College chemistry students' mental models of acids and acid strength |
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| Authors: | LaKeisha McClary Vicente Talanquer |
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| Institution: | Department of Chemistry and Biochemistry, University of Arizona, Tucson, Arizona 85721 |
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| Abstract: | The central goal of this study was to characterize the mental models of acids and acid strength expressed by advanced college chemistry students when engaged in prediction, explanation, and justification tasks that asked them to rank chemical compounds based on their relative acid strength. For that purpose we completed a qualitative research study involving students enrolled in different types of organic chemistry course sections at our university. Our analysis led to the identification of four distinct mental models, some of which resembled scientific models of acids and acid strength. However, the distinct models are better characterized as synthetic models that combined assumptions from one or more scientific models with intuitive beliefs about factors that determine the properties of chemical substances. For many students in our sample, mental models served more as tools for heuristic decision‐making based on intuitively appealing, but many times mistaken, concept associations rather than as cognitive tools to generate explanations. Although many research participants used a single general mental model to complete all of the interview tasks, the presence of specific problem features or changes in the nature of the task (e.g., prediction vs. explanation) prompted several students to change their mental model or to add a different mental representation. Our study indicates that properly diversifying and sequencing the types of academic tasks in which students are asked to participate could better foster meaningful learning as different types of cognitive resources may be activated by different students, and thus shared, analyzed, and discussed. © 2011 Wiley Periodicals, Inc., Inc. J Res Sci Teach 48: 396–413, 2011 |
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| Keywords: | chemistry college education explanations mental models reasoning |
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