Automated, Personalised, and Timely Feedback for Awareness of Programming Plagiarism and Collusion

Abstract

It is important to educate students about acceptable practices with regard to programming plagiarism and collusion. However, the current approach is quite demanding since it is manual, relying heavily on instructors. The information is delivered briefly, along with other general information, and students may not understand how it applies to their own cases. There is also no warning when students might be about to breach the rules. This doctoral project proposes a system that provides automated, personalised, and timely feedback about programming plagiarism and collusion. If a submission shares undue similarity with other students’ submissions, all involved students will be given similarity feedback, showing their program with similar code fragments highlighted and the similarities explained in natural language, and they are expected to resubmit. Students whose programs do not show clear similarities will be shown a simulation feedback with comparable information. The system is evaluated with some technical measurements and three quasi-experiments.

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