COS 24-4 - Can’t argue that: Students’ quantitative reasoning in an introductory biology course

Tuesday, August 9, 2011: 9:00 AM
9C, Austin Convention Center
Jennifer L. Momsen1, Elena Bray Speth2, Tammy Long3, Sara A. Wyse4 and Diane Ebert-May3, (1)Department of Biological Sciences, North Dakota State University, Fargo, ND, (2)Biology, Saint Louis University, Saint Louis, MO, (3)Plant Biology, Michigan State University, East Lansing, MI, (4)Biological Sciences, Bethel University, St. Paul, MN

The data deluge in science warrants a concerted effort from biology educators to reform curricula to include quantitative reasoning (QR) in instruction and assessment. Indeed, QR is one of several key competencies threaded through five core biological concept areas, as identified in the 2011 Vision and Change report. However, it remains unclear what QR skills promote biological literacy, what assessments demonstrate mastery and even, what QR skills students bring to undergraduate biology courses. We begin answering these questions through our work embedding QR into the instruction and assessment of a large-enrollment, introductory biology course (n=155). We focused our intervention on basic quantitative skills routinely applied by practicing biologists. Specifically, we assessed students’ ability to manipulate and graph data and to use their graph as evidence when articulating scientific arguments.  Through a jigsaw homework assignment on evolution, each student was given one of three datasets corresponding to 3 different regional populations. Students were asked to calculate a simple arithmetic mean, plot the data to illustrate the relationship between fitness and phenotype, and construct an argument regarding selection for their population. Each dataset illustrated a different relationship between fitness and phenotype (positive, negative, or no correlation).


Over 95% of students correctly calculated means and represented the data through a scatter or line plot. Although all students correctly identified the variables to graph (fitness and phenotype), a few (8%) had difficulty identifying the independent and dependent variables and thus inverted their axes. Students with datasets that showed a positive or negative correlation between fitness and phenotype were more likely to articulate a complete and correct claim (53% and 67%, respectively) than students with a dataset showing no correlation (30%). Few students (25%) cited the correlation between fitness and phenotype as support for their claim, and fewer still (11%) referred to their graphs as evidence for their claim. Although mathematically competent, undergraduate biology students are challenged to interpret quantitative data and reason from graphs. These results underscore both the need to further incorporate QR instruction and assessment in the undergraduate biology curriculum and to identify baseline QR skills we expect from biologically literate graduates.

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