ABSTRACT
Courses with programming assignments have long faced the issue of academic integrity violations (AIV) where cheating could harm the outcome of student learning. Checking code similarity in students' final submissions is a common way to mitigate this issue. But this single analysis is insufficient as 1) students can refactor their code to evade the check, 2) mere code similarity may not be strong enough evidence to support an AIV case, particularly for simpler assignments that may have similar solutions, and 3) code similarity cannot reveal much about the actual circumstances and behaviors of plagiarism. Due to the lack of supporting data or tools, many educators either abandon solving these challenges or rely on manual approaches that are not feasible at scale. In this paper, we propose a workflow to solve the above challenges for large programming classes by providing supporting evidence of cheating with additional learner data: detailed submission timelines with scores and source code. Running this workflow in a large advanced programming course over several years has helped us identify many cheating cases effectively and efficiently.
- Keith Adkins and David Joyner. 2022. Scaling anti-plagiarism efforts to meet the needs of large online computer science classes: Challenges, solutions, and recommendations. Journal of Computer Assisted Learning (07 2022). https://doi.org/10.1111/jcal.12710Google ScholarCross Ref
- Jiameng Du, Yifan Song, Mingxiao An, Marshall An, Christopher Bogart, and Majd Sakr. 2022. Cheating Detection in Online Assessments via Timeline Analysis. In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 1 (Providence, RI, USA) (SIGCSE 2022). Association for Computing Machinery, New York, NY, USA, 98--104. https://doi.org/10.1145/3478431.3499368Google ScholarDigital Library
- Saul Schleimer, Daniel S. Wilkerson, and Alex Aiken. 2003. Winnowing: Local Algorithms for Document Fingerprinting. In Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data (San Diego, California) (SIGMOD '03). Association for Computing Machinery, New York, NY, USA, 76--85. https://doi.org/10.1145/872757.872770Google ScholarDigital Library
Index Terms
- Programming Plagiarism Detection with Learner Data
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