Mukasa, C. (2021, August), Plagiarism detection in Programming coursework.  Paper presented at 2021 First-Year Engineering Experience, Virtual . 10.18260/1-2--38399

\bibitem{asee_peer_38399}
  Mukasa, C. (2021, August), \emph{Plagiarism detection in Programming coursework.}
  Paper presented at 2021 First-Year Engineering Experience, Virtual .
  10.18260/1-2--38399

Constantine Mukasa.  "Plagiarism detection in Programming coursework.".  2021 First-Year Engineering Experience, Virtual , 2021, August.  ASEE Conferences, 2021.  https://peer.asee.org/38399 Internet.  30 Dec, 2024

\bibitem{asee_peer_38399}
  Constantine Mukasa.
  "Plagiarism detection in Programming coursework.".
  \emph{2021 First-Year Engineering Experience, Virtual , 2021, August}.
  ASEE Conferences, 2021.
  https://peer.asee.org/38399 Internet.  30 Dec, 2024

@INPROCEEDINGS{asee_peer_38399
author = "Constantine Mukasa"
title = "Plagiarism detection in Programming coursework."
booktitle = "2021 First-Year Engineering Experience"
year = "2021"
month = "August"
address = "Virtual "
publisher = "ASEE Conferences"
note = {https://peer.asee.org/38399}
number = {10.18260/1-2--38399}
}

TY  - CPAPER
AB  - 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.  
AU  - Constantine Mukasa
CY  - Virtual 
DA  - 2021/08/09
PB  - ASEE Conferences
TI  - Plagiarism detection in Programming coursework.
UR  - https://peer.asee.org/38399
DO  - 10.18260/1-2--38399
ER  -