Reasoning using graphical representations related to particulate-level phenomena is a key part of general chemistry courses. This study aims to examine students’ cognitive resources for to interpreting and using graphical representations that relate to the varied population schema, that is, the idea that at the particulate level, there are a range of states for individual particles within a system. Central to the design and analysis of the project is the knowledge-in-pieces view of cognitive structure, which models knowledge as an ensemble of local knowledge clusters that are activated in an emergent process in response to contextual cues. Within the knowledge-in-pieces perspective, coordination class theory models a concept as involving extractions (features attended to) and the inferential net (a knowledge structure that contains relevant ideas for drawing conclusions). For our project, we model the varied population schema as a coordination class. In the context of typical qualitative research, there are limitations related to drawing conclusions about the features students are attending to in an interview context (i.e., identifying extractions). Therefore, we operationalize the construct of extractions by attending to students’ gaze patterns from eye-tracking. Here, we describe preliminary results from a mixed-methods study involving student interviews that combines the quantitative metrics of eye tracking with qualitative analysis of students’ verbal discussions. Participants were enrolled in a first-year general chemistry course, and interview prompts focused on graphical representations that relate to the varied population schema. The presentation will emphasize the role of integrating theory with methods, illustrating the affordances of combining eye-tracking with interviews to draw claims about students’ coordination class for the varied population schema.

Investigating undergraduate chemistry students’ reasoning about graphs related to particulate-level variability

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ACS Spring 2023

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