Measuring Plagiarism in Introductory Programming Course Assignments
This addresses plagiarism measurement for educators in programming courses, though it appears incremental as it builds on existing token-based methods.
The paper tackled plagiarism detection in introductory C++ programming assignments by developing a framework using three token-based similarity methods as features, achieving F1 scores of 0.955 on original data and 0.971 on synthetic data.
Measuring plagiarism in programming assignments is an essential task to the educational procedure. This paper discusses the methods of plagiarism and its detection in introductory programming course assignments written in C++. A small corpus of assignments is made publically available. A general framework to compute the similarity between a solution pair is developed that uses the three token-based similarity methods as features and predicts if the solution is plagiarized. The importance of each feature is also measured, which in return ranks the effectiveness of each method in use. Finally, the artificially generated dataset improves the results compared to the original data. We achieved an F1 score of 0.955 and 0.971 on original and synthetic datasets.