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Plagiarism detection in Programming coursework.

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Conference

2021 First-Year Engineering Experience

Location

Virtual

Publication Date

August 9, 2021

Start Date

August 9, 2021

End Date

August 21, 2021

Page Count

4

Permanent URL

https://peer.asee.org/38399

Download Count

25

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Paper Authors

biography

Constantine Mukasa Northeastern University Orcid 16x16 orcid.org/0000-0002-1851-8073

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Constantine Mukasa received a B.S. degree in Computer Engineering from Bethune-Cookman University, Daytona Beach, Florida, USA in 2007, and his M.Sc. and Ph.D. degrees in Electrical Engineering from Florida Atlantic University, Boca Raton, Florida, USA, in 2013 and 2017, respectively. He is currently an Assistant Professor at Northeastern University in Boston, MA. His research interests include Engineering Education pedagogies, Experiential learning and Teaching Technology, Team-based Learning, Summer P12 Engineering Activities, Wireless network connectivity, and interference modeling.

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Abstract

Students are taught to avoid plagiarism at all educational levels from K-12 to graduate schools, but still teachers deal with plagiarized work regularly. Plagiarism in higher education takes on various forms such as copying others' work without proper citation, illegally reusing data, artifacts, and illustrations, submitting another student’s work for credit, etc. All forms of plagiarism are unacceptable, and several studies or platforms have tried to address this issue head-on. For instance, Turnitin a software tool has been heavily used to detect plagiarism on writing assignments and it is integrated into most Learning Management Systems (LMS) like Canvas and Blackboard. However, in most First-year engineering courses, students are required to complete coursework that involves computer programming in languages such as C++ and MATLAB. These languages are specialized making it difficult for traditional tools such as Turnitin to detect plagiarism. Due to the pandemic, we have experienced a significant increase in students duplicating each other’s work. Students who participate in such acts try to hide it by making superficial changes here and there to avoid detection. This makes it hard for graders to efficiently detect plagiarism in large classes or when more than one grader was assigned to grade an assignment. Thus, in this work-in-progress, we would like to present Gradescope as an important tool in addressing plagiarism concerns when considering programming coursework. This tool detects plagiarism and provides statistical similarity values to the instructors and graders. Based on this information, the instructor has an opportunity to take adequate measures to address this issue with the student promptly. Additionally, we will discuss further advantages of using this tool based on the graders’ perspective. In future work, we plan to include students’ perspectives about the tool and its efficacy.

Mukasa, C. (2021, August), Plagiarism detection in Programming coursework. Paper presented at 2021 First-Year Engineering Experience, Virtual . https://peer.asee.org/38399

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