The attention-guiding effect and cognitive load in the comprehension of animations

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The attention-guiding effect and cognitive load in the comprehension of animationsFranck Amadieu *,Claudette Mariné,Carole LaimayCognition,Tongues,Language,Ergonomics –Work and Cognition Laboratory,University of Toulouse,Francea r t i c l e i n f o Article history:Available online 8June 2010Keywords:Animation Attention CueingComprehension Cognitive loadElement interactivitya b s t r a c tTo be effective,instructional animations should avoid causing high extraneous cognitive load imposed by the high attentional requirements of selecting and processing relevant elements.In accordance with the attention-guiding principle (Bétrancourt,2005),a study was carried out concerning the impact of cueing on cognitive load and comprehension of animations which depicted a dynamic process in a neurobiology domain.Cueing consisted of zooming in important information at each step of the process.Thirty-six undergraduate psychology students were exposed to an animation three times.Half of the participants received an animation without cueing while the other half received the same animation with cueing.Measures of cognitive load and comprehension performance (questions on isolated elements and on high-element interactivity material)were administered twice,after one and three exposures to the ani-mation.The analyses revealed two main results.First,extraneous cognitive load was reduced by cueing after three exposures.Second,retention of the isolated elements was improved in both animation groups,whereas comprehension of high-element interactive material (i.e.,the causal relations between ele-ments)increased only in the cueing condition.Furthermore,a problem solving task showed that cueing supported the development of a more elaborate mental model.Ó2010Elsevier Ltd.All rights reserved.1.IntroductionAnimations take part in multimedia instructions to teach dy-namic systems.In spite of the dynamic information conveyed by animations,these instructional devices still fail to be syste-matically efficient for learning as compared to static graphics (Bétrancourt,2005;Höffler &Leutner,2007;Tversky,Morrison,&Bétrancourt,2002).Designing effective animations for learning re-quires more investigations on the cognitive processing of anima-tions and on the difficulties experienced by learners.Although animations may reduce the cognitive cost of mental simulation of a dynamic system (Kühl,Scheiter,Gerjets,&Edelmann,2011),they also require perceptual and cognitive resources to process their spatial and temporal aspects (Bétrancourt,2005),and there-by,might hamper comprehension and learning processes such as selecting,organizing,and integrating relevant information into existing knowledge (De Koning,Tabbers,Rikers,&Paas,2009).The attention-guiding principle (Ayres &Paas,2007;Bétrancourt,2005),like signalling key information consists in directing learners’attention to specific parts of the learning material in order to support ing Cognitive Load Theory (CLT,Sweller,2003)as theo-retical background,the present study investigates how attentionalguidance (i.e.,cueing)may limit the attentional requirements for processing of animations and supports higher comprehension.1.1.Attentional requirements of learning from animations:Cognitive loadTo reach an effective comprehension of animations,perceptual and cognitive processing may be highly demanding for learners.Pro-cessing dynamic information of animations implies remembering the various steps and their relations (Bétrancourt,Dillenbourg,&Clavien,2008)and the transient nature of information may cause difficulties involving split attention over different elements (De Kon-ing et al.,2009).Therefore,during an animated presentation,learn-ers need to coordinate spatial and temporal aspects of their visual exploration of the relevant contents (Boucheix &Lowe,2010).To do so,attentional processes are crucial.The problem is that learners’attention may be distracted from the relevant information (Hill-strom &Chai,2006)by non-topic elements of animation like seduc-tive details or salient elements (Höffler &Leutner,2007;Lowe,2003)or by irrelevant movements in the animation (Lowe,1999).In order to avoid burdening the capacity of working memory unnecessarily,the animation features have to decrease the atten-tional requirements.CLT offers a relevant conceptual framework to describe and study the processing costs imposed by animations.Searching and extracting relevant elements may be viewed as an additional task (De Koning,Tabbers,Rikers,&Paas,2007).Deep learning occurs only if sufficient cognitive resources are allocated to germane cognitive load (Sweller,Van Merriënboer,&Paas,0747-5632/$-see front matter Ó2010Elsevier Ltd.All rights reserved.doi:10.1016/j.chb.2010.05.009*Corresponding author at:CLLE-LTC,University of Toulouse le Mirail,5allées Antonio Machado,F-31058Toulouse Cedex 9,France.Tel.:+33561503526;fax:+33561503533.E-mail address:amadieu@univ-tlse2.fr (F.Amadieu).1998).Therefore,to be effective,animations for learning should avoid a high extraneous cognitive load imposed by high attentional requirements due to selecting relevant elements and processing their relations.1.2.Attention-guiding principle:Cueing of relevant information‘‘When applied to animations,cueing can be defined as the addi-tion of non-content information that captures attention to those as-pects that are important in an animation...”(De Koning et al.,2007,p. 733).As well as for static illustrations with(spoken)narrations(e.g., Craig,Gholson,&Driscoll,2002;Jamet,Gavota,&Quaireau,2008; Tabbers,Martens,&Van Merriënboer,2004),evidence of positive ef-fects of cueing was obtained recently for animations.De Koning et al. (2007)confirmed that attention cueing for animations supports comprehension and transfer performance.Boucheix and Lowe (2010)also emphasized that cueing supports the construction of a mental model of causal chains.Examination of eye movements dur-ing learning confirmed that cueing(vs.no cueing)directed learners’attention within animations(Boucheix&Lowe,2010).De Koning, Tabbers,Rikers,and Paas(2010)also observed that cueing facilitates learners to look more often and for longer periods of time at cued rather than at non-cued contents.However,cueing does not improve learning performance in a systematic way(e.g.,De Koning,Tabbers, Rikers&Paas,2011;Moreno,2007).Kriz and Hegarty(2007)as well as De Koning et al.(2010)failed to prove an effect of cueing on learn-ing performance,yet eye movement recordings indicated that cue-ing effectively guided attention to the signalled region.Furthermore,cueing is expected to reduce extraneous cognitive load associated with locating relevant information(De Koning et al.,2009).Unfortunately,only a few studies included cognitive load measures in their experimental apparatus and they did not provide clear evidence of a reduction of cognitive load due to cue-ing methods(De Koning et al.,2007;Moreno,2007).Hence,more investigations of the cognitive load experienced by learners pro-cessing animations should be conducted using measurements of cognitive load throughout learning.1.3.HypothesesThe aim of the present study was to determine whether cueing leads to both better comprehension performance and lower cogni-tive load.According to CLT,it was hypothesized that helping learners focus attention to relevant information at the right time would re-duce extraneous cognitive load caused by attentional requirements, and thereby,enhance their comprehension of a causal dynamic sys-tem(elaboration of a functional mental model).Such an effect was expected to occur only for complex information,i.e.,information with the high-element interactivity because its processing is as-sumed to require more working memory resources than the pro-cessing of simple information that contains isolated elements.So, extraneous cognitive load imposed by an animation without cueing should only interfere with comprehension processes for complex information.In other words,an element interactivity effect was ex-pected(Sweller et al.,1998;Hasler,Kersten,&Sweller,2007).2.Method2.1.ParticipantsThirty-six undergraduate psychology students(6males and30 females)studied an animation displaying a dynamic process in a neurobiology domain(Long Term Potentiation–LTP).All partici-pants were volunteers and were unfamiliar with the topic of the LTP.The mean age of the participants was22.6years(SD=5.42).Two independent groups were compared(18participants in each group).One received an animation without cueing while the other one received the same animation with cueing.The level of prior knowledge in the domain of neurobiology was assessed be-fore the experimental session using a template consisting of four questions testing domain principles(i.e.,elements of a neurone, types of neurones,functions of the myelin sheath,consequences of an action potentialÀmaximum score=20).Both experimental groups had equivalent levels of prior knowledge,t(34)=0.20, p=.85.The mean scores of prior knowledge were12.06 (SD=4.05)for the no cueing animation group and11.78 (SD=4.39)for the cueing animation group.3.Material3.1.AnimationsBoth multimedia presentations were developed using power-point and a visual basic program.The animation illustrated the mechanism of Long Term Potentiation which is a chemical and an electrical phenomenon occurring in synapsis.The animation de-picted a synapsis with the neurotransmitters(glutamate),ions(so-dium,calcium,and magnesium)and neurotransmitter receptors located on the surface of the postsynaptic cell.It showed the three main steps of the process:(a)the release of glutamate from the presynaptic neuron after an electrical stimulation,(b)thefixation of glutamate on the AMPA receptors and the entrance of sodium ions in the postsynaptic cell,and(c)the depolarization of the post-synaptic cell triggering the release of the magnesium ionsfixated on the ion channels that allows the entrance of calcium ions in the postsynaptic cell.3.2.CueingThe cueing tested in the present experiment had the function of guiding attention to essential information.In order to avoid learn-ers pay(perceptual)attention to salient but irrelevant features,we strengthened the function of attention-guiding of our cueing meth-od:it consisted of a zoom on each step of the dynamic system which hides peripheral information(irrelevant information at each step).This cueing method was expected to help learners both to se-lect the central elements and to ignore the peripheral elements of the animation.A picture of a stage of the animation is given in Fig.1.At the beginning of each step of the Long Term Potentiation mechanism,a purple square appeared on the relevant region to di-rect attention to it.Next,a zoom was made on the region display-ing only the relevant information(elements)of the step(see Fig.2), i.e.,hiding the peripheral or irrelevant elements.Hiding peripheral elements was expected to avoid distraction to elements not in-volved in the step.On the basis of the recommendations of Bétrancourt(2005),a pause after each main step was inserted in order to help partici-pants conceptualize the functioning of the Long Term Potentiation mechanism(see also Spanjers,Wouters,van Gog,&van Merriënboer,2011).Besides,because the disappearance of a previous step could hamper integration of the information across the steps(Boucheix&Schneider,2009),the zoom disappeared at the end of each step displaying the overall frame.Thus,the partic-ipants could process elements previously processed as well as the structural information on the functioning of a synapse.prehension measuresIn order to study what type of information is dependent on the cueing effect,two types of measures were used(i.e.,multiple choiceF.Amadieu et al./Computers in Human Behavior27(2011)36–4037questions with4or6answering options).First,a measure tested the retention of isolated elements of the animation(e.g.,ions and ion channels).Second,a measure tested the comprehension of the high-element interactive material(i.e.,comprehension of causal relations between elements).The questions dealt with the order of events or the cause of a given event.More than one answering option was correct for each question and each correct answer was scored with2points.Moreover,to encourage the participants to avoid random answers,each wrong answer was scored with 1negative point.Two multiple choice questions were designed for isolated elements(there were5correct answering options; maximum score=10)and three others for the highly interactive ele-ments(there were4correct answering options;maximum score=8).Finally,to assess a deeper level of comprehension,a problem solving task was designed.The participants had to predict events and identify errors from the pictures of a stage in the Long Term Potentiation mechanism.This transfer task required the partici-pants to use their mental model in other situations.It consisted of three multiple choice questions(maximum score=6).3.4.Cognitive load measuresAs argued by Bétrancourt et al.(2008),studies on instructional animations would benefit from morefine-grained measurement instruments of cognitive load like ad hoc questionnaires adapted to multimedia learning situations.According to the learning taskFig.1.Screenshot of a step of the animation without cueing. Fig.2.Screenshot of a step of the animation with cueing(zoom).and the instructional material,assessing different forms of cogni-tive load becomes relevant to understanding what kind of process-ing might consume working memory resources(Amadieu,Van Gog,Paas,Tricot,&Mariné,2009;Amadieu,Tricot,&Mariné, 2009;Ayres,2006;DeLeeuw&Mayer,2008;Gerjets,Scheiter, Opfermann,Hesse,&Eysink,2009).The present study tested the effects of cueing on cognitive load and in particular on extraneous cognitive load,which would depend on attentional requirements.Measurement of cognitive load con-sisted of two types of subjective measures:a mental effort9-point scale(Paas,1992)andfive perceived difficulty9-point scales(all scales ranging from1‘‘very very low”to9‘‘very very high”).These two distinctive measures of cognitive load were considered in order to distinguish the overall cognitive load(i.e.,including intrinsic, extraneous,and germane load)assessed by the mental effort scale and the extraneous cognitive load assessed by the perceived diffi-culty scales.The perceived difficulty was expected to reflect extra-neous cognitive load because difficulty would be caused by demanding attentional processing of animations.However,what perceived difficulty reflects is still fuzzy:intrinsic and extraneous cognitive load(Gerjets et al.,2009),extraneous cognitive load(Ama-dieu,Tricot et al.,2009;Amadieu,Van Gog,et al.,2009;Kühl et al., 2011)or germane cognitive load(DeLeeuw&Mayer,2008).There-fore,the mental effort and the perceived difficulty ratings should be considered in relation to the comprehension outcomes.The statement of the mental effort scale was‘‘please indicate how much mental effort you invested to learn the mechanism of the Long Term Potentiation”.For the perceived difficulty scales, one scale assessed global difficulty(‘‘how difficult was it to learn the mechanism”),another assessed difficulty for processing iso-lated elements(‘‘how difficult was it to memorize the names of the elements”),another difficulty for processing high-element interactivity material(‘‘how difficult was it to understand the cau-sal relations between elements”)and two others attentional diffi-culty(‘‘how difficult was it to understand all elements simultaneously”;‘‘how difficult was it to concentrate your atten-tion on the relevant information”).The set of perceived difficulty scales presented high reliability after one exposure to the anima-tion(Cronbach’s Alpha=0.87)and after three exposures to the ani-mation(Cronbach’s Alpha=0.84).4.ProcedureAfter answering the prior knowledge test,participants were in-structed to memorize and learn the mechanism of Long Term Potentiation from the animation.For both animation conditions, participants had no control over the pace of the presentation. The experimental session took place in two stages in order to take into account the development of performance and cognitive load over repeated exposures to the animation.After afirst exposure to the animation,participants rated their mental effort and per-ceived difficulty and then answered the isolated elements ques-tions followed by the high-element interactive material questions.Afterwards,participants received two more exposures to the same animation and performed the same tasks,except that they additionally performed the problem solving task at the end. Testing cognitive load and performance twice indicated how the mobilized resources and the comprehension advanced during learning.5.ResultsTwo-way mixed-design ANOVAs(type of animation as a be-tween-subject factor and number of exposures as a within-subject factor)was computed for the scores of each dependent variable. Means and standard deviations of cognitive load ratings and per-formance scores are given in Table1.5.1.Mental effort and perceived difficultyThe analyses conducted on the mental effort ratings did not indicate any effect of the cueing,the number of exposures,or any interaction effect(all F s<1).However,the analyses on the mean ratings of perceived difficulty showed significant effects. While there was no effect of the cueing,F<1,the number of expo-sures significantly reduced the perceived difficulty rating,F(1, 34)=8.45,p<.01,x2=.21.After the third exposure of the anima-tion,the perceived difficulty was lower(M=6.06,SD=1.03)than after thefirst exposure(M=5.67,SD=1.06).Nevertheless,this ef-fect must be interpreted in light of a significant interaction,F(1, 34)=8.94,p<.01,x2=.23.Pairwise comparisons revealed that the perceived difficulty decreased with the number of exposures only for the cueing group,F(1,34)=17.38,p<.001,Cohen’s d=.76.prehension outcomesThe analyses conducted on the performance also indicated sig-nificant effects.For the retention of the isolated elements,the scores increased according to the number of exposures in both ani-mation groups,F(1,34)=101.54,p<.001,x2=2.87(M=5.78, SD=2.47after one exposure;M=9.42,SD=1.96after three expo-sures).There was neither a main effect of cueing,F<1,nor interac-tion effect,F<1.Concerning the comprehension of high-element interactive material,it was impacted neither by the cueing,F<1,nor by the number of exposures,F(1,34)=1.15,p>.10,but an interaction effect revealed significant differences,F(1,34)=4.12,p=.05, x2=.09.Pairwise comparisons indicated that the comprehension scores of the high-element interactive material increased with the number of exposures only in the cueing condition,F(1,34)= 4.82,p<.05,Cohen’s d=.50.Table1Means and standard deviations of cognitive load ratings and performance scores according to the type of animation and the number of exposure to the animation.After one exposure to the animation After three exposures to the animationNo cueing M(SD)Cueing M(SD)No cueing M(SD)Cueing M(SD)Cognitive load Mental effort ratings(range:1–9)5.61(1.15) 5.89(1.13) 5.78(0.94)6.06(1.34)Perceived difficulty ratings(range:1–9)5.97(0.79)6.16(1.23) 5.98(0.79) 5.37(1.22)Performance Isolated elements questions(range:0–10)5.39(2.99)6.17(1.82)9.17(2.62)9.67(0.97)High-element interactivity material questions(range:0–8)3.22(3.14) 2.78(2.78) 2.78(2.86)4.22(3.17)Problem solving questions (range:0–6)–– 2.78(2.07) 4.00(1.94)F.Amadieu et al./Computers in Human Behavior27(2011)36–4039Finally,a t-test was computed on the problem solving scores to analyse the differences between both animation groups.The result indicated that the cueing significantly improved the problem solv-ing scores,t(34)=À1.83,p<.05,Cohen’s d=0.63.6.Discussion and conclusionThe results stressed that it is important to investigate the ef-fects of exposure to animation on learning.In accordance with Schneider(2007),our results showed that repeated exposures to the animation were required to reveal significant effects.The attention-guiding by means of cueing reduced extraneous cogni-tive load,as assessed by perceived difficulty ratings,only after sev-eral exposures of the animation.The results on performance seem to indicate that the processing of isolated elements was not ham-pered by extraneous cognitive load imposed by attentional requirements.However,this result should be interpreted cau-tiously,because a ceiling effect for these scores was observed after the third exposure.Nevertheless,there was no effect of cueing after thefirst exposure.In return,processing high-element inter-active material(i.e.,comprehension of causal relations)appeared to be negatively affected by attentional requirements.The com-prehension of the causal relations was improved only with the cued animation.Moreover,providing cueing yielded higher prob-lem solving scores,suggesting the construction of a better mental model of the causal system.Hence,overall results on performance are consistent with the element interactivity effect:only high-ele-ment interactive material is affected by instructional design(Has-ler et al.,2007;Sweller et al.,1998).In sum,the set of results indicates that attentional requirements of animations may be re-duced by visual cueing which helps learners to select relevant information and ignore irrelevant information.The resources freed in working memory made the processing of complex information more effective.The present study also highlighted the fact that subjective measures of perceived difficulty may provide more sensitive mea-sures of extraneous cognitive load than a global cognitive load measure(i.e.,the mental effort scale),replicating previousfindings (Amadieu,Van Gog et al.,2009).Indeed,the patterns of the per-ceived difficulty ratings were consistent with the patterns of the comprehension outcomes.This result might explain the lack of an effect on cognitive load in the study of De Koning et al.(2007) who only used a mental effort scale.Some limitations of ourfindings should be highlighted as well as perspectives for further studies.The current study administered measures only twice during learning(after one and three expo-sures).Because the cueing effects occurred after several exposures to the animation,the observed effects might be stronger with addi-tional exposures.Conducting studies with more intermediate mea-sures would lead to more detailed data about the development of performance and thefluctuation of cognitive load and would also provide additional information about cognitive load management during learning(Tricot,Sweller,Amadieu,Chanquoy,&Mariné, 2008).Finally,on-line data is required to corroborate our interpre-tations on attentional processes during learning tasks.Eye tracking techniques supply a very suitable way of observing attentional processing obviously(Boucheix&Lowe,2010),and they also pro-vide objective measures of cognitive load(Amadieu,Van Gog et al., 2009;Van Gog&Scheiter,2010).ReferencesAmadieu,F.,Tricot,A.,&Mariné,C.(2009).Prior knowledge in learning from a non-linear electronic document:Disorientation and coherence of the reading puters in Human Behavior,25,381–388.Amadieu,F.,Van Gog,T.,Paas,F.,Tricot,A.,&Mariné,C.(2009).Effects of prior knowledge and concept-map structure on disorientation,cognitive 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