Authorship identification methods in student plagiarism detection
In the modern educational context the problem of plagiarism is urgent and requires the development of effective methods of detection and prevention of this phenomenon. The application of authorship identification methods in the field of student plagiarism detection is considered. Different check, de...
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| Format: | Article |
| Language: | English |
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Belarusian National Technical University
2023-11-01
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| Series: | Системный анализ и прикладная информатика |
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| Online Access: | https://sapi.bntu.by/jour/article/view/630 |
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| author | A. I. Paramonov I. A. Trukhanovich |
| author_facet | A. I. Paramonov I. A. Trukhanovich |
| author_sort | A. I. Paramonov |
| collection | DOAJ |
| description | In the modern educational context the problem of plagiarism is urgent and requires the development of effective methods of detection and prevention of this phenomenon. The application of authorship identification methods in the field of student plagiarism detection is considered. Different check, detect and analyze plagiarism approaches in various works are investigated. Both classical methods, which include text comparison and similarity search, and modern methods based on machine learning algorithms, as well as their combination and potential modifications, are considered. The advantages and limitations of each method are also discussed, and recommendations are given for choosing one or another approach according to the specific requirements of the research.Special attention is paid to such modern methods as metadata analysis and the application of neural networks. Stylistic analysis reveals authorial peculiarities such as word choice, preferred wording, and even punctuation. Lexical and syntactic models are used to identify repetitive phrases and structures that may indicate plagiarism. Statistical methods can identify anomalies in the use of words and phrases, and machine learning can create models to calculate the probability of plagiarism based on large amounts of data.Ultimately, an comparison of authorship identification techniques in the field of student plagiarism detection is provided, which aims to provide valuable information about different approaches and their applicability, and to help researchers and educators develop effective strategies for detecting and preventing plagiarism in educational environments. |
| format | Article |
| id | doaj-art-d31aa2e63e164916b7e718579e6f1bf9 |
| institution | Kabale University |
| issn | 2309-4923 2414-0481 |
| language | English |
| publishDate | 2023-11-01 |
| publisher | Belarusian National Technical University |
| record_format | Article |
| series | Системный анализ и прикладная информатика |
| spelling | doaj-art-d31aa2e63e164916b7e718579e6f1bf92025-02-03T11:37:40ZengBelarusian National Technical UniversityСистемный анализ и прикладная информатика2309-49232414-04812023-11-0103565910.21122/2309-4923-2023-3-56-59469Authorship identification methods in student plagiarism detectionA. I. Paramonov0I. A. Trukhanovich1Belarusian State University of Informatics and RadioelectronicsBelarusian State University of Informatics and RadioelectronicsIn the modern educational context the problem of plagiarism is urgent and requires the development of effective methods of detection and prevention of this phenomenon. The application of authorship identification methods in the field of student plagiarism detection is considered. Different check, detect and analyze plagiarism approaches in various works are investigated. Both classical methods, which include text comparison and similarity search, and modern methods based on machine learning algorithms, as well as their combination and potential modifications, are considered. The advantages and limitations of each method are also discussed, and recommendations are given for choosing one or another approach according to the specific requirements of the research.Special attention is paid to such modern methods as metadata analysis and the application of neural networks. Stylistic analysis reveals authorial peculiarities such as word choice, preferred wording, and even punctuation. Lexical and syntactic models are used to identify repetitive phrases and structures that may indicate plagiarism. Statistical methods can identify anomalies in the use of words and phrases, and machine learning can create models to calculate the probability of plagiarism based on large amounts of data.Ultimately, an comparison of authorship identification techniques in the field of student plagiarism detection is provided, which aims to provide valuable information about different approaches and their applicability, and to help researchers and educators develop effective strategies for detecting and preventing plagiarism in educational environments.https://sapi.bntu.by/jour/article/view/630machine learningplagiarismauthorship identification |
| spellingShingle | A. I. Paramonov I. A. Trukhanovich Authorship identification methods in student plagiarism detection Системный анализ и прикладная информатика machine learning plagiarism authorship identification |
| title | Authorship identification methods in student plagiarism detection |
| title_full | Authorship identification methods in student plagiarism detection |
| title_fullStr | Authorship identification methods in student plagiarism detection |
| title_full_unstemmed | Authorship identification methods in student plagiarism detection |
| title_short | Authorship identification methods in student plagiarism detection |
| title_sort | authorship identification methods in student plagiarism detection |
| topic | machine learning plagiarism authorship identification |
| url | https://sapi.bntu.by/jour/article/view/630 |
| work_keys_str_mv | AT aiparamonov authorshipidentificationmethodsinstudentplagiarismdetection AT iatrukhanovich authorshipidentificationmethodsinstudentplagiarismdetection |