Columbus, Ohio
June 24, 2017
June 24, 2017
June 28, 2017
NSF Grantees Poster Session
14
10.18260/1-2--27860
https://peer.asee.org/27860
544
Elizabeth Gross is a fellow in Engineering Education at Kettering University in Flint, MI. She is also adjunct professor in learning design and technology at Wayne State University in Detroit, MI and in the Library Science department at Sam Houston State University in Huntsville, TX.
Dr. Peters is an Assistant Professor of Mechanical Engineering at Kettering University. She is the P.I. of a grant from the NSF to study returning graduate students in engineering master's programs.
Shanna Daly is an Assistant Professor in Mechanical Engineering at the University of Michigan. She has a B.E. in Chemical Engineering from the University of Dayton (2003) and a Ph.D. in Engineering Education from Purdue University (2008). Her research focuses on strategies for design innovations through divergent and convergent thinking as well as through deep needs and community assessments using design ethnography, and translating those strategies to design tools and education. She teaches design and entrepreneurship courses at the undergraduate and graduate levels, focusing on front-end design processes.
Undergraduate student pursuing a BS in Mechanical Engineering at Kettering University. She also holds a BA in Asian Studies and Anthropology from the University of Michigan, Ann Arbor.
Engineering graduate programs and the students who attend them have become the focus of research in recent years, with researchers looking at many different aspects of the graduate student experience and at the students themselves. One area of research focuses on returning students and how they compare to direct-pathway graduate students. We identify students who matriculate directly into a graduate program after their undergraduate education as direct pathway students, and those who spend five years or more in industry before returning to school as returners. This distinction is of interest in order to understand how work experience informs learning, specifically in a graduate program. Students who return to the formal education arena after a number of years in industry bring real-world practitioner experiences to their degree programs; this can be expected to affect how they learn. The pursuit of a master’s degree is seen to promote engineers’ understanding of the practice and has been promoted as a key component of professional practice, and is the focus of this particular study; specifically, how returners construct knowledge in contrast to direct pathway students. Our research is guided by the following questions: • How do returners’ work experiences influence their learning skills? • What background material do returners forget, and how do they handle that forgotten material? • When learning new material, how do returners and direct pathway students construct and organize knowledge, particularly in relation to previous knowledge? In investigating these questions, our research study is grounded in Constructivism. It also draws on the literature investigating the differences between experts and novices.
This research project has several phases; the first phase involves a survey, distributed to a significant number of domestic engineering master’s students, both returners and direct-pathway, from across the United States. The survey asks questions in the areas of demographics, previous and current academic information, work experience, the decision to attend graduate school, and future plans. Questions were designed to characterize the engineering master’s students, and to provide information to show the contrast between direct pathway and returner experience. After development, piloting, and evaluation a final version of the survey was deployed, with a rolling recruitment method to ensure a sufficient returner population is included in the study.
In this paper, we will analyze survey data about the use of software in classes as well as in industry. The survey includes a number of questions about the use of different types of software in industry positions and in coursework, and about a student’s proficiency with the types of software. This will allow for a comparison of how closely schoolwork supports understanding of software and tools. Analysis will be done using conventional statistical analysis techniques for quantitative data. The survey also asks questions regarding students’ self-efficacy to provide a contrast between returners and direct pathway students. This information, as well as demographic information, will be used along with the software usage data to provide an understanding of other factors which might also have an impact on learner experience.
Gross, E., & Peters, D. L., & Daly, S. R., & Mann, S. L. (2017, June), Board # 46 : Perceived Self-Efficacy of Master's in Engineering Students Regarding Software Proficiency and Engineering Acumen Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--27860
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