research-article

Evaluating plagiarism detection software for introductory programming assignments

Authors:

Phatludi Modiba,

Vreda Pieterse,

Bertram HaskinsAuthors Info & Claims

CSERC '16: Proceedings of the Computer Science Education Research Conference 2016

Pages 37 - 46

https://doi.org/10.1145/2998551.2998558

Published: 04 July 2016 Publication History

Abstract

Plagiarism is an issue that all educators have had to deal with. Large numbers of students and assignments have resulted in the development of automated systems to detect code similarities with the aim of identifying cases that may have been plagiarised. These systems are of great value to assessors, allowing them to process submissions automatically. However, these automated systems do present possible disadvantages and drawbacks. In this study we explore and analyse the differences between various systems as well as how their performance compares with manual checking. We consider the different methods students use when committing plagiarism. Then we examine more closely the systems that can aid plagiarism detection, ranging from their characteristics to how they work. In the process, we determine how these systems compare with our own system and their suitability for aiding the identification of submissions which may have been plagiarised in our introductory C++ course.

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cover image ACM Other conferences

CSERC '16: Proceedings of the Computer Science Education Research Conference 2016

July 2016

52 pages

ISBN:9781450344920

DOI:10.1145/2998551

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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OU: Open University Netherlands
University of Pretoria: University of Pretoria
SIGCSE: ACM Special Interest Group on Computer Science Education

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Association for Computing Machinery

New York, NY, United States

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Published: 04 July 2016

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CSERC '16

CSERC '16: Computer Science Education Research Conference 2016

July 4 - 6, 2016

Pretoria, South Africa

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CSERC '16 Paper Acceptance Rate 5 of 14 submissions, 36%;

Overall Acceptance Rate 24 of 60 submissions, 40%

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Cited By

McLoughlin J(2024)The work of art in the age of artificial intelligibilityAI & SOCIETY10.1007/s00146-023-01845-4Online publication date: 28-Mar-2024
https://doi.org/10.1007/s00146-023-01845-4
Soares JBrito MGonçalves C(2023)Codeflex 2.0Internet of Behaviors Implementation in Organizational Contexts10.4018/978-1-6684-9039-6.ch003(40-67)Online publication date: 30-Jun-2023
https://doi.org/10.4018/978-1-6684-9039-6.ch003
Jambi KKhan ISiddiqui M(2022)Evaluation of Different Plagiarism Detection Methods: A Fuzzy MCDM PerspectiveApplied Sciences10.3390/app1209458012:9(4580)Online publication date: 30-Apr-2022
https://doi.org/10.3390/app12094580
Humayoun MHashmi MKhan A(2022)Measuring Plagiarism in Introductory Programming Course Assignments2022 8th International Conference on Information Technology Trends (ITT)10.1109/ITT56123.2022.9863961(80-87)Online publication date: 25-May-2022
https://doi.org/10.1109/ITT56123.2022.9863961
Abou Naaj MNachouki M(2022)Students’ perception of academic dishonesty in programming coursesJournal of Further and Higher Education10.1080/0309877X.2022.209363047:1(72-88)Online publication date: 12-Jul-2022
https://doi.org/10.1080/0309877X.2022.2093630
Aniceto RHolanda MCastanho CDa Silva D(2021)Source Code Plagiarism Detection in an Educational Context: A Literature Mapping2021 IEEE Frontiers in Education Conference (FIE)10.1109/FIE49875.2021.9637155(1-9)Online publication date: 13-Oct-2021
https://doi.org/10.1109/FIE49875.2021.9637155
Simon Karnalim OSheard JDema IKarkare ALeinonen JLiut MMcCauley RRößling GKrogstie BGiannakos MSindre GLuxton-Reilly ADivitini M(2020)Choosing Code Segments to Exclude from Code Similarity DetectionProceedings of the Working Group Reports on Innovation and Technology in Computer Science Education10.1145/3437800.3439201(1-19)Online publication date: 17-Jun-2020
https://dl.acm.org/doi/10.1145/3437800.3439201
Karnalim OSimon Chivers W(2020)Preprocessing for Source Code Similarity Detection in Introductory ProgrammingProceedings of the 20th Koli Calling International Conference on Computing Education Research10.1145/3428029.3428065(1-10)Online publication date: 19-Nov-2020
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Albluwi I(2019)Plagiarism in Programming AssessmentsACM Transactions on Computing Education10.1145/337115620:1(1-28)Online publication date: 9-Dec-2019
https://dl.acm.org/doi/10.1145/3371156
Karnalim OSimon Chivers W(2019)Similarity Detection Techniques for Academic Source Code Plagiarism and Collusion: A Review2019 IEEE International Conference on Engineering, Technology and Education (TALE)10.1109/TALE48000.2019.9225953(1-8)Online publication date: Dec-2019
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