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Infusing Data Science into the Undergraduate STEM Curriculum

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Conference

2022 ASEE Annual Conference & Exposition

Location

Minneapolis, MN

Publication Date

August 23, 2022

Start Date

June 26, 2022

End Date

June 29, 2022

Conference Session

NSF Grantees Poster Session

Page Count

14

Permanent URL

https://peer.asee.org/41919

Download Count

64

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Paper Authors

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Petra Bonfert-Taylor Dartmouth College

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Petra Bonfert-Taylor is the Associate Dean for Diversity and Inclusion and a Professor at the Thayer School of Engineering at Dartmouth College. She received her Ph.D. in Mathematics from Technical University of Berlin (Germany) in 1996 and subsequently spent three years as a postdoctoral fellow at the University of Michigan before accepting a tenure-track position in the Mathematics Department at Wesleyan University. She left Wesleyan as a tenured full professor in 2015 for her current position at Dartmouth College. Petra has published extensively and lectured widely to national and international audiences. Her work has been recognized by the National Science Foundation with numerous research grants. She is equally passionate about her teaching and has recently designed and created a seven-MOOC Professional Certificate on C-programming for edX for which her team won the “2019 edX Prize for Exceptional Contributions in Online Teaching and Learning”. Previously she designed a MOOC “Analysis of a Complex Kind” on Coursera. The recipient of the New Hampshire High Tech Council 2018 Tech Teacher of the Year Award, the Binswanger Prize for Excellence in Teaching at Wesleyan University and the Excellence in Teaching Award at the Thayer School of Engineering, Petra has a strong interest in broadening access to high-quality higher education and pedagogical innovations that aid in providing equal opportunities to students from all backgrounds.

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Laura Ray Dartmouth College

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Laura Ray is a Professor at the Thayer School of Engineering, Dartmouth College. Her research focuses on system dynamics, control theory, and signal processing with application to mobility of robots in unstructured terrain, machine intelligence, and sensor systems. With her students, she has developed and fielded a number of robots to support field science in Antarctica and Greenland. These robots have been deployed to these regions over a dozen times since 2006 to aid in field science and operations and have been featured in Scientific American, The New York Times, Popular Mechanics, Wired, AUGVS, and numerous other publications. Professor Ray co-founded two companies to commercialize patents resulting from her research. She participated in the NSF I-Corps National program in 2013, which provides experience with the process of transferring knowledge developed through research into products that benefit society. She is a graduate of the Drexel ELATE Leadership Development program for women in engineering. Professor Ray served as Interim Dean of the Thayer School (2018-2019) and has served as the Senior Associate Dean for Faculty Development since 2019. In this capacity, she is responsible for faculty recruiting; directs the Thayer School’s faculty mentoring programs; and manages tenure, promotion, and reappointment. Professor Ray received her B.S. and Ph.D. degrees from Princeton University and her M.S. from Stanford University.

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Scott Pauls Dartmouth College

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Lorie Loeb Dartmouth College

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Logan Sankey Dartmouth College

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James Busch Dartmouth College

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Taylor Hickey Dartmouth College

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Abstract

In this paper we describe an NSF-IUSE funded project, which seeks to expose a broad group of undergraduate students to data science methods via already existing introductory undergraduate STEM classes (as opposed to through the creation of entirely new classes). A key component is a novel process, described here, to develop data science modules that naturally fit into such existing STEM classes. This novel process takes most of the development burden off the instructor's shoulders and instead employs collaborative teams of undergraduate and graduate students as well as the PIs in consultation with the instructor. The outcomes of this process are data science modules ready to be infused throughout the undergraduate STEM curriculum. We describe best practices and lessons learned about the module development and deployment process as well as several of the modules and their fit into the curriculum. In this paper we furthermore describe data science experiential learning opportunities for students at more advanced stages of their education. We finish by outlining the methods by which we will be evaluating the effectiveness of both the modules as well as the experiential learning opportunities in exposing students to data science methods.

Bonfert-Taylor, P., & Ray, L., & Pauls, S., & Loeb, L., & Sankey, L., & Busch, J., & Hickey, T. (2022, August), Infusing Data Science into the Undergraduate STEM Curriculum Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. https://peer.asee.org/41919

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