Learning biology through mathematical modeling

Journal of Bioengineering & Biomedical Science

ISSN: 2155-9538

Open Access

Learning biology through mathematical modeling

Annual Conference on Bioscience

September 12-13, 2016 Berlin, Germany

Anita Schuchardt

University of Minnesota, USA

Posters & Accepted Abstracts: J Bioeng Biomed Sci

Abstract :

Recent advances in technology and access to big data require that current biology students have a strong foundation in mathematics. The requisite mathematics does not always have to be taught as a separate discipline and students could benefit in multiple ways by experiencing an integrated approach. Unfortunately, instruction in biology at the introductory levels typically avoids mathematics, even though mathematical modeling has been an accepted part of scientific practice for a long time and is central to understanding certain biological concepts. The field of science education is exploring how to make students├ó┬?┬? experiences in classrooms more grounded in scientific practices, because such an approach offers the potential to develop better understanding of both scientific practice and scientific content. Further, when the scientific practices that are incorporated include other disciplines, such as mathematics, there is a potential for maximizing instructional time, as well as a better understanding of the applicability of other disciplines to science. However, there are few instructional models for interdisciplinary practice based science curricula and little research on student learning effects. This talk will provide a description of a unit in biology that leverages active learning by students through mathematical modeling to support student learning of inheritance. Quantitative and qualitative analyses will be presented showing how active participation in mathematical modeling of biological phenomena benefits student understanding of biological processes and their ability to solve complex and unfamiliar quantitative problems in the domain. The instructional principles presented in this talk are broadly applicable across the biological sciences.

Biography :


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Citations: 307

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