- Title
- Reporting less coincidental similarity to educate students about programming plagiarism and collusion
- Creator
- Karnalim, Oscar; ., Simon; Chivers, William
- Relation
- Computer Science Education Vol. 34, Issue 3, p. 442-472
- Publisher Link
- http://dx.doi.org/10.1080/08993408.2023.2178063
- Publisher
- Routledge
- Resource Type
- journal article
- Date
- 2024
- Description
- Background and Context: To educate students about programming plagiarism and collusion, we introduced an approach that automatically reports how similar a submitted program is to others. However, as most students receive similar feedback, those who engage in plagiarism and collusion might feel inadequately warned. Objective: When students are likely to be engaging in plagiarism or collusion, we would like the system to apply enough pressure on them to make them reconsider their actions. Method: This study proposes a variation of the approach, which is less likely to report coincidental similarity. The variation was compared with its predecessor via quasi-experiments with 202 computing students. Findings: Students with the new approach are slightly more aware of programming plagiarism and collusion than those with the previous approach with a reduction in cases of such misconduct. Implications: There is another way to automatically educate students about programming plagiarism and collusion with appropriate pressure.
- Subject
- program similarity; plagiarism; collusion; academic integrity; computing education
- Identifier
- http://hdl.handle.net/1959.13/1511887
- Identifier
- uon:56567
- Identifier
- ISSN:0899-3408
- Language
- eng
- Reviewed
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