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PERC 2011 Abstract Detail Page

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Abstract Title: "Good looking" Animation Improves Transfer by Loading Meaning into Math
Abstract: Transferring physics principles from worked examples to new problems can be very hard for students, since they often tend to memorize math manipulations only. Elaborating verbal explanation of the meaning behind math expression often does not seem to help. Therefore, we decided to focus on visual perception. Based on knowledge from grounded cognition research, perception should be more powerful in conveying meaning. We designed several animated multimedia solutions for some difficult problems according to principles derived from grounded cognition, and compared their ability to improve transfer with two very similar animated solutions that contain equivalent spatial information but lack critical perceptual elements. The perceptually rich (good looking) solutions turn out to significantly improve transfer of physics principles, especially when the target problem involves much abstract logical reasoning, and is very different from the example in terms of surface feature.
Abstract Type: Contributed Poster

Author/Organizer Information

Primary Contact: Zhongzhou Chen
University of Illinois at Urbana Champaign
1110 West Green Street
Urbana, IL 61801
Phone: 2173330272
and Co-Presenter(s)
Gary Gladding
University of Illinois at Urbana Champaign