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Adaptive Expertise and its Manifestation in CAD Modeling: A Comparison of Practitioners and Students

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2015 ASEE Annual Conference & Exposition


Seattle, Washington

Publication Date

June 14, 2015

Start Date

June 14, 2015

End Date

June 17, 2015





Conference Session

NSF Grantees’ Poster Session

Tagged Topic

NSF Grantees Poster Session

Page Count


Page Numbers

26.155.1 - 26.155.14



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Paper Authors

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Elif Ozturk


Bugrahan Yalvac Texas A&M University

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Bugrahan Yalvac is an associate professor of science and engineering education in the Department of Teaching, Learning, and Culture at Texas A&M University, College Station. He received his Ph.D. in science education at the Pennsylvania State University in 2005. Prior to his current position, he worked as a learning scientist for the VaNTH Engineering Research Center at Northwestern University for three years. Yalvac’s research is in STEM education, 21st century skills, and design and evaluation of learning environments informed by the How People Learn framework.

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Michael Johnson Texas A&M University Orcid 16x16

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Dr. Michael D. Johnson is an associate professor in the Department of Engineering Technology and Industrial Distribution at Texas A&M University. Prior to joining the faculty at Texas A&M, he was a senior product development engineer at the 3M Corporate Research Laboratory in St. Paul, Minnesota. He received his B.S. in mechanical engineering from Michigan State University and his S.M. and Ph.D. from the Massachusetts Institute of Technology. Dr. Johnson’s research focuses on design tools; specifically, the cost modeling and analysis of product development and manufacturing systems; computer-aided design methodology; and engineering education.

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Xiaobo Peng Prairie View A&M University

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Ke Liu Prairie View A&M University

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Adaptive  Expertise  and  its  Manifestation  in  CAD  Modeling:  A   Comparison  of  Practitioners  and  Students  The state of computer-aided design (CAD) education has long been decried for focusingtoo much on the low-level skills required to do specific tasks; this often comes at theexpense of promoting the strategic knowledge associated with expertise. Theenvironment that today’s students will encounter in the workplace will require them toadapt to new challenges in innovative ways. Namely, they will need to become adaptiveexperts. To better inform CAD education, this work examines how practicing engineersadapt to a new environment and compares this behavior to that of students.To establish the “baseline” adaptive expertise among the sample population, an adaptiveexpertise survey (AES) instrument was administered to both the practicing engineers andthe students. The practicing engineers in this work are asked to model a component in aCAD program that they are not familiar with. The students are asked to model either astylized component or an artifact that they have brought from home and to which theyhave some attachment. In both cases, pre and post interviews inquire how the participantsapproach their tasks and overcome any challenges.Recordings of the interviews are transcribed and analyzed using open and axial coding.Selective coding is used to align responses with the dimensions of adaptive expertise.This coding provides the manifestation of adaptive expertise in the exercise. Statisticalanalyses are used to compare participant interview responses to their AES scores.Practitioner and student manifestations of adaptive expertise are compared.Manifestations of adaptive expertise are also compared to demographic data.

Ozturk, E., & Yalvac, B., & Johnson, M., & Peng, X., & Liu, K. (2015, June), Adaptive Expertise and its Manifestation in CAD Modeling: A Comparison of Practitioners and Students Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.23494

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