Sensitivity Preservation and Precision of Plagiarism Detection Engines for Modified Short Programs
- Conference
- Location
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Minneapolis, MN
- Publication Date
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August 23, 2022
- Start Date
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June 26, 2022
- End Date
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June 29, 2022
- Conference Session
- Page Count
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23
- DOI
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10.18260/1-2--40868
- Permanent URL
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https://peer.asee.org/40868
- Download Count
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371
Paper Authors
P.K. Imbrie University of Cincinnati
Head and Professor, Department of Engineering Education
and Professor, Department of Aerospace Engineering and Engineering Mechanics
University of Cincinnati
P.K. Imbrie received his B.S., M.S. and Ph.D. degrees in Aerospace Engineering from Texas A&M University. He is an advocate for research-based approaches to engineering education, curricular reform, and student retention. Imbrie conducts both traditional, as well as educational research in experimental mechanics, piezospectroscopic techniques, epistemologies, assessment, and modeling of student learning, student success, student team effectiveness, and global competencies He helped establish the scholarly foundation for engineering education as an academic discipline through lead authorship of the landmark 2006 JEE special reports “The National Engineering Education Research Colloquies” and “The Research Agenda for the New Discipline of Engineering Education.” He has a passion for designing state-of-the-art learning spaces. While at Purdue University, Imbrie co-led the creation of the First-Year Engineering Program’s Ideas to Innovation (i2i) Learning Laboratory, a design-oriented facility that engages students in team-based, socially relevant projects. While at Texas A&M University Imbrie co-led the design of a 525,000 square foot state-of-the-art engineering education focused facility; the largest educational building in the state.
Professor Imbrie’s expertise in educational pedagogy, student learning, and teaching has impacted thousands of students at the universities for which he has been associated. He is internationally recognized for his work in active/collaborative learning pedagogies and is a co-author of a text on teaming called Teamwork and Project Management. His engineering education leadership has produced fundamental changes in the way students are educated around the world. His current research interests include: epistemologies, assessment, and modeling of student learning, student success, student team effectiveness; experimental mechanics; and piezospectroscopic techniques.
Jeff Kastner University of Cincinnati
Dylan Ryman University of Cincinnati
Dylan is currently an undergraduate studying computer science and mathematics at the University of Cincinnati. He is preparing to begin graduate studies in engineering education. His current research interests include source code plagiarism detection and computational thinking education with a focus on visual programming languages.
Abstract
Source code plagiarism presents a continual threat to the integrity and effectiveness of engineering education, as habitual cheating often has devastating impacts on students' academic and professional careers. As programming becomes an increasingly central component of first-year engineering curricula, it is essential that instructors are able to uphold academic integrity by identifying students who engage in misconduct, either through direct plagiarism or excessive peer collaboration. Instructors have an arsenal of plagiarism detection tools at their disposal, and students are keenly aware of this. Consequently, in an attempt to evade detection, students routinely make superficial modifications to plagiarized work prior to submission. Effective plagiarism detection tools attempt to mitigate the effect of these alterations, however, the extent to which precision can be maintained for heavily modified code is limited. One aim of this paper is to quantify the effect of code modification strategies on a plagiarism detection tool's ability to preserve both sensitivity to plagiarism and precision of results. This paper will introduce a novel dimensionless metric apt for the evaluation and comparison of a plagiarism detection tool's robustness to code modification. The specific context of engineering education presents additional challenges, as research in plagiarism detection methods and performance is often not applicable to short programs in dynamically typed languages which constitute typical submissions in first-year engineering coursework. This paper will analyze the performance of relevant plagiarism detection tools on short Python programs, specifically those of fifty lines or fewer, that have been transformed by common code modification tactics, and evaluate which tools are most appropriate for use in this environment.
Imbrie, P., & Kastner, J., & Ryman, D. (2022, August), Sensitivity Preservation and Precision of Plagiarism Detection Engines for Modified Short Programs Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--40868
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