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VISUAL REPRESENTATIONS AND LEARNING:

THE ROLE OF STATIC AND ANIMATED GRAPHICS

Gary J. AnglinUniversity of Kentucky

Hossein VaezEastern Kentucky University

Kathryn L. CunninghamUniversity of Kentucky

With the proliferation of illustrations in instructional materials,it becomes increasingly important to investigate their effectson student learning. The use of illustrations in instructional ma-terials has been pervasive for a considerable amount of time(Feaver, 1977; Slythe, 1970). A substantial research literaturehas already accumulated concerning the role of illustrations ininstructional materials. The purpose of this chapter is to intro-duce researchers in instructional technology and others to theprimary theories of picture perception and to provide a surveyand critique of the visual representation research that incorpo-rates static animated illustrations.

33.1 SCOPE

The effective use of illustrations (pictures, charts, graphs, anddiagrams) in instructional materials is an important facet of in-structional message design. Fleming (1993) defines a message as“a pattern of signs (words, pictures, gestures) produced for thepurpose of modifying the psychomotor, cognitive, or affectivebehavior of one or more persons” (p. x). We define picturesas illustrations that have some resemblance to the entity thatthey stand for, whereas nonrepresentational graphics including

charts, graphs, and diagrams are more abstract but do use spatiallayout in a consequential way (Knowlton, 1966; Levie & Dickie,1973; Rieber, 1994; Winn, 1987). Levie (1987) has suggestedthat there are at least four lines of research on illustrations:(a) picture perception, (b) memory for pictures, (c) learningand cognition, and (d) affective responses to pictures. In thischapter we first present several theories of picture perception.We then present a brief discussion of selected memory mod-els that have been used to describe how words and picturesare encoded and two related topics, cognitive load theory andmultiple representations in multimedia. Next, knowledge ac-quisition studies incorporating static and animated pictures arereviewed. Finally, we critically analyze the literature and offersuggestions for future research and practice based on resultsof primary research and all literature reviews discussed in thechapter. Given the magnitude of the literature, our own exper-tise, and the economics of publishing, we reviewed only com-parative experimental research studies. Visual message designstudies completed using other research methods are certainlyreasonable and appropriate. There are many variables to con-sider when designing visual instructional messages. Our systemof classification represents only one perspective on the litera-ture. We reviewed a wide range of studies but we do not claimthat the review is exhaustive.

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866 • ANGLIN, VAEZ, CUNNINGHAM

33.2 PICTURE PERCEPTION

33.2.1 Theories of Picture Perception

When is a surface with marks on it a “picture”? How do picturescarry meaning? What kinds of meaning can pictures carry? Isthere a grammar of picturing? Is picture perception essentiallyinnate, or is it a skill that must be learned?

Questions such as these have provoked conjecture fromphilosophers, psychologists, art historians, semioticians, andcomputer scientists. It is a fascinating, disputatious literature:one with implications for researchers in educational communi-cation and technology—although widely neglected.

This section provides a concise introduction to the majorscientific theories of picture perception. To set the discussionof modern theories in historical context, we begin with a de-scription of the theory of linear perspective developed dur-ing the Italian Renaissance. Then two major conflicting theo-ries are introduced: James J. Gibson’s resemblance theory, inwhich meaning is based on the picture’s resemblance to the vi-sual environment, and E. H. Gombrich’s constructivist theory,in which meaning is based upon pictorial conventions. Nexta compromise position by Margaret Hagen is described. Thena third major theory is presented: Rudolph Arnheim’s Gestaltapproach, followed by the views of Julian Hochberg, who is inopposition to Arnheim, and John M. Kennedy, who supportsArnheim.

Next the discussion shifts to two approaches from the fieldof semiotics: James Knowlton’s analysis of the iconic sign andNelson Goodman’s theory of symbol systems. Finally, someemerging approaches from cognitive science are noted, exem-plified by David Marr’s computational theory of vision.

Only the gist of each approach is presented, but sugges-tions for further reading are provided. Overviews to the areacan be found in several edited books containing chapters ona wide range of issues: Crozier and Chapman (1984), Hagen(1980b, 1980c), Mitchell (1980), Nodine and Fisher (1979),Olson (1974), and Perkins and Leondar (1977).

33.2.1.1 Renaissance Perspective Theory: Brunelleschi.The technique of linear perspective by which three-dimensionalscenes are represented on two-dimensional surfaces has its ori-gins in ancient Greek architecture and scene design. It was notuntil 1420, however, that a theoretical basis for the techniquewas elucidated by Filippo Brunelleschi of Florence. The tech-nique involves using the pattern of light rays emanating froma natural scene. The artist draws the composition that is pro-jected onto a picture plane—a cross section of the straight linesconnecting the artist’s viewpoint with the objects in the scene.Accordingly, our ability to understand pictures is due to the opti-cal equivalence between pictures and their real-world referents.Because the picture is an optical surrogate for the scene, pic-ture perception is thought to be straightforward and essentiallyautomatic.

But there are problems with this theory. According to thetheory a picture will be perceived accurately only when theperson viewing the picture assumes the point of observation

taken by the artist. Viewing the picture from a different positionshould result in distorted perception—an outcome that does notoccur in practice. For example, when we look at a portrait froman oblique angle we do not conclude that the person portrayedactually has an elongated head; we take notice of our orientationto the picture surface and judge shapes as though our viewpointwere perpendicular to the picture (although modest distortiondue to oblique viewing may occur; Goldstein, 1987).

Another problem is that successful pictures often violate per-spective theory. For example, artists rarely obey the rules of per-spective in the vertical dimension. When a tall building is seenfrom ground level, the rules of three-point perspective stipulatethat the sides of the building should be drawn as converginglines. Such drawings are usually judged to look unnatural. Onthe other hand, when artists violate perspective in the third di-mension the “error” is visually noticed only by those few whoare attuned to watch for it. Another violation is that artists oftenuse more than one station point. Often each major figure in apicture is drawn from a different station point, a fact that goesunnoticed by most viewers. On the other hand, pictures drawnfrom a single station point can look distorted if the station pointis very close to the subject. Yet another problem—and thereare several more—is that the shapes on the picture plane areambiguous, as they can be the result of the projections of morethan one three-dimensional object.

Thus the techniques of pictorial composition used in post-Renaissance Western culture often disobey the geometric rulesof perspective. In practice, pictures are very rarely the opti-cal equivalence of the sense they represent, and Renaissanceperspective theory cannot serve as an adequate explanation ofpicture perception.

Detailed treatments of the geometry of perspective are pro-vided by Hagen (1986) and Kubovy (1986). Other commentaryon this topic is given by Greene (1983), Haber (1979), Penrice(1980), and Pirenne (1970).

33.2.1.2 Resemblance Theory: James J. Gibson. The lawsof linear perspective were the starting point for Gibson’s re-semblance theory of picture perception (sometimes called “pro-jective theory” or the “direct perception” approach). Althoughmodified somewhat by his final position on the status of pic-tures (Gibson, 1979), Gibson’s (1971) best-known definition of“picture” is, “A picture is a surface so treated that a delimitedoptic array to a point of observation is made available that con-tains the same kind of information that is found in the ambientoptic arrays of an ordinary environment” (p. 31).

But what is this “kind of information” that is found in both thepicture and the environment? According to Gibson it is some-thing beyond the static lines and shapes in the picture; it is ahigher-order kind of information consisting of formless, timelessinvariants. The concept of an invariant is described by Gibson(1979):

When a young child sees the family cat at play, the front view, side view,rear view, top view, and so on are not seen, and what gets perceived isthe invariant cat. Hence, when the child first sees a picture of a cat heis prepared to pick up the invariants, and he pays no attention to thefrozen cartoon. It is not that he sees an abstract cat, or a conceptual



33. Visualization and Learning • 867

cat, or the common features of the class of cats; . . . what he gets is theinformation for the persistence of that peculiar, furry, mobile layout ofsurfaces. (p. 271)

These stable, enduring structures that are picked up fromthe environment are also present in the optic array provided bya picture and are used to interpret the picture. An example ofan invariant is the texture of surfaces such as sand or fur. Suchtextures are represented in photographs and act as optical gra-dients that guide judgments of distances (Gibson & Bridgeman,1987). Although it is not equally clear how we are able to per-ceive the invariant shapes of the objects in a picture (e.g., Whatdoes an “invariant cat” look like?), Gibson uses the concept toavoid some of the problems of perspective theory (e.g., Howcan we identify an object in a picture if it is depicted from apoint of view we have never seen?). Nevertheless, Gibson’s the-ory of pictorial representation is based primarily on the opticalcorrespondence of the picture and the environment, and it isthe structure of the stimulus that is the driving force in pictureperception.

For recent discussion of Gibson’s work see Cutting (1982,1987), Fodor and Pylyshyn (1981), Natsoulas (1983), Reed andJones (1982), Rogers and Costall (1983), and Wilcox and Ed-wards (1982).

33.2.1.3 Constructivism: E. H. Gombrich. Perception, asNeisser (1976) puts it, is where reality and cognition meet.Whereas Gibson assigns the major role in this meeting to re-ality, constructivists such as Gombrich emphasize the role ofcognition. Pictures do not “tell their own story,” Gombrich ar-gues, the viewer must construct a meaning.

Pictures will be interpreted differently depending on the atti-tude taken by the eye of the beholder. What we see, or think wesee, is filtered through a variety of mental sets and expectations.For example, briefly shown playing cards in which hearts arecolored black are sometime seen as purple (Bruner & Postman,1949).

One special class of expectations consists of the artistic con-ventions in common use. Gombrich (1969) traces the historyof Western art showing how cultural and technological changeshave altered the criteria for pictorial realism. What is judged tobe a “good likeness” is a function of the conventions and draw-ing techniques that now look “wrong” and amateurish to ourmodern eye.

A more pervasive example of a system of pictorial conven-tion in use today is the outline drawing. The use of lines torepresent the edges of objects is a substantial departure fromnature. The objects in the world are not bounded by lines, andit is due to convention that we perceive outline drawings asdepicting shapes rather than arrangements of wires. Whereasthe convention that shapes can be represented by outlines isa rapidly acquired understanding, the ability to interpret someconventions such as implied motion cues may require extensiveexperience or even direct instruction (Levie, 1978).

Such conventions are not arbitrary. Artists are not free toadopt any technique they choose. In fact, the history of nat-uralistic art can be thought of as a series of innovations inthe technique of approximating what is seen by viewing the

environment. But Gombrich argues that realism in art is morethan just an effort to record the optical data present in nature.The artists must produce an “illusion of reality” that matchesthe viewer’s concept (schema) of what a picture of a given kindshould look like. And how are these schemata acquired? Byrepeated exposure to the art of the day. These schemata thenfunction as the standards for judging reality in subsequent pic-ture viewing.

Such schemata can also affect our perceptions of nature. “Wenot only believe what we see: to some extent we see what we be-lieve” (Gregory, 1970, p. 86). Our experience with art may leadus to look at the natural environment in new ways. For example,the sensitive museum visitor may note that the pastel patchesof impressionist paintings can be observed in nature as well. Sothe ways of representing nature can become ways of seeing na-ture. Similarly, artists vacillate between painting what they seein nature and seeing in nature what they paint on canvas.

One controversial claim by Gombrich (1972) is that pictureslack the “statement function” of words. For example, he arguesthat the statement “The cat sits on the mat” cannot be directlypictured. A picture of a cat on a mat depicts a particular cat in aparticular environment as seen from a particular viewpoint. Anequivalent verbal message would be something like “There is acat seen from behind.” Gombrich would not, however, proposethat pictures are a poor source of ideas. Indeed, the conceptualrichness of pictorial representation is a central theme of hiswork.

For further comment on this approach see Blinder (1983),Carrier (1983), Gregory (1973, 1981), Heffernan (1985), andKatz (1983).

33.2.1.4 A Generative Theory: Margaret Hagen. Is pic-ture perception primarily a bottom-up process, as Gibsonclaims, or a top-down process, as Gombrich claims? Hagen(1978, 1980a) provides a generative theory of representationthat suggests a reconciliation: “Meaning is not given by the headto the unstructured stimulus, nor is it given by the stimulus tothe unstructured head. The “relation between the two is recip-rocal and symmetrical” (1980a, p. 45).

In developing her thesis, Hagen describes differences be-tween how we perceive the natural world and how we per-ceive “the world within the picture.” For example, comparedto natural perception, picture perception compresses the per-ceived third dimension and increases the awareness of the angleamong objects (the spread). Thus picture perception has a spe-cial character that is based partly on ecological geometry (thenatural perspective of the visual environment) and partly on thecreativity or generativity of the perceiver.

Recently Hagen (1986) has provided a category system fordescribing the geometrical foundations of many styles of rep-resentational art—early Egyptian art, Roman murals, NorthwestCoast Indian art, Japanese art, Mayan art, and Ice Age cave art, toname just a few. For example, there are several options for thelocation of the artist’s station point. It can be close to the subjectof the picture, at a moderate distance, or at optical infinity—inwhich case vanishing points and the convergence of parallellines (e.g., railroad tracks meeting at the horizon) are obviated.Also, the system can involve the use of a single station point




or multiple station points. Hagen observes that each system ofdepiction is “correct” when judged according to its assump-tions. Thus in evaluating the art of other times and cultures wemust reject the premise that the prevailing post-Renaissancesystem of Western art is the only valid system for representingreality—a position also taken by Arnheim.

33.2.1.5 A Gestalt Approach: Rudolf Arnheim. Accord-ing to Arnheim, picture perception is not primarily an act of di-rect perception as Gibson claims, nor is it a response to changingconventions as Gombrich claims. Picture perception is primarilya matter of organizing the lines and other elements of a pictureinto shapes and patterns according to innate laws of structure.Arnheim (1954) applies the principles of Gestalt psychology tothe study of art. He shows how the laws of organization (e.g.,the rules of grouping, the laws of simplicity and good contin-uation) can be found in the art of many periods. Meaning, heargues, has always been embodied in the Gestalt, the wholethat is greater than the sum of its parts. Picture-making is alsoderived from Gestalt principles:

The urge to create simple shapes . . . cannot be explained as an urge tocopy nature; it can be understood only when one realizes that perceivingis not passive recording but understanding, that understanding can takeplace only through the conception of definable shapes. For this reasonart begins not with attempts to duplicate nature, but with highly abstractgeneral principles that take the form of elementary shapes. (Arnheim,1986, pp. 161–162)

Arnheim observes that our judgment of the art of other timesand cultures suffers from “a prejudice generated by the particu-lar conventions of Western art since the Renaissance” (p. 159).Furthermore, current technique is so pervasive that we assumethat it is the only correct way to make pictures. But the tech-niques of unfamiliar art styles are not, as sometimes supposed,due to lack of skill or accidentally acquired convention; nor arethey deliberate distortions devised for some artistic purpose.Each style is based on an internally consistent system of solu-tions to visual problems, solutions that are no more in need ofjustification than contemporary technique.

Arnheim (1969) is also known for his advocacy of “visualthinking.” He rejects the belief that reasoning occurs onlythrough the use of language. In fact he argues that thinkingoccurs primarily through abstract imagery. Arnheim champi-ons the role of art in education and stresses the importance ofteaching students to become fluent in thinking with shapes.

Another recurrent theme in Arnheim’s work is the natureof abstraction. Representational art involves one kind of ab-straction. Portraits, for example, are more abstract than theirreal-world referents. In such cases, “abstractness is a means bywhich a picture interprets what it portrays” (Arnheim, 1969,p. 137). On the other hand, pictures may be less abstract thanthe concepts they symbolize. For example, the silhouette ofa cow on a roadside sign, although quite abstract, is still lessabstract than the concept “cattle crossing.” Arnheim (1974) dis-cusses some of the problems faced by educators in determiningthe most effective kind and level of abstraction to use in instruc-tional illustrations.

Although Gestalt ideas have been eschewed by cognitive psy-chologists, recent discoveries in visual anatomy and physiologyand the study of perceptual organization have attracted some re-newed interest in the area (Hoffman & Dodwell, 1985; Kubovy,1981).

33.2.1.6 Picture Perception as Purposive Behavior:Julian Hochberg. Hochberg opposes the Gestalt approach,arguing that “the whole stimulus configuration cannot in gen-eral be taken as the effective determinant for perception” (Pe-terson & Hochberg, 1983, p. 192). Here is why: All aspectsof a picture cannot be perceived in a single glance. Vision issharp only in a small central area of the visual field—an areaabout the size of your thumbnail when held at arm’s length.On the retina of the eye, acuity falls off rapidly from this area(the fovea). Because detailed discriminations are possible onlyon the fovea, it is necessary to scan pictures to take in all thedetails. Scanning does not occur in smooth sweeps but, rather,as a series of very rapid jumps called “saccades” and brief stopscalled “fixations”—normally about 0.33 s each. The informa-tion obtained from these separate fixations must be integratedinto a mental map. Thus “at any given time most of the pictureas we perceive it is not only the retina of the eye, nor on theplane of the picture—it is in the mind’s eye” (Hochberg, 1972).So the whole is not perceived directly, as Arnheim claims; it isthe result of synthesis based on the analysis of parts. Theseinteractions among the picture, eye movements, and cogni-tions are “highly skilled sequential purposive behaviors” thatare, according to Hochberg, the keys to understanding pictureperception.

Hochberg (1979, 1980) describes how certain techniquesused in painting can be thought to mimic the workings of thevisual system. For example, in some of Rembrandt’s paintingsmost of the canvas is blurred; only a few areas are rendered insharp detail, simulating what is registered by the eye in a seriesof fixations. Similarly, techniques used in impressionistic paint-ings (which Hochberg calls “painting for parafoveal viewing”),pointillist paintings, and Op Art (Vitz & Glimcher, 1984) mirrorprocesses of the human perceptual system.

Another issue discussed by Hochberg concerns the questionof which picture of an object is the “best” picture. Hochberg(1980) uses the term “canonical form” to refer to “the mostreadily recognized and remembered view or ‘clean up’ ver-sion of some form or object” (p. 76). Canonical form pre-serves the most distinctive features of an object and elimi-nates noninformative features. Another factor in determiningcanonical form is the point of view from which an object isdepicted.

33.2.1.7 A Mentalistic Approach: John M. Kennedy.Kennedy is supportive of Arnheim’s approach and opposed toGibson and Gombrich. He argues that we will learn very lit-tle about how pictures are perceived by studying the opticalgeometry of naturalistic art. Understanding picture perceptionshould begin with the realization that pictures are made bypeople trying to communicate to receivers who are themselvesintelligent perceivers striving to grasp the sender’s intent. Pic-tures are made to communicate ideas, not just show scenes. To




exemplify his approach, Kennedy (1985) discusses the pictorialmetaphor:

Imagine a picture of a businessman with as many arms as an octopus,each hand holding a telephone. Or imagine a picture of a bride lookinginto a mirror and seeing a harried housewife. These pictures violate thelaws of physics; they break the rules that Gibson called on. . . . And theydo so precisely because the artist wants to put across ideas: that businessmen are overworked; that present bliss gives rise to future stress. (p.38)

Metaphoric pictures present two meanings: one false, theother intended. Understanding the perception of such picturesrequires a “mentalistic analysis” in which assumptions are madeabout the experience and mental processes of the sender andthe receiver. “The person who makes the metaphor expects therecipient to notice both meanings, and expects the recipient toknow which was intended, and expects the recipient to knowwhich was intended, and expects the recipient to know themaker expected all this from the recipient” (Kennedy, 1984b,p. 901). Kennedy also argues that pictorial cues such as impliedmotion cues can be conceived of as metaphor rather than aspictorial convention.

As a historical footnote, Kennedy was Gibson’s student atCornell and, at one time, followed in his footsteps, writing a sur-vey of the field that was based largely on Gibsonian ideas (1974).But a decade later Kennedy (1984a) would write, “Regrettablyscientific psychology as found in our universities can never beanything more than a trivial pursuit. By its very nature it is inca-pable of profound insights into humankind” (p. 30). Althoughthis represents a dramatic change in philosophy on Kennedy’spart, the attack on a competing approach is by no means un-usual. The picture perception literature is an intellectual bat-tlefield delightfully seasoned with charge and counter charge.Theorists are robustly combative in attacking opposing viewswhile defending their own.

33.2.1.8 A Semiotic Approach: James Knowlton. The the-ories discussed so far approach the topic from points of viewrelated to visual perception, either by way of perceptual psy-chology or through the analysis of visual art. The next two the-ories have a different starting point; they derive from a concernwith symbol using in general, thus placing the discussion ofpicture perception in a broader context.

The boundaries of semiotics—the science of signs—are wideand indistinct. The domain includes questions of the meaningof as well as the communication of meaning. Among the cen-tral figures in this field are Cassirer (1944), Morris (1946), Pierce(1960), and Sebeok (1976). For further commentary on the con-tribution of semiotics to picture perception see Cassidy (1982),Eco (1976), Holowka (1981), Langer (1976), Sless (1986), andVeltrusky (1976).

Here, however, we focus on the theorist in this tradition whospeaks most directly to our present concerns with visual mes-sage design research: James Knowlton. Knowlton (1964, 1966)develops a metalanguage for talking about pictures beginningwith the term sign. A sign is a stimulus intentionally producedfor the purpose of making reference to some other object orconcept. A key distinction is that between digital signs and

iconic signs. Digital signs bear no resemblance to their refer-ents. For example, the physical appearance of the signs “man”and “hombre” do not in any way look like their referent. Exam-ples of digital signs are words, numbers, Morse code, Braille, andsemaphore. Iconic signs, on the other hand, are not arbitrary intheir appearance. In some way, iconic signs include drawings,photographs, maps, and blueprints.

Usually pictures are thought to resemble their referents interms of visual appearance. Resemblance can, however, takeother forms. Knowlton broadens the concept of picture to in-clude logical pictures and analogical pictures. Logical picturesresemble their referents in terms of the relationships betweenelements. An electrical writing schematic, for example, bearsno visual resemblance to the piece of apparatus it represents;it is a picture of the pattern of connections between elements.Flowcharts and diagrams are other examples of logical pictures.In analogical pictures, the intent is to portray a resemblance infunction. For example, a pictorial analogy could be made be-tween a suit of armor and an insect’s exoskeleton. Thus Knowl-ton’s definition of “resemblance” goes far beyond Gibson’s con-cept, in which resemblance is based on the optical equivalenceof pictures and their referents. And even when resemblance isbased on physical appearance, the resemblance of a picture toits referent can, according to Knowlton, be slight. Sometimesa simple silhouette will do the job. Additionally, the ways inwhich resemblance functions in pictorial communication oftendepend on factors that are extrinsic to the picture itself:

Resemblance does not designate a single relation between pictures andtheir subjects; it designates the members of a fairly comprehensive classof relations—a class whose boundaries are not clear. And relations of re-semblance are not always immediately evident to the uneducated eye.Knowing how to look at a picture is required to discern the ways itresembles its subject. Knowledge of other matters may be required aswell—pictorial conventions, referential connections, historical, scien-tific, or mythical lore that sets the context of the work. Such mattersare not taken in at a glance. (Elgin, 1984, p. 919)

The most extreme and controversial position on the roleof resemblance is taken by Goodman (1978). He asserts thatresemblance between picture and nature is not necessary andthat “a picture is realistic to the extent that it is correct underthe accustomed system of representation” (p. 130).

33.2.1.9 Symbol Systems Theory: Nelson Goodman.Goodman (1976) has devised a detailed theory of symbol sys-tems. A symbol system consists of a set of inscriptions (e.g.,phonemes, numbers) organized into a scheme that correlateswith a field of reference. For example, musical staff notationconsists of five horizontal lines on which notes and other marksare placed that correlate with a musical performance. As anotherexample, maps consist of lines, shapes, and symbols that cor-relate with a musical performance. Also, maps consist of lines,shapes, and symbols that correlate with roads, boundaries, andlandmarks. Thus the analysis of a symbol system involves an ex-amination of (a) the scheme of representation, (b) the field of ref-erence, and (c) the rules of correspondence between the two.

Goodman provides several conceptual tools that can be usedfor analyzing symbol systems. One key concept is notationality.




Notationality is the degree to which the elements of a symbolsystem are distinct and are combined according to precise rules.Music is high in notationality. The notes on the scale are distinctin terms of pitch and duration, and the rules for combining themare clear. Mathematics systems are also high in notationality;each number is distinct and the rules for “making statements”are precise. Pictures, on the other hand, are nonnotational. The“elements” of picturing are overlapping, confusable, and lackingin syntax. The lines and shadings that pictures are built fromare without limit, and the ways they are combined to producea symbol are undefined.

Notationality is an aspect of symbol using that may have im-plications for human information processing. Gardner (1982)speculates that “a case can be made that the left hemisphereof the human brain is relatively more effective than the rightat dealing with notational symbol systems, . . . while the righthemisphere is more at ease in dealing with . . . non-notationalsystems” (p. 59).

Another key concept in Goodman’s theory is repleteness.Some symbol schemes, such as most pictures, are replete(or dense), whereas other schemes, such as printed words, arelacking in repleteness. The degree of repleteness is an index ofhow many aspects of a scheme are significant. In printed text,changes in the typeface, boldness, ink color, and other physicalparameters do not necessarily alter meaning in any significantway. Drawings, on the other hand, are relatively replete, as sev-eral aspects of the marks in a drawing are often critical. Paint-ings are very high in repleteness. “Everything about a paintingis part of it—design, coloration, brush stroke, texture and soon. A painting is “unrepeatable in the strict sense of the term”(Kolers, 1983, p. 146).

Goodman distinguishes three primary functions of symbolsystems. Symbols can represent concepts by denoting or depict-ing them. Symbols can exemplify ideas or qualities by provid-ing a sample of the concept. And symbols can express affectivemeaning (emotions).

Symbol systems differ with respect to the ease with whichthey can perform the functions of representation, exemplifi-cation, and expression. For example, music, although richlyexpressive, has no literal denotation. Music in the absenceof a title or lyrics is not “about” anything. Number systemsare limited in a different way. Numbers represent (quantities),but they normally have no expressive function. Most pictorialsystems are versatile. Line drawings, photographs, and repre-sentational paintings can depict, exemplify, and express force-fully.

Pictures exemplify qualities such as color and shape throughthe possession and presentation of them. The qualities exem-plified are properties of the picture. Pictures express through“metaphorical exemplification”—the figurative possession andpresentation of emotion. For example, when a picture expressessorrow, the feeling can be said to be “in the picture.” We must,however, learn how to decode the expressive features of picto-rial systems. “Emotions are everywhere the same; but the artisticexpression of them varies from age to age and from one countryto another” (Goodman, 1976, p. 90).

For other comments on Goodman’s theory see Coldron(1982), Gardner, Howard, and Perkins (1974), Roupas (1977),Salomon (1979a, 1979b), and Scruton (1974).

33.2.1.10 Cognitive Science: David Marr. Artificial intelli-gence research on computer vision is a rapidly developing areathat may contribute to understanding picture perception byhumans. One focus of this work involves determining the com-putations that are required to program a computer to see. Todo this, it is necessary to specify the nature of the visual input,to describe how this input is transformed into data that can behandled by a computer, and to enumerate the computationsthat are carried out on-line to produce solutions to visual prob-lems. Such problems include the detection of shape contoursand surface textures.

A central figure in this area is David Marr. Marr’s (1982) the-ory of vision involves the analysis of visual input through a seriesof stages that culminates the meaningful interpretation of an im-age. In Marr’s theory an initial analysis involves the detection offeatures such as boundaries. These determinations are used toconstruct a “primal sketch” that distinguishes the sections ofthe display. From these sections, surface data such as shadingare used to define the simple three-dimensional shapes in thescene. Finally, “generalized cones” form the basis for the repre-sentation and recognition of complex shapes such as animals.

Marr (1982) asserts that since the early days of the Gestaltschool “students of the psychology of perception have madeno serious attempts at an overall understanding of what per-ception is” (p. 9). Some psychologists are equally skeptical ofthe reciprocal value of Marr’s work. Kolers (1983), for example,comments that “although the study of human perceiving maycontinue to inform the study of machine vision, it remains to beseen whether students of computer vision will teach us muchabout human perceiving” (p. 160). For comments on Marr’swork and other recent approaches to computer vision see Con-nell and Brady (1987), Fischler and Firschein (1987), Gregory(1981), Jackendoff (1987), Kitcher (1988), Kolers and Smythe(1984), Lowe (1987), and Rosenfeld (1986).

A theory that is closely related to Marr’s approach has beenproposed by Biederman (1985, 1987). Biederman describes aprocess by which an object in a two-dimensional image canbe recognized. The process uses a set of primitive elements:36 generalized-cone components called geons. These geons arederived from the combination of only five aspects of the edgesof objects (e.g., curvature and symmetry). The process of in-terpreting a picture involves detecting the edge elements in animage, generating the resulting geons, combining these geons toproduce meaningful forms, and matching them to known formsin the visual environment. Only 36 geons are needed for theperception of all possible images, a situation that is analogousto speech perception in which only 44 phonemes are neededto encode all the words in the English language. Biederman in-vokes evidence showing that the recognition of objects is robustacross a wide range of viewing conditions (e.g., occluded views)and viewpoints (e.g., rotations in depth). Biederman’s theorywould appear to be in opposition to most other theorists, whocontend that it makes little sense to talk of a “vocabulary” and“grammar” of picturing.

Another area that should be mentioned is neurophysiology.Kosslyn (1986, 1987) suggests how neurophysiology might becombined with artificial intelligence computational theory toyield a more complete understanding of vision. After all, Kosslynobserves, perception and cognition are something the brain




does. The extreme belief regarding the potential importanceof neurophysiology is expressed by Kitcher (1988): “Ultimately,all phenomena currently regarded as psychological will eitherbe explained by neurophysiology or not at all” (p. 10).

33.2.2 Implications for MediaResearchers: An Example

Picture perception theorists have challenged many of our or-thodox beliefs about pictures. For example, consider the ques-tion of what constitutes “realism” in pictures. In the media re-search literature, realism is generally defined as matter of faith-fully copying nature. A picture is said to be “realistic” to thedegree that it mirrors the visual information provided by thereal-world referent, and researchers studying the effects of pic-torial realism have manipulated “realism cues” such as amountof detail, color, and motion. The outcomes of this research havebeen frequently disappointing.

Picture perception theorists have offered alternatives to thesimple “copy theory” of realism. Although Gibson’s approachstresses the fidelity of picture to referent, he adds the qualifi-cation that a successful picture copies the invariant visual in-formation in nature—the optical data about reality that remainsconstant across time and across different views of an object.Goodman (1976) contends that realism is “. . . not a matter ofcopying but of conveying. It is more a matter of ‘catching alikeness’ than of duplicating—in the sense that a likeness lostin a photograph may be caught in a caricature” (p. 14). ForGombrich, the criteria for realism are not in nature, but in theperceiver’s head in the form of expectations for what picturesof a given type “should” look like. These expectations are builtup during extensive experience with the prevailing pictorialsystem and function as the standards for judging realism. Arn-heim argues that perceptions of realism are relative to picto-rial style and are particularly influenced by how a style repre-sents what we know about an object (conceptual reality) com-pared to what the object looks like (perceptual reality). Marrand Biederman propose bottom-up theories that focus on thematch between abstract elementary forms in pictures and theirreferents.

Thus contrasting the copy theory of pictorial realism withthose of picture perception theorists, the copy theory empha-sizes the exact visual match between pictures and referents,whereas theorists emphasize the nature of departures of picturefrom reality—surface level vs. deeper semantic, psychologicalstimulus only vs. contribution of perceiver also.

33.3 MEMORY MODELS, COGNITIVE LOADTHEORY, AND MULTIPLE REPRESENTATIONS

33.3.1 Memory Models

There is significant evidence that generally memory for pic-tures is better than memory for words. This consistent find-ing is referred to as the picture superiority effect. At leastthree significant theoretical perspectives have been used to

explain the picture superiority effect, including (a) the dual-code model, (b) the single-code model, and (c) the sensory-semantic model.

Proponents of the dual-code theory argue that there aretwo interdependent types of memory codes, verbal and nonver-bal, for processing and storing information (Paivio, 1971, 1978,1990, 1991). The verbal code is a specialized system for process-ing and storing verbal information such as words and sentences.The nonverbal system “includes memory for all nonverbal phe-nomenon, including such things as emotional reactions, thissystem is most easily thought of as a code for images and other‘picture-like’ representations (although it would be inaccurateto think of this as pictures stored in the head)” (Rieber, 1994,p. 111). If it is assumed, as Paivio does, that the dual coding ofpictures in verbal and nonverbal memory is more likely to oc-cur for pictures than words, then the “picture superiority effect”could be explained using dual-coding theory.

Proponents of a single-code model argue that visual informa-tion is transformed into abstract propositions stored in semanticmemory (Anderson, 1978; Kieras, 1978; Kosslyn, 1980, 1981;Pylyshyn, 1981; Rieber, 1994; Shepard, 1978). Advocates for asingle-code model argue that pictures activate a single seman-tic memory system differently than do words. Individuals “pro-vided with pictures just naturally spend more time and effortprocessing pictures” (Rieber, 1994, p. 114).

Picture superiority can also be explained using a sensory-semantic model (Nelson, 1979). There may be a more distinc-tive sensory code for pictures or the probability that pictureswill be processed semantically is greater than that for words(Levie, 1987; Nelson, Reed, & Walling, 1976; Smith & Magee,1980). In many cases researchers in educational communica-tions and technology have neglected the work that has beendone concerning memory models.

33.3.1.1 Cognitive Load Theory. We believe that it is criti-cal for instructional design researchers to be aware of the knowl-edge and breakthroughs that have been made by researchers incognitive science concerning human cognitive architecture anda particular instructional theory based on current cognitive sci-ence research. In this section we provide a brief summary ofa particular information processing view (IPV) of human cog-nitive architecture similar to the one presented in the learningand memory section. We then describe an instructional theorybased on this IPV.

In our discussion of memory models we presented an IPVof human cognitive architecture. An IPV assumes that humanshave a limited working (conscious) memory and a long-termmemory (Miller, 1956). There is evidence that only seven ele-ments can be stored in working memory at a given time (Miller).Individuals are not conscious of the information stored in long-term memory. On the other hand, there is evidence that humanshave the ability to store almost unlimited amounts of informa-tion in long-term memory (Sweller, Van Merrienboer, & Pass,1998). Sweller et al. suggest that individuals real intellectualpower lies in their knowledge stored in long-term memory. Theimplications for instructional design are that we should not em-phasize general reasoning strategies that use working memorybut, rather, promote the acquisition of knowledge in specificdomains.




An additional component of human cognitive architecture isthat of schema. A schema is a network of information or classifi-cation of elements according to the way that they will be used.Schemas are stored in long-term memory. Consider the follow-ing example. If one asks an educational researcher to write aresearch paper using APA style, an experienced writer will al-ready know what APA style is and will have knowledge of thefollowing elements: order of presentation, heading structure,in-text citation format, and reference list. A schema has beendeveloped and stored for “APA” style. Most researchers that aretrue experts at writing research papers using APA style will auto-matically be able to recall and use their schema for an APA-stylepaper without performing any means–ends analysis includingthe elements of APA style.

In summary, humans have limited working memory andalmost-unlimited long-term memory, and they develop schemasthat may become automated and used to solve particular prob-lems. The result of schema development is a reduction in theload on working memoy. The goal of instruction should be tohelp learners develop and automate schemas.

Cognitive load refers to the resources used by working mem-ory at a given point in time. Two types of cognitive load havebeen identified in particular: instrinsic cognitive load and extra-neous cognitive load (Sweller et al., 1998). Intrinsic cognitiveload refers to the load placed on working memory by “difficult-to-learn” content. Extraneous cognitive load is the workingmemory load resulting from poorly designed instructional mes-sage materials and poor instructional designs. In any case, ifworking memory is cognitively overloaded, the desired learningwill not be accomplished. We believe that researchers investi-gating how pictures and animated graphics can help or hinderlearning should consider the implications of cognitive load the-ory.

33.3.1.2 Multiple Representations. It is now common formultiple representations to be used in instructional programsand situations. For example, students can now learn how tosolve quadratic equations algebraically, or they can learn todraw the “right” picture. Concepts and content can be repre-sented using pictures, animations, spreadsheets, graphs, and anumber of other external representations. Research on usingmultiple representations in instruction has yielded conflictingresults (Ainsworth, 1999). Ainsworth suggests that one findingthat is consistent across a number of studies investigating mul-tiple representations in multimedia. It is difficult for studentsto see the relationship between the multiple representationsused. The translation process may place a heavy demand onshort-term memory and cognitive overload occurs. Ainsworthsuggests that if we are to develop principles for incorporatingeffective multiple external representations (MERs) in learningsituations, we must consider the functions of MERs.

Anisworth (1999) identifies three primary functions of MERsin learning environments, “to complement, constrain, and con-struct” (p. 134). The complimentary function involves using“representations that contain complementary information orsupport complementary cognitive processes”; when using theconstrain function of MERs, “one representation is used to con-strain possible (mis)interpretations in the use of another”; the

construct function involves using MERs to “‘construct deeper’understanding of a situation” (p. 134). For each of the func-tions of MERs Ainsworth has identified, she has also identifieda number of subfunctions and discussed using MERs to supportmore than one function. We attempt to present only the gistof her perspective her. A detailed discussion can be found inAinsworth (1999).

Ainsworth also suggests that the selection of particular MERshas implications for how learning will be measured when incor-porating MERs in instructional situations. For example, whenMREs are used to complement information or processes or toconstrain interpretation, it is not critical for the learner to under-stand the relationship between the representations, so that mea-surement of performance on MERs in isolation is appropriate. Incontrast, for MERs designed to facilitate deeper understanding,it is important to assess the relationship between MERs. As withcognitive load theory, the authors believe that Ainsworth’s dis-cussion of the functions of multiple representations can be veryuseful to researchers interested in the effect of static picturesand animated graphics on learning.

33.4 PICTURES AND KNOWLEDGEACQUISITION

33.4.1 Literature Search and Reviews

Through various on-line and manual literature searches, 2,235primary research studies, reviews, books, conceptual papers,and magazine articles were identified, collected, and cata-logued. The literature search was limited to the categories ofstatic and animated graphics and knowledge acquisition. Manyof the documents collected were not appropriate for the cur-rent review. For example, numerous papers reported the resultsof memory recognition studies including pictures. In addition,several studies were not included because of methodologicalflaws such as failing to include a control group or appropriatestatistics. Many of the papers identified were not primary re-search studies or theoretical in nature. A total of 168 primaryresearch studies was included across the two categories (staticillustrations and animated graphics) used for the review. We firstreport the results of earlier literature reviews. Then an abridgedguide to the literature is presented.

33.4.1.1 Static Pictures and Knowledge Acquisition. Inthis section we first present a summary of earlier reviews of theliterature concerning the role of static pictures in the acquisi-tion of knowledge. We then discuss the results of our literaturesearch and summary. A similar approach is used for animatedpictures and knowledge acquisition.

33.4.1.2 Static Pictures and Knowledge Acquisition:Literature Reviews. Spaulding (1955) reviewed 16 researchstudies using pictorial illustrations conducted between 1930and 1953. Based on the findings of the 16 studies, Spauld-ing concluded that illustrations (a) are effective interest-gettingdevices, (b) help the learner interpret and remember the




content of the illustrated text, (c) are more effective in real-istic color than black and white, but the amount of effective-ness might not always be significant, (d) will draw more atten-tion if they are large, and (e) should conform to eye movementtendencies.

Samuels (1970) reviewed a series of 23 studies that investi-gated the effects of pictures on learning to read words, on read-ing comprehension, and on reader attitudes. Samuels’s reviewcovered the time span from 1938 to 1969. The studies reviewedincluded such treatments as (a) learning to read words in isola-tion with and without pictures, (b) acquiring a sight vocabularywith and without pictures, (c) using pictures as a response alter-native in a reading program, and (d) using pictures as prompts.Samuels concluded that (a) most studies show that, for acqui-sition of a sight vocabulary, pictures interfere with learning toread, (b) the majority of studies indicate that pictures used asadjuncts to printed text do not facilitate comprehension, and(c) pictures can influence attitudes. Many of the studiesreviewed by Samuels were narrowly focused on the use of illus-trations to learn to decode words in isolation. Illustrations usedin the context of learning to read have generally not proved tofacilitate learning.

An analysis of the pictorial research in science instructionhas also been conducted (Holliday, 1973). The general conclu-sions reached by Holliday concerning the effect of pictures onscience education were that (a) pictures used in conjunctionwith related verbal material can aid recall of a combinationof verbal and pictorial information; (b) pictures will facilitatelearning if they relate to relevant criterion test items; (c) pic-torial variables such as embellishment, size, and preference arecomplex issues, and there are almost-infinite interrelationshipsamong picture types, presentation formats, subject content, andindividual learner characteristics.

Concannon (1975) reviewed a number of studies on the ef-fects of illustrations in children’s texts (mainly basal readers).Concannon summarized the results of her review with the sin-gle conclusion that when pictures are used as motivating factors,they do not contribute significantly to helping a young readerdecode the textual information.

Levin and Lesgold (1978) reviewed studies of prose learningwith pictures and concluded that pictures do facilitate proselearning when five ground rules are adhered to.

1. Prose passages are presented orally;2. The subjects are children;3. The passages are fictional narratives;4. The pictures overlap the story content; and5. Learning is demonstrated by factual recall. (pp. 234–235)

Although Levin and Lesgold (1978) focused on oral prose,they also suggest that pictures may benefit individuals readingfor comprehension.

Schallert (1980) reviewed a number of research studies andpresented the case for and against pictures in instructional ma-terials. In the case against pictures Shallert reviewed the work ofSamuels (1967, 1970) and others. Shallert states that “the mostconvincing evidence against the use of illustrations in children’stext has been marshaled by Samuels” (p. 505). Shallert noted

that many of the early reviews completed by Samuels, Concan-non, and others reported that the use of pictures serving asmotivating factors do not facilitate a child’s ability to decodetext information. Shallert indicated that some of the reasons thepre-1970 studies did not identify picture effects were that (a)the primary emphasis in the word acquisition treatments werespeed and efficiency—with the words being spoken aloud, pic-tures used in that context are of little value; (b) the illustrationsused in many studies were not meant to convey new informationand were used only as adjuncts to the text; (c) many illustrationsused in basal readers vaguely relate to the contextual informa-tion in the text; and (d) the effects of illustrations on long-termmemory were not measured in these earlier studies.

In the case supporting positive picture effects Shallert (1980)reviewed a series of studies that covered the time period from1972 to 1977. The general conclusions reached by Schallertwere that pictures can help subjects learn and comprehendtext (a) when the pictures illustrate information central to thetext, (b) when they represent new content important to theoverall message being presented, (c) when they help depictthe structural relationships covered by the text, and (d) if theillustrated information contributes more than a simple secondrehearsal of the text.

Readence and Moore (1981) conducted a metanalytic reviewof the literature on the effect of experimenter-provided adjunctpictures on reading comprehension. The 16 studies reviewedincluded 2,227 subjects and incorporated a total of 122 mea-sures of association between the use of adjunct pictures andreading comprehension. The overall results across all studiesrevealed only minimal positive effects on reading text and sub-sequent reading comprehension when using adjunct pictures.The magnitude of picture effects was more substantial for uni-versity subjects who read text containing adjunct pictures.

One of the most comprehensive reviews of the effects of illus-trated text on learning was done by Levie and Lentz (1982). TheLevie and Lentz (1982) review compared three separate areasconcerning the role of illustration in learning: (a) learning il-lustrated text information, (b) learning nonillustrated text infor-mation, and (c) learning using a combination of illustrated andnonillustrated text information. Studies included in the Levieand Lentz review cover the time period from 1938 to 1981.Levie and Lentz also present a functional perspective, whichcould be used to explain how illustrations might function tofacilitate learning. Functional frameworks are covered in detailin a later section.

Summarizing the results across all studies included in theirreview, Levie and Lentz (1982) drew three primary conclusions:(a) Learning will be facilitated when the information in the writ-ten text is depicted in the illustrations; (b) learning of text mate-rial will not be helped or necessarily hindered with illustrationsthat are not related to the text; and (c) when the criterion mea-sure of learning includes both illustrated and nonillustrated textinformation, a modest improvement may often result from theaddition of pictures.

Using Levin’s (1981) framework to classify pictures accord-ing to the function they serve in prose learning, Levin, Anglin,and Carney (1987) conducted a metanalysis of the pictures inprose studies. The reviewers concluded that for pictures (not




TABLE 33.1. Summary of Primary Research Studies Included in the Literature Survey

Audience by Experiment Results by StudyTotalStudies Number Y H A SD NSD

Static pictures 90 75 16 29 81 33Animation 78 15 5 72 43 27

Note. Subject classifications: Y—young children, elementary school, and middle school; H—high school; A—adult. SD, significant differences; NSD, nonsignificantdifferences. Mixed effects were identified in selected studies. Some studies included more than one experiment.

mental images), serving a representation, organization, inter-pretation, or transformation function yielded at least moderatedegrees of facilitation. A substantial effect size was identified forthe transformation function.

One of the most significant programs of research on vi-sual learning has been conducted by Dwyer and his associates(Dwyer, 1972, 1978, 1987; Levie & Lentz, 1982; Rieber, 1994).The research program is unique in several ways. The studies inthe Dwyer series used similar stimulus materials. In particular,the stimulus materials included a 2,000-word prose passage de-scribing the parts, locations, and functions of the human heartalong with various types of visual materials including line draw-ings, shaded drawings, and photographs in black and white andin color. The materials were delivered in a number of formatsand combinations including written prose with illustrations, aslide tape program with audio, television, and computer-based.In addition, a rationale was provided for the inclusion of visualillustrations in the treatments. If the information tested in a par-ticular section of the text material was not difficult for the stu-dent (did not require external visualization), visual informationwould not be included and tested for this section of the text.Several types of criterion measures were developed by Dwyerand his associates including a drawing test, an identification test,a terminology test, and a comprehension test. The research hasbeen conducted with over 48,000 students (Dwyer, 1972, 1978,1987).

Levie and Lentz (1982) conducted a metanalysis using thetreatments developed by Dwyer and presented in a text formator programmed booklet. All studies included in the metanaly-sis included a text-only condition. Based on 41 comparisons oftreatments with text plus prose vs. with text only using fourcriterion measures (drawing test, identification test, terminol-ogy test, comprehension test), Levie and Lentz (1982) reportedthat 36 comparisons favored illustrated text and 4 favored textalone (see Appendix 33.1). As with other reviews of literaturediscussed, one conclusion that can be drawn from the workof Dwyer and his colleagues is that visuals are “effective someof the time under some conditions” (Rieber, 1994, p. 132).Space limitations do not permit a more detailed discussion ofthe Dwyer (1972, 1978, 1987) series.

33.4.1.3 Guide to the Literature: Static Illustrations.Based on our literature search, 90 studies investigating the roleof static pictures in knowledge acquisition were identified. The90 studies were conducted with more than 13,528 subjects rang-ing from elementary-school children to adults. (See Table 33.1.)All of the studies included at least one comparison of learningwith prose and static visual illustrations of various types vs. witha prose-only treatment. A number of the studies included written

prose materials, whereas others included prose presented orally.It should be noted that many of the studies summarized in-cluded other comparisons irrelevant to this review, and theyare not discussed. In the 118 experiments included in the 90studies, 102 significant effects for treatments including text andvisual illustrations vs. text only were identified. The results ofthe “box score” summary indicate that static visuals can havea positive effect on the acquisition of knowledge by students.The treatments used were varied and many of the studies werenot based on a particular theoretical perspective. In many ofthe studies it was not possible to identify the role or func-tion of the visual illustrations in the instructional treatments.Examples of visuals and criterion measure items should be in-cluded more regularly in published studies. It was also difficultto determine what type of information was tested using thecriterion measures in many of the studies. The reliability coef-ficients of the criterion measures were infrequently reportedin the studies reviewed. In addition, few of the studies havebeen replicated. Notable exceptions are the research programsof Dwyer and Levin. A more detailed summary of each study isreported in Appendix 33.2. The studies by Dwyer and his asso-ciates that are reported in Appendix 33.1 are not duplicated inAppendix 33.2.

Based on our review of reviews of the literature and our ownliterature summary concerning the role of visual illustrations andknowledge acquisition, we still agree with a conclusion statedby Levie (1987):

It is clear that “research on pictures” is not a coherent field of inquiry.An aerial view of the picture research literature would look like a groupof small topical islands with only a few connecting bridges in between.Most researchers refer to a narrow range of this literature in devisingtheir hypotheses and in discussing their results. Similarly, authors ofpicture memory models, for example, take little notice of theories ofpicture perception. (p. 26)

One of the primary reasons much of the research on the roleof visual illustrations in knowledge acquisition is not easily inte-grated is that the role or function of the pictures and illustrationsin the instructional treatments is not identified. We feel that itis critically important to determine, in advance of conductingresearch, the particular functions of the visual illustrations.

33.4.1.4 The Use of Functional Frameworks in StaticVisual Research. Despite the considerable amount of re-search concerning how static visuals facilitate learning, manyempirical research studies reflect an unclear perception on thepart of researchers of the manner in which illustrations functionin facilitating learning. A number of researchers have provideda variety of functional frameworks that may provide assistance




in classifying static visuals into meaningful functional categories(Alesandrini, 1984; Brody, 1984; Duchastel & Waller, 1979; Levie& Lentz, 1982; Levin, 1981; Levin et al., 1987). We provide abrief summary of several functional frameworks.

Two taxonomies have been proposed that take a morpho-logical approach (what an illustration physically looks like) topicture classification (Fleming, 1967; Twyman, 1985). But clas-sifying the role of pictures on the basis of “form” rather than“function” has not proven to be very useful (Duchastel & Waller,1979). According to Duchastel and Waller, what is needed is nota taxonomy of illustrations but a grammar of illustrations thatprovides a functional set of principles that relate illustrations tothe potential effects they may have on the learner.

Duchastel (1978) identified three general functional roles ofillustrations in text: (a) an attentional role, (b) a retentional role,and (c) an explicative role. The attentional role relies on the factthat pictures naturally attract attention. The retentional role aidsthe learner in recalling information seen in an illustration, andthe explicative role explains, in visual terms, information thatwould be hard to convey in verbal or written terms (Duchastel &Waller, 1979). Duchastel and Waller concluded that the explica-tive role of illustrations provides the most direct means withwhich to classify the role of illustrations in text. Seven subfunc-tions of explicative illustrations were identified by Duchasteland Waller.

1. Descriptive. The role of the descriptive function is to show what anobject looks like physically.

2. Expressive. The expressive role is to make an impact on the readerbeyond a simple description.

3. Constructional. The intent of the constructional role is to show howthe parts of a system form the whole.

4. Functional. The functional role allows a learner to visually follow theunfolding of a process or the organization of a system.

5. Logico-Mathematical. The purpose of this role is to show mathemat-ical concepts through curves, graphs, etc.

6. Algorithmic. The algorithmic role is used to show action possibilities.7. Data-Display. The functional role of data-display is to allow quick

visual comparison and easy access to data such as pie charts, his-tograms, dot maps, or bar graphs. (pp. 21–24)

An alternative functional framework, offered by Levie andLentz (1982), suggests that a functional framework include clas-sifying illustrations in text based on how they impact a learnerin attending, feeling, or thinking about the information beingpresented. Their framework contains four major functions: (a)attentional, (b) affective, (c) cognitive, and (d) compensatory.The attentional function attracts or directs attention to the ma-terial. The affective function enhances enjoyment or, in someother way, affects emotions and attitude. Illustrations serving acognitive function facilitate learning text content through im-proving comprehension, improving retention, or providing ad-ditional information. The last functional role identified by Levieand Lentz is the compensatory role, which is used to accom-modate poor readers. Levie and Lentz, after reviewing a largenumber of studies containing 155 experimental comparisonsof learning, have found much empirical support for the utilityof their functional framework. Such a framework can help re-searchers sort out the functions that illustrations perform and

can be used to identify the ways illustrations should be designedand used for specific cases (Levie & Lentz).

A functional framework that has proved to be useful in ex-plaining differences in research studies concerning pictures andprose is provided by Levin (1981). Levin contended that differ-ent types of text-embedded pictures serve five prose learningfunctions: (a) decoration, (b) representation, (c) organization,(d) interpretive, and (e) transformation. The decoration func-tion is associated with text-irrelevant pictures (e.g., picturesused to make a written text more attractive) and does not rep-resent the actors, objects, and activities happening in the text.Representational pictures are associated with text-relevant pic-tures and do represent the actors, objects, and activities hap-pening in the text. The role of organizational pictures is toprovide an organizational structure giving the text more co-herence. Interpretational pictures serve to clarify passages andabstract concepts or ideas that are hard to understand. Trans-formational pictures are unconventional and not often found intraditional textbooks. Transformational pictures are designed tohave a direct impact on a learner’s memory (e.g., pictures usedas a mnemonical aid serves a transformation function).

After reviewing the frameworks offered by Duchastel, Levin,Levie and Lentz, and others, Brody (1984) suggests that manyof the specific functions identified within these frameworksdo not clarify how pictures function in instructional settings.First, some functions are too broad or general in nature andadd little to gaining an understanding of the instructional rolesserved by visuals. As an example, Brody contends that a singlepicture can increase comprehension in multiple ways such asgaining attention, repeating information, offering new informa-tion, and providing additional examples. A broad functional rolesuch as increasing prose comprehension does not provide anadequate explanation of how a picture is to be used to affectprose comprehension (Brody). Brody also suggests that manypreviously defined functional roles of pictures are often too nar-row in their view. In an effort to ameliorate the limitations ofpreviously identified functional roles of pictures, Brody offershis own set of representative instructional functions served byillustrations. Brody’s approach to creating a potentially moreuseful functional framework was to identify functions in termsof what occurs during the instructional process. Another primeobjective was to make the functional framework as general aspossible in scope; that is, to make the functions independentof the specific form of instruction, content area, or types oflearning skills being taught. Brody identified 20 representativeinstructional functions served by pictures. A potential problemwith Brody’s classification system for determining the role of il-lustrations in instructional materials is that it already contains alarge number of categories. To extend his classification schemefurther would make it less practical for identifying the role ofpictures in either research or instructional design practice.

Alesandrini (1984) states that some of the previous func-tional frameworks dealt only with representational pictures, thatis, pictures that represent the actors, objects, and activities tak-ing place in the text. Alesandrini notes that other frameworksalso include arbitrary or nonrepresentational roles of picturessuch as graphs and flowcharts in the functional mix. Alesan-drini offers a functional framework based on how instructional




pictures convey meaning. Based on previous work by Grooperand Knowlton, Alesandrini classifies the role of instructional pic-tures into three functions: (a) representational, (b) analogical,and (c) arbitrary. Representational pictures can convey infor-mation in a direct way through tangible objects or concepts orindirectly by the portrayal of intangible concepts that have nophysical existence. Photos and drawings, or models and manip-ulatives, are examples of representational illustrations. Analogi-cal pictures convey meaning by acting as a substitute and thenimplying a similarity for the concept or topic being presented.Arbitrary pictures (sometimes referred to as logical pictures) arehighly schematized visuals that do not look like the things theyrepresent but are related in some conceptual or logical way.Arbitrary illustrations include schematized charts and diagrams,flowcharts, tree diagrams, maps, and networks.

33.4.1.5 Static Visuals and Knowledge Acquisition:Conclusions. Based on the conclusions of our review of earlierliterature reviews and the studies we summarize in Appendixes33.3 and 33.4, we conclude that static visual illustrations canfacilitate the acquisition of knowledge when they are presentedwith text materials. However, the facilitative effects of illustra-tions are not present across all learning situations. It is verydifficult to integrate the results across all studies due to the lackof connections (theoretical or functional) among many of them.We do offer the following broad conclusions regarding the ef-fects of illustrated visuals on learning: (a) Illustrated visuals usedin the context of learning to read are not very helpful; (b) illus-trated visuals that contain text-redundant information can facil-itate learning; (c) illustrated visuals that are not text-redundantneither help nor hinder learning; (d) illustration variables(cueing) such as size, page position, style, color, and degreeof realism may direct attention but may not act as a significantaid in learning; and (e) there is a curvilinear relationship be-tween the degree of realism in illustrations and the subsequentlearning that takes place.

There has been substantial progress in understanding howstatic illustrations affect the learning process. However, muchremains to be done. Validations for many of the functionalframeworks summarized in this chapter need to be completed.Theory-based studies that are informed by both memory re-search and theories of picture perception are lacking. Specificstudies incorporating a particular theory of picture perceptionand a particular memory model need to be conducted. Theory-based research will provide us with a deeper understanding ofthe mechanisms that contribute to the effectiveness or ineffec-tiveness of static illustrations in instructional materials. It is alsonot clear how students use illustrations in instructional materialsor that they even know how to use them. A number of methodsincluding eye movement measurements, student surveys, andsimply questioning students while they are using visual illustra-tions will provide useful data on how students use or do not useillustrations. These data will be complementary to the resultsof the recall and comprehension studies already completed. Inaddition, studies are needed that attempt to identify effectivestrategies for using illustrations included in instructional mate-rials. Assuming that strategies for effectively using illustrationsare identified, studies will then be needed that consider effec-tive ways to train students to use these strategies. The issue

of what constitutes “realism” in illustrations also needs to bereconsidered in light of the theories of picture perception dis-cussed in this chapter. Many of the criterion measures (recallor comprehension tests) are administered immediately after thepresentation of the instructional treatments. It is also impor-tant to determine if the illustration effects identified in manyof the studies reviewed in this chapter are durable over time.Finally, few of the studies reviewed systematically controlled forthe type of text or picture included. Perhaps the effects of il-lustrations on learning will vary according to the type of prosepassage or picture used.

33.4.2 Animated Pictures andKnowledge Acquisition

In this section we first review the early research on the effect ofanimated visuals on learning. We then summarize more recentreviews of the literature concerning the role of animated visualdisplays and knowledge acquisition. Finally, we present the re-sults of our literature search and analysis.

33.4.2.1 Animated Pictures and Knowledge Acquisition:Literature Reviews. Early studies examining the effects ofanimated visuals on learning can be found in instructional filmresearch. Freeman (1924) summarized 13 research studies thatcompared the effectiveness of various forms of visual instruc-tion. The treatment formats used in the 13 studies includedfilm, slides, lectures, still pictures, prints, live demonstrations,and stereographs. The motion treatments in these studies in-cluded the use of action pictures, animated drawings, and mapsor cartoons. Based on the results of the 13 studies, it was con-cluded that motion or animated sequences in film are effectivewhen (a) motion is a critical attribute of the concept being pre-sented, and (b) motion is used to cue or drew the viewer’s at-tention to the material being presented. It should be noted thatthe methodologies used in the 13 studies do not meet currentstandards for conducting comparative experimental research.A number of other investigators have conducted instructionalfilm research that examined the effect of animated visuals onlearning (Lumsdaine, Sultzer, & Kopstein, 1961; May & Lums-daine, 1958; Weber, 1926). Several conclusions can be drawnbased on the early research on the role of animated visuals ininstructional materials, including that (a) animation (motion)can lead to positive learning effects if it is a critical attributeof the concept(s) being presented, (b) animation (motion) canincrease learning of a complex procedural task, and (c) motionor action used primarily to enhance the realism of the presen-tation does not appear to have a significant effect on learning.It should be noted that the conclusions drawn are based on alimited number of studies where the motion variables were notusually tightly controlled.

Rieber (1990) summarized the results of 13 empirical studiesinvestigating the role of animated graphics in computer-based in-struction. Significant effects for animated treatments were foundin five of the primary research studies reviewed. Based on the re-sults of the 13 studies reviewed, Rieber presented three designrecommendations for the use of animated visuals in instructional




materials, including that (a) “animation should be incorporatedonly when its attributes are congruent to the learning task”(p. 79), (b) “evidence suggests that when learners are novicesin the content area, they may not know how to attend torelevant cues or details provided by animation” (p. 82), and(c) “animation’s greatest contributions to CBI may lie in interac-tive graphic applications (e.g., interactive dynamics)” (p. 82).

As discussed in the review of static visuals, a number of frame-works have been provided to classify static visual material. Asimilar functional approach would be appropriate for animatedvisual research. Rieber (1990) suggests that “generally, anima-tion has been used in instruction to fulfill or assist one of threefunctions: attention-gaining, presentation, and practice” (p. 77).

More recently, Park and Hopkins (1993) identified five im-portant instructional roles of animated visuals.

1. As an attention Guide—the animated visual can serve to guide anddirect the subject’s attention.

2. As an aid for illustration—dynamic visuals can be used as an effec-tive aid to represent the structural and functional relations amongcomponents in a domain of knowledge.

3. As a representation of domain knowledge—movement and actioncan be used to effectively represent certain domain knowledge.

4. As a device model for forming a mental image—graphical animationcan be used to represent system structures and functions which arenot directly observable (e.g. blood flowing through the heart).

5. As a visual analogy or reasoning anchor for understanding abstractand symbolic concepts or processes—animation can make abstractand symbolic concepts (e.g. velocity) become more concrete anddirectly observable. (p. 19)

When both the characteristics of the domain knowledge andthe characteristics of the subjects require one or more of thesefive instructional roles to be used, then animated visuals willmost likely be effective (Park & Hopkins).

Using their functional framework, Park and Hopkins (1993)produced a research summary of 25 studies investigating theeffects of animated versus static visual displays. The deliverymedium for 17 of the studies was computer-based instruction,whereas the delivery medium for the remaining 8 studies wasfilm or television. Fourteen of the studies yielded significant ef-fects for animated visual displays. However, “the research find-ings do not consistently support the superior effect of animatedvisual displays. The conflicting findings seem to be related to thedifferent theoretical rationales and methodological approachesused in various studies. . . ” ( p. 427).

One of the most interesting and rigorous programs of re-search on the effect of animation on learning has been con-ducted by Rieber (1989, 1994). The animation research con-ducted by Rieber included students across age groups, withrealistic instructional content (Newton’s laws of motion) andhigher-level learning outcomes. As with the static visual researchof Dwyer and his associates, the Rieber series of studies usedanimated graphics only when there was a need for externalvisualization. Results from the Rieber series are mixed and donot support the use of animated graphics across the board.

In summary, conclusions drawn from early reviews of theanimation research literature are mixed. Rieber (1990) statesthat the few serious attempts to study the instructional at-tributes of animation have reported inconsistent results. “. . . CBI

designers. . . must resist incorporating special effects, like anima-tion, when no rationale exists. . . ” (p. 84).

33.4.2.2 Guide to the Literature. Forty-two studies werelocated that included at least one animation treatment. Informa-tion concerning the authors, treatments, subjects, and results isreported in Appendix 33.5 (see also Appendix 33.6). Initially,we attempted to classify the animated treatments according tothe function they performed (Park & Hopkins, 1993). However,we later abandoned the approach due to lack of specific informa-tion concerning the treatments. It was also difficult to classifymany of the animated treatments as performing a single roleusing the classification system.

From the group of 42 studies a total of 45 comparisons wasidentified that included at least one animation treatment. Signif-icant animation effects were identified in 21 of these compar-isons. Animated treatments used by investigators have includedvarious visual content such as animated illustrations, diagramsand visuals, real-time motion graphics, animated spatial visual-ization graphics, and animated interactive maps with blinkingdots. General content areas covered by these studies includegeneral science, physics, geometry, mathematics, statistics, andelectronics. Subjects for these experiments ranged from matureadults to primary-school children in the first, second, and thirdgrade. A variety of tests was used to measure learning outcomesincluding (a) learning of facts, concepts, and procedures, (b)problem solving and visual thinking, and (c) acquisition of cog-nitive skills that are primarily spatial or perceptual in nature.

How can the mixed results of the animation research be in-terpreted? Based on these “box score” results only, one couldconclude that the use of animated graphics does not facilitatelearning. However, methodological issues need to be consid-ered. For example, in many of the studies it was not indicatedif it was determined that there was a need for external visuals,static or animated. Perhaps reading text alone is adequate. Inaddition, many of the investigators did not provide a rationalefor why motion is needed to indicate either changes over timeor changes in direction. Text or text plus static graphics maybe the optimal treatment if motion is not required. Many of theresearch reports reviewed did not specifically indicate that theanimated sequences were text relevant or at least congruentwith the text information presented. Also, both the informationtested and the test type are critical considerations when investi-gating the learning effects for both static and animated graphicdisplays. It was not always possible to determine if the informa-tion tested was presented only in the animation, only in the ani-mated sequence, or in both. It was also difficult to determine thefunction of the animated sequences. Using the lessons learnedfrom static graphic research, more attention needs to be givento the functional role of animated sequences in research studies.

Such methodological problems call into question the resultsof these studies reporting insignificant animation effects. We be-lieve that the comments of Rieber and of Park and Hopkins arestill timely and appropriate. Rieber (1990) stated that “whilespeculative explanations for these studies which did not pro-duce effects have been offered, many rival hypotheses lingerrooted in general procedural flaws such as poor conceptualiza-tion of the research problem or inappropriate implementationof methods” (p. 84).




In a later review of the literature Park and Hopkins (1993)suggested that

probably the most profound discrepancy separating the research is the-oretical in nature. One important difference between studies whichfound significant effects of DVDs [animated visuals] and studies whichfound no such effects is that the former were guided by theoretical ra-tionales which derived the appropriate uses for animated and static fea-tures of visual displays and their presumed effect. Accordingly, learnervariables, the learning requirements in the task, and/or the mediumcharacteristics were appropriately coordinated in most of the studiesthat found significant effects. (p. 439)

As is the case for static graphics, it is clear that facilitative ef-fects are not present for animated treatments across all learningsituations.

33.4.2.3 Animated Visuals and Knowledge Acquisition:Conclusions. Unlike research pertaining to static visuals,which encompass many additional studies and dozens of treat-ment conditions, research on the effects of animated visuals isvery limited. The early research lacked appropriate controls sothat the specific effects of animation on learning cannot be de-termined. Results from the limited number of completed studiesof the effect of animated visuals on learning are mixed. As dis-cussed earlier, a number of the studies are methodologicallyflawed. Thus, the verdict is still out on the effect of animatedtreatments on student learning.

More research needs to be completed concerning the func-tions of animated visuals in learning materials. Rieber’s and Parkand Hopkins’ contributions have provided a starting point forfurther work. Refinement and validation of the functional frame-works suggested by Rieber and by Park and Hopkins are needed.In addition, it has not been demonstrated if or how learners usean animated sequence in the learning process. The effect of ex-perience, prior knowledge, and aptitude patterns on the effec-tive use of animated visual displays needs to be considered. Also,will students who are naive to specific instructional content beable to determine that an animated sequence indicates changesover time or changes in direction and relate these changes tothe specific content they are learning.? Perhaps students needspecific training on how to use animated sequences for learn-ing. In almost all of the animation studies we reviewed, studentsin an animated treatment condition received visualized instruc-tion (an animated sequence) and were then tested verbally. It isan open question whether a verbal test covering content dis-played in a visual animated sequence measures the learningthat has occurred. Also, many animated sequences particularlyin simulations include a significant amount of information inci-dental to the particular purpose of the instructional package.Studies investigating the effect of such animated treatments onincidental learning are needed. Few of the animation studieswe reviewed considered the effects of developmental level onlearning. Animated treatments may differentially affect older vs.younger students. Finally, as discussed earlier, Rieber has sug-gested that animation may be most effective in computer-basedinstruction when used in interactive graphic applications. Muchwork needs to be done in this promising area of inquiry. In anycase, future research investigating the effect of animated visual

displays on learning should (a) be based on a functional frame-work (i.e., Rieber or Park and Hopkins), (b) include content forwhich external visual information is needed and that requiresthe illustration of motion or the trajectory of an object, and (c)control for the effect of static graphics.

Whereas some progress had been made since the review byAnglin, Towers, and Levie (1996), it is apparent that we stillknow very little about the effect of animated visual displays onstudent learning. Given the proliferation of visual informationin instructional material, it is imperative that the most effectivestrategies for using animated visuals be determined. Relativeto the production of static visuals and text materials, the costof producing animated sequences is high. Caraballo-Rios (1985)stated that “insisting on the used of computer animation in caseswhere it is not absolutely necessary should be considered an ex-travagance” (p. 4). Many additional theory-based studies includ-ing a range of content areas, audiences, treatment conditions,and learner characteristics are needed.

33.4.3 The Role of Static and AnimatedVisuals: Conclusions

We have emphasized the need for future research on the effect ofstatic and animated graphics on learning. Some of the studies wereviewed are theory based, whereas others are not. It is difficultto draw general conclusions across all studies given the wide va-riety of topics and perspectives represented in the studies. Thisis true particularly for the studies incorporating animated graph-ics. It was also pointed out that functional frameworks have beenhelpful when attempting to explain conflicting results identifiedacross various studies. The functional frameworks developed forstatic graphics have been particularly useful. However, we thinkit is now time for researchers to revaluate the functional frame-works that they are using in light of what we know about humanlearning and cognition. Consideration of cognitive load theoryin conjunction with Ainsworth’s (1991) taxonomy of multiplerepresentations could provide a perspective that incorporatesrecent breakthroughs in human cognitive science with a func-tional framework that could be used for various external repre-sentations of concepts and content in instructional materials, in-cluding static animated and graphics (Sweller et al., 1998). Con-sideration of cognitive load theory and taxonomy of multiplerepresentations would lead to a new set of research questions re-lated to the effectiveness of static and animated graphics. Do thestatic pictures or animated graphics we include in instructionalmaterials overload working memory, or do such pictures helpreduce cognitive load and help the learner develop automatedschemas? When should pictures and animated graphics be usedas external representations? How should pictures and animatedgraphics function when used with other forms or external rep-resentation or with each other? Should they complement in-formation and processes, constrain interpretation, or promotedeeper understanding (Ainsworth, 1999)? What strategies willbe effective is helping the learner understand the relationshipsamong multiple representations when appropriate? In additionto new research questions, the use of cognitive load theory anda taxonomy of multiple representations also has implications for




the assessment method researchers would use. In some cases itwould be appropriate to assess the effectiveness of a single ex-ternal representation on learning; in other cases it would be nec-essary to assess weather learners understand the relationshipsbetween multiple representations. In conclusion, we think thatit is critical that new research concerning the effectiveness of vi-sual representations on learning be well grounded in theory andthat the functions of external representations, including staticpictures and animated graphics, be identified.

33.5 CONCLUSIONS

We have briefly reviewed theories of picture perception,memory models, and cognitive load theory and presented ataxonomy of multiple external representations in instructionalmaterials. Then a survey of existing studies and reviewsconcerning the effect of static and animated visuals on learningwas presented. Significant progress has been made concerningour understanding of the effect of static and animated visuals onlearning. Several problems are evident in the research reviewed.

TABLE 33.A1. Summary Matrix of Studies by Dwyer and His Associates

Drawing Test Identification Test Terminology Test Comprehension Test

Better Effect Mean IT/ Better Effect Mean IT/ Better Effect Mean IT/ Better Effect Mean IT/Study Learners (N) Version Size Mean TA Version Size Mean TA Version Size Mean TA Version Size Mean TA

Dwyer (1967) College (86) IT 0.35 1.14 IT 0.34 1.09 IT 0.23 1.06 IT 0.02 1.00Dwyer (1968) 9th grade (141) IT 0.82 1.28 IT 0.57 1.24 TA −0.10 0.96 TA −0.17 0.94

Delayed retest 9th grade (129) IT 0.36 1.09 IT 0.42 1.14 IT 0.27 1.06 IT 0.50 1.18Dwyer (1969) College (175) IT 1.23 1.37 IT 0.67 1.17 IT 0.80 1.16 NSD — —Dwyer (1972) College (266) IT 0.43 1.12 IT 0.26 1.07 IT 0.16 1.04 IT 0.11 1.03Dwyer (1975) College (587) IT 0.82 1.16 IT 0.47 1.13 IT 0.52 1.11 TA −0.04 0.99Arnold & Dwyer

(1975) 10th Grade (185) — — — — — — IT 0.77 1.27 IT 0.90 1.22Joseph (1978) 10th Grade (414) IT 0.41 1.07 IT 0.14 1.02 TA −0.12 0.98 IT 0.01 1.00

Delayed retest 10th Grade IT 0.24 1.03 IT 0.13 1.02 IT 0.47 1.10 IT 0.23 1.04de Melo (1980) High school (48) — — — IT 0.23 1.11 IT 0.34 1.18 IT 0.36 1.15

Pictorial test High school (48) — — — IT 1.42 1.72 IT 1.11 1.50 IT 0.52 1.23

Note. IT, illustrated text; TA, text alone; NSD, no significant difference. Dashes indicate that the value was not provided in the published report. From ”Effects of TextIllustrations: A Review of Research,” by W. H. Levie and R. Lentz, 1982, Educational Communication and Technology Journal, 30,30(4) p. 212, pp. 195–232. Copyright1982 by the Association for Educational Communications and Technology. Reprinted by permission of the AECT.

APPENDIX 33.2 REFERENCE LIST OF DWYERSERIES REVIEWED BY W. HOWARD LEVIE

(SEE APPENDIX 33.1)

Arnold, T. C., & Dwyer, F. M. (1975). Realism in visualized instruction.Perceptual and Motor Skills, 40, 369–370.

de Melo, H. T. (1981). Visual self-paced instruction and visual testingin biological science at the secondary level (Doctoral dissertation,Pennsylvania State University, 1980). Dissertation Abstracts Inter-national, 41, 4954A.

Dwyer, F. M., Jr (1967). The relative effectiveness of varied visual illus-trations in complementing programed instruction. Journal of Ex-perimental Education, 36, 34–42.

Dwyer, F. M. (1968). The effectiveness of visual illustrations used to com-plement programed instruction. Journal of Psychology, 70, 157–162.

For both static and animated graphics, the research is frag-mented and sporadic. Notable exceptions are the research pro-grams of Dwyer, Levin, and Rieber. Over the last 6 years, thescope of animation research has broadened. In addition, manyof the researchers in instructional communication and technol-ogy have neglected the work on human cognitive architecture,memory models, perspectives on multiple external represen-tations, and theories of pictures perception. Future researchrelated to visual learning should derive from theories of pic-ture perception and incorporate memory models. We believethat consideration of cognitive load theory and Ainsworth’s(1999) taxonomy of multiple external representations wouldbe very useful to researchers interested in examining the ef-fect of static and animated graphics on student learning. Thereis much that we do not know about how to design effectivevisual representations. Future research strategies should be se-lected carefully to assure that we continue to make significantprogress.

APPENDIX 33.1

Dwyer, F. M. (1969). The effect of varying the amount of realistic de-tail in visual illustrations designed to complement programmed in-struction. Programmed Learning and Educational Technology, 6,147–153.

Dwyer, F. M. (1972). The effect of overt responses in improving visuallyprogrammed science instruction. Journal of Research in ScienceTeaching, 9, 47–55.

Dwyer, F. M. (1975). On visualized instruction effect of students’entering behavior. Journal of Experimental Education, 43, 78–83.

Joseph, J. H. (1979). The instructional effectiveness of integrating ab-stract and realistic visualization (Doctoral dissertation, PennsylvaniaState University, 1978). Dissertation Abstracts International, 39,5907A.

APPENDIX 33.3 (pp. 880–893)



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rs

Wri

tte

nS

Dd

ep

en

din

go

nin

form

atio

nty

pe

Blu

th(1

973)

1.P

rose

+il

lust

rati

on

s2.

Pro

seo

nly

Two

dif

fere

nt

clo

zep

assa

ges

of1

26w

ord

se

ach

Ele

me

nta

rysc

ho

ol(

80)

Clo

zete

stm

eas

ure

of

com

pre

he

nsi

on

Wri

tte

nS

D(g

oo

dre

ade

rs)

Bo

rge

s&

Ro

bin

s(1

980)

1.S

tory

+ap

pro

pri

ate

con

text

pic

ture

2.S

tory

+p

arti

alco

nte

xtp

ictu

re3.

Sto

ry+

no

pic

ture

Ch

arac

ter

mo

tiva

tio

nst

ory

Un

de

rgra

du

ate

(120

)1.

Re

call

bas

ed

on

14id

ea

un

its

2.M

ean

com

pre

he

nsi

on

rati

ng

Ora

lS

D,a

pp

rop

riat

e>

par

tial

>n

op

ictu

reB

ran

sfo

rd&

Joh

nso

n(1

972)

Bra

nsf

ord

&Jo

hn

son

(197

2)E

xpe

rim

en

t1

1.N

oco

nte

xt1

(he

ard

pro

sep

assa

ge)

2.N

oco

nte

xt2

(he

ard

pro

sep

assa

getw

ice

)3.

Co

nte

xtaf

ter

(pic

ture

afte

rp

assa

ge)

4.P

arti

alco

nte

xt(p

arti

alp

ictu

reb

efo

rep

assa

ge)

5.C

on

text

be

fore

(pic

ture

be

fore

pas

sage

)

Fic

titi

ou

sp

rose

pas

sage

Hig

hsc

ho

ol(

50)

1.M

ean

com

pre

he

nsi

on

2.M

ean

reca

llsc

ore

Ora

lS

D,c

on

text

pic

ture

be

fore

pas

sage

Co

vey

&C

arro

ll(1

985)

1.Te

xt+

lin

ed

raw

ings

2.Te

xto

nly

Th

ree

exp

osi

tory

scie

nce

pas

sage

so

fap

pro

xim

ate

ly30

0w

ord

se

ach

Ele

me

nta

rysc

ho

ol(

132)

Re

cogn

itio

nu

sin

g36

-ite

mm

ult

iple

-ch

oic

ete

stW

ritt

en

SD

De

an&

En

em

oh

(198

3)1.

Pic

ture

sb

efo

rere

adin

gte

xt2.

Pic

ture

saf

ter

read

ing

text

3.Te

xto

nly

Dif

ficu

ltge

olo

gyp

assa

geco

nta

inin

g26

2w

ord

s

Un

de

rgra

du

ate

(90)

Tota

lnu

mb

er

of“

ide

au

nit

s”re

call

ed

Wri

tte

nS

D,p

ictu

reb

efo

rep

assa

ge

De

Ro

se(1

976)

1.P

rose

+e

xpe

rim

en

ter-

pro

vid

ed

illu

stra

tio

n2.

Pro

se+

inst

ruct

ion

sto

sum

mar

ize

3.P

rose

+e

xpe

rim

en

ter-

pro

vid

ed

sum

mar

y4.

Pro

se+

inst

ruct

ion

sto

imag

e5.

Pro

se-o

nly

con

tro

l

A49

0-w

ord

pas

sage

fro

ma

soci

alst

ud

ies

text

bo

ok

Mid

dle

sch

oo

l(19

2)14

sho

rt-a

nsw

er

qu

est

ion

sW

ritt

en

SD

for

exp

eri

me

nte

r-p

rovi

de

dil

lust

rati

on

s

Dig

do

n,P

ress

ley,

&L

evi

n(1

985)

1.O

bje

ctp

ictu

re+

no

imag

ery

inst

ruct

ion

2.P

arti

alp

ictu

re+

no

imag

ery

inst

ruct

ion

3.O

bje

ctp

ictu

re+

imag

ery

inst

ruct

ion

4.P

arti

alp

ictu

re+

imag

ery

inst

ruct

ion

Two

10-s

en

ten

cep

rose

sto

rie

sYo

un

gch

ild

ren

(160

)S

et

ofc

ue

dre

call

qu

est

ion

sO

ral

SD

for

ob

ject

+p

arti

alp

ictu

res

wit

han

dw

ith

ou

tim

age

ryin

stru

ctio

n

Con

tin

ues

881



TAB

LE

33.A

3.(C

on

tin

ue

d)

Stu

dy

Tre

atm

en

tC

on

ten

tsS

ub

ject

s(N

)D

ep

ed

en

tV

aria

ble

(s)

Pro

seTy

pe

Re

sult

(s)

5.O

bje

ctp

ictu

re+

par

tial

pic

ture

+im

age

ryin

stru

ctio

n6.

Ob

ject

pic

ture

+p

arti

alp

ictu

re+

no

imag

ery

inst

ruct

ion

7.P

rose

+im

age

ryin

stru

ctio

n8.

Pro

se+

no

imag

ery

inst

ruct

ion

Du

chas

tel(

1980

)1.

Pro

seo

nly

2.P

rose

+il

lust

rati

on

s(i

llu

stra

tio

ns

con

veye

dth

eto

pic

alid

eas

)

A75

0-w

ord

pro

sep

assa

geo

ne

ne

rgy

Hig

hsc

ho

ol(

77)

Re

ten

tio

nb

y1.

Su

mm

ary

2.F

ree

reca

ll3.

30sh

ort

answ

ers

Wri

tte

nN

SD

Du

chas

tel(

1981

)1.

Pro

se+

illu

stra

tio

ns

2.P

rose

on

lyA

1,70

0-w

ord

his

tory

pas

sage

Hig

hsc

ho

ol(

77)

1.To

pic

alre

call

2.C

ue

dre

call

(36

qu

est

ion

s)Im

me

dia

te&

2w

ee

kd

ela

yed

Wri

tte

nS

Do

n2-

we

ek-

de

laye

do

nly

(re

call

test

)

Du

rso

&Jo

hn

son

(198

0)E

xpe

rim

en

t1

1.W

ord

s(v

erb

alo

rie

nti

ng

task

)2.

Pic

ture

s(v

erb

alo

rie

nti

ng

task

)3.

Wo

rds

(im

agin

go

rie

nti

ng

task

)4.

Pic

ture

s(i

mag

ing

ori

en

tin

gta

sk)

5.W

ord

s(r

efe

ren

tial

ori

en

tin

gta

sk)

6.P

ictu

res

(re

fere

nti

alo

rie

nti

ng

task

)(p

ictu

res

we

reli

ne

dra

win

gso

feac

ho

fth

e14

0w

ord

con

cep

ts)

Co

nta

ine

d14

0w

ord

s,e

ach

aco

nce

pt,

cho

sen

fro

mK

uce

ra&

Fra

nci

sw

ord

no

rms

Un

de

rgra

du

ate

(120

)A

resp

on

seo

feit

he

ra

pic

ture

or

aw

ord

was

take

nas

anin

dic

atio

nth

atth

eit

em

was

rem

em

be

red

ash

avin

gb

ee

np

rese

nt

du

rin

gac

qu

isit

ion

Ora

lS

Dfo

rve

rbal

ori

en

tin

gta

sks

on

ly

Exp

eri

me

nt

2S

ame

Sam

eU

nd

erg

rad

uat

e(6

0)F

ree

reca

llo

fth

eit

em

sp

rese

nte

dS

ame

Sam

e

Gib

bo

ns

et

al.

(198

6)1.

Pro

se+

visu

als

2.P

rose

on

lyD

oll

sas

acto

rsp

erf

orm

ing

inse

vera

lse

ttin

gs

You

ng

chil

dre

n(9

6)1.

Fre

ere

call

2.R

eco

nst

ruct

ion

ofs

tory

con

ten

t

Ora

lS

D,a

ud

iovi

sual

con

dit

ion

Go

ldb

erg

(197

4)1.

Pro

se(i

nci

de

nta

lin

form

atio

n)+

Illu

stra

tio

ns

2.P

rose

(in

cid

en

tali

nfo

rmat

ion

)

Sp

ell

ing

and

gram

mar

exe

rcis

eE

lem

en

tary

sch

oo

l(21

6)In

cid

en

tali

nfo

rmat

ion

:12

reco

gnit

ion

and

12re

call

qu

est

ion

s

Wri

tte

nS

D

Go

ldst

on

&R

ich

man

(198

5)1.

Pro

se+

par

tial

pic

ture

sd

uri

ng

stu

dy

2.P

rose

+p

arti

alse

nte

nce

rep

eti

tio

nd

uri

ng

stu

dy

3.P

rose

on

ly

10-s

en

ten

cen

arra

tive

sto

ryE

lem

en

tary

sch

oo

l(28

8)C

ue

d-r

eca

llm

eas

ure

sO

ral

SD

for

par

tial

pic

tori

alcu

es

Gu

ttm

ann

,Le

vin

,&

Pre

ssle

y(1

977)

Exp

eri

me

nt

11.

Imag

ery

+p

rose

2.P

arti

alp

ictu

res

+p

rose

3.C

om

ple

tep

ictu

res

+p

rose

4.P

rose

on

ly

Two

sho

rtst

ori

es,

eac

hw

ith

ap

ers

on

,ob

ject

,an

dth

ing

You

ng

chil

dre

n&

ele

me

nta

rysc

ho

ol

(240

)

Cu

ed

reca

ll,2

0q

ue

stio

ns

Ora

lS

D,k

ind

erg

arte

n,f

or

com

ple

tep

ictu

res

on

lyS

D,t

hir

dgr

ade

rs,f

or

imag

ery

=p

arti

al=

com

ple

teS

D,s

eco

nd

grad

ers

,fo

rco

mp

lete

>p

arti

al>

imag

ery

>co

ntr

ol

882



Han

naf

in(1

988)

1.P

ictu

res

+o

ral

2.P

ictu

res

3.P

rose

on

ly

Fic

titi

ou

sch

ild

ren

’sst

ory

Ele

me

nta

rysc

ho

ol(

168)

Re

call

test

-co

nta

inin

g24

-ite

msh

ort

answ

er

of

abst

ract

and

con

cre

teit

em

sIm

me

dia

tean

d1

we

ek

de

laye

d

Ora

lS

D,o

ral+

pic

ture

s,im

me

dia

te&

de

laye

d

Har

ing

&F

ry(1

979)

1.To

p-l

eve

l+lo

we

r-le

velp

ictu

res

+P

rose

2.To

p-l

eve

lpic

ture

s+

pro

se3.

Pro

seo

nly

(te

xt-r

ed

un

dan

tli

ne

dra

win

gs)

A36

0-w

ord

vers

ion

of“

Me

rcu

ryan

dth

eW

oo

dcu

tte

r”

Ele

me

nta

rysc

ho

ol&

mid

dle

sch

oo

l(15

0)F

ree

reca

llo

fbo

thle

vels

of

ide

au

nit

sIm

me

dia

tean

d5

day

de

laye

d

Wri

tte

nS

Dfo

rto

p-l

eve

lid

ea

un

its

for

bo

thim

me

dia

te&

de

laye

d

Hay

es

&R

ead

en

ce(1

983)

1.Tw

oli

ne

dra

win

gs+

pro

se+

no

inst

ruct

ion

s2.

Two

lin

ed

raw

ings

+p

rose

+in

stru

ctio

ns

top

ayca

refu

latt

en

tio

nto

pic

ture

s3.

Pro

se+

inst

ruct

ion

tofo

rmim

age

s4.

Pro

se+

no

inst

ruct

ion

s

Fo

ur

400-

wo

rdp

rose

pas

sage

sfr

om

illu

stra

ted

ed

uca

tio

nal

text

s

Mid

dle

sch

oo

l(10

8)1.

Me

ansc

ore

on

info

rmat

ion

reca

lle

d2.

Me

anp

rop

ort

ion

of

infe

ren

ces

pe

rin

form

atio

nu

nit

reca

lle

d

Wri

tte

nS

Do

fbo

thil

lust

rate

dco

nd

itio

ns

NS

Db

etw

ee

nil

lust

rate

dco

nd

itio

ns

Hay

es

&R

ead

en

ce(1

982)

1.Tw

oli

ne

dra

win

gs+

pro

se+

no

inst

ruct

ion

s2.

Two

lin

ed

raw

ings

+p

rose

+in

stru

ctio

ns

top

ayca

refu

latt

en

tio

nto

pic

ture

s3.

Pro

se+

inst

ruct

ion

tofo

rmim

age

s4.

Pro

se+

no

inst

ruct

ion

s

Fo

ur

300-

wo

rdsc

ien

cete

xts

abo

ut

sim

ple

mac

hin

es

Mid

dle

sch

oo

l(82

)S

tud

en

tsu

cce

ssat

wo

rkin

gst

ud

yp

rob

lem

sw

ith

text

avai

lab

le

Wri

tte

nS

D,i

llu

stra

ted

text

wit

ho

rw

ith

ou

tin

stru

ctio

ns

Hay

es

&H

en

k(1

986)

1.P

ictu

res

on

ly2.

Pic

ture

s+

pro

se3.

Pro

seo

nly

(fiv

esi

mp

leli

ne

dra

win

gs)

Ho

wto

tie

a“b

ow

lin

e”

kno

tH

igh

sch

oo

l(10

2)N

on

verb

alap

pli

ed

pe

rfo

rman

ceIm

me

dia

tean

d2

we

ek

de

laye

d

Wri

tte

nS

D,p

ictu

res

+p

rose

&p

ictu

res,

imm

ed

iate

test

ing

on

lyN

SD

be

twe

en

the

mH

oll

iday

(197

5)1.

Text

bo

ok-

like

illu

stra

tio

ns

+ve

rbal

2.V

erb

alV

erb

alp

rose

(23

pag

es)

abo

ut

pla

nt

gro

wth

ho

rmo

ne

s

Hig

hsc

ho

ol(

80)

Ve

rbal

com

pre

he

nsi

on

,30

-ite

mm

ult

iple

cho

ice

,ad

min

iste

red

ora

lly

Ora

lS

D

Ho

llid

ay&

Har

vey

(197

6)1.

Ad

jun

ctla

be

led

lin

ed

raw

ings

+p

rose

2.P

rose

on

ly

Bio

logy

less

on

on

de

nsi

ty,p

ress

ure

,an

dA

rch

ime

de

s’p

rin

cip

le

Hig

hsc

ho

ol(

61)

Ve

rbal

qu

anti

tati

ve(n

on

-pic

tori

al),

mu

ltip

le-c

ho

ice

test

Wri

tte

nS

D

Ho

lme

s(1

987)

1.P

rose

+p

ictu

re2.

Pic

ture

s3.

Pro

seo

nly

Fif

tee

np

assa

ges

of

150–

200

wo

rds

eac

h;m

ate

rial

fro

mp

op

ula

rm

agaz

ine

s

Ele

me

nta

rysc

ho

ol&

mid

dle

sch

oo

l(11

6)25

infe

ren

tial

qu

est

ion

sW

ritt

en

SD

,pic

ture

s+

text

>p

ictu

res

>p

rose

NS

D,p

ictu

res

vs.p

rose

Con

tin

ues

883



TAB

LE

33.A

3.(C

on

tin

ue

d)

Stu

dy

Tre

atm

en

tC

on

ten

tsS

ub

ject

s(N

)D

ep

ed

en

tV

aria

ble

(s)

Pro

seTy

pe

Re

sult

(s)

Jago

dzi

nsk

a(1

976)

1.P

rose

+sc

he

mat

icco

rre

spo

nd

en

til

lust

rati

on

2.P

rose

+re

alis

tic

corr

esp

on

de

nt

illu

stra

tio

n3.

Pro

se+

sch

em

atic

sup

ple

me

nt

illu

stra

tio

n4.

Pro

se+

real

isti

csu

pp

lem

en

til

lust

rati

on

5.P

rose

Note

:Ab

ove

inst

ruct

ion

alco

nd

itio

ns

cro

sse

dw

ith

2te

xtty

pe

s(e

sse

nti

al&

no

ne

sse

nti

al),

givi

ng

10to

tal

con

dit

ion

s

Two

vers

ion

so

fab

iolo

gyle

sso

nM

idd

lesc

ho

ol(

200)

1.R

ep

rod

uct

ion

(am

ou

nt

of

mat

eri

alre

pro

du

ced

)2.

Text

org

aniz

atio

nB

oth

imm

ed

iate

and

2-w

ee

k-d

ela

yed

test

ing

Wri

tte

nS

Dd

ep

en

din

go

np

ictu

rety

pe

and

its

rela

tio

nsh

ipto

the

text

typ

e

Jah

od

ae

tal

.(1

976)

Exp

eri

me

nt

11.

Pic

ture

s+

pro

se2.

Pic

ture

s3.

Pro

seo

nly

4.C

on

tro

l

Exp

osi

tory

text

de

sign

ed

tob

ecu

ltu

rall

yfr

ee

Mid

dle

sch

oo

l&H

igh

sch

oo

l(93

8),

Sco

tlan

d,I

nd

ia,

Gh

ana,

Ke

nya

Re

call

sco

res,

10p

icto

rial

or

verb

alq

ue

stio

ns

of

pic

ture

and

text

-re

du

nd

ant

info

rmat

ion

Wri

tte

nS

Dfo

rp

ictu

res

+te

xtN

SD

,pic

ture

sal

on

evs

.te

xtal

on

e

Jon

asse

n(1

979)

1.P

rose

+si

ngl

e-s

cre

en

pre

sen

tati

on

2.P

rose

+th

ree

-scr

ee

np

rese

nta

tio

n3.

Pro

se+

fou

r-sc

ree

np

rese

nta

tio

n4.

Pro

seo

nly

Bio

logy

less

on

on

fou

rp

lan

tty

pe

sM

idd

lesc

ho

ol(

363)

Cri

teri

on

test

ofa

verb

alan

dvi

sual

clas

sifi

cati

on

exe

rcis

eIm

me

dia

tean

d2

we

ek

de

laye

d

Ora

lS

D,F

ou

r-sc

ree

nco

nd

itio

no

nvi

sual

clas

sifi

cati

on

,im

me

dia

te&

de

laye

d

Ko

en

ke&

Ott

o(1

969)

1.P

rose

+il

lust

rati

on

s(b

oth

spe

cifi

call

yre

leva

nt

and

gen

era

lly

rele

van

tto

pas

sage

)2.

Pro

seo

nly

Th

ree

198-

wo

rdp

assa

ges

fro

mR

eader

sD

iges

t

Ele

me

nta

rysc

ho

ol&

mid

dle

sch

oo

l(60

)C

om

pre

he

nsi

on

ofm

ain

ide

asW

ritt

en

SD

(bo

thp

ictu

rety

pe

s),

sixt

hgr

ade

rso

nly

Ko

ran

&K

ora

n(1

980)

1.P

ictu

reb

efo

rete

xt2.

Pic

ture

afte

rte

xt3.

Text

on

ly

Sci

en

cele

sso

no

nh

ydro

logi

ccy

cle

Mid

dle

sch

oo

l(84

)23

-ite

mco

mp

leti

on

con

sist

ing

oft

ran

sfo

rme

dan

dp

arap

hra

seq

ue

stio

ns

Wri

tte

nS

Dfo

rse

ven

thgr

ade

rsre

gard

less

ofp

ictu

rep

lace

me

nt

NS

Dfo

re

igh

thgr

ade

rsL

esg

old

&D

eG

oo

d&

Le

vin

(197

7)

1.P

rose

+su

bje

ct-i

llu

stra

ted

sto

ryu

sin

gcu

tou

tso

na

bac

kgro

un

d2.

Pro

se+

colo

rin

gsi

mp

lefi

gure

sin

ab

oo

kle

t

Six

tee

np

rose

sto

rie

s,fo

ur

of

eac

hty

pe

(50

vs.

100

wo

rds;

on

evs

.tw

olo

cati

on

s)

Ele

me

nta

rysc

ho

ol(

32)

Fre

ean

dcu

ed

-re

call

sco

res

Ora

lS

D

Le

sgo

lde

tal

.(1

975)

Exp

eri

me

nt

11.

Pro

se+

sub

ject

sm

ade

up

illu

stra

tio

ns

fro

mcu

tou

ts(s

om

ep

ote

nti

ally

inte

rfe

rin

g)2.

Pro

se+

sub

ject

sco

pie

do

rco

lore

dge

om

etr

icfo

rms

du

rin

gil

lust

rati

on

ph

ase

Fiv

esi

ngl

e-e

pis

od

est

ori

es

of3

0–50

wo

rds

eac

h

Ele

me

nta

rysc

ho

ol(

24)

Ora

lre

call

Ora

lN

SD

884



Exp

eri

me

nt

2a1.

Pro

se+

sub

ject

sm

ade

up

illu

stra

tio

ns

fro

mfe

we

rcu

tou

tsth

ane

xpe

rim

en

t1

2.P

rose

+su

bje

cts

cop

ied

or

colo

red

geo

me

tric

form

sd

uri

ng

illu

stra

tio

np

has

e

Th

ree

sto

rie

so

f5se

nte

nce

se

ach

Ele

me

nta

rysc

ho

ol(

48)

Ora

lre

call

,bo

thfr

ee

and

cue

dS

ame

SD

Exp

eri

me

nt

2b1.

Pro

se+

exp

eri

me

nte

r-p

rovi

de

dp

ictu

res

2.P

rose

+su

bje

cts

cop

ied

or

colo

red

geo

me

tric

form

sd

uri

ng

illu

stra

tio

np

has

e

Sam

eas

2aE

lem

en

tary

sch

oo

l(24

)S

ame

as2a

Sam

eS

Dfo

rb

oth

pic

ture

con

dit

ion

sN

SD

be

twe

en

the

two

pic

ture

con

dit

ion

s

Exp

eri

me

nt

31.

Pro

se+

exp

eri

me

nte

r-p

rovi

de

dp

ictu

res

2.P

rose

+su

bje

cts

mad

eu

pil

lust

rati

on

sfr

om

few

er

cuto

uts

than

exp

eri

me

nt

12.

Pro

se+

sub

ject

sco

pie

do

rco

lore

dge

om

etr

icfo

rms

du

rin

gil

lust

rati

on

ph

ase

Sam

eas

2aE

lem

en

tary

sch

oo

l(36

)S

ame

as2a

Sam

eS

Dfo

re

xpe

rim

en

ter-

pro

vid

ed

pic

ture

so

nly

Le

vin

&B

err

y(1

980)

Exp

eri

me

nt

11.

Pro

se+

on

eco

lore

d,m

ain

-id

ea

lin

ed

raw

ing

pe

rp

assa

ge2.

Pro

seo

nly

Fiv

eh

um

anin

tere

stan

dn

ove

lty

sto

rie

s,fr

om

loca

ln

ew

spap

ers

,ap

pro

xim

ate

ly10

0w

ord

se

ach

Ele

me

nta

rysc

ho

ol(

50)

Six

sho

rt-a

nsw

er

par

aph

rase

qu

est

ion

sp

er

pas

sage

(30

tota

l);h

alft

he

qu

est

ion

sab

ou

tin

form

atio

nin

the

pic

ture

s,th

eo

the

rh

alf

abo

ut

info

rmat

ion

no

tin

pic

ture

s

Ora

lS

Dfo

rp

ictu

red

info

rmat

ion

Exp

eri

me

nt

2S

ame

(ch

ange

was

inti

me

oft

est

ing

on

ly)

Asi

xth

pas

sage

add

ed

Ele

me

nta

rysc

ho

ol(

37)

Sam

eb

ut

test

ing

too

kp

lace

on

3-d

ay-d

ela

yed

bas

isS

ame

SD

Exp

eri

me

nt

3a1.

Sin

gle

mai

nid

ea

pic

ture

+p

rose

Sam

eE

lem

en

tary

sch

oo

l(36

)16

mai

n-i

de

aq

ue

stio

ns

Sam

eS

D2.

Pro

se+

pro

mp

t(v

erb

alan

alo

gue

of

mai

nid

ea

for

eac

hp

assa

ge)

Exp

eri

me

nt

3b1.

On

em

ain

-id

ea

pic

ture

/pas

sage

+p

rose

2.P

rose

+n

op

rom

pti

ng

Sam

eas

3aE

lem

en

tary

sch

oo

l(36

)16

mai

n-i

de

aq

ue

stio

ns

plu

s24

no

n-m

ain

-id

ea

qu

est

ion

s

Sam

eS

D(b

oth

qu

est

ion

typ

es)

Le

vin

(197

6)E

xpe

rim

en

t2

1.P

rose

+e

xpe

rim

en

ter-

pro

vid

ed

culm

inat

ing

pic

ture

s2.

Pro

se+

Exp

eri

me

nte

r-p

rovi

de

dn

on

culm

inat

ing

pic

ture

s

Th

ree

sin

gle

-e

pis

od

est

ori

es

of

30to

75w

ord

se

ach

Ele

me

nta

rysc

ho

ol(

61)

Cu

ed

-re

call

,5sh

ort

-an

swe

rq

ue

stio

ns

Ora

lS

D

3.R

ep

eti

tio

nco

nd

itio

n(p

assa

gere

pe

ate

do

nce

)4.

Act

ivit

yco

ntr

ol(

pas

sage

+n

on

rele

van

tco

lori

ng

acti

vity

)5.

No

nac

tivi

tyco

ntr

ol(

pas

sage

on

ly)

Exp

eri

me

nt

3S

ame

(min

us

the

acti

vity

con

tro

lco

nd

itio

n)

Two

10-s

en

ten

cep

assa

ges

Ele

me

nta

rysc

ho

ol(

64)

Cu

ed

reca

ll,1

0q

ue

stio

ns/

sto

ryS

ame

SD

Con

tin

ues

885



TAB

LE

33.A

3.(C

on

tin

ue

d)

Stu

dy

Tre

atm

en

tC

on

ten

tsS

ub

ject

s(N

)D

ep

ed

en

tV

aria

ble

(s)

Pro

seTy

pe

Re

sult

(s)

Le

vin

et

al.(

1983

)E

xpe

rim

en

t1

1.P

rose

+co

lore

dm

ne

mo

nic

illu

stra

tio

ns

2.P

rose

on

ly

Le

arn

nu

me

rica

lo

rde

ro

f10

U.S

.p

resi

de

nts

Mid

dle

sch

oo

l(46

)1.

Tota

lre

call

2.S

eri

al-p

osi

tio

np

rofi

le3.

Re

spo

nse

late

nci

es

Ora

lN

SD

on

tota

lre

call

Exp

eri

me

nt

2S

ame

+ad

dit

ion

alst

ud

ytr

ials

add

ed

Sam

eM

idd

lesc

ho

ol(

40)

Sam

e+

nam

ere

call

add

ed

Sam

eN

SD

on

tota

lre

call

Exp

eri

me

nt

3S

ame

+3

stu

dy

tria

lsad

de

dS

ame

Hig

hsc

ho

ol(

32)

Tota

lre

call

sco

res

on

lyS

ame

SD

Le

vin

et

al.(

1982

)E

xpe

rim

en

t1

1.K

ey

wo

rdco

nte

xt(w

ord

list

+co

nte

xtu

ally

exp

lici

tco

lore

d“k

ey

wo

rd”

illu

stra

tio

n)

Le

arn

me

anin

gso

f12

chal

len

gin

gvo

cab

ula

ryw

ord

s

Ele

me

nta

rysc

ho

ol(

30)

Tota

lnu

mb

er

ofw

ord

sd

efi

ne

dco

rre

ctly

Ora

lS

D

2.C

on

tro

lco

nd

itio

n(w

ord

list

+e

xpe

rim

en

ter

read

alo

ud

+u

seo

wn

stra

tegy

)E

xpe

rim

en

t2

1.K

ey

wo

rdco

nte

xt(w

ord

list

+co

nte

xtu

ally

exp

lici

tco

lore

d“k

ey

wo

rd”

illu

stra

tio

n)

14w

ord

sto

lear

nE

lem

en

tary

sch

oo

l(64

)S

ame

Sam

eS

D,K

ey

wo

rdco

nte

xtb

est

2.P

ictu

reco

nte

xt(c

olo

red

illu

stra

tio

no

fwo

rds

de

fin

itio

n+

read

de

fin

itio

nal

ou

d)

Pic

ture

be

tte

rth

ane

xpe

rie

nti

al

3.E

xpe

rie

nti

alco

nte

xt(r

ead

3se

nte

nce

sw

ith

de

fin

itio

n+

app

lica

tio

nq

ue

stio

nw

ith

wo

rd)

4.C

on

tro

lco

nd

itio

n(w

ord

list

+e

xpe

rim

en

ter

read

alo

ud

+u

seo

wn

stra

tegy

)L

evi

ne

tal

.(19

83)

Exp

eri

me

nts

1a&

1b1.

Pro

se+

org

aniz

ed

mn

em

on

ic“k

ey

wo

rd”

pic

ture

2.P

rose

+o

rgan

ize

dsi

ngl

ep

ictu

re3.

Pro

se+

sep

arat

ep

ictu

res

4.P

rose

+su

bje

cts

use

ow

nle

arn

ing

stra

tegy

Sh

ort

pro

sep

assa

ges

abo

ut

dis

tin

guis

hin

gat

trib

ute

so

ffi

ctit

iou

sto

wn

s

Mid

dle

sch

oo

l(17

8)1.

Tota

lnu

mb

er

ofa

ttri

bu

tes

rem

em

be

red

via

mat

chin

gq

ue

stio

ns

2.C

lust

eri

ng

sco

re

Ora

lS

D,o

rgan

ize

dm

ne

mo

nic

“ke

yw

ord

”N

SD

,se

par

ate

pic

ture

Exp

eri

me

nts

2a&

2bS

ame

wit

ho

ut

org

aniz

ed

sep

arat

ep

ictu

reco

nd

itio

n(N

o.2

abo

ve)

Sam

eM

idd

lesc

ho

ol(

113)

Su

bje

ctre

spo

nse

so

f(a

)V

erb

atim

corr

ect

(b)

Ess

en

ceco

rre

ct

Sam

eS

D,o

rgan

ize

dm

ne

mo

nic

“ke

yw

ord

”N

SD

,se

par

ate

pic

ture

Le

vin

et

al.(

1986

)E

xpe

rim

en

t1

1.Te

xt+

mn

em

on

icp

ictu

res

2.Te

xt+

sum

mar

yu

sin

gfa

ctm

app

ing

3.Te

xt+

fre

est

ud

yin

stru

ctio

ns

A54

0-w

ord

text

abo

ut

min

era

lso

rgan

ize

dar

ou

nd

“nam

es”

Mid

dle

sch

oo

l(53

)N

ame

and

attr

ibu

tere

call

test

ing

Wri

tte

nS

Dfo

rm

ne

mo

nic

pic

ture

s

Exp

eri

me

nt

2S

ame

Sam

ee

xce

pt

text

org

aniz

ed

aro

un

d“a

ttri

bu

tes”

Mid

dle

sch

oo

l(11

5)S

ame

Sam

eS

Dfo

rm

ne

mo

nic

pic

ture

s

886



Man

ge&

Par

knas

(196

2)E

xpe

rim

en

t1

1.P

ictu

rein

form

atio

nsl

ide

+p

ictu

rete

stsl

ide

2.P

ictu

rein

form

atio

nsl

ide

+w

ord

test

slid

e3.

Wo

rdsl

ide

+p

ictu

rete

stsl

ide

4.W

ord

slid

e+

wo

rdte

stsl

ide

Bio

logy

less

on

on

pla

nt

typ

es

Mid

dle

sch

oo

l(22

8)R

ete

nti

on

ofp

icto

rial

or

verb

alin

form

atio

nW

ritt

en

SD

wh

en

rete

nti

on

me

asu

red

by

pic

tori

alte

stin

g

Exp

eri

me

nt

2S

ame

Sam

eC

oll

ege

(81)

Sam

eS

ame

SD

(sam

eco

nd

itio

n)

Exp

eri

me

nt

31.

Pro

se+

film

stri

p2.

Pro

seL

ess

on

on

Gre

en

lan

dM

idd

lesc

ho

ol(

192)

Re

ten

tio

nu

sin

gb

oth

verb

alan

dp

icto

rial

qu

est

ion

sS

ame

SD

(sam

eco

nd

itio

n)

Mai

n&

Gri

ffit

hs

(197

7)1.

Pri

nte

dte

xt+

pri

nte

dan

dp

icto

rial

sup

ple

me

nt

2.P

rin

ted

text

+au

dio

and

pic

tori

alsu

pp

lem

en

t3.

Pri

nte

dte

xt+

pri

nte

dsu

pp

lem

en

t4.

Pri

nte

dte

xt(c

on

tro

l)

12p

assa

ges

fro

ma

chap

ter

on

we

ath

er

Ad

ult

(120

)1.

Vo

cab

ula

ryte

st2.

A10

0-it

em

sen

ten

ceco

mp

leti

on

par

t3.

A55

-ite

mm

ult

iple

-ch

oic

ese

ctio

n

Wri

tte

nO

ral

SD

,all

exp

eri

me

nta

lgr

ou

ps

vs.c

on

tro

lN

SD

be

twe

en

exp

eri

me

nta

lgro

up

s

May

er

(198

9)E

xpe

rim

en

t1

1.Te

xt+

illu

stra

tio

ns

incl

ud

ing

lab

els

2.Te

xto

nly

Ve

hic

leb

raki

ng

syst

em

sC

oll

ege

(34)

95id

ea

un

its

ofb

oth

exp

lan

ato

ryan

dn

on

exp

lan

ato

ryin

form

atio

n

Wri

tte

nS

Do

nre

call

of

exp

lan

ato

ryin

form

atio

n

Exp

eri

me

nt

21.

Text

+la

be

led

illu

stra

tio

ns

Sam

eC

oll

ege

(44)

Sam

eS

ame

SD

on

reca

llo

fe

xpla

nat

ory

info

rmat

ion

for

lab

ele

dil

lust

rati

on

s2.

Text

+n

on

lab

ele

dil

lust

rati

on

s3.

Text

on

lyM

cCo

rmic

ke

tal

.(1

984)

1.R

ela

ted

text

+se

par

ate

mn

em

on

icil

lust

rati

on

sT

hre

efi

ctit

iou

sb

iogr

aph

ical

sto

rie

s

Co

lle

ge(1

60)

11sh

ort

-an

swe

rre

call

qu

est

ion

sW

ritt

en

SD

for

inte

grat

ed

mn

em

on

icil

lust

rati

on

s2.

Re

late

dte

xt+

inte

grat

ed

mn

em

on

icil

lust

rati

on

3.N

on

inte

rfe

ren

ceco

ntr

ol(

read

3u

nre

late

dp

assa

ges)

4.In

terf

ere

nce

con

tro

l(re

ad3

rela

ted

bu

tp

ote

nti

ally

inte

rfe

rin

gp

assa

ges)

McC

orm

ick

&L

evi

n(1

984)

Exp

eri

me

nt

11.

Text

+m

ne

mo

nic

pic

ture

s(k

ey

wo

rd-p

aire

d)

Fo

ur

fict

itio

us

bio

grap

hie

sM

idd

lesc

ho

ol(

220)

20cu

ed

-re

call

qu

est

ion

sW

ritt

en

SD

for

allt

hre

em

ne

mo

nic

con

dit

ion

sN

SD

be

twe

en

the

m2.

Text

+m

ne

mo

nic

pic

ture

s(k

ey

wo

rd-c

hai

ne

d)

3.Te

xt+

mn

em

on

icp

ictu

res

(ke

yw

ord

-in

tegr

ate

d)

4.S

imp

leco

ntr

ol(

text

+ad

dit

ion

alst

ud

ye

ach

sen

ten

ce)

5.C

um

ula

tive

con

tro

l(te

xt+

cum

ula

tive

stu

dy

ofa

llse

nte

nce

s)E

xpe

rim

en

t2

Sam

ee

xce

pt

de

lete

con

dit

ion

2ab

ove

Sam

eM

idd

lesc

ho

ol(

82)

Nam

e–a

ttri

bu

tere

cogn

itio

nte

st,b

oth

imm

ed

iate

and

2d

ayd

ela

yed

Sam

eS

Dfo

rke

yw

ord

con

dit

ion

s,b

oth

imm

ed

iate

and

de

laye

d

Con

tin

ues

887



TAB

LE

33.A

3.(C

on

tin

ue

d)

Stu

dy

Tre

atm

en

tC

on

ten

tsS

ub

ject

s(N

)D

ep

ed

en

tV

aria

ble

(s)

Pro

seTy

pe

Re

sult

(s)

Mil

ler

(193

8)1.

Pro

se+

illu

stra

tio

ns

Th

ree

sto

rie

sfr

om

bas

alre

ade

rsE

lem

en

tary

sch

oo

l(60

0)C

om

pre

he

nsi

on

Wri

tte

nN

SD

2.P

rose

on

lyM

oo

re(1

975)

1.Il

lust

rati

on

s+

pro

seto

geth

er

Text

on

lear

nin

gti

me

fro

ma

sun

dia

lE

lem

en

tary

sch

oo

l(63

)C

om

pre

he

nsi

on

,20-

ite

mm

ult

iple

-ch

oic

eW

ritt

en

NS

D2.

Illu

stra

tio

ns

be

fore

pro

se3.

Illu

stra

tio

ns

afte

rp

rose

4.P

rose

on

lyN

uge

nt

(198

2)E

xpe

rim

en

t1

1.V

isu

als

+p

rin

t+

aud

io.5

.Vis

ual

sF

ilm

abo

ut

fact

ual

life

ofa

che

eta

hE

lem

en

tary

sch

oo

l&m

idd

lesc

ho

ol(

201)

23m

ult

iple

-ch

oic

eco

mp

reh

en

sio

nte

stO

ral

NS

D,s

ingl

em

ed

ium

2.V

isu

als

+p

rin

t.6.

Pri

nt

SD

,du

alm

ed

ia3.

Vis

ual

s+

aud

io.7

.Au

dio

SD

,th

ree

me

dia

4.P

rin

t+

aud

io.

8.C

on

tro

lO

’Ke

efe

&S

olm

an(1

987)

Exp

eri

me

nts

1&

21.

Co

mp

lex

pic

ture

sb

efo

rep

rose

Sto

rie

sab

ou

t47

0w

ord

sin

len

gth

Ele

me

nta

rysc

ho

ol(

118)

Re

call

ofs

em

anti

can

dlo

gica

lne

two

rko

fsto

ryin

form

atio

n

Wri

tte

nN

SD

2.C

om

ple

xp

ictu

res

afte

rp

rose

3.N

orm

alp

ictu

res

be

fore

pro

se4.

No

rmal

pic

ture

saf

ter

pro

seP

ee

ck(1

974)

1.P

rose

+p

ictu

res

2.P

ictu

res

3.P

rose

on

ly

Pas

sage

fro

m“R

up

ert

Be

ar”

sto

ry

Ele

me

nta

rysc

ho

ol(

71)

40it

em

rete

nti

on

test

Imm

ed

iate

,1-d

ay-

and

1-w

ee

k-d

ela

yed

test

ing

Wri

tte

nS

D,i

mm

ed

iate

and

de

laye

dte

stin

g

Pe

ng

&L

evi

n(1

979)

1.P

rose

+co

lore

dli

ne

dra

win

gs2.

Pro

seo

nly

Two

10-s

en

ten

cen

arra

tive

sto

rie

sE

lem

en

tary

sch

oo

l(64

)C

ue

dre

call

usi

ng

par

aph

rase

verb

atim

qu

est

ion

s,b

oth

imm

ed

iate

and

3d

ayd

ela

yed

Ora

lS

D,i

mm

ed

iate

and

de

laye

dte

stin

g

Po

ph

am(1

969)

1.C

arto

on

-em

be

llis

he

dta

pe

/sli

de

vers

ion

Pro

gram

de

velo

pe

dfo

rp

ub

lic-

sch

oo

lad

min

istr

ato

rs

Co

lle

ge(1

75)

1.C

ogn

itiv

eac

hie

vem

en

t(5

8it

em

s)W

ritt

en

Ora

lN

SD

2.U

ne

mb

ell

ish

ed

tap

e/s

lid

eve

rsio

n2.

An

on

ymo

us

resp

on

se(4

ite

ms)

3.P

rogr

amm

ed

text

vers

ion

Pre

ssle

y,P

igo

tt,&

Bry

ant

(198

2)E

xpe

rim

en

t1

1.P

rose

+co

mp

lete

lym

atch

ed

pic

ture

Two

list

so

fco

ncr

ete

sen

ten

ces

You

ng

chil

dre

n(1

26)

Co

rre

ctre

call

resp

on

ses

Ora

lS

D,m

atch

ed

pic

ture

s>

pro

seo

nly

,pro

seo

nly

>m

ism

atch

ed

pic

ture

s2.

Pro

se+

acto

rac

tio

np

ictu

re3.

Pro

se+

acto

rst

atic

pic

ture

4.P

rose

+m

ism

atch

ed

pic

ture

/ob

ject

inco

rre

ct5.

Pro

se+

inco

rre

cto

bje

ctp

ictu

re6.

Pro

seo

nly

.E

xpe

rim

en

t2

1.P

rose

+co

mp

lete

lym

atch

ed

pic

ture

2.P

rose

+ac

tor

acti

on

/ob

ject

corr

ect

pic

ture

3.P

rose

+ac

tor

stat

ic/o

bje

ctp

ictu

re4.

Pro

seo

nly

Sam

eYo

un

gch

ild

ren

(52)

Sam

eS

ame

SD

,Mat

che

dp

ictu

res

>ac

tio

no

bje

ct>

stat

ico

bje

ct>

pro

seo

nly

NS

D,a

ctio

no

bje

ctan

dst

atic

ob

ject

888



Pre

ssle

ye

tal

.(19

83)

Co

llap

sed

exp

eri

me

nts

1,2,

2A,3

,&

3A

1.P

rose

+m

atch

ed

pic

ture

s2.

Pro

se+

mis

mat

che

dp

ictu

res

33co

ncr

ete

sen

ten

ces

or

6m

od

era

tely

dif

ficu

ltst

ori

es

Ele

me

nta

rysc

ho

ol(

414)

1.C

ue

d-r

eca

llq

ue

stio

ns

2.P

ictu

rere

cogn

itio

nin

som

ein

stan

ces

Wri

tte

nO

ral

SD

inal

lcas

es

for

mat

che

dp

ictu

res

NS

Dfo

rm

ism

atch

ed

pic

ture

svs

.pro

seo

nly

3.P

rose

on

lyN

ote

:Ab

ove

bas

icco

nd

itio

ns

we

reco

mb

ine

dw

ith

exp

lici

to

rn

on

exp

lici

tin

stru

ctio

ns

rega

rdin

gp

ictu

re–t

ext

rela

tio

nsh

ips

Ran

kin

&C

ulh

ane

(197

0)1.

Typ

ed

form

atte

xtw

ith

no

illu

stra

tio

ns

2.P

rin

ted

form

atte

xtw

ith

illu

stra

tio

ns

Ap

assa

gefr

om

“Pio

ne

er

Lif

ein

Am

eri

ca”

Mid

dle

sch

oo

l(57

)H

igh

sch

oo

l(22

)50

ite

mcl

oze

com

pre

he

nsi

on

test

Wri

tte

nN

SD

Ras

coe

tal

.(19

75)

Exp

eri

me

nt

11.

Pro

se+

dra

win

gs+

Inst

ruct

ion

alst

rate

gy2.

Pro

se+

dra

win

gs3.

Pro

se+

inst

ruct

ion

alst

rate

gy4.

Pro

seo

nly

A2,

511-

wo

rdp

rose

pas

sage

Un

de

rgra

du

ate

(91)

35-i

tem

test

wit

h28

tru

e/f

alse

,1co

nst

ruct

ed

resp

on

se,a

nd

6m

ult

iple

-ch

oic

eq

ue

stio

ns

on

the

verb

alin

form

atio

nin

the

text

Wri

tte

nN

SD

Exp

eri

me

nt

2S

ame

Sam

eH

igh

sch

oo

l(80

)S

ame

Sam

eN

SD

Exp

eri

me

nt

3S

ame

Two

sho

rte

rp

assa

ges

(429

wo

rds

and

633

wo

rds)

Ele

me

nta

rysc

ho

ol(

93)

20m

ult

iple

-ch

oic

eq

ue

stio

ns

on

verb

alin

form

atio

n

Sam

eS

D,p

rose

+st

rate

gy+

pic

ture

s

Re

id,B

rigg

s,&

Be

veri

dge

(198

3)

1.P

rose

+co

lore

dil

lust

rati

on

s2.

Pro

se+

bla

ck-&

-wh

ite

illu

stra

tio

ns

3.P

rose

on

ly

Sp

eci

fica

lly

wri

tte

nsc

ien

ceto

pic

,“s

tru

ctu

rean

dfu

nct

ion

oft

he

mam

mal

ian

he

art”

Mid

dle

sch

oo

l(33

8)1.

Clo

zete

stim

me

dia

tely

2.O

bje

ctiv

ete

stit

em

s,d

ela

yed

15m

in

Wri

tte

nS

Dfo

rp

ictu

res

on

ob

ject

ive

test

NS

Dfo

rp

ictu

res

on

clo

zete

stin

g

Ric

e,D

oan

,&B

row

n(1

981)

1.P

rose

+p

ictu

res

2.P

rose

on

lyP

rose

sto

ry,“

Lit

tle

Be

ar”

Ele

me

nta

rysc

ho

ol(

60)

Re

adin

gco

mp

reh

en

sio

nw

ith

an11

-ite

mte

stW

ritt

en

SD

Rid

ing

&S

ho

re(1

974)

1.P

rose

+vi

sual

s2.

Pro

seo

nly

Pro

sep

assa

gefr

om

“AS

tory

of

Rh

od

pis

”co

nta

inin

g18

5w

ord

s

Hig

hsc

ho

ol(

100)

Re

call

test

wit

h43

qu

est

ion

sO

ral

SD

Ro

hw

er

&M

atz

(197

5)1.

Pro

se+

pic

ture

s2.

Pro

seo

nly

Pro

seco

nta

inin

gth

ree

pas

sage

sE

lem

en

tary

sch

oo

l(12

8)To

taln

um

be

ro

fass

ert

ion

sco

rre

ctly

veri

fie

dO

ral

SD

Ro

hw

er

&H

arri

s(1

975)

1.O

ralp

rose

+w

ritt

en

pro

se+

pic

ture

sP

assa

ges

abo

ut

two

typ

es

ofm

on

keys

Ele

me

nta

rysc

ho

ol(

186)

1.S

ho

rtan

swe

rs2.

Fre

ere

call

3.V

eri

fica

tio

no

fsta

tem

en

tsin

text

Ora

lW

ritt

en

SD

,ora

l+p

ictu

res

was

sup

eri

or

2.W

ritt

en

pro

se+

pic

ture

s3.

Ora

lpro

se+

pic

ture

s4.

Ora

lpro

se+

wri

tte

np

rose

5.P

ictu

res

on

ly6.

Ora

lpro

seo

nly

7.W

ritt

en

pro

seo

nly

Ro

yer

&C

able

(197

6)1.

Ab

stra

ctp

assa

ge+

illu

stra

tio

ns

Sci

en

cele

sso

no

nh

eat

flo

wan

de

lect

rica

lco

nd

uct

ivit

y

Co

lle

ge(8

0)R

eca

llo

f“id

ea

un

its”

Wri

tte

nS

D2.

Un

em

be

llis

he

dab

stra

ctp

assa

ge3.

Ab

stra

ctp

assa

gew

ith

anal

ogu

es

4.C

on

cre

tep

assa

ge5.

Un

rela

ted

pro

se(c

on

tro

l)

Con

tin

ues

889



TAB

LE

33.A

3.(C

on

tin

ue

d)

Stu

dy

Tre

atm

en

tC

on

ten

tsS

ub

ject

s(N

)D

ep

ed

en

tV

aria

ble

(s)

Pro

seTy

pe

Re

sult

(s)

Ru

ch&

Le

vin

(197

7)1.

Par

tial

test

(par

tial

pic

ture

sw

ith

eac

hq

ue

stio

n)

2.P

arti

alst

ud

y(l

oo

kat

par

tial

pic

ture

sd

uri

ng

nar

rati

ve)

3.R

ep

eti

tio

n(e

ach

sen

ten

cetw

ice

insu

cce

ssio

n)

4.C

on

tro

l(li

ste

ne

dto

text

on

ce).

Two

10-s

en

ten

cen

arra

tive

pas

sage

sE

lem

en

tary

sch

oo

l(11

2)C

ue

dre

call

10ve

rbat

im10

par

aph

rase

Ora

lS

D(r

ela

tive

too

the

r3

con

dit

ion

s)fo

rp

arti

alp

ictu

res

du

rin

gst

ud

yo

np

arap

hra

seq

ue

stio

ns

on

ly

Ru

ch&

Le

vin

(197

9)E

xpe

rim

en

t1

1.R

ein

stat

ed

pic

ture

con

dit

ion

(pro

se+

par

tial

pic

ture

ato

nse

to

fp

assa

gean

dat

qu

est

ion

tim

e)

Two

-se

nte

nce

nar

rati

vep

assa

gem

akin

gre

fere

nce

toan

ob

ject

Ele

me

nta

rysc

ho

ol(

48)

Se

to

f10

“Wh

—”

qu

est

ion

sco

nta

inin

gb

oth

par

aph

rase

and

verb

atim

info

rmat

ion

Ora

lS

Dfo

rre

inst

ate

dp

ictu

reco

nd

itio

no

nly

2.P

arti

alp

ictu

reco

nd

itio

n(p

rose

+p

arti

alp

ictu

reat

on

set

ofe

ach

pas

sage

)3.

Pro

seo

nly

Exp

eri

me

nt

21.

Re

inst

ate

dd

esc

rip

tio

ns

(pro

se+

par

tial

pic

ture

ato

nse

to

fpas

sage

&tw

o-s

en

ten

ceve

rbal

de

scri

pti

on

pri

or

toe

ach

qu

est

ion

)

Sam

ep

lus

two

-se

nte

nce

verb

ald

esc

rip

tio

nd

eve

lop

ed

for

eac

hp

ictu

read

de

d

Ele

me

nta

rysc

ho

ol(

42)

Sam

eS

ame

SD

,re

inst

ate

dp

ictu

res

>re

inst

ate

dd

esc

rip

tio

ns

2.R

ein

stat

ed

pic

ture

s(p

rose

+p

arti

alp

ictu

res

bo

thd

uri

ng

sto

ryan

dq

ue

stio

ns)

3.P

arti

alp

ictu

res

on

lyd

uri

ng

sto

ryp

rese

nta

tio

n4.

Pro

seo

nly

Ru

ste

d&

Co

lth

ear

t(1

979b

)1.

Pro

se+

sim

ple

lin

ed

raw

ings

2.P

rose

on

lyTw

ose

tso

fco

ncr

ete

no

un

sp

lus

ash

ort

pro

sep

assa

ge

Ele

me

nta

rysc

ho

ol(

32)

Me

anre

call

,re

cogn

itio

nan

dp

ron

un

ciat

ion

sco

res

Wri

tte

nS

D

Ru

ste

d&

Co

lth

ear

t(1

979a

)E

xpe

rim

en

t1

1.P

rose

+li

ne

dra

win

gs2.

Pro

seo

nly

Six

sho

rtfa

ctu

alp

assa

ges

ofh

igh

lyu

nu

sual

pla

nt

or

cre

atu

res

Ele

me

nta

rysc

ho

ol(

72)

Fre

ere

call

,bo

thim

me

dia

tean

d5–

7m

ind

ela

yed

Wri

tte

nS

D,b

oth

imm

ed

iate

and

de

laye

dte

stin

g

Exp

eri

me

nt

21–

3.P

rose

+th

ree

pic

ture

typ

es

Sam

eE

lem

en

tary

sch

oo

l(10

0)N

um

be

ro

ffe

atu

res

reca

lle

d,b

oth

imm

ed

iate

and

de

laye

dte

stin

g

Sam

eS

Din

de

pe

nd

en

to

fp

ictu

rety

pe

,bo

thim

me

dia

tean

dd

ela

yed

test

ing

4–6.

Th

ree

pic

ture

typ

es

alo

ne

7.P

rose

Pic

ture

typ

es:

(a)

lin

ed

raw

ing,

(b)

colo

red

dra

win

g,(c

)co

lor

and

bac

kgro

un

dR

ust

ed

&H

od

gso

n(1

985)

1.Te

xt+

text

-re

leva

nt

and

text

-no

nre

leva

nt

pic

ture

sO

ne

fact

ual

and

on

efi

ctit

iou

sp

assa

geM

idd

lesc

ho

ol(

40)

Ora

lre

call

sco

res

Wri

tte

nS

Dfo

rfa

ctu

al/e

xpo

sito

ryte

xt2.

Text

on

lyS

cru

ggs

et

al.

(198

5)1.

Mn

em

on

icin

stru

ctio

n(1

0in

tera

ctiv

eil

lust

rati

on

s)P

assa

ged

esc

rib

ing

8N

ort

hA

me

rica

nm

ine

rals

Hig

hsc

ho

ol+

LD

(56)

Re

call

ofm

ine

rala

ttri

bu

tes

Wri

tte

nS

D,m

ne

mo

nic

con

dit

ion

2.D

ire

ctst

ud

y(r

eal

isti

cco

lore

dil

lust

rati

on

)3.

Fre

est

ud

y(t

ext

on

ly)

890



Se

we

ll&

Mo

ore

(198

0)1.

Car

too

nte

xt(t

ext

+43

cart

oo

ne

mb

ell

ish

me

nts

)C

arto

on

stri

pu

sed

asp

assa

geC

oll

ege

(150

)C

om

pre

he

nsi

on

usi

ng

25-i

tem

mu

ltip

le-c

ho

ice

test

Wri

tte

nS

D,a

ud

io/v

isu

al

2.V

isu

alo

nly

(car

too

ne

mb

ell

ish

me

nt)

Ora

l3.

Au

dio

/vis

ual

(au

dio

+sl

ide

so

fca

rto

on

s)4.

Au

dio

on

ly5.

Pri

nte

dte

xto

nly

Sh

erm

an(1

976)

1.G

rap

hic

par

tial

be

fore

pas

sage

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ht

70-w

ord

par

agra

ph

s(b

oth

con

cre

tean

dab

stra

ctve

rsio

ns)

Hig

hsc

ho

ol(

144)

Fre

ere

call

Tota

lwo

rds,

ide

au

nit

s,an

dth

em

atic

intr

usi

on

sre

call

ed

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tte

nS

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ral

lgra

ph

ics

vs.a

llve

rbal

con

dit

ion

s2.

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ph

icp

arti

alaf

ter

pas

sage

3.G

rap

hic

com

ple

teb

efo

rep

assa

ge4.

Gra

ph

icco

mp

lete

afte

rp

assa

ge5.

Ve

rbal

par

tial

be

fore

pas

sage

6.V

erb

alp

arti

alaf

ter

pas

sage

7.V

erb

alco

mp

lete

be

fore

pas

sage

8.V

erb

alco

mp

lete

afte

rp

assa

geS

hri

be

rge

tal

.(1

892)

Exp

eri

me

nt

11.

Pro

se+

pic

ture

sp

lus

(co

lore

d“k

ey

wo

rd”

lin

ed

raw

ings

+2

add

itio

nal

pie

ces

ofi

nci

de

nta

lin

form

atio

n)

12th

ree

-se

nte

nce

pas

sage

sab

ou

tfa

mo

us

pe

op

le

Mid

dle

sch

oo

l(48

)12

sets

oft

est

qu

est

ion

sre

lati

ng

top

assa

ges

Wri

tte

nS

Dfo

rp

ictu

res

2.P

rose

+p

ictu

res

(co

lore

d“k

ey

wo

rd”

lin

ed

raw

ings

)N

SD

be

twe

en

pic

ture

con

dit

ion

s3.

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se(1

2p

assa

ges

Exp

eri

me

nt

21.

Pro

se+

pic

ture

sp

lus

(co

lore

d“k

ey

wo

rd”

lin

ed

raw

ings

+4

add

itio

nal

pie

ces

ofi

nci

de

nta

lin

form

atio

n)

Sam

eM

idd

lesc

ho

ol(

48)

Sam

eS

ame

SD

2.Im

age

ry+

nam

e&

key

wo

rdp

age

s3.

Pro

se(1

2p

assa

ges)

Sil

vern

(198

0)1.

Pic

ture

(lis

ten

+p

ictu

re)

Two

sto

rie

s,e

ach

10se

nte

nce

slo

ng

You

ng

chil

dre

n(4

0)C

om

pre

he

nsi

on

usi

ng

10“W

h—

”q

ue

stio

ns

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lN

SD

2.P

lay

(lis

ten

+p

rete

nd

inst

ory

)3.

Re

pe

titi

on

(lis

ten

twic

e)

4.C

on

tro

l(li

ste

no

nce

)S

no

wm

an&

Cu

nn

ingh

am(1

975)

1.P

ictu

res

be

fore

rele

van

tte

xt2.

Pic

ture

saf

ter

rele

van

tte

xt3.

Pic

ture

s&

qu

est

ion

sb

efo

rere

leva

nt

text

A2,

189-

wo

rdfi

ctit

iou

sp

assa

geU

nd

erg

rad

uat

e(6

3)R

eca

llo

fsp

eci

fic

fact

ual

info

rmat

ion

for

bo

thp

ract

ice

dan

dn

on

pra

ctic

ed

ite

ms

Wri

tte

nN

SD

(wit

hre

spe

ctto

typ

eo

fad

jun

ctai

d)

4.P

ictu

res

&q

ue

stio

ns

afte

rre

leva

nt

text

5.Q

ue

stio

ns

be

fore

rele

van

tte

xt6.

Qu

est

ion

saf

ter

rele

van

tte

xt7.

Text

wit

hn

oad

jun

ctai

ds

Sto

ne

&G

lock

(198

1)1.

Pro

se+

text

-re

du

nd

ant

lin

ed

raw

ings

2.Te

xt-r

ed

un

dan

tli

ne

dra

win

gs3.

Text

on

ly

Dir

ect

sfo

ras

sem

bly

ofa

“han

dtr

uck

”to

y

Un

de

rgra

du

ate

(90)

1.N

um

be

ro

fass

em

bly

err

ors

2.C

om

pre

he

nsi

on

of

read

ing

the

inst

ruct

ion

s

Wri

tte

nS

D(d

raw

ing

+te

xt)

Str

om

ne

s&

Nym

an(1

974)

1.P

rose

+m

ne

mo

nic

illu

stra

tio

ns

pre

ced

ing

eac

hse

nte

nce

2.P

rose

on

ly

Two

30-s

en

ten

cest

ori

es

of

con

ne

cte

dd

isco

urs

e

Hig

hsc

ho

ol(

30)

Imm

ed

iate

pac

ed

reca

llw

ith

pic

ture

so

re

mp

tyfr

ame

s;p

ace

dan

dfr

ee

reca

ll1

year

de

laye

d

Wri

tte

nS

Dfo

rim

me

dia

teb

ut

NS

Dfo

rd

ela

yed

test

ing

Con

tin

ues

891



TAB

LE

33.A

3.(C

on

tin

ue

d)

Stu

dy

Tre

atm

en

tC

on

ten

tsS

ub

ject

s(N

)D

ep

ed

en

tV

aria

ble

(s)

Pro

seTy

pe

Re

sult

(s)

Tall

ey

(198

9)1.

Bas

alte

xt+

bas

alp

ictu

res

2.S

tory

gram

mar

+st

ory

gram

mar

pic

ture

s

Fo

ur

sto

rie

sfr

om

bas

alre

ade

rsE

lem

en

tary

sch

oo

l(72

)1.

Co

mp

reh

en

sio

nq

ue

stio

ns

Wri

tte

nS

Dfo

rp

ictu

reco

nd

itio

ns

2.R

eca

llm

eas

ure

s3.

Lit

era

ture

+p

ictu

res

4.B

asal

text

5.S

tory

gram

mar

6.L

ite

ratu

reT

ho

mas

(197

8)1.

Co

lor

ph

oto

grap

hs

+te

xt2.

Sim

pli

fie

dli

ne

dra

win

gs+

text

Pro

sefr

om

asc

ien

cete

xtb

oo

kE

lem

en

tary

sch

oo

l(10

8)1.

Lit

era

lco

mp

reh

en

sio

n2.

Infe

ren

tial

com

pre

he

nsi

on

Wri

tte

nN

SD

3.Te

xto

nly

Tow

ers

(199

4)E

xpe

rim

en

t1

1.P

rose

on

lyW

eat

he

rp

atte

rns

Co

lle

ge(6

9)10

sho

rt-a

nsw

er

par

aph

rase

qu

est

ion

sW

ritt

en

SD

2.P

rose

+st

atic

visu

als

Exp

eri

me

nt

2S

ame

Sam

eC

oll

ege

(64)

13sh

ort

-an

swe

rp

arap

hra

seq

ue

stio

ns

+4

com

pre

he

nsi

on

qu

est

ion

s

Sam

eN

SD

No

te:T

he

setw

oe

xpe

rim

en

tsal

soco

nta

ine

dan

anim

ate

dtr

eat

me

nt

that

isn

ot

incl

ud

ed

inth

issu

mm

ary

Ve

rno

n(1

953)

Se

rie

s1

&2

1.P

rose

+p

ho

togr

aph

sE

xpo

sito

rysh

ort

sto

rie

so

f700

–800

wo

rds

eac

h

Hig

hsc

ho

ol(

62)

Ora

lre

call

ofv

erb

alin

form

atio

n(m

ajo

rp

oin

ts)

Wri

tte

nN

SD

2.P

rose

+gr

aph

s(s

eri

es

1o

nly

)3.

Pro

seo

nly

Ve

rno

n(1

954)

Exp

eri

me

nt

11.

Pro

se+

pic

ture

s2.

Pro

seo

nly

Text

fro

mtw

osm

all

bo

oks

755

and

940

wo

rds

inle

ngt

h

Ele

me

nta

rysc

ho

ol&

mid

dle

sch

oo

l(24

)S

ixfa

irly

gen

era

lqu

est

ion

sre

late

dto

text

on

reca

llm

eas

ure

s

Wri

tte

nN

SD

Exp

eri

me

nt

21.

Pro

se+

pic

ture

scu

tou

tfr

om

bo

ok

2.P

rose

+fo

ur

sim

ple

lin

ed

raw

ings

Text

take

nfr

om

bo

ok,

Th

eSh

ape

of

Th

ings

Ele

me

nta

ryS

cho

ol(

60)

1.N

um

be

ro

fite

ms

rem

em

be

red

Ora

lN

SD

3.Te

xt+

ph

oto

grap

hs

2.Q

ue

stio

nto

test

un

de

rsta

nd

ing

Vye

et

al.(

1986

)1.

Se

nte

nce

+p

ictu

re2.

Pic

ture

3.S

en

ten

ce

20p

reci

sese

nte

nce

san

d20

imp

reci

sese

nte

nce

s

Un

de

rgra

du

ate

(168

)C

ue

dre

call

Ora

lS

Dfo

rse

nte

nce

+p

ictu

reco

nd

itio

nsu

pe

rio

r

No

te:A

bo

vein

stru

ctio

nal

con

dit

ion

scr

oss

ed

wit

he

lab

ora

tio

nty

pe

(pre

cise

,im

pre

cise

),cr

oss

ed

wit

hre

trie

valc

ue

(ve

rbal

,pic

tori

al)

,yi

eld

ing

12to

tali

nst

ruct

ion

alco

nd

itio

ns

892



Wad

dil

l,M

cDan

iel,

&E

inst

ein

(198

8)E

xpe

rim

en

t1

1.P

rose

+d

eta

ile

dp

ictu

res

2.P

rose

+re

lati

on

alp

ictu

res

3.P

rose

on

ly

Two

text

typ

es,

an

arra

tive

fair

yta

lean

dan

exp

osi

tory

text

Co

lle

ge(1

72)

1.C

om

pre

he

nsi

on

2.F

ree

reca

ll3.

Cu

ed

reca

ll

Wri

tte

nS

Dd

ep

en

de

nt

on

text

typ

ean

dp

ictu

rety

pe

Exp

eri

me

nt

2S

ame

+su

bje

cts

inst

ruct

ed

toat

ten

dto

the

typ

eo

fin

form

atio

n,n

ot

no

rmal

lye

nco

de

dfr

om

eac

hte

xtty

pe

Sam

eC

oll

ege

(72)

Sam

eS

ame

SD

de

pe

nd

en

to

nte

xtty

pe

and

pic

ture

typ

e

We

intr

aub

(196

0)1.

Pro

se+

pic

ture

s2.

Pic

ture

so

nly

3.P

rose

on

ly

Th

ree

sto

rie

sfr

om

sele

cte

db

asal

read

ers

Ele

me

nta

rysc

ho

ol(

104)

Qu

est

ion

sd

eal

ing

wit

hco

mp

reh

en

sio

nW

ritt

en

NS

D

We

isb

erg

(197

0)1.

Pro

se+

adva

nce

do

rgan

ize

r(g

rap

h)

2.P

rose

+ad

van

ced

org

aniz

er

(map

)3.

Pro

se+

adva

nce

do

rgan

ize

r(v

erb

al)

Ear

thsc

ien

ceco

nce

pts

Mid

dle

sch

oo

l(96

)40

qu

est

ion

s,ve

rbal

mu

ltip

lech

oic

eo

fkn

ow

led

geco

nte

nt

Wri

tte

nS

D,m

ap>

grap

h>

verb

al>

pro

se

4.P

rose

+n

oad

van

ced

org

aniz

er

Wo

olr

idge

et

al.

(198

2)1.

Par

tial

pic

ture

sd

uri

ng

pro

se&

qu

est

ion

ph

ase

s2.

Par

tial

pic

ture

sd

uri

ng

qu

est

ion

ph

ase

Two

10-s

en

ten

cen

arra

tive

par

agra

ph

s

Ele

me

nta

rysc

ho

ol(

80)

Two

10-s

en

ten

cen

arra

tive

par

agra

ph

sO

ral

SD

3.P

arti

alp

ictu

res

du

rin

gp

rose

ph

ase

4.P

rose

on

ly

Note

.NS

D,n

on

sign

ific

ant

dif

fere

nce

;SD

,sig

nif

ican

tst

atic

grap

hic

eff

ect

.

893




APPENDIX 33.4. STUDIES LISTED IN THE MATRIXFOR STATIC VISUALS (SEE APPENDIX 33.3)

Alesandrini, K. L. (1981). Pictorial-verbal and analytic-holistic learningstrategies in science learning. Journal of Educational Psychology,73, 358–368.

Alesandrini, K. L., & Rigney, J. W. (1981). Pictorial presentation andreview strategies in science learning. Journal of Research in ScienceTeaching, 18(5), 465–474.

Anglin, G. J. (1986). Prose-relevant pictures and older learners’ recallof written prose. Educational Communication and TechnologyJournal, 34(3), 131–136.

Anglin, G. J. (1987). Effect of pictures on recall of written prose: Howdurable are picture effects? Educational Communication and Tech-nology Journal, 35(1), 25–30.

Anglin, G. J., & Stevens, J. T. (1986). Prose-relevant pictures and recallfrom science text. Perceptual and Motor Skills, 63(3), 1143–1148.

Arnold, D. J., & Brooks, P. H. (1976). Influence of contextual organizingmaterial on children’s listening comprehension. Journal of Educa-tional Psychology, 68, 711–716.

Beck, C. R. (1984). Visual cueing strategies: Pictorial, textual, and com-binational effects. Educational Communication and TechnologyJournal, 32, 207–216.

Bender, B. G., & Levin, J. R. (1978). Pictures, imagery, and retardedchildren’s prose learning. Journal of Educational Psychology, 70,583–588.

Bernard, R. M., Petersen, C. H., & Ally, M. (1981). Can images providecontextual support for prose? Educational Communication andTechnology Journal, 29, 101–108.

Bieger, G. R., & Glock, M. D. (1984). Comprehending spatial and con-textual information in picture-text instructions. Journal of Experi-mental Education, 181–188.

Bluth, L. F. (1973). A comparison of the reading comprehension of goodand poor readers in the second grade with and without illustration(Doctoral dissertation, University of Illinois at Urbana-Champaign,1972). Dissertations Abstracts International, 34, 637A.

Borges, M. A., & Robins, S. L. (1980). Contextual and motivational cueeffects on the comprehension and recall of prose. PsychologicalReports, 47, 263–268.

Bransford, J. D., & Johnson, M. K. (1972). Contextual prerequisitesfor understanding: Some investigations of comprehension and re-call. Journal of Verbal Learning and Verbal Behavior, 11, 717–726.

Covey, R. E., & Carroll, J. L. (1985, October). Effects of adjunct pictureson comprehension of grade six science texts under three levelsof text organization. Paper presented at the annual meeting of theEvaluation Network/Evaluation Research Society, San Francisco, CA.(ERIC Document Reproduction Service No. 259-946)

Dean, R. S., & Enemoh, P. A. (1983). Pictorial organization in proselearning. Contemporary Educational Psychology, 8, 20–27.

DeRose, T. (1976). The effects of verbally and pictorially inducedand imposed strategies on children’s memory for text. Madison:Wisconsin Research and Development Center for Cognitive Learn-ing, University of Wisconsin. (ERIC Document Reproduction ServiceNo. 133–709).

Digdon, N., Pressley, M., & Levin, J. R. (1985). Preschoolers’ learningwhen pictures do not tell the whole story. Educational Communi-cation and Technology Journal, 33, 139–145.

Duchastel, P. C. (1980). Test of the role in retention of illustrations intext. Psychological Reports, 47, 204–206.

Duchastel, P. C. (1981). Illustrations in text: A retentional role. Pro-grammed Learning and Educational Technology, 18, 11–15.

Durso, F. T., & Johnson, M. K. (1980). The effects of orienting taskson recognition, recall, and modality confusion of pictures andwords. Journal of Verbal Learning and Verbal Behavior, 19, 416–429.

Gibbons, J., et al. (1986). Young children’s recall and reconstruction ofaudio and audiovisual narratives. Child Development, 57(4), 1014–1023.

Goldberg, F. (1974). Effects of imagery on learning incidental materialin the classroom. Journal of Educational Psychology, 66, 233–237.

Goldston, D. B., & Richman, C. L. (1985). Imagery, encoding, specificity,and prose recall in 6-year-old children. Journal of ExperimentalChild Psychology, 40, 395–405.

Guttmann, J., Levin, J. R., & Pressley, M. (1977). Pictures, partial pic-tures, and young children’s oral prose learning. Journal of Educa-tional Psychology, 69, 473–480.

Hannafin, M. J. (1988). The effects of instructional explicitness on learn-ing and error persistence. Contemporary Educational Psychology,13, 126–132.

Haring, M. J., & Fry, M. A. (1979). Effect of pictures on children’s com-prehension of written text. Educational Communication and Tech-nology Journal, 27, 185–190.

Hayes, D. A., & Henk, W. A. (1986). Understanding and remember-ing complex prose augmented by analogic and pictorial illustration.Journal of Reading Behavior, 18(1), 63–77.

Hayes, D. A., & Readance, J. E. (1983). Transfer of learning fromillustration-dependent text. Journal of Educational Research, 76,245–248.

Hayes, D. A., & Readence, J. E. (1982). Effects of cued attention toillustrations in text. In G. A. Niles & L. A. Harris (Eds.), New inquiriesin reading research and instruction (pp. 60–63). Thirty-first yearbook of the National Reading Conference. Rochester NY: NationalReading Conference.

Holliday, W. G. (1975). The effects of verbal and adjunct pictorial-verbalinformation in science instruction. Journal of Research in ScienceTeaching, 12, 77–83.

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Thomas, J. L. (1978). The influence of pictorial illustrations with writ-ten text and previous achievement on the reading comprehensionof fourth grade science students. Journal of Research in ScienceTeaching, 15, 401–405.




Towers, R. L. (1994). The effects of animated graphics and static graph-ics on student learning in a computer-based instructional format.Doctoral dissertation, University of Kentucky.

Vernon, M. D. (1953). The value of pictorial illustration. British Journalof Educational Psychology, 23, 180–187.

Vernon, M. D. (1954). The instruction of children by pictorial illustra-tion. British Journal of Educational Psychology, 24, 171–179.

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Weintraub, S. (1960). The effect of pictures on the comprehension ofa second grade basal reader (Doctoral dissertation, University ofIllinois, 1960). Dissertation Abstracts International, 21, 1428.

Weisberg, J. S. (1970). The use of visual advance organizers for learningearth science concepts. Journal of Research in Science Teaching,7, 161–165.

Woolridge, P., Nall, L., Hughes, L., Rauch, T., Stewart, G., & Richman, C. L.(1982). Prose recall in first-grade children using imagery, pictures,and questions. Bulletin of the Psychonomic Society, 20, 249–252.

APPENDIX 33.5 (pp. 898–912)

APPENDIX 33.6. STUDIES LISTED IN THE MATRIXFOR DYNAMIC VISUALS (SEE APPENDIX 33.5)

Al-Mulla, A. (1995). The influence of computer animation on learning(Doctoral dissertation, University of Kansas, 1995). Dissertation Ab-stracts International, 57, 0993A.

Alesandrini, K. L., & Rigney, J. (1981). Pictorial representation and re-view strategies in science learning. Journal of Research in ScienceTeaching 1(5), 465–474.

Atlas, R., Cornett, L., Lane, D. M., & Napier, A. (1977). The use of anima-tion in software training: Pitfalls and benefits. In M. A. Quinones &A. Ehrenstein (Eds.), Training for a Rapidly Changing Work-place (pp. 281–302). Washington DC: American PsychologicalAssociation.

Avons, S. E., Beveridge, M. C., Hickman, A. T., & Hitch, G. J. (1983).Teaching journey graph with microcomputer animation. HumanLearning, 2, 93–105.

Baek, Y. K., & Layne, B. H. (1988). Color, graphics, and animation ina computer-assisted learning tutorial lesson. Journal of Computer-Based Instruction, 15(4), 131–135.

Beichner, R. (1990). The effects of simultaneous motion presentationand graph generation in a kinematics lab. Journal of Research inScience Teaching, 27(8), 803–815.

Blake, T. (1977). Motion in instructional media: Some subject-displaymode interactions. Perceptual and Motor Skills, 44, 975–985.

Brasell, H. (1987). The effects of real-time laboratory graphing on learn-ing graphic representations of distance and velocity. Journal ofResearch in Science Teaching, 24(4), 385–395.

Caputo, D. J. (1982). An analysis of relative effectiveness of a graphic-enhanced microcomputer-based remedial system in a universitybasic mathematical skills deficiency removal plan. (Doctoral disser-tation, University of Pittsburgh, 1981.) Dissertation Abstracts Inter-national, 42, 3482A.

Caraballo, J. N. (1985). The effect of various visual display modes incomputer-based instruction and language background upon achieve-ment of selected educational objectives (Doctoral dissertation, Penn-sylvania State University, 1985). Dissertation Abstracts Interna-tional, 46(6), 1494A.

Caraballo-Rios, A. (1985). An experimental study to investigate the ef-fects of computer animation on the understanding and retention ofselected levels of learning outcomes (Doctoral dissertation, Pennsyl-vania State University, 1985). Dissertation Abstracts International,46, 1494A.

ChanLin, L. (1996). Enhancing computer graphics through metaphor-ical elaboration. Journal of Instructional Psychology, 23(3),196.

ChanLin, L. (1998). Animation to teach students of different knowledgelevels. Journal of Instructional Psychology, 25(3), 166.

ChanLin, L. (1999). Visual treatment for different prior knowl-edge. International Journal of Instructional Media, 26(2), 213–220.

ChanLin, L. (2000). Attributes of animation for learning scientific knowl-edge. Journal of Instructional Psychology, 24(4), 228–238.

Chien, S. C. (1986). The effectiveness of animated and interactive micro-computer graphics on children’s development of spatial visualiza-tion ability/mental rotation skills (Doctoral dissertation, Ohio StateUniversity, 1986). Dissertation Abstracts International, 46, 1494A.

Collins, A., Adams, M. J., & Pew, R. W. (1978). Effectiveness of an inter-active map display in tutoring geography. Journal of EducationalPsychology, 70(1), 1–7.

Dwyer, F. (1969). The instructional effect of motion in varied visualillustrations. Journal of Psychology, 73, 167–172.

Dyck, J., L. (1995). Problem solving by Macintosh users: The effectsof animated, self-paced written, and no instruction. EducationalComputing Research, 12(1), 29–49.

Hativa, N., & Reingold, A. (1987). Effects of audiovisual stimuli on learn-ing through microcomputer-based class presentation. InstructionalScience, 16, 287–306.

Hays, T. A. (1996). Spatial abilities and the effects of computer animationon short-term and long-term comprehension. Educational Comput-ing Research, 14(2), 139–155.

Houston, J. M., Joiner, C. L., Uddo, F., Harper, C., & Stroll, A. (1995).Computer animation in mock juries’ decision making. PsychologicalReports, 76, 987–993.

Johnson, N. C. (1985). Using a microcomputer to teach a statisticalconcept (Doctoral dissertation, University of Minnesota, 1986). Dis-sertation Abstracts International, 47, 455A.

Kann, C., Lindeman, R. W., & Heller, R. (1977). Integrating algorithm an-imation into a learning environment. Computers Education, 28(4),223–228.

King, W. A. (1975). A comparison of three combinations of text andgraphics for concept learning. Navy Personnel Research and Devel-opment Center, Report No. NPRDC-TR-76-16, San Diego, CA. (ERICDocument Reproduction Service No. ED 112936)

Kini, A. S. (1994). Effects of cognitive style and verbal and visualpresentation mode on concept learning in CBI. Paper presentedat the meeting of the American Educational Research Association,New Orleans, LA.

Kinzer, C. K., Sherwood, R. D., & Loofbourrow, M. C. (1989). Simulationsoftware vs expository text: A comparison of retention across twoinstructional tools. Reading Research and Instruction, 28(2), 41–49.

Klein, D. (1986). Conditions influencing the effectiveness of animatedand non-animated displays in computer assisted instruction (Doc-toral dissertation, University of Illinois at Urbana–Champaign, 1985).Dissertation Abstracts International, 46, 1878A.




Lai, S. (1998). The effects of visual display on analogies using computer-based learning. International Journal of Instructional Media,25(2), 151–161.

Lai, S. (2000). Influence of audio-visual presentations on learning ofabstract concepts. International Journal of Instructional Media,27(2), 199–207.

Laner, S. (1954). The impact of visual aid displays showing a manipula-tive task. Quarterly Journal of Experimental Psychology, 6, 95–106.

Laner, S. (1955). Some factors influencing the effectiveness of an in-structional film. British Journal of Psychology, 46, 280–294.

Large, A., Beheshti, J., Breuleux, A., & Reneud, A. (1995). Multimediaand comprehension: The relationship among text, animation, andcaptions. Journal of the American Society for Information Science,46(5), 340–347.

Lumsdaine, A. A., Sultzer, R. L. & Kopstein, F. F. (1961). The effect ofanimation cues and repetition of examples on learning from an in-structional film. In A. A. Lumsdaine (Ed.), Student response in pro-grammed instruction (pp. 241–269). Washington, DC: National Re-search Council.

Mayer, R. E., & Anderson, R. B. (1991). Animation needs narration: an ex-perimental test of a dual-coding hypothesis. Journal of EducationalPsychology, 83(4), 484–90.

Mayer, R. E., & Anderson, R. B. (1992). The instructive animation: Help-ing students build connections between words and pictures in mul-timedia learning. Journal of Educational Psychology, 84(4), 444–452.

Mayer, R. E., Moreno, R., Boire, M., & Vagge, S. (1999). Maximizing con-structivist learning from multimedia communications by minimizingcognitive load. Journal of Educational Psychology, 81(4), 638–643.

Mayer, R. E., & Moreno, R. (1998). A split-attention effect in multimedialearning: Evidence for dual processing systems in working memory.Journal of Educational Psychology, 90(2), 312–320.

Mayer, R. E., & Sims, V. K. (1994). For whom is a picture worth a thousandwords? Extensions of dual-coding theory of multimedia learning.Journal of Educational Psychology, 86(3), 389–401.

Mayton, G. B. (1990). The effects of the animation of visuals on thelearning of dynamic progresses through microcomputer-based in-struction (Doctoral dissertation, Ohio State University, 1990). Dis-sertation Abstracts International, 51, 4097A.

McCloskey, M., & Kohl, D. (1983). Naive physics: The curvilinear im-petus principle and its role in interactions with moving objects.Journal of Experimental Psychology: Learning Memory and Cog-nition, 9(1), 146–156.

Mccuistion, P. J. (1990). Static vs dynamic visuals in computer-assisted in-struction. (Doctoral dissertation, Texas A&M University, 1989). Dis-sertation Abstracts International, 42, 4409A.

Moore, M. V., Nawrocki, L. H., & Simutis, Z. M. (1979). The instructionaleffectiveness of three levels of graphic displays for computer-assistedinstruction. Army Research Center, ARI-TP-359, 2–14. (ERIC, EDNo. 178 057)

Myers, K. N. (1990). An exploratory study of the effectiveness of com-puter graphics and simulations in a computer-student interactiveenvironment in illustrating random sampling and the central limittheorem (Doctoral dissertation, Florida State University, 1990). Dis-sertation Abstracts International, 51, 441A.

Nicholls, C., & Merkel, S. (1996). The effect of computer animation onstudents’ understanding of microbiology. Journal of Research onComputing in Education, 28(3), 359–372.

Palmiter, S., Elkerton, J., & Baggett, P. (1991). Animated demonstrationsvs written instructions for learning procedural tasks: A preliminaryinvestigation. Instructional Journal of Man-Machine Studies, 34,687–701.

Palmiter, S., & Elkerton, J. (1993). Animated demonstrations for learning

procedural computer-based tasks. Human-Computer Interaction,8, 193–216.

Park, O. (1998). Visual displays and contextual presentations incomputer-based instruction. Educational Technology Researchand Development, 46(3), 37–50.

Park, O., & Gittelman, S. (1992). Selective use of animation and feedbackin computer-based instruction. Educational Technology Researchand Development, 40(4), 27–38.

Payne, S. J., Chesworth, L., & Hill, E. (1992). Animated demonstrationsfor exploratory learners. Interacting with Computers, 4(1), 3–22.

Peters, H. J., & Daiker, K. C. (1982). Graphics and animations as instruc-tional tools: A case study. Pipeline, 7(1), 11–13.

Ponick, D. A. (1986). Animation used as a logical organizer in vi-sualization for concept learning (Doctoral dissertation, Universityof Minnesota, 1986). Dissertation Abstracts International, 47,3300A.

Ram, S. P., & Phua, K. K. (1997). The effectiveness of a computer-aidedinstruction courseware developed using interactive multimedia con-cepts for teaching phase III MD students. Medical Teacher, 19(1),51–53.

Reed, S. K. (1985). Effects of computer graphics on improving esti-mates to algebra word problems. Journal of Educational Psychol-ogy, 77(3), 285–298.

Rieber, L. P., & Hannafin, M. J. (1988). Effects of textual and animatedorienting activities and practice on learning from computer-basedinstruction. Computers in Schools, 5(1/2), 77–89.

Rieber, L. P. (1989 ). The effects of computer animated elaboration strate-gies and practice on factual and application learning in an elemen-tary science lesson. Journal of Educational Computing Research,5(4), 431–444.

Rieber, L. P. (1990). Using computer animated graphics in science in-struction with children. Journal of Educational Psychology, 82(1),135–140.

Rieber, L. P., Boyce, M. J., & Assad, C. (1990). The effects of computeranimation on adult learning and retrieval tasks. Journal of Computer-Based Instruction, 17(2), 46–52.

Rieber, L. P. (1991a). Animation, incidental learning, and continuingmotivation. Journal of Educational Psychology, 83(3), 318–328.

Rieber, L. P. (1991b). Effects of visual grouping strategies of computer-animated presentations on selective attention in science. Educa-tional Technology Research and Development, 39(4), 5–15.

Rieber, L. P. (1996a). Animation as feedback in a computer-based sim-ulation: Representation matters. Educational Technology Researchand Development, 44(1), 5–22.

Rieber, L. P. (1996b). Animation as a distractor to learning. InternationalJournal of Instructional Media, 23(1), 53–57.

Rieber, L. P., Smith, M., Al-gharry, S., Strickland, B., Chu, G., &Spahi, F. (1996). The role of meaning in interpreting graphical andtextual feedback during a computer-based simulation. Computers &Education, 27(1), 45–58.

Rigney, J. W., & Lutz, K. A. (1976). Effects of graphic analogies of con-cepts in chemistry on learning and attitude. Journal of EducationalPsychology, 68(3), 305–311.

Roshal, S. M. (1961). Film mediated learning with varying representa-tions of the task: Viewing angle, portrayal of demonstration, mo-tion, and student participation. In A. A. Lumsdaine (Ed.), Studentresponse in programmed instruction (pp. 155–175). Washington,DC: National Research Council.

Sanger, M. J., & Greenbowe, T. J. (2000). Addressing student misconcep-tions concerning electron flow in aqueous solutions with instructionincluding computer animations and conceptual change strategies.International Journal of Science Education, 22(5), 521–537.

(Continues on p. 913)



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sts

2.A

ctiv

eh

ori

zon

tal

3.P

assi

veve

rtic

al4.

Pas

sive

ho

rizo

nta

l5.

Op

tim

alco

nd

itio

n

Exp

t2

1.V

ert

ical

lab

el(

VL

)2.

Ve

rtic

al(V

)3.

Ho

rizo

nta

l(H

)4.

Lab

elo

nly

(L)

898



Bae

k&

Lay

ne

(198

8)1.

An

imat

ed

gro

up

(co

lor)

Tuto

rial

less

on

sab

ou

tth

em

ath

em

atic

alru

les

for

calc

ula

tin

gsp

ee

d

119

hig

h-s

cho

ol

chil

dre

nS

core

on

pe

rfo

rman

cete

stan

dti

me

tofi

nis

hco

mp

ute

rm

od

ule

SD

amo

ng

the

gro

up

sfo

rb

oth

pe

rfo

rman

cete

stan

dco

mp

leti

on

tim

e.A

nim

ate

dgr

ou

ps

pe

rfo

rme

db

ett

er

than

grap

hic

san

dte

xtgr

ou

ps,

and

grap

hic

sgr

ou

ps

pe

rfo

rme

db

ett

er

than

text

gro

up

so

np

erf

orm

ance

test

.Fo

rco

mp

leti

on

tim

e,a

nim

ate

dgr

ou

ps

had

the

slo

we

stti

me

.Co

lor

had

no

eff

ect

so

nle

arn

ing

2.G

rap

hic

sgr

ou

p(c

olo

r)3.

Text

gro

up

(co

lor)

4.A

nim

ate

dgr

ou

p(B

/W)

5.G

rap

hic

sgr

ou

p(B

/W)

6.Te

xtgr

ou

p(B

/W)

Be

ich

ne

r(1

990)

1. 2. 3. 4.

Vid

eo

grap

hte

chn

iqu

e,

vie

we

dth

ere

alm

oti

on

Vid

eo

grap

hte

chn

iqu

e,

did

no

tvi

ew

the

real

mo

tio

nTr

adit

ion

alte

chn

iqu

e,

vie

we

dth

ere

alm

oti

on

Trad

itio

nal

tech

niq

ue

,d

idn

ot

vie

wth

ere

alm

oti

on

MB

Le

xpe

rim

en

tssh

ow

ing

the

ph

ysic

ale

ven

tsal

on

gw

ith

the

irgr

aph

ical

rep

rese

nta

tio

ns

237

mix

ed

sub

ject

s(1

65h

igh

-sch

oo

lst

ud

en

tsan

d72

adu

lts)

Sco

reo

np

rete

stan

dp

ost

test

(un

de

rsta

nd

ing

grap

hs)

NS

Dam

on

ggr

ou

ps

Bla

ke(1

977)

1. 2. 3.

Sti

llco

nd

itio

n(o

nly

slid

es)

Arr

ow

con

dit

ion

(sli

de

sp

lus

cue

ing

arro

ws)

Mo

tio

nco

nd

itio

nsh

ow

ing

stan

dar

dm

oti

on

vid

eo

Le

arn

ing

the

mo

vem

en

to

fch

ess

pie

ces

84ad

ult

sS

core

on

test

con

sist

ing

of3

2d

iagr

ams

ofc

he

ssb

oar

dM

ixe

dre

sult

.SD

fou

nd

amo

ng

gro

up

sfo

rlo

w-s

pat

iala

bil

ity

stu

de

nts

.Th

est

illc

on

dit

ion

pe

rfo

rme

dw

ors

eth

ane

ith

er

on

eo

fth

em

oti

on

con

dit

ion

s,w

hic

hd

idn

ot

dif

fer

fro

me

ach

oth

er.

NS

Dfo

un

dam

on

ggr

ou

ps

for

hig

h-s

pat

iala

bil

ity

stu

de

nts

Bra

sell

(198

7)1.

Sta

nd

ard

(re

al-t

ime

grap

hin

g)E

xpe

rim

en

tsfo

rle

arn

ing

grap

hin

gsk

ills

93h

igh

-sch

oo

lst

ud

en

tsS

core

on

pre

test

and

po

stte

st(u

nd

ers

tan

din

ggr

aph

s)S

Dfo

un

dam

on

ggr

ou

ps

infa

vor

of

anim

atio

n(r

eal

-tim

egr

aph

ing)

.2.

De

laye

d(d

ela

yed

-gr

aph

ing)

3.Te

sto

nly

4.C

on

tro

l(p

ape

ro

nly

)C

apu

to(1

982)

1.D

ynam

icgr

aph

icC

AI

Up

grad

ing

cert

ain

bas

icm

ath

em

atic

alsk

ills

109

adu

lts

Sco

reo

nb

asic

mat

he

mat

ical

skil

lsre

take

test

.Als

o,c

ou

rse

grad

eav

era

gefo

rco

mp

ute

rsc

ien

ce

SD

amo

ng

gro

up

s2.

Ve

rbal

CA

I3.

Ch

eck

list

CA

I

Car

abal

lo(1

985)

1. 2. 3. 4.

No

inst

ruct

ion

Text

on

lyTe

xtp

lus

stil

lgra

ph

ics

Text

plu

sst

illg

rap

hic

sp

lus

anim

ate

dgr

aph

ics

Le

arn

er’

sac

hie

vem

en

tan

dla

ngu

age

bac

kgro

un

d

80ad

ult

sF

ou

rcr

ite

rio

nte

sts

(te

rmin

olo

gy,i

de

nti

fica

tio

n,

dra

win

g,an

dco

mp

reh

en

sio

n)

and

tota

lsco

res

NS

D

Car

abal

lo-R

ios

(198

5)1. 2. 3.

Text

Text

plu

sst

illp

ictu

res

Text

plu

sst

illp

ictu

res

plu

san

imat

ion

Co

nce

pts

and

rule

sin

geo

me

try

72ad

ult

sIm

me

dia

tean

dd

ela

yed

po

stte

sts

on

pe

rfo

rman

ceN

SD

amo

ng

gro

up

s

Con

tin

ues

899



33.A

5.(C

on

tin

ue

d)

Stu

dy

Tre

atm

en

tC

on

ten

tS

ub

ject

sD

ep

en

de

nt

Var

iab

le(s

)R

esu

lts

Ch

anL

in(1

996)

1. 2. 3. 4. 5. 6.

No

ngr

aph

icw

/m

eta

ph

ors

Sta

tic

grap

hic

sw

/m

eta

ph

ors

An

imat

ed

grap

hic

sw

/m

eta

ph

ors

An

imat

ed

grap

hic

sw

/om

eta

ph

ors

Sta

tic

grap

hic

sw

/om

eta

ph

ors

No

ngr

aph

ics

w/o

me

tap

ho

rs

CA

Iexp

lain

ing

con

cep

tso

fbio

tech

no

logy

120

coll

ege

stu

de

nts

Cri

teri

on

refe

ren

ced

test

,K

ell

er’

sIM

MS

(In

stru

ctio

nal

Mat

eri

als

Mo

tiva

tio

nal

Su

rve

y)

SD

for

stu

de

nt

pe

rfo

rman

cem

eta

ph

ori

cale

lab

ora

tio

nw

/an

imat

ion

tre

atm

en

t.N

SD

for

no

me

tap

ho

rsw

/gra

ph

icre

pre

sen

tati

on

.SD

for

stu

de

nt

mo

tiva

tio

nal

sco

res

w/m

eta

ph

ors

&an

imat

ed

grap

hic

s

Ch

anL

in(1

998)

1.N

ogr

aph

ics

Bio

tech

no

logy

135

adu

lts

Test

so

fpro

ced

ura

lan

dd

esc

rip

tive

fact

sM

ixe

dre

sult

s2.

Sti

llgr

aph

ics

3.A

nim

ate

dgr

aph

ics

Ch

anL

in(1

999)

1.Te

xt(c

on

tro

lgro

up

)B

iote

chn

olo

gy13

5ad

ult

sC

rite

rio

nre

fere

nce

dte

stS

Dam

on

ggr

ou

ps

for

anim

atio

n2.

Sti

llgr

aph

ics

3.A

nim

ate

dgr

aph

ics

Ch

anL

in(2

000)

1.Te

xtP

hys

ics

less

on

357

chil

dre

nD

esc

rip

tive

test

and

pro

ced

ura

lkn

ow

led

gete

stM

ixe

dre

sult

s2.

Gra

ph

ics

wit

hte

xt3.

An

imat

ion

wit

hte

xtC

hie

n(1

986)

1. 2.H

and

s-o

nsi

mu

lati

on

sA

nim

ate

din

tera

ctiv

egr

aph

ics

Sp

atia

lvis

ual

izat

ion

72ch

ild

ren

,1st

,2n

d,

and

3rd

grad

ers

Po

stte

sto

np

erf

orm

ance

NS

Dam

on

ggr

ou

ps

Co

llin

s,A

dam

s,&

Pe

w(1

978)

1. 2. 3.

SC

HO

LA

Rm

apsy

ste

m(i

nte

ract

ive

)co

nd

itio

nA

lab

ele

dm

apco

nd

itio

nA

nu

nla

be

led

map

con

dit

ion

Le

arn

ing

geo

grap

hy

9h

igh

-sch

oo

lst

ud

en

tsan

d9

adu

lts

Sco

reo

np

rete

stan

dp

ost

test

(nu

mb

er

ofc

orr

ect

resp

on

ses)

SD

amo

ng

gro

up

sin

favo

ro

fan

imat

ion

.Sco

res

on

po

stte

stsh

ow

ed

that

the

SC

HO

LA

Rco

nd

itio

np

erf

orm

ed

sign

ific

antl

yb

ett

er

than

the

lab

ele

dm

apco

nd

itio

n,w

hic

hin

turn

sco

red

sign

ific

antl

yb

ett

er

than

the

un

lab

ele

dm

apco

nd

itio

nD

wye

r(1

969)

1. 2. 3. 4. 5.

Co

ntr

olg

rou

p(G

1),

text

-on

lygr

ou

pS

imp

leli

ne

rep

rese

nta

tio

ns

(G2)

De

tail

ed

shad

ed

dra

win

gs(G

3)P

ho

togr

aph

so

fth

eh

ear

tm

od

el(

G4)

Re

alis

tic

he

art

ph

oto

grap

hs

(G5)

Fu

nct

ion

ofh

um

anh

ear

t13

9ad

ult

sA

pre

test

,ad

raw

ing

test

,an

ide

nti

fica

tio

nte

st,a

term

ino

logy

test

,an

da

tota

lcr

ite

rio

nte

st

SD

amo

ng

gro

up

sfo

rd

raw

ing

test

s.G

2p

erf

orm

ed

be

tte

rth

anG

1.S

Dam

on

ggr

ou

ps

for

term

ino

logy

test

.G

2p

erf

orm

ed

sign

ific

antl

yb

ett

er

than

G1

SD

amo

ng

gro

up

sfo

rto

talc

rite

rio

nte

st.G

2&

G5

pe

rfo

rme

dsi

gnif

ican

tly

be

tte

rth

anG

1.N

SD

amo

ng

gro

up

sfo

rid

en

tifi

cati

on

and

com

pre

he

nsi

on

test

sD

yck

(199

5)1. 2. 3. 4.

No

inst

ruct

ion

Mo

use

/ico

nm

anu

alF

ile

man

ipu

lati

on

man

ual

An

imat

ed

inst

ruct

ion

(Mac

tou

r)

Mac

into

sho

pe

rati

ng

syst

em

48ad

ult

sP

ost

test

me

asu

rin

gth

eta

skco

mp

leti

on

tim

ean

dp

rop

ort

ion

oft

asks

com

ple

ted

wit

hfa

mil

iar

and

un

fam

ilia

rta

sks

NS

Dam

on

ggr

ou

ps

for

eit

he

rco

mp

leti

on

tim

eo

rp

rop

ort

ion

of

task

sco

mp

lete

d

900



Hat

iva

&R

ein

gold

(198

7)1. 2.

Sti

mu

lus

gro

up

wit

hso

un

d,a

nim

atio

n,a

nd

colo

rN

on

stim

ulu

sw

ith

ou

tso

un

d,a

nim

atio

n,o

rco

lor

Le

arn

ing

Eu

clid

ean

geo

me

try

929t

hgr

ade

rsIm

me

dia

tean

dd

ela

yed

po

stte

sts

me

asu

rin

gth

eap

titu

de

for

lear

nin

gan

du

nd

ers

tan

din

go

fge

om

etr

y

SD

fou

nd

for

bo

thu

nd

ers

tan

din

gan

dap

titu

de

on

bo

thim

me

dia

tean

dd

ela

yed

po

stte

sts.

Th

est

imu

lus

gro

up

pe

rfo

rme

db

ett

er.

An

imat

ion

was

he

lpfu

lin

gain

ing

atte

nti

on

,cr

eat

ing

po

siti

veat

titu

de

,an

dh

elp

ing

stu

de

nts

un

de

rsta

nd

the

sub

ject

sH

ays

(199

6)1.

Text

alo

ne

Co

nce

pts

invo

lvin

gti

me

and

mo

tio

n(d

iffu

sio

n)

131

6th

,7th

,an

d8t

hgr

ade

rsTw

ote

sts:

1.S

en

ten

ceve

rifi

cati

on

tech

niq

ue

(SV

T)

tom

eas

ure

sho

rt-t

erm

com

pre

he

nsi

on

for

spat

iala

bil

ity,

and

pre

sen

tati

on

mo

de

Sh

ort

-te

rmco

mp

reh

en

sio

n:S

Dam

on

ggr

ou

ps

for

SV

Tte

st.

An

imat

ion

he

lpe

dlo

w-s

pat

ial

abil

ity

stu

de

nts

.N

SD

amo

ng

gro

up

sfo

rm

od

eo

fp

rese

nta

tio

n.

2.C

on

cep

te

valu

atio

nst

ate

me

nt

(CE

S)

tom

eas

ure

lon

g-te

rmco

mp

reh

en

sio

nfo

rsp

atia

lab

ilit

yan

dp

rese

nta

tio

nm

od

e

Lo

ng-

term

com

pre

he

nsi

on

:SD

for

CE

Ste

st.A

nim

atio

nh

elp

ed

bo

thlo

w-

and

hig

h-s

pat

iala

bil

ity

stu

de

nts

toga

inh

igh

er

sco

reo

nco

nce

ptu

alu

nd

ers

tan

din

g.H

ow

eve

r,lo

w-s

pat

iala

bil

ity

stu

de

nts

be

ne

fite

dth

em

ost

2.Te

xtan

dst

atic

visu

als

3.Te

xtp

lus

anim

atio

n

Ho

ust

on

,Jo

ine

r,U

dd

o,H

arp

er,

&S

tro

ll(1

995)

1.R

ead

ing

ou

tlo

ud

gro

up

,li

ste

nin

gto

the

tran

scri

pt

oft

he

voic

ere

cord

er

Pla

ne

cras

hin

vest

igat

ion

72ad

ult

sA

qu

est

ion

nai

refo

rra

tin

gth

eb

lam

eo

nth

ecr

ew

for

the

cras

h

SD

amo

ng

gro

up

s.S

ub

ject

sin

the

anim

ate

dgr

ou

pp

ut

less

oft

he

bla

me

on

the

flig

ht

cre

w2.

Vo

ice

reco

rde

r’s

wri

tte

ntr

ansc

rip

t&

aud

iota

pe

gro

up

Aq

ue

stio

nn

aire

tom

eas

ure

the

sub

ject

sre

call

ofi

nfo

rmat

ion

abo

ut

the

cras

h

NS

Dfo

un

dam

on

ggr

ou

ps

for

reca

llo

fin

form

atio

nan

dre

ten

tio

n

3.A

nim

ate

dgr

ou

pli

ste

nin

gto

the

cock

pit

’svo

ice

and

flig

ht

reco

rde

rK

ann

,Lin

de

man

,&H

ell

er

(197

7)1. 2. 3. 4.

Ngr

ou

p(n

oan

imat

ion

)V

gro

up

(vie

we

dan

imat

ion

oft

he

algo

rith

m)

Cgr

ou

p(c

on

tro

lgro

up

,w

rote

pro

gram

sw

ith

ou

tan

yin

stru

ctio

n)

VC

gro

up

(su

bje

cts

wro

tep

rogr

ams

afte

rvi

ew

ing

the

anim

atio

n)

Kn

apsa

ckal

gori

thm

28ad

ult

sP

ost

test

me

asu

rin

gst

ud

en

ts’

abil

ity

tore

cogn

ize

the

typ

eo

fp

rogr

am(d

ecl

arat

ive

,p

roce

du

ral,

anal

ytic

al,a

nd

recu

rsio

n)

Qu

alit

yo

fth

ew

ritt

en

cod

es

SD

amo

ng

gro

up

sfo

rre

cogn

izin

gth

ere

curs

ive

-typ

ep

rogr

ams.

Th

ean

imat

ed

gro

up

pe

rfo

rme

db

ett

er

on

the

po

stte

st.T

he

anim

ate

dgr

ou

pal

sop

erf

orm

ed

be

tte

ro

nth

ep

ost

test

than

the

oth

ers

for

the

pro

ced

ura

l-an

dd

ecl

arat

ive

-typ

ep

rob

lem

SD

amo

ng

gro

up

sfo

rw

riti

ng

pro

gram

s.T

he

anim

ate

dgr

ou

pp

erf

orm

ed

mu

chb

ett

er

than

the

oth

er

gro

up

s.K

ing

(197

5)1.

Text

and

anim

atio

nS

ine

-rat

ioco

nce

pts

45ad

ult

sP

ost

test

me

asu

rin

gst

ud

en

ts’

un

de

rsta

nd

ing

oft

he

sin

e-r

atio

con

cep

ts

NS

Dam

on

ggr

ou

ps.

Ho

we

ver,

the

me

ansc

ore

oft

he

anim

ate

dgr

ou

pw

asth

eh

igh

est

on

the

po

stte

st2.

Text

&st

illg

rap

hic

s3.

Text

on

ly

Con

tin

ues

901



33.A

5.(C

on

tin

ue

d)

Stu

dy

Tre

atm

en

tC

on

ten

tS

ub

ject

sD

ep

en

de

nt

Var

iab

le(s

)R

esu

lts

Kin

i(19

94)

1. 2. 3. 4.

Text

on

lyTe

xtp

lus

anim

ate

dgr

aph

ics

Fie

ldin

de

pe

nd

en

ce–f

ield

de

pe

nd

en

ceP

refe

rre

dp

erc

ep

tual

mo

de

(ve

rbal

–vis

ual

)

Co

nce

pts

ofv

elo

city

and

acce

lera

tio

nin

on

e-

dim

en

sio

nal

spac

e

192

adu

lts

Co

nce

pt

test

NS

Dam

on

ggr

ou

ps

Kin

zer,

Sh

erw

oo

d,&

Lo

ofb

ou

rro

w(1

989)

1.C

om

pu

ter

anim

atio

nU

nd

ers

tan

din

gth

efo

od

chai

n52

chil

dre

n,5

thgr

ade

rsP

ost

test

me

asu

rin

gst

ud

en

ts’

un

de

rsta

nd

ing

oft

he

foo

dch

ain

SD

amo

ng

gro

up

sin

favo

ro

fth

ete

xtgr

ou

p2.

Text

Kle

in(1

986)

1. 2. 3. 4. 5. 6. 7. 8.

Tem

po

rala

nim

ate

d,e

asy

Tem

po

rala

nim

ate

d,

dif

ficu

ltTe

mp

ora

lno

nan

imat

ed

,e

asy

Tem

po

raln

on

anim

ate

d,

dif

ficu

ltS

pat

iala

nim

ate

d,e

asy

Sp

atia

lan

imat

ed

,dif

ficu

ltS

pat

ialn

on

anim

ate

d,e

asy

Sp

atia

lno

nan

imat

ed

,d

iffi

cult

Pro

ble

mso

lvin

g38

adu

lts

Tim

eb

etw

ee

np

rese

nti

ng

the

pro

ble

man

dre

ceiv

ing

answ

ers

fro

mst

ud

en

ts

Mix

ed

resu

lts

Lai

(199

8)1.

Text

on

lyL

ear

nin

gco

mp

ute

r-b

ase

dp

rogr

amm

ing

lan

guag

eth

rou

ghan

alo

gie

s

78ch

ild

ren

and

adu

lts

Am

ult

iple

-ch

oic

ete

stm

eas

uri

ng

the

un

de

rsta

nd

ing

ofp

rogr

amm

ing

con

cep

tsan

dfu

nct

ion

s

SD

amo

ng

gro

up

sin

favo

ro

fte

xtw

ith

stat

icgr

aph

ics

2.Te

xtw

ith

stat

icgr

aph

ics

3.A

nim

atio

n

Lai

(200

0)1. 2. 3.

Text

wit

hau

dio

inst

ruct

ion

Tre

atm

en

t1

plu

sst

atic

grap

hic

sTr

eat

me

nt

1p

lus

anim

ate

dgr

aph

ics

Co

mp

ute

rp

rogr

amm

ing

lan

guag

es

169

adu

lts

Am

ult

iple

-ch

oic

ete

stm

eas

uri

ng

con

cep

ts;a

nat

titu

de

qu

est

ion

nai

re(L

ike

rtty

pe

)

SD

amo

ng

gro

up

sfo

ran

imat

ion

NS

Dam

on

ggr

ou

ps

for

atti

tud

e

Lan

er

(195

4)1.

Stu

de

nts

’fil

mgr

ou

pM

ovi

ng

film

sho

win

gth

ere

pai

ro

fasa

sh-c

ord

win

do

w

75ad

ult

sIn

div

idu

alte

sts

ofp

erf

orm

ance

NS

Dam

on

ggr

ou

ps.

Ho

we

ver,

stu

de

nts

wh

ovi

ew

ed

the

film

pe

rfo

rme

db

ett

er

than

bo

thR

AF

gro

up

s

2.R

AF

film

gro

up

3.R

AF

film

stri

p

Lan

er

(195

5)1. 2.

Fil

mTe

xtan

dtw

ost

atic

pic

ture

sM

ovi

ng

film

sab

ou

tth

eB

ren

-Gu

ntr

igge

rm

ech

anis

m

50ad

ult

sIn

div

idu

alp

erf

orm

ance

test

for

dra

win

gth

em

ech

anis

m,

nam

ing

par

ts,a

nd

pe

rfo

rmin

gth

eas

sem

bly

NS

Dam

on

ggr

ou

ps

for

pe

rfo

rman

ce.

Th

ism

ean

sth

atm

ovi

ng

film

did

no

th

ave

any

eff

ect

so

np

erf

orm

ance

Lar

ge,B

eh

esh

ti,

Bre

ule

ux,

&R

en

eu

d(1

995)

1. 2. 3. 4.

Text

-on

lygr

ou

pTe

xtp

lus

anim

atio

nTe

xtp

lus

anim

atio

np

lus

cap

tio

ns

Cap

tio

ns

plu

san

imat

ion

Mu

ltim

ed

iale

arn

ing.

Th

ep

roce

du

ralt

ask

of

“ho

wto

fin

dso

uth

”

716t

hgr

ade

rsIn

div

idu

alte

stfo

rre

call

of

info

rmat

ion

and

pe

rfo

rman

ceo

fth

ep

roce

du

re

NS

Dam

on

ggr

ou

ps

for

reca

llo

fin

form

atio

nS

Dam

on

ggr

ou

ps

for

pe

rfo

rman

ceT

he

anim

ate

dgr

ou

pan

dth

eca

pti

on

gro

up

sp

erf

orm

ed

be

tte

rth

anth

ete

xt-o

nly

gro

up

902



Lu

msd

ain

ee

tal

.(1

961)

1. 2.

An

imat

ed

gro

up

give

nth

ep

refi

lmte

stA

nim

ate

dgr

ou

pgi

ven

film

wit

h6

exa

mp

les

Inst

rum

en

tre

adin

gsk

ills

1,30

0A

irF

orc

eb

asic

trai

ne

es

Ab

ilit

yto

read

the

valu

eo

fth

em

icro

me

ter

sett

ings

SD

amo

ng

gro

up

s.T

he

anim

ate

dp

refi

lmte

stgr

ou

pp

erf

orm

ed

sign

ific

antl

yb

ett

er

than

the

anim

ate

dgr

ou

pn

ot

give

nth

ep

refi

lmte

st.A

nim

ate

dgr

ou

pal

sop

erf

orm

ed

be

tte

rin

the

rep

lica

tio

ntr

ials

1.A

nim

ate

dgr

ou

pgi

ven

film

wit

h3

exa

mp

les

2.A

nim

ate

dgr

ou

pw

ith

sup

ple

me

nta

lso

un

d3.

No

nan

imat

ed

gro

up

wit

hp

refi

lmte

st4.

No

nan

imat

ed

gro

up

give

nfi

lmw

ith

6e

xam

ple

s5.

No

nan

imat

ed

gro

up

give

nfi

lmw

ith

3e

xam

ple

s6.

No

nan

imat

ed

gro

up

wit

hsu

pp

lem

en

tals

ou

nd

May

er

&A

nd

ers

on

(199

1)E

xpt

11.

Wo

rds

wit

hp

ictu

res

Op

era

tio

no

fab

icyc

lep

um

p30

adu

lts

Ap

rob

lem

-so

lvin

gte

st(f

ou

re

ssay

qu

est

ion

s)S

Dam

on

ggr

ou

ps

infa

vor

ofw

ord

sw

ith

pic

ture

gro

up

2.W

ord

sb

efo

rep

ictu

res

Exp

t2b

1.C

on

tro

lS

ame

asab

ove

48ad

ult

sA

pro

ble

m-s

olv

ing

test

and

ave

rbal

reca

llte

stS

Dam

on

ggr

ou

ps

for

pro

ble

mso

lvin

gN

SD

amo

ng

gro

up

sfo

rre

call

2.W

ord

so

nly

3.P

ictu

res

on

ly4.

Wo

rds

wit

hp

ictu

res

May

er

&A

nd

ers

on

(199

2)E

xpt

11. 2. 3. 4. 5.

Co

ncu

rre

nt

gro

up

,3(A

+N)

Su

cce

ssiv

egr

ou

p,3

(AN

,NA

,A,N

,N,A

)A

nim

atio

n-a

lon

egr

ou

pN

arra

tio

n-a

lon

egr

ou

pN

oin

stru

ctio

ngr

ou

p(c

on

tro

lgro

up

)

Op

era

tio

no

fab

icyc

lep

um

p13

6ad

ult

sA

po

stte

stm

eas

uri

ng

reca

llo

fin

form

atio

nan

dn

um

be

ro

fso

luti

on

sge

ne

rate

dto

fix

the

give

np

rob

lem

s

SD

amo

ng

gro

up

sfo

rre

ten

tio

nan

dp

rob

lem

solv

ing

infa

vor

oft

he

exp

eri

me

nta

lgro

up

s.T

he

con

curr

en

tgr

ou

pp

erf

orm

ed

be

tte

rth

anan

yo

the

rgr

ou

po

np

rob

lem

solv

ing.

All

exp

eri

me

nta

lgro

up

sp

erf

orm

ed

be

tte

rth

anth

eco

ntr

ol

gro

up

,bu

tth

eir

pe

rfo

rman

ced

idn

ot

dif

fer

fro

me

ach

oth

er.

Exp

t2

Sam

etr

eat

me

nt

asE

xpt

1O

pe

rati

on

ofa

nau

tom

ob

ile

bra

kin

gsy

ste

m

144

adu

lts

Sam

eas

Exp

t1S

Dam

on

gth

ee

xpe

rim

en

talg

rou

ps

and

the

con

tro

lgro

up

for

pro

ble

mso

lvin

g.A

lle

xpe

rim

en

talg

rou

ps,

exc

ep

tA

AA

gro

up

pe

rfo

rme

dsi

gnif

ican

tly

be

tte

rth

anth

eco

ntr

ol

gro

up

.

Con

tin

ues

903



33.A

5.(C

on

tin

ue

d)

Stu

dy

Tre

atm

en

tC

on

ten

tS

ub

ject

sD

ep

en

de

nt

Var

iab

le(s

)R

esu

lts

May

er,

Mo

ren

o,

Bo

ire

,&V

agge

(199

9)E

xpt

11. 2. 3. 4. 5.

Co

ncu

rre

nt

pre

sen

tati

on

Sm

allb

ite

so

fnar

rati

on

be

fore

anim

atio

nS

mal

lbit

es

ofa

nim

atio

nb

efo

ren

arra

tio

nL

arge

bit

es

ofn

arra

tio

nb

efo

rean

imat

ion

Lar

geb

ite

so

fan

imat

ion

be

fore

nar

rati

on

Ho

wli

ghte

nin

gfo

rms,

and

ho

wau

tom

ob

ile

bre

ak-

ing

syst

em

op

era

tes

60ad

ult

sR

ete

nti

on

test

,mat

chin

gte

st,

and

tran

sfe

rte

st(o

pe

nre

spo

nse

and

op

en

-en

de

dq

ue

stio

ns)

SD

amo

ng

gro

up

sfo

rb

oth

exp

eri

me

nts

,exc

ep

tN

SD

fou

nd

for

the

mat

chin

gte

stin

Exp

t2.

Exp

t2,

rep

lica

tio

no

fE

xpt

1M

aye

r&

Mo

ren

o(1

998)

Exp

t1

1. 2.

Co

ncu

rre

nt

anim

atio

np

lus

nar

rati

on

(AN

gro

up

)C

on

curr

en

tan

imat

ion

plu

ste

xt(A

Tgr

ou

p)

Pro

cess

ofl

igh

tin

gfo

rmat

ion

78ad

ult

sA

mat

chin

gte

st,t

om

eas

ure

stu

de

nts

’ab

ilit

yto

mat

che

ach

anim

atio

nfr

ame

toa

par

ticu

lar

sen

ten

ceth

atd

esc

rib

es

it,a

rete

nti

on

test

,an

da

tran

sfe

rte

st,t

oas

sess

stu

de

nts

’ab

ilit

yto

app

lyth

ele

arn

ed

kno

wle

dge

toa

ne

wsi

tuat

ion

SD

amo

ng

gro

up

s.In

bo

the

xpe

rim

en

ts;t

he

AN

gro

up

ou

tpe

rfo

rme

dth

eA

Tgr

ou

pin

all

test

s.

Exp

t2

1. 2.

Co

ncu

rre

nt

anim

atio

np

lus

nar

rati

on

(AN

gro

up

)C

on

curr

en

tan

imat

ion

plu

ste

xt(A

Tgr

ou

p)

Op

era

tio

no

fan

auto

mo

bil

eb

raki

ng

syst

em

68ad

ult

s

May

er

&S

ims

(199

4)E

xpt

11. 2.

Co

ncu

rre

nt

gro

up

,3(A

+N),

con

sist

ed

of1

0H

SA

&12

LS

AS

ucc

ess

ive

gro

up

,3(A

N,N

A),

con

sist

ed

of2

1H

AS

and

22L

SA

Op

era

tio

no

fan

auto

mo

bil

eb

reak

ing

syst

em

86ad

ult

sA

po

stte

stm

eas

uri

ng

the

nu

mb

er

ofs

olu

tio

ns

gen

era

ted

for

the

give

np

rob

lem

by

hig

h-

and

low

-sp

atia

lab

ilit

yst

ud

en

ts

SD

amo

ng

gro

up

sfo

rth

en

um

be

ro

fso

luti

on

sge

ne

rate

dfo

re

ach

pro

ble

m.T

he

pe

rfo

rman

ceo

fth

eco

ncu

rre

nt

gro

up

was

sign

ific

antl

yb

ett

er

than

that

oft

he

succ

ess

ive

or

no

inst

ruct

ion

gro

up

s,w

hic

hd

idn

ot

dif

fer

fro

me

ach

oth

er.

3.C

on

tro

lgro

up

(no

inst

ruct

ion

),co

nsi

ste

do

f7

HA

San

d14

LS

AS

Dam

on

gth

esp

atia

lab

ilit

ygr

ou

ps

for

pro

ble

mso

lvin

g.T

he

hig

h-s

pat

iala

bil

ity

stu

de

nts

wh

ovi

ew

ed

con

curr

en

tan

imat

ion

gen

era

ted

twic

eas

man

yso

luti

on

sas

the

hig

h-s

pat

iala

bil

ity

stu

de

nts

wh

ore

ceiv

ed

succ

ess

ive

anim

atio

n.

904



Exp

t2

1. 2. 3.

Co

ncu

rre

nt

gro

up

,3(A

+N)

Su

cce

ssiv

egr

ou

p,3

(N,A

)C

on

tro

lgro

up

(no

inst

ruct

ion

)

Op

era

tio

no

fhu

man

resp

irat

ory

syst

em

97ad

ult

sP

ost

test

me

asu

rin

gth

en

um

be

ro

fso

luti

on

sge

ne

rate

db

yth

eh

igh

-an

dlo

w-s

pat

iala

bil

ity

stu

de

nts

Th

ere

sult

sw

ere

alm

ost

sim

ilar

toe

xpe

rim

en

t#1

.SD

was

fou

nd

for

pro

ble

mso

lvin

g.T

he

pre

sen

tati

on

ofa

nim

atio

nan

dn

arra

tio

nge

ne

rate

d50

%m

ore

solu

tio

ns

toth

egi

ven

pro

ble

ms.

May

ton

(199

0)1. 2. 3

Sta

tic

grap

hic

sS

tati

cgr

aph

ics

wit

hso

me

imag

ery

cue

sA

nim

ate

dgr

aph

ics

&im

age

rycu

es

Fu

nct

ion

so

fth

eh

um

anh

ear

t72

adu

lts

Fiv

ete

sts.

T1

&T

2to

ide

nti

fyth

eh

ear

tp

arts

inb

oth

fre

ean

dcu

ed

reca

ll.T

3to

ide

nti

fyp

arts

oft

he

hu

man

he

art.

T4

&T

5to

me

asu

reth

eu

nd

ers

tan

din

go

fth

eh

ear

t’s

fun

ctio

ns,

wit

hcu

ed

and

fre

ere

call

.

SD

fou

nd

amo

ng

gro

up

s.A

nim

ate

dgr

ou

po

utp

erf

orm

ed

the

oth

er

gro

up

s

McC

losk

ey

&K

oh

l(1

983)

Exp

t1

1. 2. 3

Co

mp

ute

ran

imat

ion

Co

mp

ute

ran

imat

ion

sho

win

gth

ep

oss

ible

traj

ect

ori

es

Vis

ual

grap

hic

sw

ith

no

mo

tio

n

Pe

rce

ivin

gtr

aje

cto

rie

sin

the

bal

l-an

d-s

trin

gp

rob

lem

90ad

ult

sF

ind

ing

the

corr

ect

traj

ect

ory

for

the

bal

lN

SD

fou

nd

amo

ng

the

gro

up

sin

eac

he

xpe

rim

en

t

Exp

t2

1.T

he

no

-mo

tio

ngr

ou

p(c

ho

seth

eco

rre

ctp

ath

wit

ho

ut

vie

win

gan

imat

ion

72ad

ult

s

2.T

he

traj

ect

ory

gro

up

(Vie

we

dan

anim

atio

n,

the

np

icke

dth

eco

rre

ctp

ath

fro

mth

egi

ven

six

alte

rnat

ive

s)E

xpt

31.

Sam

ein

stru

ctio

ns

for

allo

nth

eta

sk50

adu

lts

Mcc

uis

tio

n(1

990)

1.S

tati

cvi

sual

Sp

atia

lab

ilit

ies

137

adu

lts

Ap

erf

orm

ance

test

and

am

en

tal

rota

tio

nte

stN

SD

amo

ng

gro

up

s2.

Dyn

amic

visu

alM

oo

re,N

awro

cki,

&S

imu

tis

(197

9)1. 2. 3.

Lo

w-l

eve

lgra

ph

icgr

ou

p,c

on

sist

ed

of

alp

han

um

eri

csan

dsc

he

mat

ics

Me

diu

m-l

eve

lgra

ph

icgr

ou

p,c

on

sist

ed

ofl

ine

dra

win

gH

igh

-le

velg

rap

hic

gro

up

,co

nsi

ste

do

flin

ed

raw

ing

and

anim

atio

n

Ho

wth

ein

ne

re

arw

ork

s90

adu

lts

Fiv

ete

sts

we

reu

sed

:on

ete

stto

me

asu

reth

eco

mp

leti

on

tim

eo

fth

ele

sso

n;o

the

rco

nte

nt

test

su

sed

tom

eas

ure

term

ino

logy

,fac

ts,

ide

nti

fica

tio

n,a

nd

pri

nci

ple

s

NS

Dam

on

ggr

ou

ps

for

com

ple

tio

nti

me

NS

Dam

on

ggr

ou

ps

for

the

oth

er

fou

rte

sts

Mye

rs(1

990)

1. 2.Tr

adit

ion

alle

ctu

reIn

tera

ctiv

em

eth

od

Le

arn

ing

stat

isti

cal

con

cep

ts52

adu

lts

Aco

nce

pt

test

and

anap

pli

cati

on

test

SD

amo

ng

gro

up

sfo

rco

nce

pt

test

NS

Dam

on

gth

egr

ou

ps

for

app

lica

tio

nte

st

Con

tin

ues

905



33.A

5.(C

on

tin

ue

d)

Stu

dy

Tre

atm

en

tC

on

ten

tS

ub

ject

sD

ep

en

de

nt

Var

iab

le(s

)R

esu

lts

Nic

ho

lls

&M

erk

el

(199

6)1.

An

imat

ed

tuto

rial

Nit

roge

ncy

cle

44ad

ult

s10

mu

ltip

le-c

ho

ice

qu

est

ion

san

dsi

xsh

ort

-an

swe

rq

ue

stio

ns

NS

Dam

on

ggr

ou

ps

2.Te

xtw

ith

stil

ldia

gram

Pal

mit

er,

Elk

ert

on

,an

dB

agge

tt(1

991)

1.A

nim

ate

dd

em

on

stra

tio

ngr

ou

pP

erf

orm

ing

auth

ori

ng

task

sin

Hyp

erC

ard

on

the

Mac

into

sh

28ad

ult

sIm

me

dia

tean

dd

ela

yed

po

stte

sts

on

dif

fere

nt,

ide

nti

cal,

and

sim

ilar

task

sm

eas

uri

ng

init

iala

cqu

isit

ion

,re

ten

tio

n,a

nd

tran

sfe

r

NS

Dam

on

ggr

ou

ps.

Th

ere

was

atr

ade

-off

be

twe

en

trai

nin

gp

erf

orm

ance

and

late

rsp

ee

dan

dtr

ansf

er.

Th

ean

imat

ed

gro

up

was

50%

fast

er

inth

etr

ain

ing

sess

ion

than

the

text

gro

up

,bu

tth

eir

pe

rfo

rman

cew

asw

ors

eo

nb

oth

imm

ed

iate

and

de

laye

dp

ost

test

.

2.W

ritt

en

-te

xtgr

ou

p

Pal

mit

er

&E

lke

rto

n(1

993)

1. 2. 3.

Text

-on

lygr

ou

pA

nim

ate

dd

em

o-o

nly

gro

up

Text

plu

san

imat

ed

de

mo

gro

up

Pe

rfo

rmin

gH

ype

rCar

dta

sks

48ad

ult

sIm

me

dia

tean

dd

ela

yed

test

s(7

day

s)m

eas

uri

ng

acq

uis

itio

n,r

ete

nti

on

,an

dtr

ansf

er

ofH

ype

rCar

dta

sks

NS

Dam

on

ggr

ou

ps.

Th

ed

em

ogr

ou

ps

we

refa

ste

ran

dm

ore

accu

rate

than

the

text

gro

up

du

rin

gth

etr

ain

ing

sess

ion

bu

tb

eca

me

slo

we

rd

uri

ng

the

test

sess

ion

s.T

he

rew

ere

NS

Db

etw

ee

nth

ed

em

ogr

ou

pan

dth

ed

em

op

lus

text

gro

up

.P

ark

(199

8)1. 2. 3.

An

imat

ion

Sta

tic

grap

hic

sw

/mo

tio

ncu

es.

Sta

tic

grap

hic

w/o

mo

tio

ncu

es

Ele

ctro

nic

circ

uit

san

dtr

ou

ble

sho

oti

ng

pro

ced

ure

s

96ad

ult

sTe

sto

fpe

rfo

rman

cean

dte

sto

ftr

ansf

er

(tro

ub

lesh

oo

tin

g)S

Dam

on

ggr

ou

ps

for

anim

ate

dgr

ou

ps

Par

k&

Git

telm

an(1

992)

1. 2. 3. 4. 5. 6.

An

imat

ion

wit

hn

atu

ral

fee

db

ack

An

imat

ion

wit

hkn

ow

led

geo

fre

sult

sfe

ed

bac

kA

nim

atio

nw

ith

exp

lan

ato

ryfe

ed

bac

kS

tati

cvi

sual

sw

ith

nat

ura

lfe

ed

bac

kS

tati

cvi

sual

sw

ith

kno

wle

dge

ofr

esu

lts

fee

db

ack

Sta

tic

visu

als

wit

he

xpla

nat

ory

fee

db

ack

Pro

ble

mso

lvin

g,te

ach

ing

ele

ctro

nic

str

ou

ble

sho

oti

ng

90ad

ult

sA

test

me

asu

rin

gn

um

be

ro

ftr

ials

atte

mp

ted

tofi

xth

efa

ult

yci

rcu

its

and

tim

esp

en

to

nth

efa

ult

yci

rcu

itd

uri

ng

pra

ctic

ean

dte

stse

ssio

ns

SD

amo

ng

gro

up

sfo

rn

um

be

ro

ftri

als

and

infa

vor

ofa

nim

atio

nN

SD

amo

ng

gro

up

sfo

rti

me

spe

nt

on

circ

uit

and

fee

db

ack

typ

e

Pay

ne

,Ch

esw

ort

h,&

Hil

l(19

92)

Exp

t1

1. 2. 3. 4.

No

inst

ruct

ion

gro

up

Car

ds-

on

lyin

stru

ctio

ns

Vid

eo

-on

lyin

stru

ctio

ns

Car

dan

dvi

de

oin

stru

ctio

ns

Pe

rfo

rmin

gta

sks

on

the

Mac

Dra

wso

ftw

are

pac

kage

Un

de

rsta

nd

ing

and

pe

rfo

rmin

gd

iffe

ren

tta

sks

inM

acD

raw

32ad

ult

sA

mo

un

to

ftim

esp

en

tto

com

ple

teal

lsix

task

sS

Dam

on

ggr

ou

ps

infa

vor

of

anim

atio

n.T

he

anim

ate

dgr

ou

ps

pe

rfo

rme

dal

ltas

ksin

38m

inle

ssth

anth

ete

xtgr

ou

pS

Dam

on

ggr

ou

ps

infa

vor

of

anim

atio

nfo

ru

nd

ers

tan

din

gan

dp

erf

orm

ance

.Th

ean

imat

ed

gro

up

fin

ish

ed

allf

ou

rta

sks

15m

infa

ste

rth

anth

eco

ntr

olg

rou

p

906



Exp

t2

1. 2.V

ide

o(a

nim

ate

d)

gro

up

Co

ntr

ol(

no

inst

ruct

ion

)gr

ou

p

16ad

ult

sA

mo

un

to

ftim

esp

en

tto

pe

rfo

rmal

lfo

ur

task

s

Pe

ters

&D

aike

r(1

982)

1. 2. 3. 4.

An

imat

ion

-on

lygr

ou

pIn

tera

ctio

n-o

nly

gro

up

An

imat

ion

plu

sin

tera

ctio

ngr

ou

pC

on

tro

lgro

up

;pla

yed

anu

nre

late

dga

me

Un

de

rsta

nd

ing

org

anic

che

mis

try

35ad

ult

sS

core

on

the

po

stte

stN

SD

amo

ng

gro

up

s

Po

nic

k(1

986)

1. 2. 3. 4.

Ran

do

mse

lect

ion

wit

hse

qu

en

tial

pre

sen

tati

on

Ran

do

mse

lect

ion

wit

hsi

mu

ltan

eo

us

pre

sen

tati

on

Gu

ide

dse

lect

ion

wit

hse

qu

en

tial

pre

sen

tati

on

sG

uid

ed

sele

ctio

nw

ith

sim

ult

ane

ou

sp

rese

nta

tio

n)

Co

nce

pt

lear

nin

gin

mat

he

mat

ical

curv

esk

etc

hin

g

71ad

ult

sId

en

tify

ing

the

fun

ctio

nth

atre

pre

sen

ted

the

give

ngr

aph

,fr

om

the

set

ofs

ixal

tern

ativ

es

SD

amo

ng

the

anim

ate

dgr

ou

pan

dst

atic

visu

algr

ou

ps.

Su

bje

cts

assi

gne

dto

anim

ate

dtr

eat

me

nt

(No

.4)

pe

rfo

rme

dsi

gnif

ican

tly

be

tte

ro

nle

arn

ing

con

cep

tsre

late

dto

mat

he

mat

ical

fun

ctio

ns

than

sub

ject

sas

sign

ed

tost

atic

grap

hic

str

eat

me

nts

Ram

&P

hu

a(1

997)

1.C

on

ven

tio

nal

lect

ure

Re

vie

wo

fco

nge

nit

alh

ear

td

ise

ase

.64

adu

lts

10m

ult

iple

-ch

oic

eq

ue

stio

ns

SD

amo

ng

gro

up

sin

favo

ro

fan

imat

ion

2.A

nim

atio

nco

urs

ew

are

Re

ed

(198

5)1. 2.

Exp

eri

me

nta

lgro

up

,vi

ew

ing

sim

ula

tio

ns

wit

hre

gard

tosp

ee

de

stim

atio

n,f

illi

ng

tan

ke

stim

atio

n,a

nd

mix

ture

est

imat

ion

Co

ntr

olg

rou

p,r

ece

ivin

gu

nre

late

din

form

atio

n

Teac

hin

gal

geb

raw

ord

pro

ble

ms

rela

ted

to:

1.S

pe

ed

sim

ula

tio

n2.

Tan

kfi

llin

g3.

Mix

ing

ofd

iffe

ren

tco

nce

ntr

atio

ns

180

adu

lts

Ne

tga

inin

stu

de

nts

est

imat

eo

fth

ep

rob

lem

fro

mth

ep

req

ue

stio

nn

aire

toth

ep

ost

test

qu

est

ion

nai

re

Mix

ed

resu

lts

we

rere

po

rte

d.

Wat

chin

gsi

mu

lati

on

(an

imat

ion

)w

asu

sefu

lwh

en

fee

db

ack

was

pro

vid

ed

(le

arn

ing

by

do

ing)

.H

ow

eve

r,in

mo

stca

ses

vie

win

gal

on

e(l

ear

nin

gb

yse

ein

g)w

asn

ot

en

ou

gh.T

his

me

ans

that

grap

hic

sd

isp

lays

alo

ne

did

no

tp

rod

uce

anin

cre

ase

inin

stru

ctio

nal

eff

ect

ive

ne

ss.

Rie

be

r&

Han

naf

in(1

988)

1. 2. 3.

Text

gro

up

wit

hp

ract

ice

Text

gro

up

wit

ho

ut

pra

ctic

eA

nim

atio

ngr

ou

pw

ith

pra

ctic

e

Ru

leu

sin

gan

dp

rob

lem

solv

ing,

Ne

wto

n’s

law

so

fmo

tio

n

111

4th

,5th

,an

d6t

hgr

ade

rsA

24-i

tem

po

stte

stm

eas

uri

ng

rule

usi

ng

and

pro

ble

mso

lvin

g

NS

Dam

on

ggr

ou

ps

for

ori

en

tin

gac

tivi

tie

s.N

om

ain

eff

ect

was

fou

nd

for

pra

ctic

e.

Asi

gnif

ican

tin

tera

ctio

nw

asfo

un

db

etw

ee

np

ract

ice

and

lear

nin

go

utc

om

es.

Th

ese

resu

lts

sugg

est

sth

at“o

rie

nti

ng

acti

viti

es

wh

eth

er

text

-bas

ed

or

anim

ate

dd

on

ot

exe

rtin

flu

en

ceo

nle

arn

ing”

(p.8

5)

4.A

nim

atio

ngr

ou

pw

ith

ou

tp

ract

ice

5.Te

xtp

lus

anim

atio

ngr

ou

pw

ith

pra

ctic

e6.

Text

plu

san

imat

ion

gro

up

wit

ho

ut

pra

ctic

e7.

No

-act

ivit

ygr

ou

pw

ith

pra

ctic

e8.

No

-act

ivit

ygr

ou

pw

ith

ou

tp

ract

ice

Con

tin

ues

907



33.A

5.(C

on

tin

ue

d)

Stu

dy

Tre

atm

en

tC

on

ten

tS

ub

ject

sD

ep

en

de

nt

Var

iab

le(s

)R

esu

lts

Rie

be

r(1

989)

1.S

tati

cgr

aph

ics

Ne

wto

n’s

law

so

fmo

tio

n19

24t

h,5

than

d6t

hgr

ade

rsA

po

stte

stm

eas

uri

ng

lear

nin

go

utc

om

es

and

tran

sfe

rN

SD

amo

ng

gro

up

sfo

rle

arn

ing

ou

tco

me

s.T

his

me

ans

no

anim

atio

ne

ffe

cts.

2.S

tati

cgr

aph

ics

wit

ho

ut

rele

van

tp

ract

ice

3.S

tati

cgr

aph

ics

wit

ho

ut

text

4.S

tati

cgr

aph

ics

5.A

nim

ate

dgr

aph

ics

wit

hre

leva

nt

pra

ctic

e6.

An

imat

ed

grap

hic

sw

ith

ou

tre

leva

nt

pra

ctic

e7.

An

imat

ed

grap

hic

sw

ith

ou

tte

xt8.

An

imat

ed

grap

hic

sw

ith

text

9.N

ogr

aph

ics

wit

hre

leva

nt

pra

ctic

e10

.N

ogr

aph

ics

11.

No

grap

hic

sw

ith

ou

tte

xt12

.N

ogr

aph

ics

Rie

be

r(1

990)

1.S

tati

cgr

aph

ics

wit

hb

eh

avio

ralp

ract

ice

Ne

wto

n’s

law

so

fmo

tio

n11

94t

han

d5t

hgr

ade

rsA

po

stte

stm

eas

uri

ng

six

lear

nin

go

bje

ctiv

es

(p.1

37)

SD

amo

ng

gro

up

sfo

rvi

sual

ela

bo

rati

on

.Sig

nif

ican

tin

tera

ctio

nb

etw

ee

nvi

sual

ela

bo

rati

on

and

pra

ctic

e.S

tud

en

tsin

the

cogn

itiv

egr

ou

psc

ore

dh

igh

er

than

the

oth

er

gro

up

so

nth

ep

ost

test

.

2.S

tati

cgr

aph

ics

wit

hco

gnit

ive

pra

ctic

e3.

Sta

tic

grap

hic

sw

ith

no

pra

ctic

e4.

An

imat

ed

grap

hic

sw

ith

be

hav

iora

lpra

ctic

eS

Dam

on

ggr

ou

ps

for

lear

nin

go

bje

ctiv

es

infa

vor

ofa

nim

atio

n5.

An

imat

ed

grap

hic

sw

ith

cogn

itiv

ep

ract

ice

6.A

nim

ate

dgr

aph

ics

wit

hn

op

ract

ice

Rie

be

r,B

oyc

e,&

Ass

ad(1

990)

Th

isis

the

rep

lica

tio

no

fth

e19

90b

stu

dy

usi

ng

adu

ltsu

bje

cts.

Ne

wto

n’s

law

so

fmo

tio

n14

1ad

ult

sA

po

stte

stm

eas

uri

ng

six

lear

nin

go

bje

ctiv

es

(p.4

8)N

SD

amo

ng

gro

up

sfo

rvi

sual

ela

bo

rati

on

Sig

nif

ican

tin

tera

ctio

nb

etw

ee

nvi

sual

ela

bo

rati

on

and

pra

ctic

e.T

he

pra

ctic

eh

adm

ore

eff

ect

so

nle

arn

ing

than

the

stra

tegy

.Th

ean

imat

ed

gro

up

ou

tpe

rfo

rme

dth

eo

the

rgr

ou

ps

wh

en

the

rew

asn

ofe

ed

bac

k.S

Dam

on

ggr

ou

ps

for

lear

nin

go

bje

ctiv

es

and

resp

on

sela

ten

cyin

favo

ro

fan

imat

ion

Rie

be

r(1

991a

)1. 2. 3.

An

imat

ion

wit

hq

ue

stio

ns/

sim

ula

tio

ns.

An

imat

ion

wit

hsi

mu

lati

on

/qu

est

ion

sS

tati

cgr

aph

ics

wit

hq

ue

stio

ns/

sim

ula

tio

ns

Ne

wto

n’s

law

so

fmo

tio

n70

chil

dre

n,

4th

grad

ers

A24

-ite

mp

ost

test

(im

me

dia

tean

d2

day

sd

ela

yed

)m

eas

uri

ng

bo

thty

pe

so

fru

lele

arn

ing:

inte

nti

on

alan

din

cid

en

tal

SD

amo

ng

gro

up

sfo

rin

cid

en

tal

lear

nin

gin

bo

thim

me

dia

tean

dd

ela

yed

po

stte

stin

favo

ro

fan

imat

ion

SD

amo

ng

gro

up

sfo

rin

ten

tio

nal

lear

nin

gin

favo

ro

fan

imat

ion

908



4.S

tati

cgr

aph

ics

wit

hsi

mu

lati

on

s/q

ue

stio

ns

SD

amo

ng

gro

up

sfo

rti

me

late

ncy

.T

he

anim

ate

dgr

ou

ps

too

ksi

gnif

ican

tly

less

tim

eto

answ

er

the

inci

de

nta

lqu

est

ion

s.T

his

was

no

tth

eca

sefo

rin

ten

tio

nal

lear

nin

g.R

ieb

er

(199

1b)

1.G

rou

pe

dan

imat

ion

(ch

un

ked

)N

ew

ton

’sla

ws

ofm

oti

on

39ch

ild

ren

,4th

grad

ers

A24

-ite

mp

ost

test

me

asu

rin

gin

cid

en

tala

nd

inte

nti

on

alle

arn

ing

SD

amo

ng

gro

up

sin

favo

ro

fan

imat

ion

.Th

ech

un

ked

gro

up

sco

red

sign

ific

antl

yb

ett

er

on

the

po

stte

stth

anth

est

atic

gro

up

s.N

SD

be

twe

en

the

un

gro

up

ed

anim

ate

dco

nd

itio

nan

dan

yo

fth

eo

the

rth

ree

gro

up

s.S

Dam

on

ggr

ou

ps

on

inci

de

nta

lle

arn

ing

infa

vor

ofa

nim

atio

n

2.U

ngr

ou

pe

dan

imat

ion

(no

chu

nki

ng)

3.G

rou

pe

dst

atic

grap

hic

s(c

hu

nke

d)

4.U

ngr

ou

pe

dst

atic

grap

hic

s(n

och

un

kin

g)

NS

Dam

on

ggr

ou

ps

for

inte

nti

on

alle

arn

ing

Rie

be

r(1

996a

)E

xpt

11. 2. 3.

An

imat

ed

fee

db

ack

gro

up

Text

ual

fee

db

ack

gro

up

Text

ual

plu

san

imat

ed

fee

db

ack

Le

arn

ing

the

rela

tio

nsh

ipb

etw

ee

nsp

ee

d&

velo

city

ofa

bal

l

40ad

ult

sA

12-i

tem

pre

test

and

po

stte

stto

me

asu

resc

ore

(tim

e/s

),in

tera

ctiv

ity

(No

.ofh

its

on

keys

),an

dfr

ust

rati

on

leve

l

NS

Dam

on

gsi

mu

lati

on

vers

ion

sfo

rth

ep

erf

orm

ance

test

.SD

for

the

gam

esc

ore

.Th

ean

imat

ed

fee

db

ack

gro

up

had

alo

we

rsc

ore

SD

amo

ng

sim

ula

tio

nve

rsio

ns

for

inte

ract

ivit

y.T

he

anim

ate

dfe

ed

bac

kve

rsio

nh

ada

low

er

inte

ract

ivit

yle

vel(

un

de

rsto

od

the

rela

tio

nsh

ipb

etw

ee

nsp

ee

dan

dve

loci

ty).

NS

Dfo

un

dam

on

gth

esi

mu

lati

on

vers

ion

sfo

rth

ele

velo

ffr

ust

rati

on

s.E

xpt

2T

his

isth

ere

pli

cati

on

of

Exp

t1.

Th

eo

nly

chan

ges

are

the

amo

un

to

fpra

ctic

ean

dth

en

um

be

ro

fsu

bje

cts.

49ad

ult

sS

Dam

on

gsi

mu

lati

on

vers

ion

s.T

he

anim

ate

dfe

ed

bac

kan

dan

imat

ed

fee

db

ack

plu

ste

xtgr

ou

ps

pe

rfo

rme

dsi

gnif

ican

tly

be

tte

rth

anth

ete

xtu

algr

ou

p.S

Dam

on

gth

esi

mu

lati

on

gro

up

sfo

rsc

ore

and

inte

ract

ivit

y.T

he

anim

ate

dfe

ed

bac

kve

rsio

nh

adth

elo

we

stti

me

and

nu

mb

er

ofh

its.

SD

fou

nd

for

fru

stra

tio

nle

veli

nfa

vor

oft

he

anim

ate

dgr

ou

ps.

Rie

be

r(1

996b

)1.

Hig

h-d

istr

acti

on

con

dit

ion

(sp

ace

ship

mo

ved

fro

mL

toR

)

Ne

wto

n’s

law

so

fmo

tio

n36

45t

hgr

ade

rsA

po

stte

stm

eas

uri

ng

pe

rfo

rman

cean

dp

roce

ssin

gti

me

(tim

en

ee

de

db

ye

ach

stu

de

nt

top

roce

sse

ach

inst

ruct

ion

alfr

ame

)

NS

Dam

on

ggr

ou

ps

for

pe

rfo

rman

cete

st.T

he

dis

trac

ter

did

no

taf

fect

stu

de

nts

’pe

rfo

rman

ceo

nth

ep

ost

test

.SD

amo

ng

gro

up

sfo

rp

roce

ssin

gti

me

.Th

eh

igh

-an

dm

ed

ium

-dis

trac

tio

ngr

ou

ps

pai

dat

ten

tio

nto

the

dis

trac

ters

and

too

kle

ssti

me

tovi

ew

the

inst

ruct

ion

alfr

ame

s.

2.M

ed

ium

-dis

trac

tio

nco

nd

itio

n(s

pac

esh

ipm

ove

dat

top

ofp

age

)3.

No

-dis

trac

tio

nco

nd

itio

n(n

osp

ace

ship

)

Con

tin

ues

909



33.A

5.(C

on

tin

ue

d)

Stu

dy

Tre

atm

en

tC

on

ten

tS

ub

ject

sD

ep

en

de

nt

Var

iab

le(s

)R

esu

lts

Rie

be

re

tal

.(19

96)

1.M

ean

ingf

ulc

on

text

gro

up

Un

de

rsta

nd

ing

Ne

wto

n’s

law

so

fmo

tio

nth

rou

ghsi

mu

late

dga

me

s(m

inia

ture

golf

)

41ad

ult

sA

pre

test

and

po

stte

stm

eas

ure

dst

ud

en

ts’

pe

rfo

rman

ce;g

ame

sco

re,

inte

ract

ivit

y,an

dfr

ust

rati

on

leve

lals

om

eas

ure

d

NS

Dam

on

ggr

ou

ps

for

pe

rfo

rman

ce.

NS

Dam

on

ggr

ou

ps

for

inte

ract

ivit

y.H

ow

eve

r,su

bje

cts

did

be

tte

rw

ith

anim

ate

dfe

ed

bac

k.N

SD

amo

ng

gro

up

sfo

rfr

ust

rati

on

leve

l.S

Dam

on

ggr

ou

ps

for

sco

rein

favo

ro

fan

imat

ion

.

2.A

rbit

rary

con

text

gro

up

Rig

ne

y&

Lu

tz(1

976)

1.V

erb

altr

eat

me

nt

gro

up

(ve

rbal

on

ly)

Le

arn

ing

the

con

cep

to

fasi

mp

leb

atte

ry40

adu

lts

On

ere

cogn

itio

n(m

em

ory

)te

stan

dth

ree

reca

llte

sts

(kn

ow

led

ge,c

om

pre

he

nsi

on

,an

dap

pli

cati

on

)

SD

amo

ng

gro

up

sfo

rre

call

and

atti

tud

ein

favo

ro

fan

imat

ed

gro

up

.2.

Imag

ery

gro

up

(ve

rbal

info

rmat

ion

plu

san

imat

ion

)

NS

Dam

on

ggr

ou

ps

for

reco

gnit

ion

test

.Ho

we

ver,

the

anim

ate

dgr

ou

pp

erf

orm

ed

be

tte

ro

nth

ere

cogn

itio

nte

st.

Ro

shal

(194

9)1.

Gro

up

1Ty

ing

thre

ety

pe

so

fkn

ots

3314

adu

lts

(Nav

yre

cru

ittr

ain

ee

s)P

erf

orm

ing

the

task

corr

ect

ly(t

yin

gth

ekn

ots

)S

Dam

on

ggr

ou

ps

infa

vor

ofm

oti

on

.P

ort

rayi

ng

con

tin

uo

us

chan

ges

thro

ugh

mo

vem

en

tw

asan

eff

ect

ive

lear

nin

gst

rate

gy.

2.G

rou

p2

3.G

rou

p3

4.G

rou

p4

5.G

rou

p5

6.G

rou

p6

7.G

rou

p7

8.G

rou

p8

San

ger

&G

ree

nb

ow

e(2

000)

1. 2.A

nim

atio

nC

on

cep

tual

chan

gein

stru

ctio

n

Ch

em

ical

pro

cess

es

135

adu

lts

Eig

ht

mu

ltip

le-c

ho

ice

qu

est

ion

san

do

ne

ess

ayN

SD

amo

ng

gro

up

s

Sp

ange

nb

erg

(197

3)E

xpt

11. 2.

An

imat

ed

vid

eo

gro

up

Sti

ll-S

eq

ue

nce

gro

up

Le

arn

ing

the

dis

asse

mb

lyp

roce

du

refo

ran

M-8

5m

ach

ine

gun

40ad

ult

s(A

rmy

sold

iers

)In

div

idu

alp

erf

orm

ance

test

SD

amo

ng

gro

up

s.T

he

pe

rce

nta

geo

fsu

bje

cts

wh

oco

rre

ctly

dis

asse

mb

led

the

pro

ced

ure

was

59%

for

the

vid

eo

gro

up

and

25%

for

the

stil

l-se

qu

en

cegr

ou

p.

Exp

t2

1.A

nim

ate

dvi

de

ogr

ou

p80

adu

lts

(en

list

ed

sold

iers

)S

Dam

on

ggr

ou

ps.

Th

ep

erc

en

tage

of

sub

ject

sw

ho

corr

ect

lyd

isas

sem

ble

dth

ep

roce

du

rew

as43

%fo

rth

evi

de

ogr

ou

ps

and

15%

for

the

stil

l-se

qu

en

cegr

ou

ps.

2.A

nim

ate

dvi

de

ogr

ou

pp

lus

cue

ing

arro

ws

3.S

till

-se

qu

en

cegr

ou

p4.

Sti

ll-s

eq

ue

nce

gro

up

plu

scu

ein

gar

row

sS

pan

gle

r(1

994)

1.Tr

adit

ion

alin

stru

ctio

nD

ep

icti

ng

2Dan

d3D

ob

ject

s57

adu

lts

A2D

&3D

test

;me

nta

lro

tati

on

test

NS

Dam

on

ggr

ou

ps

2.A

nim

atio

nw

ith

colo

r3.

An

imat

ion

wit

ho

ut

colo

r4.

Sta

tic

pic

ture

sw

ith

colo

r5.

Sta

tic

pic

ture

sw

ith

ou

tco

lor

Sp

ott

s&

Dw

yer

(199

6)1. 2. 3.

Text

plu

sst

atic

grap

hic

sTe

xtp

lus

anim

atio

nTe

xtp

lus

anim

atio

np

lus

sim

ula

tio

n

Le

arn

ing

par

tsan

dfu

nct

ion

so

fhu

man

he

art

63ad

ult

sF

ou

rcr

ite

rio

nte

sts:

dra

win

g,id

en

tifi

cati

on

,te

rmin

olo

gy,

and

com

pre

he

nsi

on

test

s

SD

amo

ng

gro

up

sfo

rd

raw

ing

test

infa

vor

oft

he

anim

atio

np

lus

text

gro

up

SD

amo

ng

gro

up

sfo

rto

talc

rite

rio

nte

st(c

om

bin

atio

no

fth

efo

ur

crit

eri

on

test

s).T

he

anim

atio

np

lus

text

gro

up

ou

tpe

rfo

rme

dth

esi

mu

lati

on

gro

up

.Th

ead

dit

ion

of

sim

ula

ted

blo

od

flo

wd

idn

ot

affe

ctle

arn

ing.

910



Sw

eze

y,P

rez,

&A

lle

n(1

991)

1.P

roce

du

ralg

rou

p:

(a)

vid

eo

pre

sen

tati

on

,(b

)sl

ide

pre

sen

tati

on

Tro

ub

lesh

oo

tin

ge

lect

rom

ech

anic

alsy

ste

ms

ina

die

sel

en

gin

e

120

adu

lts

Fo

ur

test

s:a

crit

eri

on

-bas

ed

refe

ren

ceta

sk,a

han

ds-

on

tran

sfe

rta

sk,a

nab

stra

cttr

ansf

er

task

,an

da

con

cep

tual

kno

wle

dge

task

NS

Dam

on

gsu

bje

cts

wh

ore

ceiv

ed

stat

icve

rsu

sd

ynam

icp

rese

nta

tio

ns

oft

he

trai

nin

gm

ate

rial

s,re

gard

less

oft

he

inst

ruct

ion

alst

rate

gyco

nd

itio

ns

em

plo

yed

2.G

en

eri

csy

ste

mst

ruct

ure

and

fun

ctio

ngr

ou

p:(

a)vi

de

op

rese

nta

tio

n,(

b)

slid

ep

rese

nta

tio

n3.

Inte

grat

ed

gro

up

:co

mb

inat

ion

oft

he

abo

veS

zab

o&

Po

oh

kay

(199

6)1.

Text

on

lyC

on

stru

ctio

no

fatr

ian

gle

usi

ng

aco

mp

ass

173

adu

lts

Co

nst

ruct

ion

pro

ble

mte

stan

dL

ike

rt-t

ype

atti

tud

ete

stN

SD

amo

ng

gro

up

s2.

Text

plu

sst

atic

grap

hic

s3.

Text

plu

san

imat

ed

grap

hic

sT

ho

mp

son

&R

idin

g(1

990)

1.N

oco

mp

ute

rize

dil

lust

rati

on

gro

up

Le

arn

ing

Pyt

ho

gora

s’th

eo

rem

thro

ugh

mat

he

mat

ical

de

mo

nst

rati

on

s

108

chil

dre

n,1

1to

14ye

ars

old

Two

test

sgi

ven

tom

eas

ure

stu

de

nts

’un

de

rsta

nd

ing

of

the

rota

tio

ns

and

she

ars

SD

amo

ng

gro

up

sin

favo

ro

fan

imat

ion

.Th

em

ean

sco

reo

fth

ean

imat

ed

gro

up

was

sign

ific

antl

yh

igh

er

than

the

me

ansc

ore

so

fth

eo

the

rtw

ogr

ou

ps.

2.P

yth

ago

rean

pro

gram

wit

hil

lust

rati

on

s3.

Pyt

hag

ore

anp

rogr

amw

ith

com

pu

teri

zed

anim

atio

n(e

xpe

rim

en

tal

gro

up

)To

do

rov,

Sh

adm

eh

r,&

Biz

zi(1

997)

Exp

t1

1. 2. 3.

Vie

we

dre

alp

erf

orm

ance

plu

sve

rbal

coac

hin

gV

iew

ed

sim

ula

ted

pe

rfo

rman

cean

dan

imat

ed

pad

dle

(pil

ot

gro

up

)V

iew

ed

sim

ula

ted

pe

rfo

rman

cesh

ow

ing

anim

ate

dp

add

lean

db

all

(tra

inin

ggr

ou

p)

Le

arn

ing

dif

ficu

ltm

oto

rsk

ills

(hit

tin

ga

pin

g-p

on

gb

all)

42ad

ult

sN

um

be

ro

ftim

es

toh

itth

eta

rge

tfo

rtw

ose

tso

f50-

bal

ltr

ials

Sig

nif

ican

tin

tera

ctio

nre

po

rte

d.T

he

pe

rfo

rman

ceo

fth

ep

ilo

tgr

ou

pw

assi

mil

arto

that

oft

he

con

tro

lgro

up

for

the

firs

t50

tria

lsan

dw

ors

efo

rth

ese

con

d50

tria

ls.S

Dam

on

ggr

ou

ps

inth

ese

con

dtr

ial.

Th

esi

mu

late

dtr

ain

ing

gro

up

pe

rfo

rme

dsi

gnif

ican

tly

be

tte

rth

anth

eo

the

rgr

ou

ps.

SD

for

pe

rfo

rman

ce.T

he

trai

nin

ggr

ou

pth

atp

ract

ice

din

the

sim

ula

tor

pe

rfo

rme

db

ett

er

than

the

con

tro

lgro

up

that

pra

ctic

ed

on

the

task

inth

ere

ale

nvi

ron

me

nt.

Exp

t2

1.C

on

tro

lgro

up

(re

alp

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