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A kind of expertise reversal effect: Personalisation effect can depend on domain-specific prior knowledge

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A kind of expertise reversal effect: Personalisation effect can depend on domain-specific prior knowledge
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  Australasian Journal of Educational Technology 2010, 26(1), 133-149 A kind of expertise reversal effect: Personalisation effectcan depend on domain-specific prior knowledge Klaus D. Stiller   and Rosemarie Jedlicka University of RegensburgIn instructional multimedia design, it is often recommended that text accompanyingpictures be presented in a personalised style to promote learning. The superiority of personalised over formal text may be explained using social agency theory (Mayer,2005b), but it has not been investigated empirically whether such effects are valid inclassroom settings, or depend on learner characteristics such as domain-specific priorknowledge. In this research, 65 pupils from the 10th grade of German grammarschools received computerised instructions about the structure of the human eye,containing static pictures and on screen text. The texts were written either in formal orin personalised style. Personalisation of the formal text was reached by replacing 130impersonal articles with second person possessive pronouns and 24 third personarticles with second person constructions. For learners with low prior knowledge,personalisation improved drawing and labeling performance, as well as theverbalisation of structural knowledge and transfer. For learners with higher priorknowledge, only labeling and drawing performance were improved, whilst structuralknowledge was not affected and transfer performance was reduced. Introduction We can enhance learning from texts and pictures, i.e. multimedia learning, byoptimising the cognitive or motivational conditions of the learning situation (Mayer,Fennell, Farmer & Campbell, 2004). In cognitive aspects, well and badly designedmultimedia instructions differ in the way they impose load on working memory.According to the cognitive load theory (Sweller, 1999), learning occurs when theoverall cognitive load does not overburden working memory capacity and when asmuch capacity as available can be dedicated to schema acquisition and automation.Cognitive overload is mostly created by extraneous and intrinsic load. Extraneous loadarises from the presentation manner of the material and is thought to be detrimental tolearning because it has nothing to do with the construction or automation of schemas.Intrinsic load depends on the complexity of the learning task and the difficulty of thelearning material and is the basic amount of processing required for understanding apresentation. Minimising extraneous load means designing the informationpresentation in an optimal fashion. Minimising intrinsic load means controllinginformation complexity by means of didactics. By minimising these loads, learners aremore likely to engage in schema acquisition and automation that impose germaneload. Maximising germane load means fostering schema acquisition. In motivationalaspects, means of fostering learning are intended to increase germane load. In thisaspect, one way to affect motivation was found in the personalisation of texts.  134 Australasian Journal of Educational Technology, 2010, 26(1) In the following, the personalisation principle and its empirical base are introduced,followed by two theoretical explanations of personalisation effects. Then a researchgap is revealed, i.e. the dependence of personalisation effects on domain-specific priorknowledge, and the experimental hypotheses are derived. The personalisation principle and its empirical evidence The personalisation principle of multimedia design recommends presenting texts inconversational rather than formal style in conjunction with dynamic or static pictures, because learners learn more effectively with personalised texts (Mayer, 2005b). Thesuperiority of personalised over formal texts was demonstrated in multiple studiesand mainly observed in measures of retention and transfer (Moreno & Mayer, 2000,2004; Mayer et al., 2004). Table 1 provides an overview about experiments onpersonalisation effect, also showing the effect sizes d and the statistical significance of group comparisons.Table 1: Overview about personalisation effects on retention and transfer performance Effect sizes d Media contextContentBased on …RetentionTransferLightningMoreno & Mayer(2000)Exp. 1.151.04*Narration and animationRespirationMayer et al.(2004)Exp. 1Exp. 2Exp. 3.00-.23.39.52*.99*.79*Visual text and animationLightningMoreno & Mayer(2000)Exp. 2.201.61*Interactive, agent-basedmultimedia game usingvisual textBotanyMoreno & Mayer(2000)Exp. 4.63*1.48*Moreno & Mayer(2000)Exp. 3Exp. 51.05*.62*1.84*1.08*Interactive, agent-basedmultimedia game usingnarration (desktop comp-uter and virtual reality)BotanyMoreno & Mayer(2004).92*1.75* Notes for Table 1 a.Personalisation effects on retention and transfer performance were measured by testsrequiring learners to remember the presented information and to use the presentedinformation to solve new problems (Mayer, 2005b), respectively. b.Effect sizes d were computed as the difference between the means of the personalised andthe non-personalised groups divided by the pooled standard deviations (Cortina & Nouri,2000; * statistically significant effect).c.Effect sizes d of range .20 to .50 are interpreted as small, of range .50 to .80 as medium, and of > .80 as large (Cohen, 1988). Hence, effect sizes d of .00 reflect no differences and no effect;effect sizes of 1.00 reflect a large effect, meaning that the average score of the personalisedgroup is a standard deviation greater than the average score of the non-personalised group. Two major techniques for creating personalised texts are (1) to use  you and I  and theassociated possessive pronouns, rather than solely relying on third-personconstructions, and (2) to add sentences in which the instructor makes direct commentsto the learner (Mayer, 2005b). Table 2 gives an overview about the means of personalisation used in the experiments.  Stiller and Jedlicka 135 Table 2: Overview about the means of personalisation used in the experiments Media contextContentBased on …Means of personalisationNarration accompany-ing an animationLightningMoreno & Mayer(2000): Exp. 1On screen text accom-panying an animationLightningMoreno & Mayer(2000): Exp. 2Using first and second personconstructions and direct comments tothe learnerNarrationaccompanying ananimationRespirationMayer et. al. (2004):Exps. 1, 2, and 3Replacing twelve instances of thearticle the by  your in a 100 wordnarrationInteractive, agent-basedmultimedia game usingnarrationBotanyMoreno & Mayer(2000): Exp. 3Interactive, agent-basedmultimedia game usingon screen textBotanyMoreno & Mayer(2000): Exp. 4Using first and second personconstructions, adding questions anddirect comments to the learner;transforming formal to informal style(including some greater grammaticalchanges)Interactive, agent-basedmultimedia game usingnarrationBotanyMoreno & Mayer(2000): Exp. 5Using first and second personconstructions (including some greatergrammatical changes)Interactive, agent-basedmultimedia game usingnarrationBotanyMoreno & Mayer(2004)Using first and second person constr-uctions, adding questions and directcomments to the learner; transformingformal to informal style (includingsome greater grammatical changes) Mayer et al. (2004; Exps. 1 to 3) used a 60 second narrated animation about the humanrespiratory system. They only minimally changed the text used, by replacing twelveinstances of the article the by  your in a 100 word narration. This was the smoothest textmodification of formal to personalised style appearing in literature. No pre-selection of probands in respect of prior knowledge was made. Introducing greater changes inpersonalisation, Moreno and Mayer (2000; Exps. 1 and 2) used a 140-second animationwith either narration or on screen text about lightning formation. The personalisationof the text was reached by using first and second person constructions and addingdirect comments to the learner. Only learners with low prior knowledge onmeteorology were recruited. All five experiments revealed the superiority of personalised over formal texts in respect of transfer performance, but retention was notaffected.The most invasive changes in order to create a personalised style out of formal textswere done by Moreno and Mayer (2000, Exps. 3 to 5; 2004). They used anenvironmental science simulation game in which the learners interacted with an onscreen agent named Herman-the-Bug either presented on a desktop computer (Moreno& Mayer, 2000, 2004) or by using a head-mounted display (Moreno & Mayer, 2004).Personalisation was mainly reached by using first and second person constructionsand inserting questions (only Exp. 5 didn’t use questions) to the learner. In exceptionto Moreno and Mayer (2000; Exps. 1 and 2) and Mayer et al. (2004), the sentences werealso frequently accompanied by a variation in the grammatical structure. Moreno andMayer (2004) used instructions lasting between 14 and 16 minutes and low priorknowledge learners. Moreno and Mayer (2000) used instructions lasting between 24and 28 minutes, but no observations about learners’ prior knowledge were given. Allof the four experiments revealed the superiority of personalised over formal texts inrespect of retention and transfer performance.  136 Australasian Journal of Educational Technology, 2010, 26(1) Explaining personalisation effects It was argued that personalised texts inspire learners to engage in deeper meaningfulprocessing of information, resulting in a more coherent and complete mental model of the subject, which finally results in a better learning outcome (Mayer et al., 2004).There are mainly two competing ways – a motivational and a cognitive one – toexplain how this deeper processing is reached. The motivational view stresses socialaspects and the cognitive view the aspect of self-referencing. Both aspects are notmutually exclusive.In a motivational view, personalisation is seen as an instance of using social cues inmultimedia instructions (Mayer, 2005b). In order to explain effects of social cues, thesocial agency theory was introduced (Mayer, 2005b; see Figure 1). Against this background, it is assumed that social cues may prime the activation of social responsesin learners, “such as the commitment to try to make sense out of what the speaker issaying” (Mayer, 2005b, p. 202). Especially, Reeves and Nass (1996) provided evidenceagainst a communicational background that people have a natural predisposition toapply the same dynamics from human-human to human-computer interactions. Asocial response should lead to a higher motivation – mostly called interest (e.g. Mayeret al., 2004) – to engage in active cognitive processing and subsequently to deeperprocessing during learning, which was also evidenced by research on interest (Hidi &Baird, 1988; Renninger, Hidi & Krapp, 1992; Wade, 1992). Consequently, thisimprovement of learning should be reflected in a better test performance (Hidi &Baird, 1988; Mayer, 2001; Renninger, Hidi & Krapp, 1992; Wade, 1992).Figure 1: How social cues should work according tothe social agency theory (Mayer, 2005b, p. 203).Some evidence for the social cues explanation was found by Moreno and Mayer (2004),who tried to measure if learners feel a social relation with a virtual instructor (Reeves& Nass, 1996). Indeed, they found evidence with learners’ ratings of their physicalpresence (being in and interacting in a place) and the instructor’s social presence(friendliness and helpfulness of the program) as well as some other indicators, whichwere theoretically assumed to be consecutively influenced by social cues like difficultyratings of the instruction. On the other hand, there are also studies that did not supportthe social cues assumption (Mayer et al., 2004; Moreno & Mayer, 2000), but they werecriticised for using inadequate measures of social response like program ratingsreflecting a joint measure of interest, motivation, and understanding as well as theprogram’s perceived difficulty and friendliness, or measures of interest like countingsmiles while learning.From a cognitive view, the personalisation effect is considered to be related to the self-reference effect (Moreno & Mayer, 2000). The self-reference effect reflects the aspectthat people remembered information better, when it was encoded with respect tothemselves rather than with respect to other referential frames (Symons & Johnson,1997). Turco (1996, p. 1047) thinks that information only needs to be “somehow related  Stiller and Jedlicka 137 to the self” in order to be more easily remembered. Research has shown that self-reference promotes elaboration and organisation of information (Rogers, Kuiper &Kirker, 1977; Symons & Johnson, 1997). Thereby, it was also practised to vary self-reference by varying the communicational style of the experimental instructions. Highself-referencing was realised by addressing probands directly and encouraging themto refer information to themselves, e.g. to their thoughts and feelings (Truco, 1996). Thematerial to be learned was not manipulated in a self-referencing way. From this pointof view, personalised instructional explanations are considered to enhance learning because personalisation is a way to force referencing information to the self, thusenhancing elaboration and organisation of information and leading to a better learningoutcome. Especially, learners are more disposed to activate their relevant priorknowledge and to relate new information to it, hence to process the material moredeeply (Moreno & Mayer, 2000). So it might not be a social response and an associatedincrease in the learner’s motivation that mediates processing, but rather the learner’sfeeling that the learning material is personally relevant. It was also theorised andempirically shown that this kind of relevance increases motivation (e.g. Keller &Suzuki, 1988).The motivational view is preferred and dominates the field of explainingpersonalisation effects, although evidence for a social explanation of personalisationeffects is relatively scarce and not convincing in general. The cognitive view wasmostly neglected and hence also not empirically proven. Therefore, in respect of thesocial agency theory, the core aspect is to show that social processes are involved toproduce personalisation effects, because a self-referencing view also predicts anincrease of interest, active cognitive processing, and learning outcome. Research gap In a variety of studies and instructional designs, it was found that design principlesdepend on domain-specific prior knowledge (Kalyuga, 2007), but how thepersonalisation principle might depend on it has not been investigated yet. This is thecentral research gap, which is addressed in this study. Kalyuga (2007) gave recently anoverview about studies, revealing interaction effects of domain-specific priorknowledge with well founded instructional design principles. In general, it was shownthat what is effective for low prior knowledge learners might be ineffective or evendisturbing for learners with high prior knowledge, i.e. what is known as the expertisereversal effect (Kalyuga, 2007). Higher prior knowledge in a specific domain can beregarded as being a protection factor against badly designed informationpresentations, but also can handicap information processing in the way that for highknowledge learners even the badly designed instruction could be better. One way toexplain the latter effect is as follows. With experienced learners, guidance of information processing by instruction presentation collides with guidance by schemaof long-term memory. Two active guidances are cross-related and integratedconcerning their overlapping components; thus producing unnecessary load, becauseit is difficult to ignore the instructional guidance. Low experienced learners do notpossess any schemas of their own that help them to deal with new information. Forlow experienced learners, well-designed instructions guide information processingand serve as a substitute for schemas (Kalyuga, 2007).Further on, some other aspects are addressed. The following experiment wasconducted with pupils within a classroom setting at school using a learner-sequenced
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