Investigating student plagiarism patterns and correlations to grades

Jonathan Pierce, Craig Zilles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We analyzed 6 semesters of data from a large enrollment data structures course to identify instances of plagiarism in 4 assignments. We find that the majority of the identified plagiarism instances involve cross-semester cheating and are performed by students for whom the plagiarism is an isolated event (in the studied assignments). Second, we find that providing students an opportunity to work with a partner doesn't decrease the incidence of plagiarism. Third, while plagiarism on a given assignment is correlated with better than average scores on that assignment, plagiarism is negatively correlated with final grades in both the course that the plagiarism occurred and in a subsequent related course. Finally, we briey describe the Algae open-source suite of plagiarism detectors and characterize the kinds of obfuscation that students apply to their plagiarized submissions and observe that no single algorithm appears to be suficient to detect all of the cases.

Original languageEnglish (US)
Title of host publicationSIGCSE 2017 - Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education
PublisherAssociation for Computing Machinery
Pages471-476
Number of pages6
ISBN (Electronic)9781450346986
DOIs
StatePublished - Mar 8 2017
Event48th ACM SIGCSE Technical Symposium on Computer Science Education, SIGCSE 2017 - Seattle, United States
Duration: Mar 8 2017Mar 11 2017

Publication series

NameProceedings of the Conference on Integrating Technology into Computer Science Education, ITiCSE

Other

Other48th ACM SIGCSE Technical Symposium on Computer Science Education, SIGCSE 2017
Country/TerritoryUnited States
CitySeattle
Period3/8/173/11/17

ASJC Scopus subject areas

  • Education
  • Computer Science Applications

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