Teaching Real Data Interpretation with Models (TRIM): Analysis of Student Dialogue in a Large-Enrollment Cell and Developmental Biology Course
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CBE Life Sci Educ-2016-Zagallo-.pdf
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Final Published Version
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Univ Arizona, Dept Mol & Cellular BiolIssue Date
2016-05-18
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AMER SOC CELL BIOLOGYCitation
Teaching Real Data Interpretation with Models (TRIM): Analysis of Student Dialogue in a Large-Enrollment Cell and Developmental Biology Course 2016, 15 (2):ar17 Cell Biology EducationJournal
Cell Biology EducationRights
© 2016 P. Zagallo et al.CBE—Life Sciences Education © 2016 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
We present our design for a cell biology course to integrate content with scientific practices, specifically data interpretation and model-based reasoning. A 2-yr research project within this course allowed us to understand how students interpret authentic biological data in this setting. Through analysis of written work, we measured the extent to which students' data interpretations were valid and/or generative. By analyzing small-group audio recordings during in-class activities, we demonstrated how students used instructor-provided models to build and refine data interpretations. Often, students used models to broaden the scope of data interpretations, tying conclusions to a biological significance. Coding analysis revealed several strategies and challenges that were common among students in this collaborative setting. Spontaneous argumentation was present in 82% of transcripts, suggesting that data interpretation using models may be a way to elicit this important disciplinary practice. Argumentation dialogue included frequent co-construction of claims backed by evidence from data. Other common strategies included collaborative decoding of data representations and noticing data patterns before making interpretive claims. Focusing on irrelevant data patterns was the most common challenge. Our findings provide evidence to support the feasibility of supporting students' data-interpretation skills within a large lecture course.ISSN
1931-7913Version
Final published versionAdditional Links
http://www.lifescied.org/cgi/doi/10.1187/cbe.15-11-0239ae974a485f413a2113503eed53cd6c53
10.1187/cbe.15-11-0239
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Except where otherwise noted, this item's license is described as © 2016 P. Zagallo et al.CBE—Life Sciences Education © 2016 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).