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Deconstructing and Decoding Complex Process Diagrams in University Biology

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Part of the book series: Models and Modeling in Science Education ((MMSE,volume 7))

Abstract

This chapter explores a type of external representation used widely in biology instruction but rarely in physics and chemistry (Griffard, Decoding of visual narratives used in university biology. Paper presented at the National Association for Research in Science Teaching (NARST) Annual Conference, Philadelphia, PA, 2010a). Complex process diagrams are static images composed of pictures, icons, and symbols that represent a dynamic biological system or process involving numerous structures interacting at multiple levels of organization in a temporal sequence and thus can be considered as multiple external representations (MERs). Deconstruction of such diagrams reveals how graphic designers layer shapes, color, arrows, and text and use devices such as shading, telescoping, and distortions of scale to represent the many structures interacting in that process. Because of the information demands of such representation, graphic designers “reduce chaos” (D. Mikhael, personal communication, May 30, 2010) with respect to number, position, color, and prominence of icons and symbols, requiring learners to correctly infer the implicit meanings imbedded among the explicit elements of the diagram. Such inference may require special dimensions of representational competence. Research findings on how premedical students decoded information-dense process diagrams identified procedural skills and habits used by successful biology learners, which inform several recommendations for teaching with complex process diagrams. The ubiquity of these diagrams in university biology teaching and research suggests that such diagrams present unique pedagogical challenges.

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Correspondence to Phyllis B. Griffard .

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Griffard, P.B. (2013). Deconstructing and Decoding Complex Process Diagrams in University Biology. In: Treagust, D., Tsui, CY. (eds) Multiple Representations in Biological Education. Models and Modeling in Science Education, vol 7. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4192-8_10

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