TY - GEN
T1 - Investigating student plagiarism patterns and correlations to grades
AU - Pierce, Jonathan
AU - Zilles, Craig
N1 - Publisher Copyright:
© 2017 ACM.
PY - 2017/3/8
Y1 - 2017/3/8
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85018307915&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85018307915&partnerID=8YFLogxK
U2 - 10.1145/3017680.3017797
DO - 10.1145/3017680.3017797
M3 - Conference contribution
AN - SCOPUS:85018307915
T3 - Proceedings of the Conference on Integrating Technology into Computer Science Education, ITiCSE
SP - 471
EP - 476
BT - SIGCSE 2017 - Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education
PB - Association for Computing Machinery
T2 - 48th ACM SIGCSE Technical Symposium on Computer Science Education, SIGCSE 2017
Y2 - 8 March 2017 through 11 March 2017
ER -