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Investigating Student Plagiarism Patterns and Correlations to Grades

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Published:08 March 2017Publication History

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 briefly 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 sufficient to detect all of the cases.

References

  1. C/C+ Obfuscator. http://stunnix.com/prod/cxxo/.Google ScholarGoogle Scholar
  2. Clang: A C language family frontend for LLVM. http://clang.llvm.org/index.html.Google ScholarGoogle Scholar
  3. K. W. Bowyer and L. O. Hall. Experience using "MOSS" to detect cheating on programming assignments. In Frontiers in Education Conference, 1999. FIE'99. 29th Annual, volume 3, pages 13B3--18. IEEE, 1999. Google ScholarGoogle ScholarCross RefCross Ref
  4. S. Burrows, S. M. Tahaghoghi, and J. Zobel. Efficient plagiarism detection for large code repositories. Software: Practice and Experience, 37(2):151--175, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. C. Collberg, C. Thomborson, and D. Low. A taxonomy of obfuscating transformations. Technical report, Department of Computer Science, The University of Auckland, New Zealand, 1997.Google ScholarGoogle Scholar
  6. J. L. Donaldson, A.-M. Lancaster, and P. H. Sposato. A plagiarism detection system. SIGCSE Bull., 13(1):21--25, Feb. 1981. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. S. Engels, V. Lakshmanan, and M. Craig. Plagiarism detection using feature-based neural networks. SIGCSE Bull., 39(1):34--38, Mar. 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. M. Freire, M. Cebrián, and E. Del Rosal. AC: An integrated source code plagiarism detection environment. arXiv preprint cs.IT/0703136, 2007.Google ScholarGoogle Scholar
  9. D. Gitchell and N. Tran. Sim: A utility for detecting similarity in computer programs. SIGCSE Bull., 31(1):266--270, Mar. 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. S. Grier. A tool that detects plagiarism in pascal programs. SIGCSE Bull., 13(1):15--20, Feb. 1981. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. C. J. Hwang and D. E. Gibson. Using an effective grading method for preventing plagiarism of programming assignments. SIGCSE Bull., 14(1):50--59, Feb. 1982. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Y.-C. Jhi, X. Wang, X. Jia, S. Zhu, P. Liu, and D. Wu. Value-based program characterization and its application to software plagiarism detection. In Proceedings of the 33rd International Conference on Software Engineering, ICSE '11, pages 756--765, New York, NY, USA, 2011. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. J. Pierce. Algae, 2015. http://www.github.com/JonathanPierce/Algae.Google ScholarGoogle Scholar
  14. L. Prechelt, G. Malpohl, and M. Philippsen. Finding plagiarisms among a set of programs with jplag. J. UCS, 8(11):1016, 2002.Google ScholarGoogle Scholar
  15. S. Schleimer, D. S. Wilkerson, and A. Aiken. Winnowing: Local algorithms for document fingerprinting. In Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, SIGMOD '03, pages 76--85, New York, NY, USA, 2003. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. J. Sheard, M. Dick, S. Markham, I. Macdonald, and M. Walsh. Cheating and plagiarism: Perceptions and practices of first year it students. SIGCSE Bull., 34(3):183--187, June 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. G. Whale. Software metrics and plagiarism detection. Journal of Systems and Software, 13(2):131--138, 1990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. M. Zeidner. Test Anxiety The State of the Art. Plenum Press, 1998.Google ScholarGoogle Scholar

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        cover image ACM Conferences
        SIGCSE '17: Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education
        March 2017
        838 pages
        ISBN:9781450346986
        DOI:10.1145/3017680

        Copyright © 2017 ACM

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        Publication History

        • Published: 8 March 2017

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        SIGCSE '17 Paper Acceptance Rate105of348submissions,30%Overall Acceptance Rate1,595of4,542submissions,35%

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