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Spatial Learning and Computer Simulations in ScienceRobb Lindgren a; Daniel L. Schwartz aa Stanford University, California, USA

To cite this Article Lindgren, Robb and Schwartz, Daniel L.(2009) 'Spatial Learning and Computer Simulations in Science',International Journal of Science Education, 31: 3, 419 438To link to this Article: DOI: 10.1080/09500690802595813URL:http://dx.doi.org/10.1080/09500690802595813

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International Journal of Science EducationVol. 31, No. 3, 1 February 2009, pp. 419438

ISSN 0950-0693 (print)/ISSN 1464-5289 (online)/09/03041920 2009 Taylor & FrancisDOI: 10.1080/09500690802595813

RESEARCH REPORT

Spatial Learning and ComputerSimulations in Science

Robb Lindgren * and Daniel L. SchwartzStanford University, California, USATaylorandFrancis Ltd TSED_A_359749.sgm10.1080/09500690802595813InternationalJournalof ScienceEducation0950-0693 (print)/1464-5289 (online)ResearchArticle2008Taylor&Francis3130000002008Mr.RobbLindgre [email protected]

Interactive simulations are entering mainstream science education. Their effects on cognition andlearning are often framed by the legacy of information processing, which emphasized amodal prob-lem solving and conceptual organization. In contrast, this paper reviews simulations from thevantage of research on perception and spatial learning, because most simulations take a spatialformat and the pedagogical intent is to promote learning. Four learning effects help clarify thepositive and negative aspects of current simulation designs: picture superiority, noticing, structur-ing, and tuning.

Keywords: K-12; Spatial learning; Perceptual learning; Computer simulations;Undergraduate

Interactive simulations are a powerful tool for scientific thinking. They are dynamic;they can be highly interactive; they can scaffold inquiry; they can provide multiplerepresentations; and they can be readily disseminated and incorporated into bothindustry and classroom settings. Simulations are a growing part of the scientificenterprise. A worthwhile goal for science education is to develop simulation pedago-gies that maximize student learning. Thus far, simulation research in science educa-

tion has been informed largely by the information processing literature, and morerecently the socio-cultural literature. A third relevant field of work comes fromresearch on spatial learning. One purpose of this paper is to introduce four reliableeffects from spatial learning research. A second purpose is to introduce readers tothe variety of current science education simulations and to use the learning effects toilluminate the strengths and missed opportunities in their designs.

*Corresponding author. Stanford University, Building 160, 450 Serra Mall, Stanford, CA 94305,

USA. Email: [email protected]


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420 R. Lindgren and D. L. Schwartz

From Amodal Problem Solving to Spatial Learning

A well-leveraged literature in science education comes from the informationprocessing tradition of cognitive science. Information processing grew from informa-tion theory (Shannon, 1948), which proposed that communication between orwithin systems can be described as the manipulation of information. The amount of information being communicated can be quantified, which made it possible totheorize much more precisely about cognitive processes. Information processing, asoriginally developed, is not an ideal match for examining the spatial aspect of simu-lations and their effects on learning. One mismatch comes from its primary emphasison problem solving (e.g., Newell & Simon, 1972), and the constructs that supportproblem solving such as working memory, mental models, search, and metacogni-tion. A good deal of science education research examines problem solving on theassumption that learning is a likely side effect of successful problem solving, which it

can be. Nevertheless, a stronger focus on learning would have led to a more refinedvocabulary for describing the mechanisms and contexts by which knowledgechanges. For example, an emphasis on learning would have entailed includingmotivation and reward in the information processing lexicon, but this has not beenthe case.

A second mismatch is that the turn to information as an abstract unit of measure-ment came with a concomitant argument that cognition is amodal (cf. Goldstone& Barsalou, 1998). Amodal means that with respect to the description of cognition,it does not matter whether people experience words, images, or feelings. All thoughtshould be described in terms of information units, which are independent of aspecific internal or external communicative medium. For example, it does notmatter whether an internet service comes by cable, phone, or wireless; the informa-tion is the same. The turn to amodal representations meant less attention was dedi-cated to determining what made spatial cognition unique. As a result, there is less of a theoretical vocabulary for analyzing the properties and learning that may comefrom spatial simulations as opposed, for example, to solving a series of word prob-lems. For instance, in a seminal paper by Larkin and Simon (1987) the benefits of spatial diagrams were described as effectively indexing branch points in problemsolving; there was no mention of the affordances of visual displays for detecting

holistic form (e.g., Wertheimer, 1938).In the shadow of information processing, however, there were several lines of

research that took perceptual-motor phenomena and learning as primary includingGestalt psychology, perceptual learning, and spatial cognition. This work yieldedimportant vocabulary and empirical findings for describing learning with spatialphenomena. Schwartz and Heiser (2006) provide a review on the internal (mental)processes relevant to spatial thinking and learning. Here, we recast these in ways thatmake them more relevant to the design of external interactive environments that cansupport spatial learning. We focus on four effects:

(1) The picture superiority effect . People have an impressive memory for visual infor-mation and spatial structure. Given peoples constant need for navigation and


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Spatial Learning and Computer Simulations 421

tool manipulation, it should not be surprising that we have evolved a specialfacility for remembering space. Integrating visual components into the presenta-tion of conceptual knowledge can have benefits for learning.

(2) The noticing effect . A characteristic of perceptual learning is the increasing ability

to perceive more in a given situation. Experts can notice important subtletiesthat novices simply do not see. This literature helps explain how people cancome to perceive what they previously could not, and how the ability to noticeoften corresponds to competence in a domain.

(3) The structuring effect . Perception differs from sensation, because perception isstructured experience. Sensory input is continually changing based on factorssuch as distance or angle of observation, and yet people will perceive stabilityand constant form. Perception can help extract structure that is difficult touncover in a verbal presentation.

(4) The tuning effect . Perception is dedicated to action, and therefore, it is tightlycoupled with the motor system. Recalibration tunes perceptual expectations andmotor activity. For example, when first learning to use a new computer, peopleoften find the mouse moves too quickly or too slowly, but over time, they adjust.Recalibration learning is typically automatic, and it has relevance for immersivesimulations that involve mapping motor activity to visual changes.

Current Research on Pedagogical Simulations

Practicality alone may be sufficient for simulations to pervade science education.

Simulations typically save money over physical experiments; they permit access toactivities that would otherwise depend on specialized equipment or travel; and theycan seamlessly collect data on student performance, with the prospect of real-timefeedback and direction (Hickey, Kindfeld, Horwitz, & Christie, 2003; Shavelson,Baxter, & Pine, 1992).

Simulations are major scientific and industrial tools. Simulations permit profes-sionals to ask what if questions and model phenomena that are difficult todescribe in closed form. They are also becoming available to the public; for example,people frequently run financial simulations using software from their investment

companies. These types of simulations have been specifically designed to helppeople answer questions in domains they know relatively well. They are not neces-sarily designed for novices who are trying to learn about a domain in the first place.Pedagogical simulations may require additional features to support learning.

One concern is that instructors will simply show students which simulationparameters to set and ask the students to record the answers, or they may use thesimulation as a demonstration experiment at the front of the class. These practicesundermine the potential of simulations for supporting authentic inquiry practicesthat include formulating questions, hypothesis development, data collection, andtheory revision (de Jong & van Joolingen, 1998; Edelson, Gordin, & Pea, 1999;Hennessy et al. , 2007; Singer, Marx, Krajcik, & Chambers, 2000; Windschitl,2000). Support for the use of simulations to promote inquiry comes from teacher


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reports of students increased adherence to standard inquiry procedures (Hennesseyet al. , 2007), an increase in student reflections on their own learning (Soderberg &Price, 2003), and improved content knowledge (Huppert, Yaakobi, & Lazarowitz,1998). Windschitl and Andre (1998), for example, had two groups of students use

the same cardiovascular simulation. Students who were directed to follow proce-dures, per a demonstration experiment, learned less than students who used thesimulation to develop and test hypotheses per an inquiry experiment. More gener-ally, studies have found that the inclusion of optional and just-in-time supports inthe context of inquiry have a positive effect on learning relative to more oppressivesupports or directives (Hulshof & de Jong, 2006).

Most studies of simulations have considered how to facilitate verbal problemsolving, which is appropriate given an emphasis on inquiry. For example, the simula-tion described in Fund (2007) takes a set of problems from a science textbook andposes them to students in the context of a virtual science laboratory. The focus oninquiry-driven problem solving is important. However, there are other aspects of simulations and learning that are also beneficial, which we discuss further.

Method for Selecting Simulations

Spatial simulations are highly versatile, so it is not surprising that authors focus ondifferent active ingredients. Some have stressed the importance of simulations forsimplifying and communicating abstractions (Baudrillard, 1983), while othersemphasize the phenomenological aspects of simulations, such as Gredler (1994) who

defines simulations as experiential exercises. People have also made distinctionsamong different types of simulations. Winn et al. (2006), for example, distinguishbetween model-based simulations and physical simulations such as commercialflight simulators. In our review of simulations, we will be agnostic with respect tothese and other distinctions. Instead, our approach will be to look at the properties of representative simulations from a perceptual learning perspective.

We reviewed simulations described or referenced in peer-reviewed journal articlespublished in the last decade. Using the ERIC and PsychInfo databases, we searchedfor articles using the keywords simulation and science education. In all, weidentified more than a hundred simulation packages. Whenever possible we down-loaded the simulation software so that we could assess its affordances for spatialreasoning and learning. In what follows, we describe the perceptual learning effectsin more detail, and we use them to analyze a representative subset of the simulationswe found in our search.

The Picture Superiority Effect

Memory for Spatial Material

Spatial information and visual displays have a unique relation to memory. This hasbeen known at least since the time of the Greek method of loci , which was based onthe discovery that people could remember speeches better by tying specific


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components of the speech to statues set systematically around a theater. Shepard(1967) conducted one of the first studies of visual memory. Participants saw approx-imately 600 colored photographs. Immediately afterwards, they completed a forcedchoice task in which they had to judge which of two pictures they had seen before.

Accuracy for the pictures was 98%, which compared favorably to 90% for singlewords and 88% for sentences.

This so-called picture superiority effect is even more potent for striking or vividpictures. Standing (1973) found that subjects presented with 1,000 pictures at 5seconds each showed a subsequent recognition of 91%, but their memory for vividpictures was 95% (e.g., a picture of a dog with a cigar in its mouth compared to apicture of a dog). Media studies indicate peoples memory for vivid images is posi-tively correlated both with physiological measures and subjective ratings of arousal(Bradley, Greenwald, Petry, & Lang, 1992).

To explain the picture superiority effect, Paivio (1971) proposed the dual coding hypothesis: people encode both a picture and its verbal interpretation. Thus, peoplecreate two retrieval paths, which increase the chances of remembering. Moreover,visual and verbal processes often enhance one another through elaborative process-ing. For example, a persons memory for verbal information is enhanced if it is asso-ciated with a relevant image. Reciprocally, a drawing with an enlightening caption ismore likely to be remembered and accurately re-created than the drawing alone(Anderson & Bower, 1973). The permeability and mutual reinforcement of visualand verbal processes is important for science education. Simply remembering strik-ing visual information runs the risk of excellent memory for irrelevant surface

features and little understanding. Verbal processes can help make sense of visualimages in useful ways. At the same time, visual presentation supports subsequentmemory, so students can reconstruct their original understanding.

Memory Effects of Images in Simulation Environments

Many of the science education simulations that we found used symbolic spatialconventions. The prototypical configuration is an interface comprising numericparameters and a line graph. A simulation of disease transmission (Figure 1a) allows

the user to specify four disease parameters (e.g., rate of transmission). Given theparameters, the simulation plots the number of organisms infected by the diseaseover time. Simple spatial representations are a powerful means for communicatingcomplicated structural relations. Nevertheless, it would seem that some of the screenreal estate could include richer images that would enhance retention and under-standing without distraction. One approach is the selective presentation of actualphotographs, animations, or video clips of scientific phenomena and practice thatcan help students ground abstract uses of space.Figure1. (a)A simulationofdiseasetransmission(BiologicalSciences CurriculumStudy,1999).(b)LiveChem(Adcock,2005):Students choosechemicals,andthesimulationshows videoclips ofthephysicalreactions.(c)SimQuest(Universityof Twente,2008):The linegraphandcarboth simulatevelocitygivenconstantacceleration.(d)PhETsimulationofprojectilemotion(Dubson,2008):Theinclusionof incongruous images shouldenhancememory

One example comes from the LiveChem simulation (Figure 1b), which is part of the Virtual Chemistry suite developed at the University of Oxford. Students click anddrag a salt from the top menu to the central staging area, and then choose a reagentfrom the menu at the bottom. Students click play movie to view the effects of this


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reaction (e.g., color change, bubbling, etc.). These video snippets provide groundingwhile avoiding the minutiae involved in an extended chemistry experiment, so thestudent can rapidly examine different reactions.

A similar approach is to yoke graphs with an unfolding visual event. SimQuest (Figure 1c) combines a dynamic graph with the phenomenon being simulated (van

der Meij & de Jong, 2006). Thus, in addition to a fairly rich resource of spatialinformation, the simulation is highly suggestive of familiar situations. The resultshould be a strong verbal and spatial memory (cf. Richards, Barowy, & Levin,1992). Further advances in graphics and video technologies will make it increasinglypossible to render realistic visualizations of simulated phenomena. The vividness of the images should also enhance memory.

The literature on bizarre imagery may also be useful to consider for improvingmemory (Collyer, Jonides, & Bevan, 1972). Images that are incongruous or do notmake initial sense are well-remembered if they can eventually be interpreted . This isdue to a combination of the distinctiveness of the image (McDaniel & Einstein,1986), and the elaborative processing of interpretation. For example, there arenumerous simulations of projectile motion. Most of these simulations allow the

Figure 1. (a) A simulation of disease transmission (Biological Sciences Curriculum Study,1999). (b) LiveChem (Adcock, 2005): Students choose chemicals, and the simulation shows

video clips of the physical reactions. (c) SimQuest (University of Twente, 2008): The line graph

and car both simulate velocity given constant acceleration. (d) PhET simulation of projectilemotion (Dubson, 2008): The inclusion of incongruous images should enhance memory


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learner to specify a few parameters (e.g., initial velocity), and then observe theoutcome typically via a moving dot that leaves a trail. Figure 1d shows a simulationdeveloped by the PhET group at the University of Colorado at Boulder that has thesame basic ingredients of setting parameters and illustrating a trajectory (Wieman &

Perkins, 2006). However, the PhET simulation uses a more memorable visualimplementation. Users can choose from a pumpkin, a piano, and even a person toblast out of simulated cannon. Each object has default parameters (e.g., mass), butthese quantities can be changed by a curious learner. The simulation also has animplicit goal of hitting a target. While these relatively minor visual elements mayseem trivial, they are likely to provoke an elaborative effort (e.g., would a piano anda baseball shot out of the cannon at the same velocity really travel the samedistance?), which leads to better memory of the simulated events.

Gratuitous imagery is not the best way to improve learning. Visual components of simulations need to be designed so that students remember the right things, andcogent guidelines for how to incorporate pictures into science education materialsexist (e.g., Reid, 1990). Our point here is simply that science education simulationscould be enhanced by selecting visual elements that improve memory and evokeelaborative processing. Ideal visual elements are vivid or distinctive, provide ground-ing, and invite interpretive effort.

The Noticing Effect

Differentiation of Perception

Learning is often characterized as the development of abstract knowledge that permitsinferences that go beyond the information given (Bruner, 1957). By this account,learning moves one progressively further from the world through abstraction (Gibson& Gibson, 1955). However, for those who study perceptual learning, the consequenceof learning is that one gets closer to the world, not further. For example, a wineconnoisseur can perceive flavors that a novice cannot, and an expert teacher can noticesubtle differences in student responses that a novice might simply characterize aswrong (Marton & Booth, 1997). Appropriate experience enables people to extractmore information from the stimulus array. In their seminal paper on perceptual differ-entiation, Gibson and Gibson (1955) describe a study in which participants werepresented with nonsense scribbles. People had to identify which scribbles were thesame as a target scribble. Despite a lack of feedback, people improved over time. Theirdescriptions of the squiggles became more attuned to a set of dimensions thatdifferentiated the squiggles (e.g., degree of horizontal compression). The authorsconcluded:

There is a great quantity of evidence about progressive change in acuity, variability, andaccuracy of perception, including both relative judgments and absolute judgments. Itproves beyond a shadow of doubt that the notion of fixed thresholds for a certain set of innate sensory dimensions is oversimplified. Discrimination gets better with practice,both with and without knowledge of results. (p. 39)


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Perceptual learning has been documented in many domains. For example, radiolo-gists ability to detect abnormal signs in complex X-rays is correlated with thenumber of years of experience in radiology (Myles-Worsley, Johnston, & Simmons,1988). Appropriate instruction can support perceptual learning. Goodwin (1994)

describes the ways that archeology experts will articulate and reinforce the importantperceptual distinctions as a way of establishing professional vision (e.g., the color,texture, and consistency of dirt at an excavation site). Biederman and Shiffrar(1987) were able to bring novices to near expert levels at determining the sex of chicks by using carefully selected cases that highlighted distinctive features.

The benefits of perceptual learning do not stop with perception. The ability to seeimportant distinctions prepares students to understand conceptual treatments thatdepend on and explain these differences. For example, many beginning studentsgloss the mean, mode, and median as the average, which means they are unpre-pared to learn about non-normal distributions. Discrimination activities can preparestudents to better learn from relevant expository instruction (Schwartz & Bransford,1998).

To promote perceptual learning, people need exposure to appropriate variability.Often times, instruction strips out all the variability so that students can focus on anabstraction (e.g., a formula). Variability, however, is essential for learning to noticewhat is important and what is not important. Posner and Keele (1968) found thatgreater variability led to slower learning in the short term, but resulted in a morepotent learning that could be applied to a set of transfer problems. This suggests thatgiving learners more time and breadth for exploring a variable problem space can

lead to more adaptive behaviors, compared to, for example, applying a told proce-dure to a set of similar word problems. To create optimal variability for perceptuallearning, Bransford, Franks, Vye, and Sherwood (1989) suggest the use of carefullyselected contrasting cases . Contrasting cases are similar instances that vary on one ortwo dimensions, like tasting two wines one after the other. People notice the featuresthat differentiate the cases.

Perceptual Differentiation in Simulation Environments

The controlled and replicable nature of simulations makes them ideal for deliveringoptimal variability for perceptual learning. Simulations, for example, can presentside-by-side contrasts. Figure 2a shows a screen shot from Crocodile Physics , partof the Crocodile Clips software suite. Basketballs, dropped from identical heights, arebouncing on three different surfaces: the Earth, the Moon, and Mars. The simula-tion displays the weight of the ball in each location, and the student has the opportu-nity to observe how high and for how long the ball bounces. Students could bouncea ball in their classrooms and be told how it would behave differently on the moon,but this would likely lead to declarative knowledge, and students may never learn tonotice the contrasts that a full understanding of gravity explains.Figure2. (a)A CrocodilePhysics simulationofa ballbouncinginthreedifferentgravitationalenvironments (CrocodileClips,2008):Theside-by-sidecontrastshouldaidperceptualdifferentiation.(b)RockSim(ApogeeComponents,2008):A designenvironmentformodelrockets thatallows theuserto simulatea launchundervarious conditions.(c)StarLogoTNG (MITSchellerTeacherEducationProgram,2006):Codeassembledon theleftdictates thebehavioroftheinteractive3D environmentontheright

The simulation examples offered so far have been experimentation simulations,which are ideal for juxtaposing contrasting cases and emphasizing the empirical side


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of science activities. Another class of relevant simulations emphasizes the modelbuilding activities of science. With modeling simulations, students design andimplement models of empirical phenomena. Modeling simulations often providegeneric drag-and-drop components and a basic physics engine that implements

constraints on how things will behave. An example of a modeling simulation fordesigning model rockets is shown in Figure 2b. The power of these design environ-ments for creating perceptually realistic simulations will continue to grow as 3Dgraphics engines and virtual reality development tools become more accessible. Forexample, the visual programming environment StarLogo TNG allows users tospecify and tinker with character behavior in a virtual world (Figure 2c).

A key feature of both experimentation and modeling simulations is the iterativeprocess of configuring and testing. This creates two potential contrasts for learning.One contrast is the difference between expected and observed, which helps studentsalign their mental model with the perceptual phenomena (Monaghan & Clement,1999). The second contrast, more consonant with perceptual learning, is the differ-ence between two runs of the simulation.

Figure 2. (a) A Crocodile Physics simulation of a ball bouncing in three different gravitationalenvironments (Crocodile Clips, 2008): The side-by-side contrast should aid perceptual

differentiation. (b) RockSim (Apogee Components, 2008): A design environment for modelrockets that allows the user to simulate a launch under various conditions. (c) StarLogo TNG

(MIT Scheller Teacher Education Program, 2006): Code assembled on the left dictates thebehavior of the interactive 3D environment on the right


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An important direction for research is determining how to design environmentsthat help students engage in inquiry that creates optimal variability. Children do notnaturally vary only one thing at a time to maximize the contrast value (Kuhn, 1989).Left to their own devices, they may create so much variability that they cannot

extract perceptual regularities. At the same time, dictating what contrasts to makeundermines inquiry. The noticing effect is particularly useful in the context of assessment, because it provides convenient ways to measure student learning from asimulation. One simple technique is to show students a visual display and ask themto write down what they notice. For example, if a geology expert and a novice see aphotograph of a landslide, the expert will be much more likely to notice the featuresthat contributed to the landslide. The same is true for all domains. Even abstractvisual presentations like a line graph can be useful as an assessment in this context do students notice the slope, an inflection point, the intercept, and so forth? Askingstudents what they notice in a simulation is an illuminating assessment.

The Structuring Effect

Appreciation of Structure

If you rotate a piece of office paper in your hands, you will continue to see it as arectangle, even though the images that are hitting your retina comprise an evershifting trapezoid. Human perception extracts form from the buzzing confusion of sensory data. For humans it is effortless, whereas computers still have difficulty

segmenting pixels into objects and sound waves into words. Evolution hasafforded human perception the ability to make rapid and cumulative assessmentsof structure.

Gestalt Psychologists demonstrated that the perception of whole configurationshas structural characteristics that are not present when viewing the individual parts(Wertheimer, 1938). Leveraging the structuring abilities of perception is a powerfulway to help students see the forest and not just the trees. Scientific visualization toolshelp scientists see patterns that might be overlooked in a stream of numbers. Moregenerally, converting concepts to space has been an important scientific tool for thediscovery of structure. Kekul famously reported that it was visions of molecules andatoms dancing in the air that led him to formulate the structure of the benzene ring(Rocke, 1985). In his studies of expert scientists, Clement (1994) describes how newdiscoveries and inferences are often made using imagistic simulations, rather thanrelying on declarative principles or equations (cf. Finke, 1990).

Several researchers in science education and elsewhere have prescribed the use of visualizations to promote learning difficult concepts (e.g., Clark & Jorde, 2004; Wilder& Brinkerhoff, 2007). Visualizations may be especially useful for helping students seestructure in phenomena and processes that are traditionally invisible to students. Aprocess can be invisible if it is too small (bacterial reproduction), too big (tectonic

shifting), too fast (chemical reactions), or too slow (evolution). Visualizations canmake these processes accessible so learners can perceive the important structures.


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Appreciation of Structure in Simulation Environments

We consider uses of simulations for supporting the appreciation of three typesof structure: invisible, temporal, and thought. Science education simulations canilluminate the invisible deep structure beneath surface changes. Simulations of molecules, for example, can explain the surface expansion of a balloon. In somecases simulations purify phenomena, so it is easier to see the deep structure with-out the noise of incidental surface variation. Figure 3a shows a refraction simulationthat uses a simple visual representation to show the path of a single ray of light as ittravels from one medium to another, such as air to water. Students vary the angle of incidence to perceive the non-linear relationship between angle of incidence andangle of refraction. More generally, simulations may help students develop an intui-tive spatial understanding of many science phenomena, such as quantum mechanics,that otherwise depend on advanced mathematics for conveying structure (McKagan

et al. , 2008).Figure3. (a)Components ofa Physics JavaAppletsimulatingtherefractionoflight(Fendt,2008).(b)A ConnectedChemistrysimulationshowingthe relationshipoftemperatureandpressureonmolecularactivity(Wilensky,2005).(c)Avieda-Ed(Pennock,2007):A simulationofmicroorganismgrowth.(d)Bettys Brain(TeachableAgents GroupatVanderbiltUniversity,2005):A simulationofan agents thinkingaboutenvironmentalfactorsTemporal structure is another important place for spatial representation and simu-lation. Simple examples involve spatial displacement over time, as in the case of

Figure 3. (a) Components of a Physics Java Applet simulating the refraction of light (Fendt,2008). (b) A Connected Chemistry simulation showing the relationship of temperature and

pressure on molecular activity (Wilensky, 2005). (c) Avieda-Ed (Pennock, 2007): A simulation of microorganism growth. (d) Bettys Brain (Teachable Agents Group at Vanderbilt University,

2005): A simulation of an agents thinking about environmental factors


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projectile motion. More complex examples occur when changes do not involvespatial displacement, and it is important to visualize the underlying invariant struc-ture. Echevarria (2003) presented students with a genetics simulation that wasdesigned to confront the students with anomalous outcomes of genetic inheritance in

fruit flies. By observing the patterns over time, students learned more about thestructure of trait inheritance and could explain the anomalies. Even more complexexamples involve emergent structure, where there appears to be a qualitative changein the structure of the system. Agent-based simulations use elements with local inter-action rules that combine to make higher-order structure over time. The Connected Chemistry suite (Figure 3b) promotes understanding of chemical interactions bydemonstrating the effects of changes in an environments parameters (e.g., concen-tration of a particular element) (Stieff & Wilensky, 2003). This is in contrast tosimply giving students the formulas that define the structural relationships. Figure 3cis a similar simulation that shows the process of bacterial reproduction. Thesecomplex system simulations all typically include an area for inputting and adjust-ing parameters and an area for displaying a visual representation of the system overtime. The graphic display permits students to make qualitative assessments of thesystem state (e.g., reaction equilibrium, over-population, etc.).

Not only is it important for students to see structure in phenomena, but it is alsoimportant for them to appreciate the structure in scientific thought about thosephenomena. Teachable Agents simulate how one might reason about a domain bymaking thinking visible (Schwartz, Blair, Biswas, Leelawong, & Davis, 2007).Students teach a computer agent, such as Bettys Brain (Figure 3d), by using

predefined spatial forms to input and organize important causal relations (e.g., animalsexhale carbon dioxide). Using simple artificial intelligence techniques, the agent cananimate its path of reasoning when asked inference questions (e.g., if the number of factories increases, what happens to the temperature of the earth?). This helps studentsdevelop better abilities to reason through the causal chains that are ubiquitous inscience.

The Tuning Effect

Tuning PerceptionAction Links

The power of the human perceptual system lies in its ability to entertain and facilitatepossible actions. Perception and action are so tightly linked that action influencesperception. People are quicker to perceive a revealed object if their hand waspositioned at the right orientation for grasping (Craighero, Fadiga, Rizzolatti, &Umilta, 1999). People are also better at predicting the destination of an objectmoving behind an occluder if they had previously controlled the objects movement(Wexler & Klam, 2001). Perception helps guide action, and action helps shape thepredictions of perception.

Perceptionaction links are important not only for performance, but also forunderstanding. The literature on embodied cognition, in direct response to amodal


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brands of information processing, argues that all thought is an evolved simulation of perceptual-motor activity (Barsalou, 1999). Empirical support for embodied cogni-tion comes from Glenberg, Gutierrez, Levin, Japuntich, and Kaschak (2004) whohad second graders read a short passage describing a scenario (e.g., feeding animals

on a farm). Children scored higher on measures of comprehension for the givenpassage if they had an opportunity to manipulate figurines described in the passage.Further, when the children were instructed to simply imagine manipulating thefigures, they were better at comprehending new passages.

A good deal of everyday perceptual learning involves tuning perception and actionthrough a process called recalibration (Redding & Wallace, 2006). This happensautomatically, for example, when learning to drive a new car where applying pres-sure on the gas or brake pedals has different effects. In one study, participantscompleted a driving simulation with the goal of stopping at a specified target (Fajen,2005). After numerous trials, the braking strength was changed, but it took just afew trials for participants to recalibrate and use the brake effectively. Prism adapta-tion studies show recalibration to the extreme. Kohler (1964), for example, woreprism glasses that made the world appear upside down. At first it was extremelydifficult for him to interact with the world, but over a period of several days theworld eventually flipped upright as his motor and visual system recalibrated.

The human capacity for quick recalibration suggests the utility of supporting spatialinteractions that engage the perceptual-motor system. The driving simulator studyabove and other studies of recalibration in virtual environments (e.g., Richardson &Waller, 2007) indicate that even mainstream 3D software run on a desktop computer

are capable of achieving this effect. The impact of using virtual worlds for tuningperceptionaction links has not yet been fully explored, but the relevance of recalibra-tion to learning seems to be a ripe area for investigation, especially in light of rapidlyadvancing media technologies.

Tuning PerceptionAction Links in Simulation Environments

The most obvious simulations relevant to tuning are embodied, immersive learningenvironments. Although we do not know of academic research, it seems obvious that

people like embodied simulationswitness the enthusiastic reception of theNintendo Wii. Input devices in embodied simulations can have a direct mapping tothe real-world input devices, as in the use of a brake pedal. However, training simu-lations do not need to copy the input devices from the real world, unless the goal isimmediate high proficiency at the real task. Rather, most pedagogical simulationssimply need to map the structural correspondences between input and output. Acase in point is the dissection simulation BioLab Frog (Figure 4a). It includes a real-istic rendering of a frogs anatomy and the ability to remove and classify organs.Although students used a mouse, the virtual activity of dissecting the frog transferredto improve physical dissection of an actual frog (Akpan & Andre, 1999).Figure4. (a)BiolabFrog(Doltar,2002):A dissectionsimulationshowingtherealisticconfigurationofafrogs internalorgans.(b)MolecularRover(ConcordConsortium,2008):A simulationfornavigatingthroughconfigurations ofmolecules in3D

One reason for considering motor activity in simulation design is that people oftenrecruit the motor system to make complex physical inferences. Schwartz and Black


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(1996), for instance, demonstrated that people fallback to explicit motor simula-tions to work out gear problems (e.g., moving their hands as if they were gears),when they cannot solve the problems verbally. One goal of simulation design can beto help students tune motor movements to perception, so they can recruit the motorsystem to help develop intuitive inferences.

In the design of simulations that rely on tuning effects, the key issue is motorrealism not visual realism. After all, many simulations represent invisible processes.The key question is: Do the motor activities map into spatial changes in the simula-tion such that people could recruit those same motor patterns later to help withinferences? If people complete a simulation by taking discrete button presses, thenthe motor system is unlikely to get tuned to spatial changes in their environment. Inthis scenario, the tuning involves learning a fundamentally new relation rather thanrecalibrating an old one. It takes a long time before people can simulate playing apiano to help imagine what a song will sound like. However, if the motor action

takes a continuous form with continuous spatial consequences to the simulation,then there should be better prospect of connecting motor activity to help futuremental simulations.

The Molecular Rover simulation (Figure 4b) is a nice instance of mapping themotor system into a science concept. Students use a mouse to navigate throughcomplex molecular configurations, such as those that comprise greenhouse gases orhemoglobin. Students can view these molecules from different angles, observe inter-actions between molecules, and even apply forces to test the strength of molecularbonds. A good study would compare mouse navigation in this environment withtextual commands for making changes to perspective. By hypothesis, the mouseversion would help students subsequently conduct mental simulations of moleculesmore effectively.

Figure 4. (a) Biolab Frog (Doltar, 2002): A dissection simulation showing the realisticconfiguration of a frogs internal organs. (b) Molecular Rover (Concord Consortium, 2008): A

simulation for navigating through configurations of molecules in 3D


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As technologies for creating virtual objects and virtual environments become moreaccessible and easier to use, there is the additional potential for students to learnthrough creating their own three-dimensional models of systems in science. Someresearchers have touted the educational benefits of having students use VRML and

other platforms for creating physically accurate three-dimensional representations(Barnett, Yamagata-Lynch, Keating, Barab, & Hay, 2005). Just like those who haveadvocated for the building of hands-on physical models to increase understanding of the constraints and complexities of spatial processes (e.g., Erdogan, 2006), thebuilding of virtual models has been said to have similar affordances for learning. Theunderlying assumption in these claims is that students can recalibrate quickly to anew virtual space. The knowledge that people are capable of constructing and tuningperceptionaction links opens up numerous possibilities for learning in simulatedenvironments. Much like researchers in the area of communication have demon-strated that people treat their computers like real people (Reeves & Nass, 1996), thehope is that students will treat virtual phenomena enough like real physical objectsand events that they facilitate powerful and authentic learning experiences.

Conclusions

We have examined each of the four spatial learning effects in turn and offeredexamples of simulations that potentially support each effect. These have includedsimulations of experimentation, modeling activity, navigation, and manipulation.While we have separated out the effects for exposition, they do not operate in isola-

tion. The structure effect, for example, is complemented by the picture superiorityeffect by making instances of important structures more likely to be rememberedthan if the structures had been described verbally. As another example, the noticingeffect supports tuning by allowing the motor system to respond to more and morefine-grained perceptual distinctions. That said, it may not be possible for a singlesimulation module to support all four effects simultaneously. For example, a visuallyimmersive environment that promotes new perceptionaction links may not be veryeffective at leveraging memory effects because the user will be exposed to a steadystream of vivid images that lack distinctiveness. The design of simulations should try

to maximize these four effects while remaining conscious of their trade-offs andinteractions.The design of a simulation should be explicit about the types of learning that it

hopes to elicit. Improved problem solving is one type of learning, but as we haveargued, there are others. For example, effective memory of spatial structure and theability to notice the relevant information is a prerequisite to problem solving. Oneway to help be explicit about desired learning outcomes in simulation design is tothink in terms of assessment. While most current assessments use problem solving,the four spatial learning effects discussed here provide natural ways to measurespecific types of learning that do not exclusively rely on verbal problem solving.

For the picture superiority effect, one simple assessment is to show a picture thatstudents have seen alongside a slightly varied picture that includes impossible


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states. The students task is to decide which one they saw. For the structure effect, itis useful to ask students to redraw what they saw. The drawing will be readilyanalyzable for its inclusion of important structural relations (e.g., ratios) versus itsinclusion of uninformative surface features (e.g., locations of buttons). For the

noticing effect, students can be given a new, but relevant photograph of a situation,for example from GIS data. If they are asked to annotate what they notice, it will beeasy to separate whether students notice the diagnostic features versus incidentalfeatures. For tuning, the simplest assessments are for training simulations. Have thestudents calibrated their motor behaviors so they can perform the task precisely? Formore conceptually directed simulations, the question is whether students havedeveloped facility with imagining transformations to the system. An appropriateassessment question would be, what would happen if you moved this object thisway? The most direct measure of tuning would be whether they use their hands tohelp model the system. More indirectly, one can look for evidence of mental imag-ery; for example, do they close their eyes or look off into space? Swaak and de Jong(2001) had students make speeded judgments of which outcomes of a ball collisionis correct on the assumption that this tapped their intuitive simulations of thesystem.

In conclusion, most pedagogical simulations are designed to help students learn sothey can subsequently operate in non-simulation environments. One approach is tomake the simulation environment as similar to the non-simulation environment aspossible. However, doing so would undermine many of the affordances of simula-tions for pedagogy; for example, well-chosen images, contrasting cases, and affor-

dances for detecting structure. An alternative is to view simulations as preparationfor future learning (Bransford & Schwartz, 1999). Simulations prepare studentsto learn and adapt more effectively when the students eventually reach the non-simulation context. Simulations that explicitly capitalize on peoples innate spatiallearning mechanisms are a powerful way to help them get started.

Acknowledgments

This material is based upon work supported by the National Science Foundation

under grant SLC-0354453 and the Department of Education under grant IESR305H060089. Any opinions, findings, and conclusions or recommendationsexpressed in this material are those of the authors and do not necessarily reflect theviews of the granting agencies.

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