Abstract
According to statistics, over the past year, the quality of education has fallen due to the pandemic, and the percentage of plagiarism in the work of students has increased. Modern plagiarism detection systems work well with external plagiarism, they allow to weed out works and answers that completely copy someone else’s published ideas. Using natural language processing methods, the proposed algorithm allows not only detecting plagiarism, but also correctly classifies students’ responses by the amount of plagiarism. This research paper implements a two-step plagiarism detection algorithm. In the experiment, the text was converted into a vector form by the GloVe method, and then segmented by K-means and the result was obtained by the FP-Growth unsupervised learning algorithm.
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Acknowledgment
This research is conducted within the framework of the grant num. AP09058174 “Development of language-independent unsupervised methods of semantic analysis of large amounts of text data”.
The work was done with partial support from the Mexican Government through the grant A1-S-47854 of the CONACYT, Mexico and grants 20211784, 20211884, and 20211178 of the Secretaría de Investigación y Posgrado of the Instituto Politécnico Nacional, Mexico. The authors thank the CONACYT for the computing resources brought to them through the Plataforma de Aprendizaje Profundo para Tecnologías del Lenguaje of the Laboratorio de Supercómputo of the INAOE, Mexico.
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Nurlybayeva, S., Akhmetov, I., Gelbukh, A., Mussabayev, R. (2021). Plagiarism Detection in Students’ Answers Using FP-Growth Algorithm. In: Batyrshin, I., Gelbukh, A., Sidorov, G. (eds) Advances in Soft Computing. MICAI 2021. Lecture Notes in Computer Science(), vol 13068. Springer, Cham. https://doi.org/10.1007/978-3-030-89820-5_12
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DOI: https://doi.org/10.1007/978-3-030-89820-5_12
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