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Research Papers

Profiling the Desirable CAD Trainee: Technical Background, Personality Attributes, and Learning Preferences

[+] Author and Article Information
Ramsey F. Hamade

 American University of Beirut, Beirut 1107-2020, Lebanonrhamade@aub.edu.lb

J. Mech. Des 131(12), 121009 (Nov 18, 2009) (10 pages) doi:10.1115/1.4000455 History: Received October 20, 2008; Revised August 31, 2009; Published November 18, 2009; Online November 18, 2009

This research aims to explore some of the underlying reasoning for why some individuals acquire mechanical computer-aided design (CAD) skills with relative ease while some others seem to falter. A methodical study was performed by monitoring 74 mechanical engineering seniors (over a 3 year period) in a semester-long formal training on a commercial three-dimensional (3D) CAD package (PRO/ENGINEER , version WILDFIRE ). The study methodically explored the trainees’ (1) technical background, (2) personality attributes, and (3) learning preferences. Investigating the technical background included quantifying the trainees’ following technical foundations: basic math, advanced math, CAD-related math, computer science and engineering, methodologies related to CAD, graphics, and mechanical design. Determining the trainees’ personality attributes included exploring their willingness-to-learn CAD, perception, gauging their actual behavior (practice), and CAD syntax learned throughout the training. Trainees’ learning preferences were determined according to the index of learning styles (ILS). Furthermore, and in order to assess the trainees’ progress in CAD knowledge acquisition, competency tests were conducted at four intervals throughout the semester-long study. The assessment involved hands-on modeling of CAD test parts of comparable complexity. At the conclusion of the study, statistical methods were used to correlate the trainees’ attributes with their monitored performance. Only a fraction (17 out of a class of 74 trainees or 1 in 4) of the trainees were found to fit the “star CAD trainee” mold, which is defined here as someone who is fast on the tube and perceptive enough to see through the procedure of building progressively more sophisticated CAD models. A profile of the star CAD trainee character emerges as an individual who is technically competent, perceptive, and motivated. The study also reveals these most desirable trainees to possess an active, sensor, visual, and sequential learning style.

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Copyright © 2009 by American Society of Mechanical Engineers
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Figures

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Figure 1

CAD solid models (test parts 1–4 from top left to bottom left)

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Figure 2

An illustration of a declarative task in which PRO/ENGINEER WILDFIRE GUI is utilized to sketch the base feature of test part 1)

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Figure 3

An illustration of an unsophisticated build procedure where a large (eight) number of relatively simple features is used to construct test part 1

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Figure 4

An illustration of a sophisticated CAD part build procedure where a few number (two) of relatively complex features is required to construct test part 1

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Figure 6

A plot showing build-time for each of the four groups (according to Fig. 5), as well as that of the entire class

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Figure 7

Post-training analysis of individual trainee’s actual initial number of features utilized versus rate of sophistication progress

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Figure 8

A plot showing model-sophistication for each of the four groups (according to Fig. 7), as well as that of the entire class

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Figure 9

Technical scores for the star CAD trainees versus class average scores

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Figure 10

Learning preferences for the star CAD trainees versus class average scores (according to ILS)

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Figure 5

Post-training analysis of individual trainees’ initial performance time (in minutes) versus rate of speed progress

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