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In this study, we examined representations produced by students while they solved reversible multiplicative relationship problems, with a focus on fractions content. We developed an emergent coding scheme with three umbrella codes: Type of Representation, Visual Precision, and Mathematical Relationships. Our preliminary results suggest that word problems with objects that possess directly relevant quantitative attributes (e.g., the length of peppermint sticks), may support more symbolic, precise, and useful representations than word problems with objects that do not possess relevant quantitative attributes (e.g., a $1 bill is the same size as a $5 bill).
Yan Tian, University at Albany - SUNY
Caroline Cassie-Marie Williams-Pierce, University at Albany - SUNY