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Generating self-explanations leads to improved effectiveness of attention cueing in complex animations PowerPoint Presentation
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Generating self-explanations leads to improved effectiveness of attention cueing in complex animations

Generating self-explanations leads to improved effectiveness of attention cueing in complex animations

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Generating self-explanations leads to improved effectiveness of attention cueing in complex animations

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  1. Generating self-explanations leads to improved effectiveness of attention cueing in complex animations Björn de Koning, Huib Tabbers, Remy Rikers & Fred Paas Erasmus University Rotterdam The Netherlands

  2. Animations can be too complex (simultaneous depiction of multiple changes), and/or too fast (distributed nature of the presentation). This learning “under time pressure” could be harmful to understanding because of information loss and/or high cognitive load Directing learners’ attention to the right place at the right time should facilitate processing (Schnotz & Lowe, 2008) Attention cueing (content-free guidance) Instructional animations

  3. Visual (e.g. color change) and temporal (e.g. speed) manipulations emphasizing critical information may improve performance (De Koning et al. (2007); Fischer et al., 2008) Although (visual) cues help learners in attending to essential parts, cues do not necessarily lead to improved learning (Kriz & Hegarty, 2007; De Koning et al. in press) Thus, cues may be helpful but not sufficient! Cues: Mixed findings

  4. Stimulate learners’ cognitive engagement: generating self-explanations (Chi et al., 1994) Constructive but WM intensive activity Diagrams that provide guidance to relevant parts improve meaningful self-explanations (Butcher, 2006) Thus, reducing ineffective WM load (search processes) and increasing learning activities (SE) might improve learning from animations Improving learning

  5. Cueing and SE in animation Cueing + SE No cueing + SE Hypothesis: significant Cueing x SE interaction, indicating better learning in ‘cued animation + SE’ than in ‘uncued animation + SE’ condition

  6. Independent variables: Cueing (y,n) and SE (y,n) uncued animation cued animation uncued animation +SE cued animation +SE (SE: aloud and content-free prompts) Dependent variables: retention, inference, transfer tests SE protocols mental effort after animation and each test Experiment (N = 90)

  7. Animation of the cardiovascular system (5 subsystems) No narration and no interactivity Animation

  8. Results – Performance measures Hypothesized significant interaction-effect confirmed!

  9. Combining learning outcomes and mental effort (at test) to compute instructional efficiency Results on efficiency scores parallel those of learning performance: Cued animation +SE obtained higher learning performance without investing more mental effort Results - Efficiency

  10. Verbal protocol SE conditions analyzed for different types of SE statements (paraphrase, goal-directed, elaborative, errors, monitoring, see Ainsworth & Burcham, 2007) ’Cued animation + SE’ generated more meaningful SE’s and fewer errors and (negative) monitoring statements than ‘Uncued animation +SE’ Results – SE statements

  11. Self-explaining can be extended to animation Self-explaining improves learning from animation, at least when the animation is cued Explanations: Cues reduce visual search and increase WM resources Cues provided a structured sequence of meaningful units In sum, animations need cues to effectively verbalize the operation of dynamic systems Discussion

  12. Thank you for your attention! Contact information: dekoning@fsw.eur.nl