Plagiarism detection and prevention techniques in engineering education

Halak, Basel; El‐Hajjar, Mohammed

doi:10.1109/ewme.2016.7496465

Cited by 26 publications

(11 citation statements)

References 9 publications

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“…Most detection techniques assume that all inputted source code files are written in the same programming language [2,21]. This can be an issue when more than one programming language is allowed to complete a particular assessment.…”

Section: Related Workmentioning

confidence: 99%

“…In some computer science courses, students are free to choose their preferred programming language for completing assignments. This can lead to an issue when plagiarism or collusion occurs; most existing detection techniques are not specifically designed to handle such cases in a cross-language manner where the involved source code files are written in different programming languages [2,21]. Some detection techniques have addressed the issue by considering the source code as raw text [7,50], removing any needs for language-specific components.…”

Section: Introductionmentioning

confidence: 99%

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TF-IDF Inspired Detection for Cross-Language Source Code Plagiarism and Collusion

Karnalim

2020

csci

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Several computing courses allow students to choose which programming language they want to use for completing a programming task. This can lead to cross-language code plagiarism and collusion, in which the copied code file is rewritten in another programming language. In response to that, this paper proposes a detection technique which is able to accurately compare code files written in various programming languages, but with limited effort in accommodating such languages at development stage. The only language-dependent feature used in the technique is source code tokeniser and no code conversion is applied. The impact of coincidental similarity is reduced by applying a TF-IDF inspired weighting, in which rare matches are prioritised. Our evaluation shows that the technique outperforms common techniques in academia for handling language conversion disguises. Further, it is comparable to those techniques when dealing with conventional disguises.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TF-IDF Inspired Detection for Cross-Language Source Code Plagiarism and Collusion

Karnalim

2020

csci

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“…Academic dishonesty is a concerning issue in engineering education [1], [2], especially when the classes are going online due to Covid-19 pandemic [3]. In online environment, assignments are completed without direct supervision by the lecturer and this might tempt the students to cheat [4].…”

Section: Introductionmentioning

confidence: 99%

Initial Suspicion on Detecting Code Plagiarism and Collusion in Academia: Case Study of Algorithm and Data Structure Courses

Ayub

Karnalim

Wijanto

et al. 2021

JITeCS

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In engineering education, some assessments require the students to submit program code, and since that code might be a result of plagiarism or collusion, a similarity detection tool is often used to filter excessively similar programs. To improve the scalability of such a tool, it is suggested to initially suspect some programs and only compare those programs to others (instead of exhaustively compare all programs one another). This paper compares the ef-fectiveness of two common techniques to raise such initial suspicion: focusing on the submissions of smart students (as they are likely to be copied), or the submissions of slow-paced students (since those students are likely to breach academic integrity to get higher assessment mark). Our study shows that the latter statistically outperforms the former by 13% in terms of precision; slow-paced students are likely to be the perpetrators, but they fail to get the submissions of smart students.

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“…Maintaining academic integrity is a serious concern in engineering education [1], [2], especially with the introduction of MOOC [3], [4]. Several strategies have been proposed in which one of the most popular ones is the use of Turnitin [5].…”

Section: Introductionmentioning

confidence: 99%

A Scalable Code Similarity Detection with Online Architecture and Focused Comparison for Maintaining Academic Integrity in Programming

2020

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<p>Many code similarity detection techniques have been developed to maintain academic integrity in programming. However, most of them assume that the student programs are locally available, and the computation can be run on any computer specification. Further, their comparison in raising suspicion is time-consuming as the student programs are pairwise compared to one another. This paper proposes a scalable code similarity detection with online architecture and focused comparison. The former enables student programs shared among lecturers and guarantees that the computation is runnable. The latter shortens the execution time as only some students are considered, with inclusion criteria determined by the lecturers. To boost up the scalability, the similarity algorithm is cosine correlation, which computation is linear time. Our evaluation shows that focused comparison leads to fewer comparisons and cosine correlation leads to shorter execution time.</p>

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Plagiarism detection and prevention techniques in engineering education

Cited by 26 publications

References 9 publications

TF-IDF Inspired Detection for Cross-Language Source Code Plagiarism and Collusion

TF-IDF Inspired Detection for Cross-Language Source Code Plagiarism and Collusion

Initial Suspicion on Detecting Code Plagiarism and Collusion in Academia: Case Study of Algorithm and Data Structure Courses

A Scalable Code Similarity Detection with Online Architecture and Focused Comparison for Maintaining Academic Integrity in Programming

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