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RESEARCH PAPERS

Visualization, Intuition, and Mathematics Metrics as Predictors of Undergraduate Engineering Design Performance

[+] Author and Article Information
Bruce W. Field

Department of Mechanical Engineering, Monash University, Clayton, Victoria 3800 Australiabruce.field@eng.monash.edu.au

J. Mech. Des 129(7), 735-743 (Feb 22, 2007) (9 pages) doi:10.1115/1.2722790 History: Received September 25, 2006; Revised February 22, 2007

Many undergraduate engineering students perform relatively poorly in design courses, even though they are otherwise academically very strong. Some average students perform exceptionally well in design courses. While there are generally strong correlations between the results that each student gains at university, the design outcomes seem somewhat anomalous. It is hypothesized that some of the variation in relative success at design courses is due to the influence of otherwise unused and unmeasured nonacademic attributes. One clue to the existence of additional attributes exploited in design courses arises from an appreciation of “hemispheric preference,” since many of the special tasks in design projects rely on creativity, holistic problem solving, visualization, and intuition; skills normally associated with the brain’s right hemisphere. Students in the second year of the engineering program at Monash University were tested for spatial skill, and completed a management survey that identified their willingness to use intuition during problem solving. Separately, their grades in a series of mathematics and computing courses were obtained, and the set of results was grouped in a multiple regression against their engineering design grades. Whereas the correlation coefficients for the students’ paired grades in several mathematics and other analytical courses were all high, the correlation coefficient between mathematics and their grade in engineering design was weak, but significant. However, when measures of their spatial skills and their willingness to use intuition were factored with their mathematics grade, the composite score was highly correlated with a student’s design grade. It was concluded that while general academic competence was of prime importance, a student’s spatial skill and their comfort in making assumptions were important factors in predicting their design grade.

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Copyright © 2007 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Kolb’s four-staged learning cycle and the four learning styles (14)

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Figure 2

Data for 238 students’ grades in level 1 mathematics and computing

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Figure 3

Data for 97 students’ grades in mathematics and engineering design

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Figure 4

Data for 50 students’ design grade and factored results

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