Work-in-Progress: Syntactic Code Similarity Detection in Strongly Directed Assessments

Simón²,

ACM Trans. Comput. Educ.

et al. 2022

Self Cite

To help address programming plagiarism and collusion, students should be informed about acceptable practices and about program similarity, both coincidental and non-coincidental. However, current approaches are usually manual, brief, and delivered well before students are in a situation where they might commit academic misconduct. This paper presents an assessment submission system with automated, personalized, and timely formative feedback that can be used in institutions that apply some leniency in early instances of plagiarism and collusion. If a student’s submission shares coincidental or non-coincidental similarity with other submissions, personalized similarity reports are generated for the involved submissions and the students are expected to explain the similarity and resubmit the work. Otherwise, a report simulating similarities is sent just to the author of the submitted program to enhance their knowledge. Results from two quasi-experiments involving two academic semesters suggest that students with our approach are more aware of programming plagiarism and collusion, including the futility of some program disguises. Further, their submitted programs have lower similarity even at the level of program flow, suggesting that they are less likely to have engaged in programming plagiarism and collusion. Student behavior while using the system is also analyzed based on the statistics of the generated reports and student justifications for the reported similarities.

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Section: Discussionmentioning

confidence: 99%

Section: The Approachmentioning

confidence: 99%

Educating Students about Programming Plagiarism and Collusion via Formative Feedback

Simón²,

ACM Trans. Comput. Educ.

et al. 2022

Self Cite

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“…Not all assessments are open to many distinct solutions. Some are either trivial or strongly directed [27], leading to an expectation that they will all be highly similar when compared to using common similarity detectors. Consequently, a number of similarity detectors capturing more subtle variations have been developed.…”

Section: Related Workmentioning

confidence: 99%

“…Detecting programming plagiarism and collusion on such assessments is not always easy. Some weekly assessments are either trivial or strongly directed [27]. Trivial assessments expect simple solutions with limited variation (e.g., writing a program to print the Fibonacci sequence).…”

Section: Introductionmentioning

confidence: 99%

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Layered similarity detection for programming plagiarism and collusion on weekly assessments

Simón²,

Comp Applic In Engineering

2022

Self Cite

When weekly programming assessments are used, it is often the case that some of them are either trivial or strongly directed. Common code similarity detectors are not particularly helpful with such assessments: some potential instances of misconduct are not selected for manual investigation as all submissions are expected to be similar and it is not feasible to check them all. Several dedicated similarity detectors have been developed to work with such assessments, but the experience is required to determine when to use them. This paper presents a similarity detector that works on many kinds of weekly assessments. It combines three‐layered types of similarity so that even within a set of highly similar submissions, program pairs are still sorted according to their levels of similarity. Our similarity detector is more effective than JPlag in distinguishing similar programs and helping to identify plagiarism and collusion. The similarity detector is slower than JPlag, but the longer execution time is partly offset by some optimization that has no negative impact on the effectiveness. As weekly assessments seldom entail large submissions, the execution time does not appear to be a barrier to use.

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Reporting less coincidental similarity to educate students about programming plagiarism and collusion

Simón²,

Computer Science Education

2023