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Undergraduate data science: Biological connections and assessing impacts

By Lou Gross1, Suzanne Lenhart1, Robin Taylor1, Pam Bishop1, Kelly Sturner1

University of Tennessee Knoxville

Presentation made by Lou Gross et al. as part of the "Bringing Research Data to the Ecology Classroom: Opportunities, Barriers, and Next Steps” Session at the Ecological Society of America annual meeting, August 8th, 2017, Portland Oregon

Listed in Teaching Materials

Version 1.0 - published on 15 Aug 2017 doi:10.25334/Q41660 - cite this

Licensed under CC Attribution-ShareAlike 4.0 International according to these terms



As an outgrowth of the availability of "big data" across many domains, institutions are developing diverse pathways for data science education. There are ongoing discussions at the national level as well as within educational institutions about what topics fall under the data science discipline and how its core concepts and skills relate to those in many quantitative domain disciplines. Undergraduate data science texts are just beginning to appear, some with emphasis on biology. There are an immense collection of formal and informal materials on data science available online that might be beneficial to undergraduate education, but there is little guidance on how to effectively "downscale" the concepts to mesh with the quantitative level of many undergraduates. Despite efforts over many years to enhance quantitative foundations for life science students, in general this has been limited to basic statistics and calculus. We will summarize the history of such efforts and note some lessons from these that may be beneficial as biology educators collaborate with colleagues developing data science programs. One such lesson derives from the development of methods to assess the impact on quantitative concept comprehension from inclusion of biological examples.


With NSF support, a group of educators, evaluators, and mathematical biologists have been developing a Quantitative Biology Concept Inventory (QBCI) as an instrument to assess student's mathematics abilities as affected by the use of real-world examples from the life sciences. One objective is to investigate whether life science students taking a course that places mathematical concepts in a biological context are more readily able to understand the underlying concepts, and apply them to other examples, than students who have not had this exposure to examples from the life sciences. This addresses a fundamental question in teaching mathematics: whether placing the mathematics in a concrete, real-world context helps students learn the mathematical ideas and enhances their skills in applying the mathematics. The process of developing the QBCI has included consultation with a large cadre of individuals with extensive teaching experience in quantitative biology, several focus groups with biology undergraduates, development of a pilot version, and revisions based upon implementation in courses. We will present preliminary results from additional piloting of the QBCI, empasizing comparisons of responses from biology students with a standard calculus course to those from a course emphasizing biological examples.

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