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Dolos 2.0: Towards Seamless Source Code Plagiarism Detection in Online Learning Environments

Published: 29 June 2023 Publication History

Abstract

With the increasing demand for programming skills comes a trend towards more online programming courses and assessments. While this allows educators to teach larger groups of students, it also opens the door to dishonest student behaviour, such as copying code from other students. When teachers use assignments where all students write code for the same problem, source code similarity tools can help to combat plagiarism. Unfortunately, teachers often do not use these tools to prevent such behaviour.
In response to this challenge, we have developed a new source code plagiarism detection tool named Dolos. Dolos is open-source, supports a wide range of programming languages, and is designed to be user-friendly. It enables teachers to detect, prove and prevent plagiarism in programming courses by using fast algorithms and powerful visualisations.
We present further enhancements to Dolos and discuss how it can be integrated into modern computing education courses to meet the challenges of online learning and assessment. By lowering the barriers for teachers to detect, prove and prevent plagiarism in programming courses, Dolos can help protect academic integrity and ensure that students earn their grades honestly.

References

[1]
D. Chuda, P. Navrat, B. Kovacova, and P. Humay. 2012. The Issue of (Software) Plagiarism: A Student View. IEEE Transactions on Education, Vol. 55, 1 (Feb. 2012), 22--28. https://doi.org/10.1109/TE.2011.2112768 Conference Name: IEEE Transactions on Education.
[2]
R. Maertens, C. Van Petegem, N. Strijbol, T. Baeyens, A. C. Jacobs, P. Dawyndt, and B. Mesuere. 2022. Dolos: Language-agnostic plagiarism detection in source code. Journal of Computer Assisted Learning, Vol. 38, 4 (2022), 1046--1061. https://doi.org/10.1111/jcal.12662
[3]
L. Prechelt, G. Malpohl, and M. Philippsen. 2002. Finding Plagiarisms among a Set of Programs with JPlag. Journal of Universal Computer Science, Vol. 8, 11 (Nov. 2002), 1016--1038. https://doi.org/10.3217/jucs-008--11--1016
[4]
S. Schleimer, D. S. Wilkerson, and A. Aiken. 2003. Winnowing: local algorithms for document fingerprinting. In Proceedings of the 2003 ACM SIGMOD international conference on Management of data (SIGMOD '03). Association for Computing Machinery, New York, NY, USA, 76--85. https://doi.org/10.1145/872757.872770 io

Cited By

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  • (2025)Detecting AI-Generated Pseudocode in High School Online Programming Courses Using an Explainable ApproachProceedings of the 56th ACM Technical Symposium on Computer Science Education V. 110.1145/3641554.3701942(701-707)Online publication date: 12-Feb-2025
  • (2025)What are the differences between student and ChatGPT-generated pseudocode? Detecting AI-generated pseudocode in high school programming using explainable machine learningEducation and Information Technologies10.1007/s10639-025-13385-zOnline publication date: 1-Feb-2025

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cover image ACM Conferences
ITiCSE 2023: Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 2
June 2023
694 pages
ISBN:9798400701399
DOI:10.1145/3587103
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 June 2023

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Author Tags

  1. plagiarism detection
  2. programming education
  3. similarity detection

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  • Poster

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  • Research Foundation - Flanders (FWO) for ELIXIR Belgium

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ITiCSE 2023
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Overall Acceptance Rate 552 of 1,613 submissions, 34%

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ITiCSE '25
Innovation and Technology in Computer Science Education
June 27 - July 2, 2025
Nijmegen , Netherlands

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View all
  • (2025)Detecting AI-Generated Pseudocode in High School Online Programming Courses Using an Explainable ApproachProceedings of the 56th ACM Technical Symposium on Computer Science Education V. 110.1145/3641554.3701942(701-707)Online publication date: 12-Feb-2025
  • (2025)What are the differences between student and ChatGPT-generated pseudocode? Detecting AI-generated pseudocode in high school programming using explainable machine learningEducation and Information Technologies10.1007/s10639-025-13385-zOnline publication date: 1-Feb-2025

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