The BIO2010 report recommended that students in the life sciences receive a more rigorous education in mathematics and physical sciences. The University of Delaware approached this problem by (1) developing a bio-calculus section of a standard calculus course, (2) embedding quantitative activities into existing biology courses, and (3) creating a new interdisciplinary major, quantitative biology, designed for students interested in solving complex biological problems using advanced mathematical approaches. To develop the bio-calculus sections, the Department of Mathematical Sciences revised its three-semester calculus sequence to include differential equations in the first semester and, rather than using examples traditionally drawn from application domains that are most relevant to engineers, drew models and examples heavily from the life sciences. The curriculum of the B.S. degree in Quantitative Biology was designed to provide students with a solid foundation in biology, chemistry, and mathematics, with an emphasis on preparation for research careers in life sciences. Students in the program take core courses from biology, chemistry, and physics, though mathematics, as the cornerstone of all quantitative sciences, is given particular prominence. Seminars and a capstone course stress how the interplay of mathematics and biology can be used to explain complex biological systems. To initiate these academic changes required the identification of barriers and the implementation of solutions.
In this work, we describe our effort to develop, pilot, and evaluate a model for infusing computational thinking into undergraduate curricula across a variety of disciplines using multiple methods that previously have been individually tried and tested, including: (1) multiple pathways of computational thinking, (2) faculty professional development, (3) undergraduate peer mentors, and (4) formative assessment. We present pilot instantiations of computational thinking integration in three different disciplines including sociology, mathematics and music. We also present our professional development approach, which is based on faculty support rather than a co-teaching model. Further, we discuss formative assessment during the pilot implementation, including data focusing on undergraduate students' understanding and dispositions towards computational thinking. Finally, we reflect on what worked, what did not work and why, and identify lessons learned. Our work is relevant to higher education institutions across the nation interested in preparing students who can utilize computational principles to address discipline-specific problems.
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