that underlie the range and
distribution of the surface temperature. Consider Figure 2 (data from Barkstrom; 1984), which shows a
monthly mean of the insolation (i.e., energy coming to Earth from the sun) during January, 1987.
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This is an unusually simple SciV, since each horizontal line has a constant value. Hence, it could also
be rendered as a two dimensional graph. However, there are benefits to presenting the insolation as a
colorful SciV. Showing the horizontal lines accentuates that there are spatial regions (in this case
along lines of latitude) which are receiving the same amount of energy or radiation. The colors
dramatically encode the energy levels, ranging from the hot reds in the Southern Hemisphere to the
chilly blues and purples in the Northern Hemisphere. In beginning to explore this SciV certain familiar
places on the globe can be examined for their value. For students in North America a natural place to
look first is where they live. Look at the thick band of blue covering North America -- these chilly
blues present a sharp indication of winter's cold. This can be used to demonstrate the causal relation
between sunlight and our temperature, namely that it is sunlight that warms our planet. However, other
examples are not as clear. Find the equator at 0° latitude, which shows up as a bright yellow, thus
revealing a high value, but by no means the highest. This provokes intriguing questions: If the equator
isn't getting the most heat why is it generally considered the hottest place? and If the South Pole is
receiving so much solar energy why is it so cold? To try to answer these questions look at another SciV
of insolation, this one from July, 1987 (Figure 3; data from Barkstrom, 1984).
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Insert Figure 3 about here
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The SciV of July's insolation provides a seemingly flipped image from January's insolation. Here (in
Figure 3) the North American summer is consistent with the red band crossing our continent. The earlier
questions can now be partially answered, since in this SciV the equator is still a bright yellow while
the South Pole is a dark purple. Hence, a partial explanation for higher temperatures at the equator is
that relatively high amounts of solar energy are received there throughout the year, whereas the amount
received by the poles varies widely between seasons.
Now we may examine a SciV of the Earth's albedo (i.e., the reflectivity of the Earth's atmosphere
system) for January, 1987 (Figure 4; data from Barkstrom, 1984) to obtain additional insight into the
varying patterns of surface temperature we are attempting to understand.
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Insert Figure 4 about here
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Note that this SciV uses a different palette than the rainbow palette of the preceding SciVs. Here the
palette varies from dark blue to white to dark red. The values of albedo shown range from zero to
eighty, where eighty means that eighty percent of the sun's energy would be reflected back to outer
space and zero means none of the sun's energy would be reflected, rather it would all be absorbed. Find
the continents of South America and Africa in Figure 4, which show substantial areas of white in
contrast to the dark blue of the surrounding oceans, thus indicating the continental land masses
reflect more energy than their surrounding oceans. The high albedo (shown as bright red in Figure 4) of
the South Pole reveals another reason for its extreme cold -- most of the sun's energy is reflected away
from it by polar ice. In contrast, a wide blue swath surrounds the equator signifying its low albedo
and the fact that it will absorb most of the sun's energy that comes to it. The complexity of the
albedo also provides one reason why the temperature patterns on Earth are not as simple as the straight
lines of insolation in Figures 1 and 2 would indicate. Temperature patterns come from a combination of
the influences of insolation and albedo as can be demonstrated using Figure 1, which shows a SciV of
temperature for January, 1987. Compare the bands of color denoting temperature in the Northern
Hemisphere in Figure 1 with the bands of color denoting amount of incoming sunlight (or more
technically, amount of incoming energy or insolation) in Figure 3. There is a parallel between the two,
in that bands of purple, blue, and yellow can be roughly seen in both, running from the North Pole to
the equator. The pattern in the Southern Hemisphere is more complex. Whereas the insolation shows a
band of yellow followed by a large band of red, the temperature shows successive bands of yellow,
green, and blue. Examine the albedo in Figure 4 to see why high temperatures, signified in Figure 1 by
red, do not show up uniformly in the Southern Hemisphere. Around the equator there are high
temperatures since the low albedo (shown as blue in Figure 4) lets in (i.e., does not reflect) the
insolation (shown as yellow in Figure 2), however the higher values of the albedo (shown as white in
Figure 4) in the middle of the Southern Hemisphere reflect away the insolation causing the temperatures
to sink to a middling value (shown as green in Figure 1). The high albedo of Antarctica (shown as deep
red in Figure 4) reflects away almost all of the sun's energy, resulting in very cold temperatures
(shown as blue and purple in Figure 1).
The seasonal change in temperature (see Figure 5) provides another illustration of how SciVs can be
used to explore phenomena.
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Whereas the earlier examples derived insights by flipping back and forth between SciVs and finding
causal patterns and connections, this SciV represents a derivation. Hence, the data in it was never
directly observed, rather it was calculated by subtracting the surface temperature of January, 1987,
from July, 1987. A prominent feature of this seasonal difference SciV is the predominance of color
(both red and blue) is on the continents while the oceans are either white or very pale. Looking at the
scale, white signifies a small change in temperature whereas the darker blue and red hues signify large
changes. Hence, this SciV demonstrates that much larger changes in surface temperatures occurs on land
masses than on the ocean. Such an observation could give rise to a line of inquiry to understand the
cause.
As presented above, SciVs provide the ability to pursue scientific inquiry through reasoning qualitatively about pictorial patterns. In the example above, swaths of color were used to reason about the distribution of temperature on Earth. This style of reasoning utilizes patterns and color in performing a qualitative analysis of basic scientific phenomena. Finding these patterns relies on having particular knowledge about the world and the way that data sets represent it. Thus, rather than presenting an understanding, the SciVs form the basis around which an understanding can be woven. This is similar to the way a path can be constructed using a map. The map does not supply the path, rather the map provides an analogical space in which the referents of the origin and destination can be connected through a constructed path. Like maps, SciVs are not territory but symbolic constructions with a purposive or intentional context. What aspects of phenomena we choose to place on these representations and how we place them tell as much about us as the SciVs tell about the physical reality from which they were abstracted. Yet, once constructed SciVs can provide a powerful vantage point from which to make observations on the "constructed world" shown in them. In addition, these observations can give rise to questions or hypotheses about the world far more readily than reading arrays of numerical values or textual descriptions. Through manipulations on the underlying data sets these hypotheses can be tested and verified. In this way, SciVs can help to integrate the too often separated areas of model design and model testing.
2.3 SciV within Scientific Community
A description of SciV must include its contexts of use. These contexts are helpful in understanding the
uses to which SciV has been put and what material and social requirements exist for its appropriation.
These considerations are especially important when we come to consider how science education can
appropriate SciV. Below the fields of climatology and biology are briefly considered. Particular
attention is drawn to the accumulation and standardization of data sets to facilitate the usage of SciV
within these fields. The promulgation of these standardized data sets and SciVs derived from them has
allowed scientists to distribute their knowledge and practices. These developments can be considered as
part of a continual and ubiquitous reification of knowledge into tools that has been called the
creation of "distributed intelligence" (Pea, 1993a).
Within the scientific community the search for patterns within SciVs have been aided by the complementary practices of creating large standardized data sets that are used to construct SciVs and provide the bases for experiments. Examples of these paired practices, of searching for patterns and accumulating standardized data sets, can be found in several scientific fields. Climatologists have built up impressive repositories of data in recent years that are investigated through the coupled usage of SciV and Global Circulation Models (GCMs). Data sets are compiled and certified at national laboratories like the National Meteorological Center, the Carbon Dioxide Information Analysis Center (CDIAC) and the European Community Medium-Range Weather Forecasting (ECMWF) and then sent to researchers over high speed computer networks or sent as CD-ROMs (Domenico, Bates & Fulker,1994). Much of the experimentation performed with this data uses GCMs to understand the climate of the past (paleoclimatology) and to predict the climate of the future (Hall, 1992). These experiments function by seeding the GCMs with observed data and then iteratively applying differential equations, defined by the model, to predict the future climate. Often, these models are run through hundreds or thousands of iterations to predict the Earth's climate ten thousand years in the future. The end results are displayed as SciVs so that scientists can look for patterns of change in the amount and distribution of temperature. Analogous practices are found within the biology community in the effort to map out the genetic codes that determine the development of life. Substantial progress has been made in mapping out the entire human genome. SciV has been an integral part of the history of understanding the structure of DNA as is recounted in Watson's classic account (Watson, 1968) of discovering that DNA is structured as a double helix. While Watson and Crick's original model was built from wire the more recent SciVs are generated on computer screens. The discovery and visualization of the structure of DNA has provided a basic foundation for later work in genetics and biochemistry, such as helping to build maps of chromosomes, thus leading to the localization of genes responsible for diseases. For example, the gene responsible for Hutchinson's disease has been localized, enabling researchers to test for its presence, thus offering crucial information to couples planning to have children. In summary, the basic practices of establishing standardized data setsfor experimentation and understanding these data sets through SciV are present in biology as in climatology. Similar descriptions could be constructed of analogous practices in many other scientific fields, such as medicine, chemistry, astronomy, and meteorology (for an excellent collection of SciVs from a wide variety of fields see Wolff & Yaeger, 1993).
The representations and tools utilized by a field often have a pervasive effect on which issues are examined and what problems explored (Toulmin, 1953). This has often been overlooked within science, where scientists have argued that their representations are mirrors of Nature or are deductively inferred from Nature. Yet, as Toulmin has persuasively argued the relationship between representations and phenomena is not deductive: rather the representations provide a model, complete with symbols that can be formally manipulated, such that points of connection can be forged from the model to the phenomena. The strength of a model lies in its ability to encourage fruitful analogies to the phenomena to which it has been connected, thus leading to novel explorations. These models can also be considered as systems in which knowledge is produced or manufactured. This point of view has been elaborated by sociologists of science.
2.4 Scientific Visualization as an Inscriptional Medium
In recent years sociologists have provided a startlingly different account of what scientists do when
they do science, the roles which the external representations they produce play in that process, and a
characterization of why various representations are successful in scientific discourse (e.g., Latour
& Woolgar, 1979; Latour, 1988; Lynch & Woolgar, 1988; Knorr-Cetina, 1981). Since this account
helps explicate the culture of science and its use of SciV, we see it as useful for both understanding
how to bridge the practices of high school classroom with those of scientists' laboratories and the
benefits that students may be able to obtain from using SciV. Two methodological assumptions underlie
much of this work and serve to illuminate why it differs from many conventional accounts of scientific
practice. First, sociologists of science engage in a practice of anthropological strangeness. This
refers to a conscious suspension of preconceptions and knowledge about the role of scientists in
society, the meaning and purpose of the representations that scientists use, and the epistemological
status of the products of science. Second, sociologists assume that the cognitive and perceptual
mechanisms practiced by people are roughly the same independent of both culture and time. Hence, the
abilities of scientists do not rely on uncommon insight or a qualitative jump in intellect but utilize
creativity and perceptions in an analogous way to that shown by Aborigines hunting for game, Bedouins
navigating the deserts, and high school students angling for a top grade.
In this characterization of science, developed from empirical studies in diverse fields (e.g., Lynch & Woolgar, 1988), the various formulas, diagrams, pictures, and textual accounts have been collectively labeled inscriptions to emphasize their nature as materially embodied representations. These inscriptions play a pivotal role in the actions of scientists. We may only briefly review these claims and their implications here. Vast amounts of work go into the production of inscriptions; the preparation for one inscription uses others; and debates over issues raised in these inscriptions dominate routine conversations in scientific workplaces. "Science" is thereby defined in these studies as the creation of these inscriptions and the social practices that accompany their creation and use. This perspective does not deny that "science" is about providing powerful explanatory models of the phenomena of the physical world, but seeks to highlight the dominant character of inscriptions in scientific practice. In particular, two characteristics of inscriptions describe their use and success in the scientific enterprise. First, these inscriptions are used agonistically in argumentation as persuasive devices that can be used to compel agreement among peers. For example, a conversation could center on the quickest way to traverse a maze. In order to demonstrate a solution, a map of the maze is drawn with different paths shown in different colors and the lengths of the paths are annotated. This inscription then becomes a powerful tool in resolving the debate. The victors of these arguments are rewarded socially and economically. Winning a scientific debate often determines the bottom line on societal awards such as grants, scientific stature, academic tenure, and influence.
Second, various formal and material aspects of inscriptions imbue them with power. These attributes of inscriptions are enumerated as: immutable, mobile, flat, scaleable, reproducible, recombinable, superimposable, the ability to be placed in written texts, and mergeable with geometry (Latour, 1988). While we will not review accounts and definitions for these attributes here, these qualities can again be seen in the map of the maze discussed above when it is drawn on paper. Indeed, the enormous utility of paper for achieving these goals becomes an important factor in the success of inscriptions. Thus, paper becomes a common medium that permits various inscriptions to be combined, referred to, and accumulated. This reconstruction of science by sociologists of science continues onto the epistemological status of inscriptions. Whereas scientists frequently ascribe a basis in nature for their inscriptions, this sociological account finds them to be constructed in practice through a layering of representations. Using the maze example above, the specialized map that is constructed builds on accepted representations of maps for describing spatial relationships, and also builds on representations in mathematics for summing distances. This example underscores the observation that representations are overlaid and built upon after they have become widely accepted. In addition, an effective inscription often relies upon resemblances with the object it represents. These resemblances are crafted, and as such, their use may undergo substantial development as was the case with realistic painting. The view of scientific material thus changes from a reflection of nature, as some theories of science would have it, to the products of craft or even a manufacturing process (e.g., see case studies in Lynch & Woolgar, 1988).
Using the above framework, SciV can be considered as another inscriptional mechanism, thus providing for a more nuanced perspective on the role of SciV in science than that we described earlier. Specifically, the following points can be made:
The above sociological portrait of science highlights the role of inscriptions within the practice of science. In this framework the activity of science can be described as the production of inscriptions and of argumentation around those inscriptions. As an increasingly ubiquitous new inscriptional mechanism, SciV plays an important part in the way science is practiced. In particular, SciV provides a new medium in which scientists can observe patterns that are taken to represent the natural world. Alternatively, SciV provides a new medium in which patterns can be crafted to depict nature, thus forming the basis for analyses and descriptions of the natural world. In short, SciVs provide a new plastic medium in which to craft the inscriptions that constitute a primary activity in the practice of science. Similar to any representational system, SciV can be said to promote or "afford" certain modes of inquiry more than others. The animated and colorful images of SciV promote the use of pictorial patterns. The colorful representations, such as those in Figures 1-5, afford visual inferences and reasoning through pattern differences and cross-figure relationships due to human perceptual discriminative capacities. Once again it is not enough to say that the patterns are found, just as often they are crafted. This essential tension has also been described with respect to our visual perception through two competing rules of thumb: we see what we expect to see versus our attention is caught by unusual features (Szlichcinski, 1979). Though seemingly contradictory, these complementary practices are basic to perceptual experience and are commonly practiced in the temporal sequence of work patterns utilizing SciVs. First, the choice of which data to examine and the way it is examined is determined by preconceptions that there is something interesting to be seen. Yet, given an "open mind" in subsequent search for patterns, new possibilities can intrude. An interesting case in point comes from recent scientific history of how the ozone hole was detected (Hall, 1992). In brief, the story is that satellite data detailing a precipitous decline in ozone was collected for several years without anyone noticing it. The problem was that during data analysis any measurements falling outside of "reasonable" limits were thrown out. Only after a different research team reported a drop in ozone levels, as determined from simpler ground instruments, were the data reexamined and found to show a dramatic drop in ozone during the Antarctic spring months, roughly from August to November. Despite the failure to use satellite data in discovering the problem, it subsequently has served as the basis for many dramatic SciVs depicting the grave condition of the earth's ozone shield. Arguably, these images, as much as any scientific arguments, helped consolidate the international communities' agreement to legislate the use of CFCs in an effort to reverse the depletion of the ozone layer.
3.0 SciV within Education
3.1 Roles for SciV within Education
The above discussion described SciV and its role within science, thus setting the stage for a
consideration of its potential within education. Areas where SciV can aid science education include the
following:
Making Science Accessible
Often science education has consisted of memorizing formulas and learning to apply them using
algorithmic processes (Linn, diSessa, Pea & Songer, in press; Linn, Songer & Eylon, 1993).
These abstractions contrast sharply with the complexity and beauty of the physical world that is the
subject of science. SciVs can provide a more accessible inscriptional system for students to understand
the subject matter, processes, and results of science. Through the use of color and animation SciV aim
at achieving verisimilitude with the phenomena they represent. These correspondences, unlike
mathematical formulas, rely on pictorial similarities that students can often readily assimilate. The
dynamic time-based processes that science studies can be shown dynamically as animations that
themselves move through time, thus providing a more accessible means for reasoning about scientific
processes than typical graphs, formulas and quantitative analyses. For example, an animation showing
monthly mean temperatures for a year (similar to Figure 1) could provide a basis for investigating and
understanding patterns of seasonal change. This style of reasoning attempts to describe basic scientific
principles qualitatively, through referring to patterns of color in the SciV. For example, the role of
albedo in redirecting sunlight can be discussed using Figure 4 as was done in the example above. We do
not imply that making the SciVs available will allow student to immediately understand the lines of
inferences presented. Rather, these SciVs provide visible referents for the scientific processes that
science education encourages them to appropriate and use as anchors in their inquiries.
This use of pictures and animations might also correspond to the strengths of "the Nintendo (or Sega) generation," so designated due to children's increased use of television and video games. The emergence of a more "visual culture" has been derided by educators seeking to foster written literacy (Postman, 1992), yet it can also play to the advantage of education through the use of engaging and intellectually stimulating images, such as SciVs.
Provides the means for authentic scientific inquiry
As mentioned already, teaching science through abstract formulas, schematized methodologies, and rote
procedures has been the norm in our high school and college classrooms. Yet, this does not capture what
scientists do. Nor is this pedagogy successful in helping students appropriate the fundamental
practices and concepts of science. In reaction to this failure a new pedagogy has been articulated that
strives to connect the practices of scientists and students. This new pedagogy, called cognitive
apprenticeship (Brown, Collins & Duguid, 1989; Pea, 1992) seeks to reemploy the practices of
apprenticeship to provide education in areas not commonly associated with skills or trade, such as
mathematics and science. A similar reconceptualization has emphasized education as enculturation into a
community of practice, where students are peripheral members of the community (Lave & Wenger, 1991;
Hawkins & Pea, 1987). As such, it is important that students observe how full members of the
communities perform their tasks and that they have opportunities to use similar tools and inscriptional
systems. Inquiry using SciVs can link students with the authentic practice of scientists. Through
utilizing the same SciVs techniques and data sets as scientists, students gain a common ground with
them that can promote mentor to apprentice relationships. Further, the SciVs and their data sets
provide the means for students to engage in authentic inquiry on basic scientific questions. Often
students' experiments have had only a distant relationship to the phenomena under study (e.g., studying
a tornado by swirling water in an aquarium). Though this type of experimentation can be valuable, SciVs
allow students to interact with current scientific data collected from actual phenomena and ask their
questions in relation to it.
Significant increases in student motivation can also result when students have the opportunity to study scientific questions that can have important ramifications for their lives. For example, many students are aware of a problem with the amount of ozone in the atmosphere. While students may not understand the details behind the loss of ozone nor why the thinning of ozone has occurred over the Antarctica, they are aware that it poses a potential danger to them; hence it is an issue that engages their attention. Students can inspect SciVs of the ozone hole in order to begin their inquiry. In order to acquire a robust understanding, numerous issues must be investigated: the chemical composition of ozone, the reason the ozone hole occurs over Antarctica in the spring rather than over North America, and how fast the ozone depletion is occurring. The general point is that when inquiry begins with an issue that engages students' interest they are more likely to energetically pursue an inquiry. Further, students are interested in important issues, like the depletion of ozone, that affect them. SciVs can help students begin inquiry on these issues by providing dramatic images of the phenomena they want to study, thus providing them with referents for their inquiry and discussion. As they seek to progress further, these same SciVs can provide the basis for conversations with scientists or other mentors, including more knowledgeable peers. Visual patterns in these SciVs can suggest basic lines for inquiry: Why does the ozone hole grow and shrink over the course of each year? Why has the size of the ozone hole oscillated instead of getting steadily bigger? Later, the underlying data sets can be used as the basis for experimental exploration of these questions. The questions which arise from inspecting such SciVs can quickly reach the limit of scientific knowledge. While challenging for teachers, this too is an advantage of students engaging in inquiry in areas of current work, since students become aware that scientific knowledge is not complete, that it has boundaries and is not static but evolving as scientists continue to perform inquiry and engage in debate.
Several important issues arising when students engage in inquiry through projects are glossed over in this account. How will the student locate the appropriate data sets? How will they find scientists willing to act as mentors? How will students understand the tacit scientific conventions, such as the units employed (e.g., ozone levels are given in Dobson units). In addition, inquiry directed by the needs of a project requires topics presented outside of "textbook sequences" (e.g., students investigating ozone would need to understand the basic chemistry of ozone and how CFCs interact, but they may not have previously studied chemistry). These are difficult questions which must be addressed. Below, these and similar issues are discussed further, along with some tentative directions for their solution.
Empower students with important tools
Experience using SciV can benefit students by providing them with new capabilities that could aid them
in a variety of enterprises. Science is only one of the areas where data is being transformed into
virtual realities through the universal imaging abilities of computer graphics. Diverse areas are
taking advantage of viewing and exploring their subject matter through computer images. Financiers view
profit and loss risks within imagined six dimensional spaces in which they can investigate strategic
directions through navigation in visualization representations (e.g., Feiner & Beshers, 1990).
Architects inspect and refine their designs by virtually walking through their buildings before a
single stone has been laid. Similarly, manufacturers test the stress levels of proposed equipment while
it exists only as CAD-CAM diagrams. There is reason to believe that working with SciV and related
virtual realities will be a common part of many enterprises.
Lay Groundwork to Understand and Critique Scientific Issues
SciV also offers the means to acquire a scientific literacy that is instrumental for an informed
citizenry, who do not choose to become scientists, yet wish to participate in keeping abreast of
critical issues affecting their lives through, for example, medical and scientific policies. This
literacy requires familiarity with rhetorical means, like SciVs, with which these issues are argued.
When SciVs are used in support of project inquiry, students have an opportunity to focus their study on
current scientific issues, such as, environmental change, depletion of the ozone, and global warming.
Further, with appropriate pedagogical support, students may gain expertise in the proper use of SciVs
for advancing arguments and gain critical practice in discerning when SciVs are used inappropriately.
3.2 Examples of SciV within Education
Many educators are already seeking to use SciV within their classrooms, though most have occurred
within post-secondary settings. In order to more concretely survey both the opportunities and potential
problems of using SciV we briefly review three existing programs that incorporate SciV as a fundamental
part of their curriculum. The programs we review are all actively working with teachers and/or students
as opposed to some programs that are primarily dedicated to providing access to SciVs (e.g. Sampson,
et al., 1994). Two of the projects surveyed here are working with secondary students and one is a
university.
The ChemViz Project
The ChemViz Project (Koker & Rowe, 1993) is working with high school chemistry classrooms to
revitalize their curriculum and pedagogy through SciVs of atomic interactions. This project is run by
the National Center for Supercomputing Activities (NCSA) and is funded by the National Science
Foundation. When it began in 1991, four high school teachers were taught to generate the SciVs. Through
the successful completion of several teacher training workshops, over thirty high schools, located
around the country, are now participating in ChemViz. Initially, the students were required to come to
NCSA and program supercomputers in order to generate the SciVs. However, the difficulties involved in
transportation and in programming the supercomputers prompted the NCSA researchers to write a
"front-end" to the supercomputer software that was easy to use and that could be run over a
remote connection (i.e., either TCP/IP or modem).
The SciVs that the chemistry students request show the electron densities between atoms at various stages of bonding along with their energy levels (e.g., Koker & Rowe, 1987). SciVs showing successive bonding stages can be combined into animations. These SciVs allow students to explore chemical bonding in depth, including the sharing of electrons, strong and weak bonds, and the effect of distance and angle on energy levels.
Informal evaluation by NCSA researchers has reported increased student motivation and increased access to difficult scientific concepts. Students request that specific SciVs be generated, in effect designing their own computational chemistry experiments. This search for answers to their own questions has reportedly combined with the SciV technology to increase students' enthusiasm and led them to spend long hours on their projects. In addition, NCSA researchers report that students are led to explore a more complex model of chemical interaction than is normally taught. A commonly taught model is the Bohr atom where electrons circle the atom in a fashion similar to how planets circle their sun. The more complex electron cloud model does not place an atom's electrons at a fixed distance, but describes them using probability regions. This latter model is often not used with high school students since its understanding is often tied to other advanced material, such as probability, three dimensional geometry, wave-particle duality, and quantum mechanics. In addition, it is much harder to construct images of the electron cloud model. However, the NCSA researchers claim that using ChemViz SciVs, which show electron clouds as atoms interact, high school students need not be limited to the simpler Bohr model but can understand and work with the electron cloud model. This process is aided by chemistry teachers who provide guidance. These teachers take part in workshops and have continuing interactions with each other and NCSA researchers through electronic mail.
Image Processing for Teaching Project
The Image Processing for Teaching Project (IPT) (Greenberg, 1992; Greenberg, et al., 1993) reports that
its innovations have produced significant changes in student motivation, particularly among
under-represented groups and at-risk students. Begun at the University of Arizona's Lunar and Planetary
Laboratory, this National Science Foundation sponsored project was begun in 1990 with a focus on
planetary imaging. A series of four week workshops were held for teachers where a combination of
lecture, hands-on training, and informal work combined to introduce the idea of teaching science
through image processing . Over 85 teachers in sixteen states, from grades 5 to 12, have now
participated in these workshops and are leading image processing activities in their science
classrooms. In addition to teacher training, IPT develops curriculum and CD-ROMs containing images,
though some on-site visits and continuing assistance are also available. The IPT project encourages a
pedagogical style of active inquiry, styled after constructivist models of learning. In accounting for
their successes, detailed further below, they ascribe them to this style of learning and to the
benefits of learning through images, rather than text. The values of images for learning is described
as owing to, at least in part, the enormous amount of information that is transmitted with images as
opposed to written or spoken words.
The IPT project has not limited itself to a single area of science, rather they supply images from a broad range of domains and have found innovative ways to perform quantitative experiments on them. Examples include, measuring volcanic activity on Jupiter's moon Io, measuring lung capacity from CAT scans, and identifying seasonal and long-term changes in vegetation on a planetary scale. The analysis of images is done with a program developed by the National Institute of Health (NIH) called Image in conjunction with CD-ROMs, which the IPT project has distributed, that contain over 35,000 images.
The IPT project has found significant effects from its curriculum of scientific image processing, particularly with under-represented groups and "at-risk" students. Relying on informal assessment, the IPT project reports that minority groups, females, and "learning disabled" students were largely successful in this style of scientific inquiry. Further, participating teachers reported that the negative attitudes of "at-risk" students were improved through engaging in supervised image processing work. In addition, the IPT project reports the teachers perceptions of themselves changed through working with University researchers.
Undergraduate Geophysical Sciences class at the University of Chicago
Whereas, the two earlier examples involved large numbers of teachers and students at middle and
secondary schools, this example focuses on a single undergraduate class at a research university. The
class is called Introduction to the Dynamics of Planetary Atmospheres, and is taught by Professor
Raymond Pierrehumbert at the University of Chicago. This class uses SciV as a fundamental tool to
motivate the study of climate and as a tool of inquiry. The course provides a broad introduction to the
primary forces underlying Earth's climate, using the climates of Venus and Mars as contrasts.
The class time is approximately evenly split between providing qualitative descriptions of the phenomena and processes under study and deriving formal mathematical models. In particular, a series of radiative equilibrium models are introduced, where each successive model provides a more detailed picture at the expense of introducing more complexity. SciVs are used to illustrate and analyze basic climatic patterns (e.g., the slower rate of change of ocean temperature versus land temperature). The SciVs were constructed through the use of Apple Macintoshes, CD-ROM drives, and a weather data set from the National Meteorological Commission (NMC). The NMC data set provides daily measurements of temperature vs. pressure and altitude vs. pressure for the Northern Hemisphere over a forty year time span. The data is particularly amenable to interpretation since it consists of measurements over a uniform latitude and longitude grid.
Broad experiential questions are used to motivate the study of climate: What is the relation between solar radiation and the Earth's temperature? How is solar radiation distributed by the Earth's atmosphere? Why are there seasons? How can the differences between seasons be quantified? How does the temperature of the ocean relate to the temperature of land masses? These initial questions give way to questions concerning phenomena less directly experienced: What would be the climactic consequences of a comet evaporating ten percent of the oceans? How is the global heat economy maintained? Are we moving towards another ice age (i.e., glaciers covering much of the continents) or towards another warm period (i.e., eras when there are no polar ice caps)? Each of these subjects is accompanied by data sets that, using SciV, serve to illustrate the phenomena under consideration.
The investigations posed by the class are divided into problem sets and data labs. The problem sets require using the models produced in class in order to explain various phenomena and extending the models to handle variations in the situations considered (e.g., producing a radiative equilibrium model for Earth taking into account a "nuclear winter" scenario where the atmosphere is surrounded by a film of black dust). The data labs draw on SciV and the climactic data sets to provide an empirical sounding board for the models constructed.
Models can also be experimentally tested using SciV. For example, a radiative transfer model can be combined with a data set of surface temperature to calculate the incoming solar radiation for comparison with measurements of insolation. Using similar techniques, "what-if" scenarios can be investigated, such as, to what extent would a thicker atmosphere (i.e., an atmosphere that absorbs more of the Earth's terrestrial radiation and therefore which re-emits more radiation back to Earth) produce global warming? Hence, the tools of SciV are used to test the effectiveness of models and to form predictions by combining models with data.
This course provides a example of using SciV as an integral tool in learning science. In many ways this course exemplifies the pedagogy of cognitive apprenticeship, discussed above. Consistent with that framework, students have contact with a practicing scientist -- the instructor, who does research in climatology. The students are encouraged to conduct original research, thus providing them with authentic practice in the field. While not all of these features can be easily replicated within pre-college education, the course provides a working example of how SciV has been incorporated into modern day scientific apprenticeships.
Summary
The three examples presented above show that the potential of SciV within education have already begun
to be realized. The ChemViz project reports that students are able to comprehend a more advanced model
of the atom through SciVs that students request from NCSA supercomputers. The IPT project reports that
students who traditionally have difficulties learning science are able to engage the subject matter
through guided practice in image processing. The undergraduate Geophysics course shows students
learning SciV as one of the essential tools they will use as practicing scientists. Thus, these
students, running the gamut from "at-risk" students to future scientists, have benefited from
linking their science studies with the new inscriptional system of SciV. Yet, these successes have been
won in spite of considerable obstacles that these projects have faced. Problems ranging from finding
appropriate data sets to training teachers in the use of SciVs must be met. These problems and some
tentative solutions, many of them evolving from these example projects, are surveyed below.
4.0 Potential Problems and Solutions to Placing SciV in Education
The successful incorporation of SciV into classrooms requires the resolution of complex problems. This
section focuses primarily on these problems of utilizing SciV within high school classrooms and
proposes possible solutions. The discussion of problems is divided into understanding the knowledge and
practices of scientists or the substance of science; the personal and social dynamics in the classroom,
such as impact of social roles and individual diversity; and physical environment and resources.
4.1 Problems Arising from Working with Data Sets
When scientists use the inscriptional system of SciV they draw on a large background of scientific
knowledge. This knowledge is used in at least the following areas: categorizing data sets, visualizing
and operating on data sets, and connecting the SciVs to scientific issues. The ramifications of
students lacking this knowledge is discussed below.
Data Sets
Data can influence students' inquiry by virtue of the data that is available; limitations in accuracy,
resolution or appropriateness; and the ease or difficulty in understanding the units in which the data
is measured.
· The availability of data limits the domain of inquiry (e.g., if there are no data sets on black holes then the subject cannot be studied). Hence, an important component of scientific inquiry is getting access to the data. Further, the data has to be in a format that is accessible. This problem was surmounted in the IPT project by supplying CD-ROMs filled with thousands of images.
Visualizing and Operating on Data
The exploration and crafting of SciVs can be difficult for students because of the numerous ways
numeric values can be mapped to colors and the complexity of the operations used to combine and
manipulate the underlying data sets.
Connecting SciV to Scientific Issues
The quantities that SciVs represent are ones defined and designed by science. As such, students
preconceptions about scientific categories and processes will interact with their understanding of
SciVs.
4.2 Problems Arising from Social Organization
The social organization of the classroom has an enormous impact on what the students learn. While a
full consideration of this topic is beyond the scope of this paper, the issues are too fundamental to
neglect completely. The social organization of many science classrooms can be described as rigidly
hierarchical with the teacher occupying a lead role and the students following. This organization has
also been described as following an information transmission model of instruction (Pea & Gomez,
1992; Pea, 1994). In contrast, project-enhanced science learning (PESL; Ruopp, Gal, Drayton &
Pfister, 1993) proposes student centered projects. This pedagogy is partially inspired by Dewey's
educational philosophy, but seeks to avoid the pitfalls of discovery learning by providing guidance.
SciV can do little to improve secondary science education unless the social organization of the
science classroom transforms in this manner. Little benefit will accrue if SciVs are merely added as
new representations to existing science textbooks or as additional audio-visual aids for lectures.
The potential of SciVs for education lies with an adoption of its culture of use, not just its images.
A more detailed consideration of the problems that can arise from issues of social organization and individual differences are discussed below in the categories of teachers, students, scientists, and time.
Teachers
Students' use of SciVs for project inquiry relies on teachers facilitating their access and on teachers
being comfortable with using SciVs themselves.
Students
Project inquiry relies on students having the executive skills to manage their own work. In performing
these projects students are differentially capable of utilizing resources; for example, visually
impaired students may not be able to use SciVs.
Scientists
Impeding the process of involving scientists directly in the activity of science classrooms are the
difficulties of providing specific incentives for involvement, the current paucity of communication
networks in schools, and the difficulty in forging common conceptual ground.
Time
The fragmentation of time in most schools makes in-depth investigations difficult to accomplish.
4.2 Problems Related to Material Resources
Introducing SciV into secondary science classrooms also requires computers and software, networks, and
data sets.
Computers and Software
The use of SciV relies on an computational infrastructure that is not present in most schools.
Networks
The synergies between high speed communication and SciV are significant since the ability to obtain
large data sets, particularly for near real-time topics (e.g., weather), depends on high bandwidth
connections.
Data Sets
Many data sets are expensive to obtain.
These problems do not have a simple solution. However, the exemplar projects provide insight into how these difficulties can be overcome and the potential of SciV realized. The proposed solutions are grouped into three areas: one is infrastructure-- the creation of software and accompanying data sets; one is social organization -- building a community of practice that links teachers, students and scientists; and one is a combination of both infrastructure and social organization -- connecting secondary schools to a high speed network. These suggestions are elaborated below.
Customized Software and Data Sets
Student difficulties in understanding data sets and their SciVs can be partially solved through
reifying scientists' knowledge within software environments. These software environments can provide
supplementary context for SciVs, thus making explicit knowledge that scientists use in their
interpretation. In order to do this, the software environments need to be tied to specific data sets.
This approach is consistent with the "front-end" built by NCSA researchers to aid high school
students in obtaining SciVs from their supercomputers. Software environments of this sort can provide
explanatory contexts by describing what is being shown, how the data was collected, its numeric range,
and its spatial and temporal extent. The contextual information does not differ appreciably from what
one expects to find on a map. These cartographic standards need to be enunciated for SciVs because they
so often are lacking. In addition, when data is requested it is shown with units and useful operations
are provided on it. For example, after examining a SciV of temperature, a student could request a SciV
showing the extent to which it is anomalous. Research is needed to find the means to easily build
software environments with these features.
The consolidation of data sets is another important area to promote educational use of SciVs. The data sets could be packaged with pre-existing tools and accompanied by a proposed set of open-ended projects as "curriculum." This is the approach the IPT project has chosen by creating CD-ROMs of images and designing curriculums. Some data sets seem particularly well suited to student use, such as, national census data to allow students to understand the social makeup of their community or country; Landsat or SPOT data of the surrounding community to allow the geographic, biological and urban characteristics to be examined; global climate and environmental data sets to understand and critique the possibilities of global change; the human genome data set to present students with the beginnings of a map of the biological mechanisms which determine physical life; and astronomical data sets that allow students to orient themselves within our solar system, galaxy, and universe. This list is not meant to be exhaustive, but rather to be evocative of the potential diversity that students can explore.
Build a Community of Practice Linking Students, Teachers, and Scientists
Transforming our schools into places where cognitive apprenticeships can take place and where students
can successfully carry out authentic science projects requires a community of practice be built up
involving students, teachers, and scientists. There are many possibilities for productive relationships
between these groups. Scientists can lead in-service training for teachers to introduce them to new
inscriptional mediums, like SciV. Training of this sort could lead to scientists acting as mentors and,
in conjunction with science teachers, guiding students to participate in genuine scientific inquires.
Thus viewed, the population of students and teachers is an enormous untapped resource for generating
scientific knowledge. Examples of this type of cooperation have occurred in the TERC Global Lab Project
(TERC, 1991) and are also reported in the IPT project. In addition, the educational community can help
scientists in their quest for consensus and economic backing. A recent example of a collaboration
between education and science is the National Research Education Network (NREN). The stated goals of
the network were to connect public schools and to provide an "information super-highway" for
data and communication links for scientific research. The combined support from these two communities
helped pass the High Performance Computing Act of 1991 (Public Law 102-194), thus guaranteeing several
billions of dollars in investment for what is now described as the National Information Infrastructure
(NII). Now however, discussions of what exact services to build for the NII have led to some
controversies between the two communities, with the extremes being scientists hoping to connect
supercomputers and educators wanting to provide network access to all public schools. While the goals
are not fundamentally incompatible, federal subsidies and network communication bandwidth are limited
commodities. Hence, this example can be viewed as a possible model and as a cautionary tale for how
these two communities can work together. The common ground between science and educators is elusive.
More experiments are needed to see how schools can join with expert practitioners, like scientists, in
the formation of communities that benefit all of their participants.
Connect Schools to a High Speed Network
An important first step towards creating a community that spans students, teachers, and scientists is
to forge a commonly accessible communication medium. Connecting high schools to a high speed computer
network would provide them with the means to interact with scientists and other mentors (Pea, 1993b).
Currently, students and teachers in high schools and scientists have little means with which to contact
each other and few organizing structures to promote such contact. Providing flexible asynchronous and
synchronous connections can promote a new level of interaction that might result in the type of shared
community described above. Such a computer network could promote the transfer of data sets and
technical data as well as supporting the social interactions that undergird successful working
relationships. In addition, students could collaborate with students located at remote locations or
seek guidance from them or their teachers. The network also provides a means to disseminate the results
of student work, thus providing them an audience for their work. As our sociological account of
scientific work detailed, the work of science can be described as the production of inscriptions that
garner consensus and social acclaim. Through linking students to one another and to the main avenues of
society, students are provided with authentic motivation to engage in the scientific inquiry and the
construction of scientific inscriptions. Hence, students could generate SciVs from scientific data sets
received over their computer network connection and then funnel their work back over this same
connection to other science students and into the general scientific community. This possibility is
particularly enticing since our capacity to collect data is out stripping our ability to analyze the
data. This overload is guaranteed to increase as tens of more satellites are sent into space as part of
the Earth Observing System and as powerful new eyes and ears come on-line (e.g., the scanning tunneling
microscope and underwater networks of sonic detectors).
5.0 Conclusion
The case has been made above that SciV shows remarkable potential to help students learn science. As an
extraordinarily plastic medium, SciV affords the construction of provocative images that can resemble
physical phenomena and serve as the basis for the construction, debate, and negotiation of meaning that
stands at the heart of the process of education. However, research is needed to understand how students
come to be able to read and understand SciVs and how this process can be facilitated by material and
social supports. When students succeed in appropriating the inscriptions of SciV, grounds are laid to
connect the education of students with the practices of scientists. This connection holds promise for
students' studies to achieve authenticity or consonance with the practices and goals of science. Yet,
considerable research is needed to forge a clear plan for how scientists and students can best
interact. It is here, in the interface between scientist and student, that the science educator can
play the profoundly useful role of diplomat and interpreter. This role is especially challenging, since
the practices and goals of science and education have drifted apart, leaving each stranded in separate
communities. Yet, the resulting lack of connection is not to the benefit of either one. The integration
of science and education will require the searching gaze of ethnography to understand the starting
goals and practices of each community, brave educational experiments that seek to act on these
understandings, and rigorous assessment to interpret the results. While starting points in this process
are often elusive, the ground between learners' preconceptions of the physical world and scientists'
tools or inscriptional mediums, such as SciV, can provide an advantageous place to begin.
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Figure Captions
Figure 1.
Raster image showing surface temperature for January, 1987, based on data from the European Community
Medium-Range World Forecast (ECMWF). Resolution is 2.5° square. Black areas signify missing data.
Figure 2.
Insolation for January, 1987, shown as a raster image, based on data from the Earth Radiation Budget
Experiment (ERBE). Resolution is 2.5° square. Black areas signify missing data.
Figure 3.
Insolation for July, 1987, shown as a raster image based on data from the Earth Radiation Budget
Experiment (ERBE). Resolution is 2.5° square. Black areas signify missing data.
Figure 4.
Albedo for January, 1987, shown as a raster image, based on data from the Earth Radiation Budget
Experiment (ERBE). Resolution is 2.5° square. Black areas signify missing data.
Figure 5.
Seasonal difference in surface temperature, computed by subtracting January, 1987, from July, 1987,
based on data from the Earth Radiation Budget Experiment (ERBE). Resolution is 2.5° square.