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Detection and Incidence of Plagiarism in a Solid Modeling Course

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Conference

2018 ASEE Annual Conference & Exposition

Location

Salt Lake City, Utah

Publication Date

June 23, 2018

Start Date

June 23, 2018

End Date

July 27, 2018

Conference Session

EDGD: Potpourri

Tagged Division

Engineering Design Graphics

Page Count

10

Permanent URL

https://peer.asee.org/30287

Download Count

20

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Paper Authors

biography

Steven Joseph Kirstukas Central Connecticut State University

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Steve Kirstukas is an Associate 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. He has degrees in civil and mechanical engineering, with a Ph.D. from the University of Minnesota. Steve has worked in industry as a civil engineer, software developer, biomechanics researcher, and mechanical design engineer.

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Abstract

This research paper presents the method used and results of a study in plagiarism detection of solid models. To aid in detecting plagiarism, a computer program was written that runs within a specific CAD package on the instructor’s computer. When a solid model is opened, this program reads the complete history that is stored within the CAD file. This history contains all save dates and times, a hardware identifier corresponding to each save time, and previous file names. For files that were created at the same times on the same machines, are older than expected, or have unexpected originating names, these files can be placed into one of twelve different categories that that predict whether a particular file is probable plagiarism, near-certain plagiarism, near-certain self-plagiarism, or a false positive. In this paper, the program was used to examine the incidence of these types of plagiarism over fourteen semesters during a seven-year period. We have found plagiarism in this CAD course to be a relatively uncommon occurrence, but nevertheless present in 1-2% of all submitted files, and has involved as many as 16% of all students in a particular section. Because we can now detect plagiarism, we hope that this will discourage future plagiarism and instead help students learn to be effective and efficient part modelers.

Kirstukas, S. J. (2018, June), Detection and Incidence of Plagiarism in a Solid Modeling Course Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. https://peer.asee.org/30287

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