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A Study of Variations in Motivation Related to Computational Modeling in First-year Engineering Students

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2022 ASEE Annual Conference & Exposition


Minneapolis, MN

Publication Date

August 23, 2022

Start Date

June 26, 2022

End Date

June 29, 2022

Conference Session

NSF Grantees Poster Session

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Alison Polasik Campbell University

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Dr. Alison Polasik joined the Campbell School of Engineering in August 2018. Previously, she was an assistant professor of practice at Ohio State University’s Materials Science & Engineering Department. In this position, she designed curriculum for and implemented a number of active learning strategies in large courses on materials engineering, processing, and selection. She was part of the first cohort of instructors to design and implement a new required 3-semester computational lab sequence in the MSE curriculum at OSU in 2013. From 2014 – 2017, she developed and led a program providing materials-science focused professional development to high school science teachers that was funded by the Ohio Department of Education. Much of her work in these areas has been presented at ASEE National Conferences and published in the peer-reviewed proceedings. Polasik has also presented her work at the North American Materials Education Symposium (2014 – 2017) and Materials Science and Technology (2015 and 2017) conferences.

At Campbell, Dr. Polasik teaches courses in materials science, statics and mechanical behavior and thermodynamics. She spearheaded the initial development of the Energy Lab in the Engineering Annex. In 2018, Dr. Polasik became an ABET program evaluator for materials engineering programs and joined the TMS Accreditation Committee. She is a member of TMS, ASM, ASEE, and SWE.

Dr. Polasik received her bachelor’s degree in Materials Science and Engineering from Arizona State University in 2002, followed by MS and PhD degrees in materials science and engineering from Ohio State University in 2015 and 2014, respectively. Her doctoral research focused on understanding microstructural effects on fatigue in titanium alloys, and was funded by the US Air Force and the NSF Graduate Research Fellowship Program.

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It is increasingly critical that engineering students develop proficiency with computational modeling tools, and many curricula include some introduction to such tools during their first year. It is clear that student interest and skill can vary significantly based on prior experiences, but it is less clear whether student motivation specifically related to computational modeling varies as well. This study hypothesizes that the self-efficacy and utility value related to computational methods varies significantly in students’ first year and that engineering students pursuing some disciplines (such as computer, software, and electrical engineering) will begin with a higher initial self-efficacy than others (such as chemical, materials, and biomedical engineering). A survey was used to investigate the utility value and efficacy of approximately 700 undergraduate students in their first year of engineering studies at both a large public institution and a small private institution. Data is analyzed for variations in baseline motivation based on the students’ intended major. This analysis also considers known confounding factors such as gender, race, and prior experience with programming. The results of this survey will help determine whether efficacy and interest related to computational methods vary based on intended major early in an engineering student’s academic career. Ultimately, it is hoped that this study can inform future studies related to what types of interventions might benefit students.

Polasik, A. (2022, August), A Study of Variations in Motivation Related to Computational Modeling in First-year Engineering Students Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--42120

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