Interpreting Data: The Hybrid Mind

The central goal of this study was to characterize major patterns of reasoning exhibited by college chemistry students when analyzing and interpreting chemical data. Using a case study approach, we investigated how a representative student used chemical models to explain patterns in the data based on structure-property relationships. Our results elicited various reasoning challenges: undifferentiation of concepts, overreliance on surface explicit features, oversensitivity to contextual features, unconstrained application of ideas, hybridization of chemical and intuitive knowledge, and overreliance on nonmechanistic explanatory schemas. Our findings also revealed several affordances in student thinking: cognitive flexibility, responsiveness to probing and scaffolding, rich knowledge base, and pragmatism in the search for explanations. Our investigation provides insights into curriculum design and teaching and assessment strategies that can better leverage students' cognitive resources to scaffold learning.