Computers in Human Behavior 25 (2009) 348–353

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Computers in Human Behavior

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Learning hand manipulative tasks: When instructional animations are superiorto equivalent static representations

Paul Ayres a,*, Nadine Marcus b, Christopher Chan b, Nixon Qian b

a School of Education, University of New South Wales, Sydney, NSW 2052, Australiab School of Computer Science and Engineering, University of New South Wales, Australia

a r t i c l e i n f o

Article history:Available online 3 January 2009

Keywords:Instructional animationsHuman movementMirror-neuronsCognitive load theory

0747-5632/$ - see front matter � 2008 Elsevier Ltd. Adoi:10.1016/j.chb.2008.12.013

* Corresponding author.E-mail address: p.ayres@unsw.edu.au (P. Ayres).

a b s t r a c t

Cognitive load theory was used to argue why instructional animations are more effective in teachinghuman motor skills than static representations. A key aspect to this argument is the role played by thetransitory nature of animation and the newly discovered human mirror-neuron system. In two experi-ments students were taught to tie knots or complete puzzle rings either through an animated presenta-tion or an equivalent sequence of static diagrams. In both experiments students learnt more from theanimation mode than the static one, thus supporting the general thesis of the paper.

� 2008 Elsevier Ltd. All rights reserved.

1. Introduction

It is often assumed that technology is so advanced and sophis-ticated now that it will inevitably lead to enhanced learning. It isalso frequently suggested that dynamic representations will bevery useful in depicting systems that change or move as a functionof time, particularly compared with static diagrams where the lear-ner has to infer such changes. But as Chandler (2004) observed ourdevelopment of new technologies is much more advanced than ourunderstanding of how humans can best learn from the technology.In the field of learning and instruction there has been much re-search into testing the effectiveness of instructional animations.One focus of this research has been to compare dynamic visualiza-tions (animations) with static visualizations (statics). The results sofar have been far from conclusive. A review of the literature byTversky, Morrison, and Betrancourt (2002) found little evidencethat animations were superior to graphics (see also Mayer, He-garty, Mayer, & Campbell, 2005, who found statics to be the bestlearning environment), whereas a recent meta-analysis by Höfflerand Leutner (2007) identified a number of studies where anima-tions produced better learning outcomes than statics. The conclu-sions of Tversky et al. (2002) were direct evidence of themismatch between expectation and reality, concluding that foranimations to be beneficial they must correspond to congruenceand apprehension principles. That is the animated content mustcorrespond very closely to the content to be learnt, and be accu-rately perceived. However, even if these two principles are closelyfollowed, according to CLT researchers the transitory nature of ani-

ll rights reserved.

mations may still be a major impediment to learning (Ayres & Paas,2007a).

In spite of these seemingly conflicting findings, and a number ofalternative theories offered in explanation, some convergence isdeveloping. One promising approach has been to use cognitive loadtheory (Paas, Renkl, & Sweller, 2003; Sweller, 2005) as a theoreticalframework. Through this paradigm a more comprehensive theoryhas been proposed which identifies some of the conditions underwhich instructional animations may be effective (Ayres & Paas,2007a, 2007b). Furthermore, it has also been proposed that anima-tions may be particularly apt in teaching about motor skills due tothe mirror-neuron system (Van Gog, Paas, Marcus, Ayres, & Swel-ler, in press). The main aim of this current paper is to use a cogni-tive load theory (CLT) approach to expand the research-base oncomparing animations with statics in a human motor skills do-main. The following section gives a brief introduction to CLT andoutlines why transitory information may be a problem for somelearners.

The central idea of CLT is that working memory (WM) plays ahighly significant role in learning (schema development). However,because WM is very limited in both capacity and duration (seeMiller, 1956; Peterson & Peterson, 1959), learning can be seriouslyinhibited if instructional designers fail to take account of these lim-itations. CLT identifies three categories of cognitive load: intrinsic,extraneous and germane (see Sweller, van Merriënboer, & Paas,1998). Intrinsic load is the load caused by the complexity of thematerials to be learnt, extraneous load is the load caused bypoorly-designed instructional procedures which interfere withschema acquisition, and germane load is the load directly investedin schema acquisition, aided by well-designed instructional condi-tions. Intrinsic load is dependent upon element interactivity (see

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Marcus, Cooper, & Sweller, 1996; Sweller & Chandler, 1994), thenumber of elements that need to be processed simultaneously bythe learner. If element interactivity is high, learning becomes diffi-cult and WM resource intensive, whereas for low element interac-tivity material, learning is easier requiring fewer WM resources.When instructional material is poorly constructed, extraneous loadis generated because the learner is diverted away from schemaacquisition and uses up precious WM resources by trying to prob-lem solve for example (see Sweller et al., 1998). A combination ofhigh intrinsic and/or extraneous load inevitably leads to reducedlearning as few if any WM resources are left to engage directly inlearning (germane load). A good instructional design lowers extra-neous load and induces germane load.

CLT theorists have argued that instructional animations aregenerally ineffective because they often create high extraneousloads (see Ayres, Kalyuga, Marcus, & Sweller, 2005; Ayres & Paas,2007a, 2007b). Extraneous load can be caused by a number of poordesign features, for example separating text and diagram leadingto a split-attention effect (see Ayres & Sweller, 2005), or moreendemically by the very transitory nature of animations (see Ains-worth & VanLabeke, 2004; Hegarty, 2004). By their intrinsic nature,animations show change over time, which often involves informa-tion disappearing from the computer screen. Under such condi-tions learners are required to process new information whilesimultaneously trying to remember and integrate important pastinformation, thus creating extraneous load as WM resources arefocused on dealing with the demands of the presentation, ratherthan a focus on learning. The transitory effect provides a possibleexplanation as to why some forms of user-interactivity (see Hasler,Kersten, & Sweller, 2007; Schwan & Riempp, 2004) or segmenta-tion (Mayer & Chandler, 2001; Moreno, 2007) improves the effec-tiveness of animations. In particular, by stopping or dividing theanimation into smaller parts, there is less transitory information,and consequently the demands on WM are reduced. The transitoryinfluence also explains why in many cases animations are no moreeffective than static presentations. With statics, there is less needto hold information in WM because in many research designs thesequence of static displays are readily available and do not disap-pear, and thus can easily be reviewed by the learner.

The transitory argument based on CLT provides a plausible rea-son why animations have not produced the desired learning re-sults, particularly when compared with static diagrams, but itdoes not seemingly support the research that has found animationsto be more effective. The meta-analysis of Höffler and Leutner(2007) identified some conditions under which animations aremore effective than equivalent statics. In particular, superior learn-ing was found when the animations were highly realistic and pro-cedural-motor knowledge was involved. Although it was alsonoted by the authors that it was sometimes difficult to isolatewhich actual factors were most responsible for the positive effectsbecause often combinations of different factors were embedded inthe research designs. Nevertheless, it was concluded that the larg-est effect size found was in learning about procedural-motorknowledge such as disassembling a machine gun (Spangenberg,1973, as cited by Höffler & Leutner, 2007). On the surface, the Höf-fler and Leutner findings are not consistent with the CLT transitoryexplanation, as the Spangenberg study for example, used a non-interactive video-based recording that clearly contained transientinformation, yet it was superior to an equivalent static presenta-tion. To explain this potential contradiction Van Gog et al. (inpress) have extended the CLT argument to include a special casefor human movement.

Van Gog et al. have proposed that the high WM demands cre-ated by transitory information in animations is less a problem ifthe learning focus is related to human movement, because of themirror-neuron system. Some recent research conducted in neuro-

science suggests that mirror-neurons (for a review, see Rizzolatti& Craighero, 2004) allow humans to engage in imitative learning(see Blandin, Lhuisset, & Poteau, 1999). Van Gog et al. argue, basedon Geary’s (2007) evolutionary primary knowledge concept, thathumans have evolved the ability to learn certain types of knowl-edge effortlessly. If, as part of this primary knowledge, humanshave evolved to observe movement and copy it (through mirror-neurons), then asking learners to observe an animation in orderto learn a motor skill may not place an excessive burden on WMresources, as we have biologically evolved to cope with it. In con-trast, learning about secondary knowledge (see Geary, 2007), suchas mechanical systems, or using static diagrams to represent hu-man movement, may require more WM resources, because wedo not have the same biological (neural networks) advantages.

The discussion outlined above potentially provides some an-swers to the seemingly contradictory results found in comparinginstructional animations with equivalent static presentations. Iftransitory information is a WM issue then many animation presen-tations will not be more effective than static ones because of extra-neous cognitive load. In some cases static presentation may bemore effective than animations when they involve learning aboutmechanical systems (Mayer et al., 2005). However, if the anima-tions depict human movement then they may more effective thanstatics due to the mirror-neuron system. The two experiments re-ported in this study were conducted to add to the literature oninstructional animation–static comparisons and to provide furthersupport for the hypothesis developed by Van Gog et al. (in press)based on the mirror-neuron system. It was hypothesized thatwhen learning a human motor skill, an animated presentationwould be more effective than a static one. In this study, the humanmotors skills to be learnt were hand manipulations during knot ty-ing and solving puzzle rings.

2. Experiment 1

The learning focus of this experiment was to learn how to con-struct a series of 3 knots, commonly referred to as Scoubidou knots.These knots were chosen because they were thought to be fairlyuncommon amongst the targeted group of participants, eliminat-ing the influence of expertise and because they directly involvemanipulation of the hands, and thus meeting the criterion of a mo-tor skill. To animate the presentation a direct video recording of ahuman (hands only) constructing the knots was made. A videorecording was chosen because it meets the highly realistic condi-tion identified by Höffler and Leutner (2007), and because videoshave been successfully used to teach knot tying (Schwan & Riempp,2004). Unlike the Schwan and Riempp study in learning to tie nau-tical knots, the video was not interactive, thus ensuring that noconfounding variables were introduced. For the same reason notext, neither written nor spoken was included. Participants wereexpected to learn entirely by observation only. To test the generalhypothesis of the study two equivalent presentations were con-structed; one in animated form, the other in static form.

2.1. Method

2.1.1. Participants and designThe participants were 36 (21 male and 15 female) Sydney high

school students (age range 16–18) and were randomly assigned toeither the animation or static group (n = 18 per group).

2.1.2. MaterialsParticipants in this experiment were required to learn to con-

struct a series of 3 Scoubidou knots (sometimes called Scoobystrings). To make the knots, two hollow plastic strings (tubes) wereprovided (one red and one green). Each tube was hollow, had a

350 P. Ayres et al. / Computers in Human Behavior 25 (2009) 348–353

length of 40 cm and a diameter of 2–3 mm, and was very flexible,without being too flimsy, and easily manipulated. The completedknots are shown in Fig. 1. Knots 1, 2 and 3, required 10, 6 and 6steps, respectively. For each knot a digital video recording, usinga PanasonicTM NV-GS50 camera, was made of one of the researchersconstructing it. The recording captured only the researcher’shands, as each step in the process was carefully shown. Feedbackfrom pilot testing allowed an earlier version to be amended so thatthe position of hands and cameras angles were optimised to ensurethe targeted population could learn from the materials. The re-corded footage was transferred onto a computer via a firewire con-nection and saved using MicrosoftTM Windows Movie MakerVersion 5. This computer-based recording became the presentationfor the animated group and had no sound. The completion of knots1, 2 and 3 took 80, 60 and 60 s, respectively. The complete video

Fig. 1. Final states of the three knots used in Experiment 1.

recording took 210 s to play through, which included two 5-s inter-vals when switching between knots.

For the static group presentation, the continuous recording wasdivided into 42 static images, which depicted each step in con-structing the 3 knots. In most cases more that one frame was re-quired to adequately demonstrate each step, particularly formore complex steps. For knot-1, 14 frames were used to showthe 10 steps; for knot-2 13 frames were used for 6 steps; and forknot-3, 15 frames for 6 steps. The Movie Maker software packagewas used to pause and capture a still frame, which was saved asa separate file. These 42 files (each 209 � 175 pixels) were thenloaded into a HTML webpage in a matrix formation (6 rows of 7images). Only 5 rows could be fitted on the whole computer screenat a time and therefore learners were required to scroll down to seethe last row. Frames were the same size for both the static and ani-mation materials.

A set of instructions were provided on a single sheet of A4 pa-per, which detailed what was expected of the participants in termsof learning and what controls they had over the presentations. Forthe static group the instructions stated that each image proceededto the next one and they had to scroll down using the mouse toview the last row, whereas for the animation group it was statedthat no interaction was possible.

2.1.3. ProcedureDuring the learning phase both groups were presented with

the relevant presentation on a laptop computer. For the animatedgroup, the presentation proceeded without stopping. In contrast,the static group was presented the matrix of static diagrams,for which they had to scroll down the page to view the lastrow. However, before a new knot was introduced the computerscreen was wiped clean so as not to mix up the knots. Each groupwas given 210 s (time for the video to play through once) to ob-serve the presentation and learn the three knots. This activity wasthen repeated giving a total learning time of 420 s for bothgroups. For the testing phase, all participants were given thetwo coloured strings and asked to make the three knots consecu-tively using the same strings. They were given a maximum timeof 600 s to accomplish all three knots. In order to tie the secondor third knot, the previous knot had to be completed correctly. Ifthe first or second knot was not completed successfully and aparticipant was starting on the next knot, a completed knot ofthe previous stage was handed out. For each knot the time takento complete the task was recorded. A performance score was re-corded for each knot by counting how many steps were com-pleted correctly.

2.2. Results

The mean number of steps completed successfully and time forsolution (correct or incorrect) was calculated for each group (seeTable 1). It is notable that the mean average for this animatedgroup is extremely high (20.4 out of a maximum of 22) indicatinga high level of learning. It should also be noted that the distributionof scores for this measure, and others in Experiment 2, were notnormally distributed, consequently non-parametric tests wereconducted throughout this paper. Mann–Whitney U tests indicatedthat the animated group took significantly less time to complete

Table 1Group means (and SD) in Experiment 1.

Animated group Static group

Total number of correct steps (max. score = 22) 20.4 (3.04) 12.3 (6.30)Total time taken (s) 334.5 (213.7) 524.4 (142.2)

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the task (Z = 3.7, p < .01) and completed more steps correctly(Z = 2.7, p < .01). Both results indicate large effect sizes.

Fig. 2. Initial state of the two puzzle rings used in Experiment 2.

3. Experiment 2

The results of Experiment 1 support the stated hypothesis: inlearning to tie a series of three knots an animated presentationwas superior to a static representation. Simply by observing a com-puter-based video of a pair of human hands constructing the knots,the animated group was able to perform at a very competent level.Even though the information depicted in the animation was highlytransitory, this was not an impediment to learning. The purpose ofthis next experiment was to test this hypothesis further by inves-tigating different content – two puzzle rings of different complex-ity, but still motor-based and involving only hands. In a similarstudy, comparing animation and statics in a human movement do-main (paper folding), Wong et al. (2009) investigated whether theanimation advantage would translate beyond the manipulationskill to a more cognitively based skill. To do so learners were re-quired to recognize the previous or next fold of the paper-foldingtask, given a particular state of completion. However, the resultsindicated that there were no group differences on the cognitivetasks. Nevertheless to explore the impact of animation on develop-ing cognitive skills further, similar tasks to the Wong et al. studywere added to this study. Learners were asked to recognize previ-ous and past states of the puzzle rings. Furthermore, a transfer taskwas added and cognitive load measures were collected (see Paas,Tuovinen, Tabbers, & van Gerven, 2003).

3.1. Method

3.1.1. ParticipantsThirty-six undergraduates attending an Australian university

(16 female and 20 male, with a mean age of 22.7 years,SD = 6.37) participated. They were randomly assigned to one ofthe two groups giving 18 students in each group.

3.1.2. MaterialsThe material to be learnt consisted of two puzzles of varying

difficulty. The simplest puzzle is called Earring and the more com-plex one Quadring (see Fig. 2 showing the initial stage of each puz-zle). The animated and static presentations were constructed in asimilar fashion to those in Experiment 1. For the animated presen-tation, two video recording (Sony digital camcorder) were made ofan investigator taking apart (deconstructing the rings into theirconstituent pieces) each of the two puzzles (rings) with the handsvisible and clearly showing how the fingers manipulate the ringsfor each step. These recordings were then transferred onto a com-puter using S-Video (VIVO). The Earring and Quadring presenta-tions took 24 and 51 s, respectively to complete all the stepsrequired.

For the static group presentations the animations were brokendown into key frames (static images) that demonstrated each stepof the puzzle. However, some steps were more complicated thanothers and pilot testing revealed that for the more complicatedsteps more than one frame was required to adequately show theprocedures. Adobe Premiere (7.0) software was used to pauseand capture a still frame. As a result, 12 frames were capturedfor the Earring puzzle and 20 frames for the Quadring puzzle. Foreach puzzle, a matrix array was constructed consisting of the staticframes that were placed from left-to-right one after each other intemporal order. In each row of the array, 4 frames were shown.Two rows (8 images) were fitted onto the computer screen atany particular time, meaning that the static group had to scrolldown one row to observe the final row for the Earring puzzle,

and three rows for the Quadring puzzle. Frames were the same sizefor both the static and animation materials.

The motor skills tasks consisted of asking the participants totake the two rings apart and then to put them back together again.The latter was considered a transfer task as no instruction was pro-vided on how to do it, and the process was in the reverse order. Forthe Earring task a score of 1 was assigned if the task was completedcorrectly, and 0 if it was not correct. For the Quadring task, whichhad 7 discrete steps, a score of 1 was awarded for every correctstep completed, giving a maximum score of 7. For the purely cog-nitive tasks paper-based materials were constructed. For each puz-zle a screen capture of a particular state was given, followed by 4other pictures of different states of the same puzzle. Participantswere required to choose which picture represented the next stepin the deconstruction of the puzzle. Three of these forward ques-tions were constructed. In a similar fashion, three backward ques-tions were constructed, which asked for the previous step to beselected from the given options. one mark was awarded for eachcorrect question giving a maximum score possible of 3 for the setof backward and also forward questions for each puzzle.

To collect self-rating measures of understanding and problemdifficulty two cognitive load measures were constructed on sepa-rate sheets of A4 paper. Participants were asked to rate: ‘‘How easywas for you to understand the learning material?” after given thepresentation material and ‘‘How easy was it for you to solve thepuzzle?” after they have attempted to solve the puzzles. In bothcases a 9-point scale was provided and participants were askedto tick one of the 9 boxes ranging from 1 (extremely easy) to 9 (ex-tremely difficult).

3.1.3. ProcedureAt the start of the experiment participants were informed what

was expected of them including the fact that there was no user-

352 P. Ayres et al. / Computers in Human Behavior 25 (2009) 348–353

interaction (animation group only) and the need for scrolling down(static group only). For both groups the presentations were shownon a laptop computer and each participant was tested individually.Participants were initially seated in front of the computer and anyquestions on the procedure were answered. After this introductionthe following 6 steps were completed for each puzzle separately inthe given order. (1) For the animated group the Earring presenta-tion was shown through from beginning to end, and then repeatedafter a few seconds giving a total viewing time of 48 s. The staticgroup was allowed the same time as the animated group to exam-ine the static diagrams in two 24-s intervals. (2) Both groups wererequired to fill in the cognitive load measure pertaining to under-standing the materials. (3) Both groups were given a completedring and asked to take it apart. One hundred and twenty secondswere allowed for this task. (4) Both group were required to fill inthe cognitive load measure again. (5) Both groups were given theseparate constituents of the puzzle and asked to put it together.Again 120 s was allowed for the task. (6) Both groups were giventhe cognitive tasks and allowed sufficient time to answer all thequestions. (7) Both groups were required to fill in the cognitiveload measure pertaining to difficulty in solving the puzzle. The ex-act same process was repeated for Quadring puzzle, the only differ-ence in procedure was that the learning time (Phase 1) was longer:two periods of 51 s. There was no overlapping of information con-cerning the two different puzzles on the computer screen at anytime.

3.2. Results

The mean group scores for the two puzzle tasks are shown inTables 2 and 3. Mann–Whitney U tests were conducted on eachmeasure

3.2.1. Earring puzzle taskU tests revealed that the animated group performed signifi-

cantly better than the static group on the motor task (Z = 3.2,p < .01), the transfer task (Z = 3.3, p < .01) the forward cognitivetask (Z = 3.0, p < .01), and the backward cognitive task (Z = 3.1,p < .01).

For the cognitive load measures U tests revealed that the ani-mated group found it significantly easier to understand the learn-ing materials (Z = 4.0, p < .01) and solve the puzzle (Z = 4.6, p < .01)than the static group.

Table 2Means (and SD) for Earring puzzle in Experiment 2.

Animation Static

Motor (max. score = 1) 0.9 (0.24) 0.4 (0.51)Transfer (max. score = 1) 0.8 (0.38) 0.3 (0.46)Cog. Load (pre-motor) 1.8 (0.92) 4.3 (1.71)Cog. Load (post-motor) 3.3 (1.81) 7.6 (1.82)Forward cognitive (max. score = 3) 2.3 (0.57) 1.6 (0.61)Backward cognitive (max. score = 3) 2.0 (0.91) 0.9 (0.96)

Table 3Means (and SD) for Quadring puzzle in Experiment 2.

Animation Static

Motor (max. score = 7) 2.3 (0.83) 0.9 (0.83)Transfer (max. score = 7) 0.9 (0.83) 0.3 (0.49)Cog. Load (pre-motor) 5.4 (1.82) 7.8 (1.31)Cog. Load (post-motor) 8.2 (0.86) 8.7 (0.59)Forward cognitive (max. score = 3) 2.1 (0.83) 1.7 (0.91)Backward cognitive (max. score = 3) 1.6 (0.78) 1.1 (0.64)

3.2.2. Quadring puzzle taskThe same statistical analyses were conducted on the Quadring

puzzle as the Earring puzzle. U tests revealed that the animatedgroup performed significantly better than the static group on themotor task (Z = 4.1, p < .01), the transfer task (Z = 2.2, p < .05), andthe backward cognitive task (Z = 1.9, p = .05), but not the forwardcognitive task (Z = 1.6, p = .12). For the cognitive load measures Utests revealed that the animated group found it significantly easierto understand the learning materials (Z = 3.6, p < .01) and solve thepuzzle (Z = 2.0, p = .05) than the static group.

3.3. Discussion

For the tasks that required hand manipulation of the puzzlesthe animated group outperformed the static group in taking bothpuzzles apart and putting them back together again (transfer task).Concerning the purely cognitive tasks, the results varied slightlyaccording to the two tasks. For the Earring puzzle the animatedgroup significantly outperformed the static group on identifyingboth the next step and the previous step. For the Quadring puzzlethe animated group outperformed the static group only in identify-ing the previous step (backward cognitive task). However, the ani-mated group had a higher, although not significant, mean score onthe forward cognitive task. These results suggest that animatedgroup were superior not only in manipulating the puzzles (motorskills) but also in step recognition (cognitive task). On the cognitiveload measures the animated group rated the instructions easier tofollow and the puzzles easier to complete than the static group.Overall performance scores and self-rating cognitive load measureswere in concordance. The animated group found the instructionseasier to understand, believed that the puzzles were easier to solveand performed better on both motor and cognitive tasks.

4. General discussion

The results of the studies are consistent with our expectationthat instructional animations would lead to superior learningwhen compared with static diagrams. Students who received acontinuous animated presentation showing either knots being con-structed (Experiment 1) or two puzzle rings being taken apart(Experiment 2) were able to perform the demonstrated tasks sig-nificantly better than students who were presented a series of sta-tic diagrams. In Experiment 2 on the transfer task that required thetwo rings to be assembled (the opposite process to the video pre-sentations) the animated group performed better than the staticgroup. On the cognitive non-manipulative tasks of recognizingthe next or previous steps in the solution the animated groupwas superior. The results of Experiment 2 indicate that studentscould not only imitate the actions shown in the computerized vi-deo, but also reverse the process and complete related cognitivetasks. Furthermore, cognitive load measures indicated that stu-dents in the animated group found it significantly easier to under-stand the learning materials and solve the puzzles, an indicationthat cognitive load was reduced in comparison to the static group.

Clearly, the results found in this study support the findings ofHöffler and Leutner (2007), that is, animations can be more effec-tive than statics if they are realistic and involve procedural-motorknowledge. Both these conditions were met in the study. The re-sults are also consistent with the CLT prediction of Van Gog et al.(in press) that animations can be effective, even if transitory, pro-vided they are teaching human motor skills. The results do not ofcourse prove that the mirror-neuron system has led to more learn-ing in the animated mode but lends some support to this hypoth-esis. If transitory information is a problem in learning fromanimations, unless human movement is involved, then something

P. Ayres et al. / Computers in Human Behavior 25 (2009) 348–353 353

must be aiding WM to overcome the extraneous load. An argumentbased on mirror-neurons providing additional resources to WM isplausible, but further research is needed to validate this hypothe-sis. In a similar study in this domain Wong et al. (2009) went a fur-ther theoretical step and used the mirror-neuron argument topropose the existence of an additional WM processor that dealsexclusively with human movement. Using paper-folding tasksWong et al. also demonstrated that animations were superior tostatics in learning human motor skills. Our results are in concor-dance with those of Wong et al. and added some indirect evidenceto their theory, but this study did not seek to test it directly. To getmore direct evidence for a mirror-neuron explanation, or even ahuman movement based WM processor, research needs to be con-ducted that compares human movement tasks with equivalentnon-human movement tasks. A dual-task paradigm (see Brünken,Plass, & Leutner, 2003) could be a useful tool in such a comparison.If a movement based task is compared to an equivalent non-move-ment task, and they are both placed under the same additional load(using a secondary task), data may be obtained that demonstratesthat the movement based task requires less WM resources.

A number of other future research directions follow from thestudy. Firstly, only hand manipulations were used and thereforeother motor skills could be studied involving other parts of thebody. Secondly, it was notable that the Wong et al. (2009) studydid not find that the animation group was significantly better onsimilar cognitive tasks, and therefore this difference between thetwo studies warrants further investigation, including the use ofmore complex cognitive tasks. Furthermore, the cognitive tasksused in our study were very low-level multiple-choice questionswith no complexity other than to choose the proceeding or nextstate. Future research could explore whether complexity of thecognitive task interacts with instructional format.

Finally, research by Arguel and Jamet (2009) found that the num-ber of static pictures used in a sequence, is an important moderatingfactor. In the present study, pilot trials were used to assess howmany key frames were required for students to learn from this mode.However, no comparison was made between different combinationsof static diagrams, which again, require further investigation. Hencethe number of frames used in the static formats was potentially anexperimental design weakness of the static format. A further possi-ble limitation of the static presentation was the way that the frameswere presented on the computer screen, in both experiments somescrolling down was required, which may have required additionalcognitive processing. However, frames could be retrieved if neces-sary by scrolling backwards. Furthermore moving from the end ofone row to the beginning of the next row may require additional vi-sual searches. Nevertheless, the pilot testing did not identify theseconditions as problematical, however, future experiments shouldinvestigate and control these variables more precisely. Time duringinstruction may also be an issue. If the static groups needed moretime to infer the motion adequately then these groups may be disad-vantaged. However, the fact that the animation groups were able tolearn more effectively in this limited period, suggest that the anima-tion mode is more efficient. Nevertheless, learning time is an impor-tant consideration and could be investigated in future.

In conclusion, the study showed that learning from a dynamicrepresentation was better than an equivalent static one, whenthe content was tying knots and completing ring puzzles (humanmotor skills). The study also demonstrated that learning took placepurely from observation. The hypothesis (Van Gog et al., in press;Wong et al., 2009) that the human mirror-neuron system helpsto overcome transitory information in a human movement basedlearning environment is plausible, and the findings of this studysupport this hypothesis. Yet significantly more research is requiredto add further weight to this promising theory.

Acknowledgement

The authors wish to acknowledge the support of the UNSW Fac-ulty of Arts and Social Sciences, who awarded the first author ofthis paper a research grant.

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