A unified approach to automate the usage of plagiarism detection tools in programming courses

Portillo-Dominguez, A. Omar; Ayala-Rivera, Vanessa; Murphy, E. C.; Murphy, John

doi:10.1109/iccse.2017.8085456

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…The institution may also choose to use a reactive method like plagiarism detection combined with disciplinary action to limit future plagiarism. Both proactive and reactive approaches and their respective techniques applied to prevent or detect plagiarism, present problems that need to be examined and addressed (Ganguly et al, 2018;Haskins & Pieterse, 2016;Louw & Pieterse, 2015;Portillo-Dominguez et al, 2017).…”

Section: Research Problemmentioning

confidence: 99%

Systematic Literature Review to Identify and Rank the Most Common Reasons for Plagiarism

2020

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Aim/Purpose: In this paper the authors explore and analyse the literature to determine the common reasons why a student may choose to plagiarise. The predominant purpose of the research formed part of a larger study to develop metrics and methods to identify potential plagiarism within coding-based assignments. Methodology: A systematic literature review with the objective to identify and rank the most common reasons for plagiarism was conducted. This was achieved by identi-fying primary studies conducted on the reasons for plagiarism. The identified studies were subsequently subjected to a top down quality assessment with a number of criteria. In total, 37 studies made it through the selection process. The results of the selected studies were synthesized to obtain a ranked list of reasons why students plagiarise. Contribution: This paper contributes a ranked list of reasons that may influence a student’s decision to plagiarise, based on a set of categories emerging from the literature. Findings: Eleven possible categories indicating the common reasons behind a student’s decision to plagiarise are identified. The literature revealed that aspects such as the external values of the student and attitudes towards academia and teaching styles are significant factors that impact a student’s willingness and decision to plagiarise. Impact on Society: Instructors may gain a better understanding on why their students decided to plagiarise.

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Section: Research Problemmentioning

confidence: 99%

Systematic Literature Review to Identify and Rank the Most Common Reasons for Plagiarism

2020

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“…Some works extend existing techniques instead of directly proposing new ones, such as a meta-tool which combines several source code plagiarism detection techniques (Portillo-Dominguez et al, 2017). A technique requiring steps for detecting (and investigating) source code plagiarism is expected to form guidance for examiners (Kermek & Novak, 2016).…”

Section: A Review Of Automated Source Code Plagiarism Detectionmentioning

confidence: 99%

Source Code Plagiarism Detection in Academia with Information Retrieval: Dataset and the Observation

Karnalim¹,

Budi²,

Toba³

et al. 2019

Informatics in Education

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Source code plagiarism is an emerging issue in computer science education. As a result, a number of techniques have been proposed to handle this issue. However, comparing these techniques may be challenging, since they are evaluated with their own private dataset(s). This paper contributes in providing a public dataset for comparing these techniques. Specifically, the dataset is designed for evaluation with an Information Retrieval (IR) perspective. The dataset consists of 467 source code files, covering seven introductory programming assessment tasks. Unique to this dataset, both intention to plagiarise and advanced plagiarism attacks are considered in its construction. The dataset's characteristics were observed by comparing three IR-based detection techniques, and it is clear that most IR-based techniques are less effective than a baseline technique which relies on Running-Karp-Rabin Greedy-String-Tiling, even though some of them are far more time-efficient.

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“…Works in [25], [26], for example, proposed a mechanism to replace method invocations with their respective contents so that the effectiveness of the detection tool can be improved. A work in [27] proposed a meta-tool which combines several existing detection tools. A work in [28] proposed dynamic thresholding mechanisms to mitigate the number of false results.…”

Section: Literature Reviewmentioning

confidence: 99%

A Language-Independent Library for Observing Source Code Plagiarism

Franclinton

Karnalim

2019

JISEBI

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Background: Most source code plagiarism detection tools are not modifiable. Consequently, when a modification is required to be applied, a new detection tool should be created along with it. This could be a problem as creating the tool from scratch is time-inefficient while most of the features are similar across source code plagiarism detection tools.Objective: To alleviate researchers' effort, this paper proposes a library for observing two plagiarism-suspected codes (a feature which is similar across most source code plagiarism detection tools).Methods: Unique to this library, it is not constrained by the selected programming language for development. It is executed from command line, which is supported by most programming languages.Results: According to our evaluation, the library is integrable and functional. Moreover, the library can enhance teaching assistants' accuracy and reduce the tasks' completion time.Conclusion: The library can be beneficial for the development of source code plagiarism detection tools since it is integrable, functional, and helpful for teaching assistants.Keywords:Language independency, Plagiarism detection, Reusable library, Source code, Tool development

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A unified approach to automate the usage of plagiarism detection tools in programming courses

Cited by 5 publications

References 13 publications

Systematic Literature Review to Identify and Rank the Most Common Reasons for Plagiarism

Systematic Literature Review to Identify and Rank the Most Common Reasons for Plagiarism

Source Code Plagiarism Detection in Academia with Information Retrieval: Dataset and the Observation

A Language-Independent Library for Observing Source Code Plagiarism

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