Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-28T00:56:39.645Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  27 July 2021

John Clay ,
Xingang Li ,
Molla Hafizur Rahman ,
Darya Zabelina ,
Charles Xie  and
Zhenghui Sha
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

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

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.

Keywords

Design cognitionComputer Aided Design (CAD)Human behaviour in design
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 2157 - 2166
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

References

Baddeley, A. (1992), “Working memory”, Science, 255(5044), pp. 556–559.CrossRefGoogle ScholarPubMed
Brandt, N. D., Lechner, C. M., Tetzner, J., & Rammstedt, B. (2020), “Personality, cognitive ability, and academic performance: Differential associations across school subjects and school tracks”, Journal of personality, 88(2), pp. 249–265.CrossRefGoogle ScholarPubMed
Condon, D. M., & Revelle, W. (2014), “The International Cognitive Ability Resource: Development and initial validation of a public-domain measure”, Intelligence, 43, pp. 52–64.CrossRefGoogle Scholar
Dworak, E. M., Revelle, W., Doebler, P., & Condon, D. M. (2021), “Using the International Cognitive Ability Resource as an open source tool to explore individual differences in cognitive ability”, Personality and Individual Differences, 169, 109906.CrossRefGoogle Scholar
Dym, C. L., Agogino, A. M., Eris, O., Frey, D. D., & Leifer, L. J. (2006), “Engineering design thinking, teaching, and learning”, IEEE Engineering Management Review, 34(1), pp. 65–65.Google Scholar
Feist, G. J. (1998), “A meta-analysis of personality in scientific and artistic creativity”, Personality and social psychology review, 2(4), pp. 290–309.CrossRefGoogle ScholarPubMed
Frank, M. (2000), “Engineering systems thinking and systems thinking”, Systems Engineering, 3(3), pp. 163–168.3.0.CO;2-T>CrossRefGoogle Scholar
Frank, M. (2010), “Assessing the interest for systems engineering positions and other engineering positions’ required capacity for engineering systems thinking (CEST)”, Systems Engineering, 13(2), pp. 161–174.Google Scholar
Frey, M. C., & Detterman, D. K. (2004), “Scholastic assessment or g? The relationship between the scholastic assessment test and general cognitive ability”, Psychological science, 15(6), pp. 373–378.CrossRefGoogle ScholarPubMed
Gero, J. S. (1990), “Design prototypes: a knowledge representation schema for design”, AI magazine, 11(4), pp. 26–26.Google Scholar
Goff, K., Torrance, E.P. (2002), “Abbreviated Torrance Test for Adults manual”, Bensenville, IL: Scholastic Testing Service.Google Scholar
Greene, M. T., & Papalambros, P. Y. (2016, March), “A cognitive framework for engineering systems thinking”, 2016 Conference on Systems Engineering Research pp. 1–7.Google Scholar
Guilford, J.P. (1967), “The Nature of Human Intelligence”, New York: McGraw-Hill.Google Scholar
Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008), “Improving fluid intelligence with training on working memory”, Proceedings of the National Academy of Sciences, 105(19), pp. 6829–6833.CrossRefGoogle ScholarPubMed
Koenig, K. A., Frey, M. C., & Detterman, D. K. (2008), “ACT and general cognitive ability”, Intelligence, 36(2), pp. 153–160.CrossRefGoogle Scholar
Kontoangelos, K., Economou, M., & Papageorgiou, C. (2020), “Mental health effects of COVID-19 pandemia: a review of clinical and psychological traits”, Psychiatry Investigation, 17(6), 491.CrossRefGoogle ScholarPubMed
Kühn, S., Ritter, S. M., Müller, B. C., Van Baaren, R. B., Brass, M., & Dijksterhuis, A. (2014), “The importance of the default mode network in creativity—A structural MRI study”, The Journal of Creative Behavior, 48(2), 152–163.CrossRefGoogle Scholar
McComb, C., Cagan, J., & Kotovsky, K. (2017), “Capturing human sequence-learning abilities in configuration design tasks through markov chains”, Journal of Mechanical Design, 139(9): 091101 pp. 1–12.CrossRefGoogle Scholar
McCrae, R. R., John, O. P.; John, (1992), “An introduction to the Five-Factor Model and its applications”, Journal of Personality. 60 (2): pp. 175–215. https://dx.doi.org/10.CrossRefGoogle ScholarPubMed
Meilă, M. (2003), “Comparing clusterings by the variation of information”, Learning theory and kernel machines, pp. 173–187.CrossRefGoogle Scholar
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000), “The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis”, Cognitive psychology, 41(1), pp. 49–100.CrossRefGoogle ScholarPubMed
Rahman, M. H., Gashler, M., Xie, C., & Sha, Z. (2018, August), “Automatic Clustering of Sequential Design Behaviors”, International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 51739, pp. V01BT02A041). American Society of Mechanical Engineers.Google Scholar
Rahman, M. H., Schimpf, C., Xie, C., & Sha, Z. (2019), “A Computer-Aided Design Based Research Platform for Design Thinking Studies”, Journal of Mechanical Design, 141(12): 121102 pp. 1–12.CrossRefGoogle Scholar
Scott, G., Leritz, L.E., Mumford, M.D. (2004) “Types of creativity training: Approaches and their effectiveness”, The Journal of Creative Behavior, 38(3), pp. 149–179.CrossRefGoogle Scholar
Starkey, E. M., Hunter, S. T., & Miller, S. R. (2019), “Are creativity and self-efficacy at odds? an exploration in variations of product dissection in engineering education”, Journal of Mechanical Design, 141(1).CrossRefGoogle Scholar
Von Luxburg, U. (2007), “A tutorial on spectral clustering”, Statistics and computing, 17(4), pp. 395–416.CrossRefGoogle Scholar
Xie, C., Schimpf, C., Chao, J., Nourian, S., & Massicotte, J. (2018), “Learning and teaching engineering design through modeling and simulation on a CAD platform”, Computer Applications in Engineering Education, 26(4), pp. 824–840.CrossRefGoogle Scholar
Zabelina, D. L., & Condon, D. M. (2019), “The Four-Factor Imagination Scale (FFIS): A measure for assessing frequency, complexity, emotional valence, and directedness of imagination”, Psychological Research, pp. 1–13.Google Scholar