Through an agnostic programming languages methodology for plagiarism detection in engineering coding courses

Gonzalo Herrera, Miguel Nunez-Del-Prado, Juan G. Lazo Lazo, Hugo Alatrista

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Nowadays all engineering students must possess coding skills in several programming languages. Regrettably, students do not perceive the importance of such skills often. Instead, they look for risky shortcuts to accomplish projects or homework in programming courses by copying the code from their colleagues or internet sources. Therefore, in programming languages courses it is necessary to identify this kind of activity well known as plagiarism. The idea behind plagiarism detection is to correct this attitude, raise the morale of the course and show the importance of programming for the training of the future engineer. In the present effort, we develop an automatic, fast and efficient methodology to detect code plagiarism without the professor intervention or code comparison. The results seem promising since the applied mythology detected plagiarism that was confirmed by students afterwards.
Original languageEnglish
Title of host publicationEDUNINE 2019 - 3rd IEEE World Engineering Education Conference
Subtitle of host publicationModern Educational Paradigms for Computer and Engineering Career, Proceedings
EditorsClaudio da Rocha Brito, Melany M. Ciampi
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)978-1-7281-1666-2
DOIs
StatePublished - 1 Mar 2019
EventEDUNINE 2019 - 3rd IEEE World Engineering Education Conference: Modern Educational Paradigms for Computer and Engineering Career, Proceedings -
Duration: 1 Mar 2019 → …

Conference

ConferenceEDUNINE 2019 - 3rd IEEE World Engineering Education Conference: Modern Educational Paradigms for Computer and Engineering Career, Proceedings
Period1/03/19 → …

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

  • plagiarism
  • plagiarism detection

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