Plagiarism deterrence for introductory programming

TL;DR

This paper introduces a novel class-wide statistical approach and a compression-based similarity detection algorithm to deter plagiarism in introductory programming, providing transparent feedback and improving educational integrity.

Contribution

It proposes a new class-wide statistical characterization method and an automated deterrence system that enhances detection accuracy and transparency over existing pairwise comparison approaches.

Findings

System provides meaningful independence measurements from week one

Improves detection accuracy with compression-based similarity detection

Enhances transparency and student awareness of plagiarism risks

Abstract

Plagiarism in introductory programming courses is an enormous challenge for both students and institutions. For students, relying on the work of others too early in their academic development can make it impossible to acquire necessary skills for independent success in the future. For institutions, widespread student cheating can dilute the quality of the educational experience being offered. Currently available solutions consider only pairwise comparisons between student submissions and focus on punitive deterrence. Our approach instead relies on a class-wide statistical characterization that can be clearly and securely shared with students via an intuitive new p-value representing independence of student effort. A pairwise, compression-based similarity detection algorithm captures relationships between assignments more accurately. An automated deterrence system is used to warn students that their behavior is being closely monitored. High-confidence instances are made directly available for instructor review using our open-source toolkit. An unbiased scoring system aids students and the instructor in understanding true independence of effort. Preliminary results indicate that the system can provide meaningful measurements of independence from week one, improving the efficacy of technical education.