Reporting less coincidental similarity to educate students about programming plagiarism and collusion
Oscar Karnalim, Simon, William Chivers · Computer Science Education · 2023
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.