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Supporting multimedia learning with visual signalling and animated pedagogical agent: moderating effects of prior knowledge

心理学 信号 考试(生物学) 视觉感受 认知心理学 神经科学 感知 生物 古生物学 经济 微观经济学
作者
Amy M. Johnson,Gamze Özoğul,Martin Reisslein
出处
期刊:Journal of Computer Assisted Learning [Wiley]
卷期号:31 (2): 97-115 被引量:113
标识
DOI:10.1111/jcal.12078
摘要

Abstract An experiment examined the effects of visual signalling to relevant information in multiple external representations and the visual presence of an animated pedagogical agent ( APA ). Students learned electric circuit analysis using a computer‐based learning environment that included C artesian graphs, equations and electric circuit diagrams. The experiment was a 2 (visual signalling, no visual signalling) × 2 (visual APA presence, no visual APA presence) between‐subjects design, resulting in four experimental conditions: visual signalling with APA presence ( APA + S ), visual signalling without APA presence ( S ), no visual signalling with APA presence ( APA ) and no visual signalling without APA presence ( C ). Signalling was provided via gestures of the APA in the APA + S condition and via dynamic arrows in the S condition. To investigate potential moderating effects of prior knowledge on APA presence and visual signalling factors, middle school students were grouped into low prior knowledge ( LPK ) and high prior knowledge ( HPK ) groups using scores on a domain pre‐test. Results revealed that LPK students had higher post‐test scores after learning with visual signalling, resulting in equivalent post‐test performance to their HPK counterparts. LPK students also had higher post‐test scores, higher ratings of graphics understanding and lower perceived difficulty ratings in conditions that included the visual image of the APA . Conversely, HPK students had better post‐test scores after learning without the APA . These results indicate that the effectiveness of visual signalling techniques and the visual presence of an APA is dependent on learner characteristics, including prior domain knowledge.
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