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Plagiarism in introductory programming courses is an enormous challenge forboth students and institutions. For students, relying on the work of others tooearly in their academic development can make it impossible to acquire necessaryskills for independent success in the future. For institutions, widespreadstudent cheating can dilute the quality of the educational experience beingoffered. Currently available solutions consider only pairwise comparisonsbetween student submissions and focus on punitive deterrence. Our approachinstead relies on a class-wide statistical characterization that can be clearlyand securely shared with students via an intuitive new p-value representingindependence of student effort. A pairwise, compression-based similaritydetection algorithm captures relationships between assignments more accurately.An automated deterrence system is used to warn students that their behavior isbeing closely monitored. High-confidence instances are made directly availablefor instructor review using our open-source toolkit. An unbiased scoring systemaids students and the instructor in understanding true independence of effort.Preliminary results indicate that the system can provide meaningfulmeasurements of independence from week one, improving the efficacy of technicaleducation.

Plagiarism deterrence for introductory programming