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MODELLING AND PROFILING STUDENT DESIGNERS’ COGNITIVE COMPETENCIES IN COMPUTER-AIDED DESIGN

Published online by Cambridge University Press:  27 July 2021

John Clay
Affiliation:
Department of Psychological Science, University of Arkansas;
Xingang Li
Affiliation:
Department of Mechanical Engineering, University of Arkansas;
Molla Hafizur Rahman
Affiliation:
Department of Mechanical Engineering, University of Arkansas;
Darya Zabelina
Affiliation:
Department of Psychological Science, University of Arkansas;
Charles Xie
Affiliation:
Institute for Future Intelligence
Zhenghui Sha*
Affiliation:
Department of Mechanical Engineering, University of Arkansas;
*
Sha, Zhenghui, University of Arkansas, Mechanical Engineering, United States of America, zsha@uark.edu

Abstract

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There are three approaches to studying designers – through their cognitive profile, design behaviors, and design artifacts (e.g., quality). However, past work has rarely considered all three data domains together. Here we introduce and describe a framework for a comprehensive approach to engineering design, and discuss how the insights may benefit engineering design research and education. To demonstrate the proposed framework, we conducted an empirical study with a solar energy system design problem. Forty-six engineering students engaged in a week-long computer-aided design challenge that assessed their design behavior and artifacts, and completed a set of psychological tests to measure cognitive competencies. Using a machine learning approach consisting of k-means, hierarchical, and spectral clustering, designers were grouped by similarities on the psychological tests. Significant differences were revealed between designer groups in their sequential design behavior, suggesting that a designer's cognitive profile is related to how they engage in the design process.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

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