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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2506.17355 |
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| _version_ | 1866909654557130752 |
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| author | McDonald, Jesse Robertson, Scott Peruma, Anthony |
| author_facet | McDonald, Jesse Robertson, Scott Peruma, Anthony |
| contents | Introductory Computer Science classes are important for laying the foundation for advanced programming courses. However, students without prior programming experience may find these courses challenging, leading to difficulties in understanding concepts and engaging in academic dishonesty such as plagiarism. While there exists plagiarism detection techniques and tools, not all of them are suitable for academic settings, especially in introductory programming courses. This paper introduces PasteTrace, a novel open-source plagiarism detection tool designed specifically for introductory programming courses. Unlike traditional methods, PasteTrace operates within an Integrated Development Environment that tracks the student's coding activities in real-time for evidence of plagiarism. Our evaluation of PasteTrace in two introductory programming courses demonstrates the tool's ability to provide insights into student behavior and detect various forms of plagiarism, outperforming an existing well-established tool.
A video demonstration of PasteTrace and its source code, and case study data are made available at https://doi.org/10.6084/m9.figshare.27115852 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17355 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PasteTrace: A Single Source Plagiarism Detection Tool For Introductory Programming Courses McDonald, Jesse Robertson, Scott Peruma, Anthony Computers and Society Introductory Computer Science classes are important for laying the foundation for advanced programming courses. However, students without prior programming experience may find these courses challenging, leading to difficulties in understanding concepts and engaging in academic dishonesty such as plagiarism. While there exists plagiarism detection techniques and tools, not all of them are suitable for academic settings, especially in introductory programming courses. This paper introduces PasteTrace, a novel open-source plagiarism detection tool designed specifically for introductory programming courses. Unlike traditional methods, PasteTrace operates within an Integrated Development Environment that tracks the student's coding activities in real-time for evidence of plagiarism. Our evaluation of PasteTrace in two introductory programming courses demonstrates the tool's ability to provide insights into student behavior and detect various forms of plagiarism, outperforming an existing well-established tool. A video demonstration of PasteTrace and its source code, and case study data are made available at https://doi.org/10.6084/m9.figshare.27115852 |
| title | PasteTrace: A Single Source Plagiarism Detection Tool For Introductory Programming Courses |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2506.17355 |