Minneapolis, MN
August 23, 2022
June 26, 2022
June 29, 2022
12
10.18260/1-2--40644
https://peer.asee.org/40644
300
Steve Kirstukas is a Professor at CCSU, where he teaches courses in solid modeling, MATLAB programming, and engineering mechanics. He is exploring the use of computer-aided assessment of CAD files to give consistent, accurate, and quick feedback to students so that they can design parts and assemblies that incorporate design intent.
There are numerous acceptable ways to create a solid model, but all good models share the same characteristics — good solid models incorporate the design intent that was expressed in the original layout drawing. The result is a robust, changeable model that behaves as the user expects: if any dimensional values are altered, the model changes appropriately, without breaking or losing features.
Of the many advantages promoted by creators of computer-based grading programs, one feature that most benefits students is the ability to get feedback quickly. For the last several years, students have been permitted to upload some homework files before the assignment deadline. These submittals were graded by a computer program and detailed feedback was provided to the students. Students were permitted to revise and resubmit CAD files multiple times up until the hard deadline.
This resubmittal policy was used for three different homework assignments, comprising seven different models of increasing complexity, from 2D sketches to fairly complex 3D parts. Some students enthusiastically took advantage of this learning opportunity. As a result, their homework grades increased, and they became more aware of the instructor’s grading scheme and the importance of creating a solid model that honored design intent. This study examined the correlation between making heavier use of the multiple submission homework assignment policy and performance in the first exam which focused on modeling and in the modeling portion of the final exam.
Kirstukas, S. (2022, August), Reinforcing Design Intent with a Computer Grading Program Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--40644
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