We investigated the patterns of behavior exhibited by chemistry undergraduate students in attempting to solve visual problems in stoichiometry using multimodal data. Eye-tracking provided fixation data for each problem solved and by combining cluster analysis and principal component analysis patterns of viewing were identified between and within participant video segments. Additionally we performed a qualitative analysis of the multimodal problem solving strategies used by the students. This talk will discuss the implications for teaching and the opportunities gained by combining mixed method analysis and multimodal data to yield insight into how the students solve chemistry problems with symbolic, submicroscopic, and algorithmic representations.

Gaining insight into visual problem solving by combining eye-tracking with multimodal data analysis

249th National Meeting (2015)

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