What is it about?
The paper introduces a tool for the detection of plagiarism in modeling assignments. This tool judges the similarity of the assignments by comparing structural properties. Thus, it detects plagiarism even when one tries to hide it by altering the copied assignment. We evaluate our tool with real modeling assignments and generated plagiarisms based on categories of such alteration attempts.
Featured Image
Photo by Hanna Morris on Unsplash
Why is it important?
Plagiarism is a widespread problem in computer science education. Manual inspection is impractical for large courses, and the risk of detection in these courses is thus low. Software artifacts can be easily copied and altered in order to hide plagiarism. Many plagiarism detectors are available for programming assignments. However, very few approaches are available for modeling assignments.
Read the Original
This page is a summary of: Token-based plagiarism detection for metamodels, October 2022, ACM (Association for Computing Machinery),
DOI: 10.1145/3550356.3556508.
You can read the full text:
Resources
Contributors
The following have contributed to this page
