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Successes and Lessons Learned in an Undergraduate Computational Lab Sequence for Materials Science and Engineering

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2017 ASEE Annual Conference & Exposition


Columbus, Ohio

Publication Date

June 24, 2017

Start Date

June 24, 2017

End Date

June 28, 2017

Conference Session

Materials Division Technical Session 1

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Alison K. Polasik Ohio State University Orcid 16x16

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Alison K Polasik received a B.S.E. degree in Materials Science and Engineering from Arizona State University in 2002, and M.S. and Ph.D. degrees from The Ohio State University in 2005 and 2014, respectively. She has been part of the adjunct faculty at Columbus State Community College, and was a full-time lecturer at OSU from 2013 until 2015. In 2015, she was hired as an assistant professor of practice in the Department of Materials Science and Engineering at OSU.

Dr. Polasik's research interests include modeling of microstructure-property relationships in metals, assessment of educational outcomes, and engineering-specific epistemology in undergraduate students.

Dr. Polasik is a member of ASM, TMS, and ASEE.

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In 2012, with a switch from quarters to semesters of instruction during the academic calendar year, the Materials Science & Engineering Department added a series of computational labs to the required undergraduate curriculum. This series of courses included computational modules and assignments aligned with lecture courses taken previously or concurrently. Over the course of the next 4 academic years, the achievement of student outcomes and student feedback on the courses were monitored while minor changes were made to the curriculum. While student outcomes were generally achieved, student dissatisfaction with the course structure was high. In the 2016- 2017 academic year, several substantial changes were made to the sophomore and junior lab courses in response to this data. Curricular changes included an increased emphasis on pseudo-code development, routine reflection on assumptions and limitations of models used in lab meetings, and a move of the lectures and discussions to after the in-depth lab assignments. In addition, short modules on data analysis, elementary statistics, and linear algebra were included. Interestingly, student feedback revealed that a number of “problems” with the lab sequence stem from the perception that either computational thinking is not a relevant skill for a materials engineer, or that students were not in fact learning more than how to use a specific software package. To combat these factors and increase students’ self-efficacy, a “marketing campaign” was implemented for these courses. The results of these five years of aggressively including computational modeling into the undergraduate materials science curriculum, including student perceptions and achievement before and after these changes, can provide valuable insight for any department interested in making similar changes.

Polasik, A. K. (2017, June), Successes and Lessons Learned in an Undergraduate Computational Lab Sequence for Materials Science and Engineering Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--28877

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