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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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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
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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
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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
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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.
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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
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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.
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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
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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
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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
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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
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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
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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).
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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
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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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ture
sb
ase
do
nca
rniv
oro
us
pla
nts
Ele
me
nta
rysc
ho
ol(
256)
Re
call
1-d
ay-d
ela
yed
mu
ltip
le-c
ho
ice
test
Wri
tte
nS
Dfo
rco
mb
in-r
atio
nal
cue
ing
on
ly
Be
nd
er
&L
evi
n(1
978)
1.S
tory
+il
lust
rati
on
s2.
Sto
ry+
gen
era
tevi
sual
imag
es
3.S
tory
(lis
ten
twic
e)
4.S
tory
(lis
ten
on
ce)
20se
nte
nce
fict
itio
us
sto
ryM
en
tall
yre
tard
ed
chil
dre
n(9
6)R
eca
llsc
ore
s10
verb
atim
+10
par
aph
rase
dq
ue
stio
ns
Ora
lS
D(i
llu
stra
tio
ns)
NS
D(o
the
r3
con
dit
ion
s)
880
P1: gsb/gsb P2: gdo/gdo QC: MRM/UKS T1: MRM
PB378C-33 PB378-Jonassen-v3.cls September 2, 2003 20:24 Char Count= 0
Be
rnar
d,
Pe
tte
rso
n,&
All
y(1
981)
1.V
erb
alo
rgan
ize
r2.
Co
nte
xtu
alim
age
(pic
ture
)3.
No
-org
aniz
er
con
tro
l4.
Pla
ceb
oco
ntr
ol
An
800-
wo
rdp
assa
geab
ou
tfu
nct
ion
of
the
bra
in
Un
de
rgra
du
ate
(104
)R
eco
gnit
ion
18p
arap
hra
sean
dn
on
par
aph
rase
qu
est
ion
sIm
me
dia
te&
de
laye
dte
stin
g(2
we
eks
)
Wri
tte
nS
Dfo
rb
oth
verb
alan
dim
age
org
aniz
ers
NS
Db
etw
ee
nth
em
Bie
ger
&G
lock
(198
4)1.
Ten
com
bin
atio
ns
oft
ext
+p
ictu
res
by
info
rmat
ion
typ
e2.
No
thin
gco
ntr
olI
nfo
rmat
ion
typ
es:
no
no
pe
rati
on
al,o
pe
rati
on
al,
con
text
ual
,sp
atia
l,o
pe
rati
on
al+
con
text
ual
,op
era
tio
nal
+sp
atia
l
Two
asse
mb
lyta
sks
(han
dtr
uck
&w
all
han
gin
g)
Un
de
rgra
du
ate
(120
)1.
Me
anas
sem
bly
tim
es
2.M
ean
nu
mb
er
of
asse
mb
lye
rro
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
P1: gsb/gsb P2: gdo/gdo QC: MRM/UKS T1: MRM
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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
P1: gsb/gsb P2: gdo/gdo QC: MRM/UKS T1: MRM
PB378C-33 PB378-Jonassen-v3.cls September 2, 2003 20:24 Char Count= 0
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
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PB378C-33 PB378-Jonassen-v3.cls September 2, 2003 20:24 Char Count= 0
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
P1: gsb/gsb P2: gdo/gdo QC: MRM/UKS T1: MRM
PB378C-33 PB378-Jonassen-v3.cls September 2, 2003 20:24 Char Count= 0
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
P1: gsb/gsb P2: gdo/gdo QC: MRM/UKS T1: MRM
PB378C-33 PB378-Jonassen-v3.cls September 2, 2003 20:24 Char Count= 0
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
P1: gsb/gsb P2: gdo/gdo QC: MRM/UKS T1: MRM
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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
P1: gsb/gsb P2: gdo/gdo QC: MRM/UKS T1: MRM
PB378C-33 PB378-Jonassen-v3.cls September 2, 2003 20:24 Char Count= 0
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
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PB378C-33 PB378-Jonassen-v3.cls September 2, 2003 20:24 Char Count= 0
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
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PB378C-33 PB378-Jonassen-v3.cls September 2, 2003 20:24 Char Count= 0
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
P1: gsb/gsb P2: gdo/gdo QC: MRM/UKS T1: MRM
PB378C-33 PB378-Jonassen-v3.cls September 2, 2003 20:24 Char Count= 0
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
Eig
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
Wri
tte
nS
Dfo
ral
lgra
ph
ics
vs.a
llve
rbal
con
dit
ion
s2.
Gra
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.
Pro
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
Ora
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
P1: gsb/gsb P2: gdo/gdo QC: MRM/UKS T1: MRM
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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
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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
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894 • ANGLIN, VAEZ, CUNNINGHAM
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.
Holliday, W. G., & Harvey, D. A. (1976). Adjunct labeled drawings inteaching physics to junior high school students. Journal of Researchin Science Teaching, 13, 37–43.
Holmes, B. C. (1987). Children’s inferences with print and pictures.Journal of Educational Psychology, 79(1), 14–18.
Jagodzinska, M. (1976). The role of illustrations in verbal learning. PolishPsychological Bulletin, 7, 95–104.
Jahoda, G., Cheyne, W. M., Deregowski, J. B., Sinha, D., & Collings-bourne, R. (1976). Utilization of pictorial information in classroomlearning: A cross cultural study. AV Communication Review, 24,295–315.
Jonassen, D. H. (1979). Implications of multi-image for concept acqusi-tion. Educational Communication and Technology Journal, 27(4),291–302.
Koenke, K., & Otto, W. (1969). Contribution of pictures to children’scomprehension of the main idea in reading. Psychology in theSchools, 6, 298–302.
Koran, M. L., & Koran, J. J. J. (1980). Interaction of learner characteristicswith pictorial adjuncts in learning from science text. Journal ofResearch in Science Teaching, 17(5), 477–483.
Lesgold, A. M., DeGood, & Levin, J. R. (1977). Pictures and young chil-dren’s prose learning: A supplementary report. Journal of ReadingBehavior, 9, 353–360.
Lesgold, A. M., Levin, J. R., Shimron, J., & Guttmann, J. (1975). Picturesand young children’s learning from oral prose. Journal of Educa-tional Psychology, 67, 636–642.
Levin, J. R. (1976). What have we learned about maximizing what chil-dren learn? In J. R. L. & & V. L. Allen (Eds.), Cognitive learning in
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children: Theories and strategies. (pp. 105–134). New York: Aca-demic Press.
Levin, J. R., & Berry, J. K. (1980). Children’s learning of all the newsthat’s fit to picture. Educational Communication and TechnologyJournal, 28, 177–185.
Levin, J. R., McCormick, C. B., Miller, G. E., Berry, J. K., & Pressley, M.(1982). Mnemonic versus nonmnemonic vocabulary-learning strate-gies for children. American Educational Research Journal, 19,121–136.
Levin, J. R., Morrison, C. R., McGivern, J. E., Mastropieri, M. S., & Scruggs,T. E. (1986). Mnemonic facilitation of text-embedded science facts.American Educational Research Journal, 23, 489–506.
Levin, J. R., et al. (1983). Learning via mnemonic pictures: Analysis ofpresidential process. Educational Communication and Technol-ogy Journal, 31(3), 161–173.
Levin, J. R., Shriberg, L. K., & Berry, J. K. (1983). A concrete strategyfor remembering abstract prose. American Educational ResearchJournal, 20(2), 277–290.
Magne, O., & Parknas, L. (1962). The learning effects of pictures. BritishJournal of Educational Psychology, 33, 265–275.
Main, R. E., & Griffiths, B. (1977). Evaluation of audio and pictorialinstructional supplements. AV Communication Review, 25(2), 167–179.
Mayer, R. E. (1989). Systematic thinking fostered by illustrations in sci-entific text. Journal of Educational Psychology, 81(2), 240–246.
McCormick, C. B., & Levin, J. R. (1984). A comparison of different prose-learning variations of the mnemoic keyword method. American Ed-ucation Research Journal, 21, 379–398.
McCormick, C. B., Levin, J. R., Cykowski, F., & Danilovics, P. (1984).Mnemonic-strategy reduction of prose-learning interference. Ed-ucational Communication and Technology Journal, 32, 154–152.
Miller, W. A. (1938). Reading with and without pictures. ElementarySchool Journal, 38, 676–682.
Moore, A. M. (1975). Investigation of the effect of patterns of illus-trations on third graders’ comprehension of information (Doctoraldissertation, Kent State University, 1974). Dissertation AbstractsInternational, 36, 1275A.
Nugent, G. C. (1982). Pictures, audio, and print: Symbolic represen-tation and effect on learning. Educational Communications andTechnology Journal, 30(3), 163–174.
O’Keefe, E. J., & Solman, R. T. (1987). The influence of illustrations onchildren’s comprehension of written stories. Journal of ReadingBehavior, 19(4), 353–377.
Peeck, J. (1974). Retention of pictorial and verbal content of a text withillustrations. Journal of Educational Psychology, 66, 880–888.
Peng, C. Y., & Levin, J. R. (1979). Pictures and children’s story recall:Some questions of durability. Educational Communication andTechnology Journal, 27, 179–192.
Popham, W. J. (1969). Pictorial embellishments in a tape-slide instruc-tional program. AV Communication Review, 17(1), 28–35.
Pressley, M., Levin, J. R., Pigott, S., LeComte, M., & Hope, D. J. (1983).Mismatched pictures and children’s prose learning. EducationalCommunication and Technology Journal, 31, 131–143.
Pressley, M., Pigott, S., & Bryant, S. L. (1982). Picture content andpreschoolers’ learning from sentences. Educational Communica-tion and Technology Journal, 30, 151–161.
Rankin, E. F., & Culhane, J. W. (1970). One picture equals 1,000 words?Reading Improvement, 7, 37–40.
Rasco, R. W., Tennyson, R. D., & Boutwell, R. C. (1975). Imagery in-structions and drawings in learning prose. Journal of EducationalPsychology, 67, 188–192.
Reid, D. J., Briggs, N., & Beveridge, M. (1983). The effect of pictures
upon the readability of a school science topic. British Journal ofEducational Psychology, 53, 327–335.
Rice, D. R., Doan, R. L., & Brown, S. J. (1981). The effects of pictureson reading comprehension, speed and interest of second grade stu-dents. Reading Improvement, 18, 308–312.
Riding, R. J., & Shore, J. M. (1974). A comparison of two methods of im-proving prose comprehension in educationally subnormal children.British Journal of Educational Psychology, 44, 300–303.
Rohwer, W. D., & Matz, R. D. (1975). Improving aural comprehensionin white and in black children. Journal of Experimental Child Psy-chology, 19, 23–36.
Rohwer, W. D. J., & Harris, W. J. (1975). Media effects of prose learningin two populations of children. Journal of Educational Psychology,67, 651–657.
Royer, J. M., & Cable, G. W. (1976). Illustrations, analogies, and facilita-tive transfer in prose learning. Journal of Educational Psychology,68, 205–209.
Ruch, M. D., & Levin, J. R. (1977). Pictorial organization versus verbalrepetition of children’s prose: Evidence for processing differences.AV Communication Review, 25, 269–280.
Ruch, M. D., & Levin, J. R. (1979). Partial pictures as imagery-retrievalcues in young children’s prose recall. Journal of Experimental ChildPsychology, 28, 268–279.
Rusted, J., & Coltheart, M. (1979a). Facilitation of children’s prose recallby the presence of pictures. Memory and Cognition, 7(5), 354–359.
Rusted, J., & Coltheart, V. (1979b). The effect of pictures on the reten-tion of novel words and prose passages. Journal of ExperimentalChild Psychology, 28, 516–524.
Rusted, J., & Hodgson, S. (1985). Evaluating the picture facilitation effectin children’s recall of written texts. British Journal of EducationalPsychology, 55(3), 288–294.
Scruggs, T. E., Mastropieri, M. A., Levin, J. R., & Gaffney, J. S. (1985).Facilitating the acquisition of science facts in learning disabled stu-dents. American Educational Research Journal, 22, 575–586.
Sewell, E. H., & Moore, R. L. (1980). Cartoon embellishments in informa-tive presentations. Educational Communication and TechnologyJournal, 28, 39–46.
Sherman, J. L. (1976). Contextual information and prose comprehen-sion. Journal of Reading Behavior, 8, 369–379.
Shriberg, L. K., Levin, J. R., McCormick, C. B., & Pressley, M. (1982).Learning about “famous” people via the keyword method. Journalof Educational Pscyhology, 74, 238–247.
Silvern, S. B. (1980). Play, pictures, and repetition: Mediators in auralprose learning. Educational Communication and Technology Jour-nal, 28, 134–139.
Snowman, J., & Cunningham, D. J. (1975). A comparison of pictorial andwritten adjunct aids in learning from text. Journal of EducationalPsychology, 67, 307–311.
Stone, D. E., & Glock, M. D. (1981). How do young adults read directionswith and without pictures? Journal of Educational Psychology, 73,419–426.
Stromnes, F. J., & Nyman, J. (1974). Immediate and long-term retentionof connected concrete discourse as a function of mnemonic picture-type sequence and context. Scandinavian Journal of Psychology,15, 197–202.
Talley, J. E. (1989). The effect of pictures and story text structure on re-call and comprehension. (Doctoral dissertation, Auburn University,1988). Dissertation Abstracts International, 49, 2604A.
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.
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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.
Vye, N. J., Bransford, J. D., Symons, S. E., & Acton, H. (1986, April).Constraints on elaborations in visual domains. Paper presentedat the meeting of the American Educational Research Association,San Francisco.
Waddill, M. A., & McDaniel, M. A. (1988). Illustrations as adjuncts toprose: A text-appropriate processing approach. Journal of Educa-tional Psychology, 80(4), 457–464.
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.
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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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TAB
LE
33.A
5.S
um
mar
yM
atri
xo
fRe
sear
chR
esu
lts
for
Dyn
amic
Vis
ual
s
Stu
dy
Tre
atm
en
tC
on
ten
tS
ub
ject
sD
ep
en
de
nt
Var
iab
le(s
)R
esu
lts
Al-
Mu
lla
(199
5)1.
Sta
tic
visu
als
Ae
rod
ynam
ic,
air-
con
dit
ion
er,
&jo
ysti
ck
50ad
ult
sIm
me
dia
tean
dd
ela
yed
po
stte
sts
SD
for
anim
atio
no
nae
rod
ynam
icin
tell
ect
ual
skil
lsd
ela
yed
po
stte
st,
ove
rall
inte
lle
ctu
alsk
ills
de
laye
dp
ost
test
,an
dim
me
dia
tean
dd
ela
yed
joys
tick
inte
lle
ctu
alsk
ills
2.A
nim
ate
dvi
sual
s
Ale
san
dri
ni&
Rig
ne
y(1
981)
Exp
t1
1.P
icto
rial
less
on
&p
icto
rial
revi
ew
Fu
nct
ion
ofa
bat
tery
96ad
ult
sP
icto
rial
test
&ve
rbal
test
2.P
icto
rial
less
on
&n
ore
vie
wS
Dfo
ran
imat
ion
on
pic
ture
reco
gnit
ion
3.V
erb
alle
sso
n&
pic
tori
alre
vie
wN
SD
amo
ng
gro
up
sfo
rve
rbal
test
4.V
erb
alle
sso
n&
no
revi
ew
Exp
t2
1.V
erb
alle
sso
n&
pic
tori
alre
vie
w50
adu
lts
2.V
erb
alle
sso
n&
verb
alre
vie
wA
tlas
,Co
rne
tt,L
ane
,&
Nap
ier
(199
7)E
xpt
11. 2. 3.
Text
An
imat
ion
An
imat
ion
plu
sve
rbal
info
rmat
ion
Trai
nin
gin
Hyp
erC
ard
auth
ori
ng
task
s39
adu
lts
Imm
ed
iate
test
&7
day
de
laye
dte
stM
ixe
dre
sult
s:A
nim
ate
dgr
ou
pp
erf
orm
ed
be
tte
ro
nim
me
dia
tete
stb
ut
wo
rse
on
de
laye
dte
st
Exp
t2
1. 2.Te
xtA
nim
atio
np
lus
verb
alin
form
atio
n
Trai
nin
gin
anim
atio
np
lus
verb
alin
form
atio
nle
dto
gre
ate
rim
pro
vem
en
to
nd
ela
yed
test
22ad
ult
s
Avo
ns,
Be
veri
dge
,H
ickm
an,&
Hit
ch(1
983)
Exp
t1
1.A
ctiv
eve
rtic
alR
ela
tio
nsh
ipb
etw
ee
nsp
ee
dan
dco
rre
spo
nd
ing
slo
pe
so
fth
egr
aph
inan
anim
ate
dsi
mu
lati
on
of
am
ovi
ng
car
60ch
ild
ren
,9to
11ye
ars
old
48ch
ild
ren
,10
to11
year
so
ld
Po
stte
stm
eas
uri
ng
com
pre
he
nsi
on
,pro
du
ctio
n,
and
con
cep
tual
un
de
rsta
nd
ing
Inb
oth
exp
eri
me
nts
,NS
Dw
ere
fou
nd
amo
ng
the
gro
up
sfo
rco
nce
ptu
al,
com
pre
he
nsi
on
,an
dp
rod
uct
ion
test
s.H
ow
eve
r,ch
ild
ren
pe
rfo
rme
db
ett
er
on
com
pre
he
nsi
on
and
pro
du
ctio
nte
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
P1: gsb/gsb P2: gdo/gdo QC: MRM/UKS T1: MRM
PB378C-33 PB378-Jonassen-v3.cls September 2, 2003 20:24 Char Count= 0
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
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PB378C-33 PB378-Jonassen-v3.cls September 2, 2003 20:24 Char Count= 0
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
P1: gsb/gsb P2: gdo/gdo QC: MRM/UKS T1: MRM
PB378C-33 PB378-Jonassen-v3.cls September 2, 2003 20:24 Char Count= 0
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
P1: gsb/gsb P2: gdo/gdo QC: MRM/UKS T1: MRM
PB378C-33 PB378-Jonassen-v3.cls September 2, 2003 20:24 Char Count= 0
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
P1: gsb/gsb P2: gdo/gdo QC: MRM/UKS T1: MRM
PB378C-33 PB378-Jonassen-v3.cls September 2, 2003 20:24 Char Count= 0
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
P1: gsb/gsb P2: gdo/gdo QC: MRM/UKS T1: MRM
PB378C-33 PB378-Jonassen-v3.cls September 2, 2003 20:24 Char Count= 0
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
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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
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PB378C-33 PB378-Jonassen-v3.cls September 2, 2003 20:24 Char Count= 0
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
P1: gsb/gsb P2: gdo/gdo QC: MRM/UKS T1: MRM
PB378C-33 PB378-Jonassen-v3.cls September 2, 2003 20:24 Char Count= 0
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
P1: gsb/gsb P2: gdo/gdo QC: MRM/UKS T1: MRM
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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
P1: gsb/gsb P2: gdo/gdo QC: MRM/UKS T1: MRM
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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
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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
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PB378C-33 PB378-Jonassen-v3.cls September 2, 2003 20:24 Char Count= 0
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
ract
ice
)21
adu
lts
2.Tr
ain
ing
gro
up
(sim
ula
tin
gp
ract
ice
)To
we
rs(1
994)
Exp
t1
1.Te
xto
nly
Pre
dic
tio
no
fwe
ath
er
pat
tern
49ad
ult
sA
10-i
tem
reca
llte
stS
Dam
on
ggr
ou
ps
infa
vor
oft
ext
2.Te
xtp
lus
stat
icvi
sual
s3.
Text
plu
san
imat
ed
visu
als
Con
tin
ues
911
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PB378C-33 PB378-Jonassen-v3.cls September 2, 2003 20:24 Char Count= 0
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
Exp
t2
1.Te
xto
nly
64ad
ult
sA
nim
me
dia
tete
stan
da
14-d
ay-d
ela
yed
test
on
reca
llan
dco
mp
reh
en
sio
no
fin
form
atio
n
NS
Dam
on
ggr
ou
ps
2.Te
xtp
lus
stat
icvi
sual
s3.
Text
plu
san
imat
ed
visu
als
Vae
z(2
000)
1.P
ure
anim
atio
nO
pe
rati
on
ofa
nin
tern
alco
mb
ust
ion
en
gin
e60
adu
lts
Imm
ed
iate
and
de
laye
dte
sts
con
sist
ing
ofa
33-i
tem
rete
nti
on
test
,an
11-i
tem
mat
chin
gte
st,a
nd
a7-
ite
mtr
ansf
er
test
SD
amo
ng
gro
up
sfo
rtr
eat
me
nt
2.A
nim
atio
np
lus
text
ual
nar
rati
on
NS
Dam
on
ggr
ou
ps
for
exp
osu
reti
me
toan
imat
ion
.NS
Dam
on
ggr
ou
ps
for
seq
ue
nce
ofv
iew
ing
3.A
nim
atio
np
lus
verb
aln
arra
tio
nW
est
en
do
rp(1
996)
1.Te
xtp
lus
spat
ial
info
rmat
ion
Se
ttin
gu
pa
tele
ph
on
esy
ste
mN
ot
give
nTi
me
spe
nt
read
ing
inst
ruct
ion
s;ti
me
spe
nt
pe
rfo
rmin
g13
task
sim
me
dia
tely
afte
rin
stru
ctio
nan
d1
we
ek
late
r
SD
amo
ng
gro
up
sfo
rim
me
dia
tete
st.
Inth
ed
ela
yed
test
NS
Dam
on
ggr
ou
ps
for
pe
rfo
rman
ce2.
Text
on
ly3.
Pic
ture
w/s
pat
ial
info
rmat
ion
on
ly4.
Pic
ture
on
ly5.
An
imat
ion
wit
h/s
pat
ial
info
rmat
ion
6.A
nim
atio
no
nly
Wil
liam
s&
Ab
rah
am(1
995)
Un
it5
1. 2. 3.
Sta
tic
visu
als
(co
ntr
ol
gro
up
)A
nim
atio
nin
lect
ure
sA
nim
atio
nin
lect
ure
san
dla
bo
rato
rie
s
Un
de
rsta
nd
ing
the
mo
lecu
lar
be
hav
ior
of
mat
ters
(PN
M)
124
adu
lts
Fo
rb
oth
un
its:
Test
ofc
on
cep
tual
un
de
rsta
nd
ing
(PN
ME
T),
cou
rse
ach
ieve
me
nt
test
,re
aso
nin
gab
ilit
yte
st(T
OLT
),an
dat
titu
de
test
(BA
R)
Fo
rU
nit
s5
and
7:S
Dam
on
ggr
ou
ps
for
con
cep
tual
un
de
rsta
nd
ing.
Bo
than
imat
ed
gro
up
sp
erf
orm
ed
50%
be
tte
rth
anth
eco
ntr
olg
rou
p(e
ffe
ctsi
zeo
f0.5
).H
ow
eve
r,th
ey
did
no
td
iffe
rfr
om
eac
ho
the
r.U
nit
71.
Sta
tic
visu
als
(co
ntr
ol
gro
up
)12
4ad
ult
sN
SD
amo
ng
gro
up
sfo
rre
aso
nin
gab
ilit
ies,
atti
tud
eto
war
din
stru
ctio
n,a
nd
cou
rse
ach
ieve
me
nt
2.A
nim
atio
nin
lect
ure
s3.
An
imat
ion
inle
ctu
res
and
lab
ora
tori
es
Zav
otk
a(1
987)
1. 2. 3. 4.
No
anim
atio
n(c
on
tro
lgr
ou
p)
An
imat
ion
ord
er
1A
nim
atio
no
rde
r2
An
imat
ion
ord
er
3
Un
de
rsta
nd
ing
3Do
rth
ogr
aph
icd
raw
ings
101
adu
lts
Am
en
talr
ota
tio
np
rete
st,a
nim
me
dia
tem
en
talr
ota
tio
nte
st,a
nd
afi
nal
ide
nti
fica
tio
nte
st
Mix
ed
resu
lts:
NS
Dam
on
ggr
ou
ps
for
me
nta
lro
tati
on
test
.An
imat
ion
did
no
th
ave
any
eff
ect
on
the
me
nta
lro
tati
on
test
.S
Dam
on
ggr
ou
ps
for
ide
nti
fica
tio
nte
st.O
ne
exp
eri
me
nta
lord
er
(nat
ura
l3D
ton
atu
ral2
Dto
a3D
wir
efr
ame
toa
2Dfl
atli
ne
dra
win
gfo
rm)
sco
red
hig
he
rth
anth
eco
ntr
olg
rou
p.
Not
e.A
du
ltsu
bje
cts
are
un
de
rgra
du
ate
and
grad
uat
est
ud
en
ts.O
the
rsar
esp
eci
fie
d.
912
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33. Visualization and Learning • 913
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