Vol.66, No.2, 2021, pp.1215-1226, doi:10.32604/cmc.2020.012658
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ARTICLE
University Learning and Anti-Plagiarism Back-End Services
  • Manjur Kolhar*, Abdalla Alameen
Prince Sattam Bin Abdulaziz University, Wadi Ad Dawaser, 11990, Saudi Arabia
* Corresponding Author: Manjur Kolhar. Email:
Received 08 July 2020; Accepted 12 August 2020; Issue published 26 November 2020
Abstract
Plagiarism refers to the use of other people’s ideas and information without acknowledging the source. In this research, anti-plagiarism software was designed especially for the university and its campuses to identify plagiarized text in students’ written assignments and laboratory reports. The proposed framework collected original documents to identify plagiarized text using natural language processing. Our research proposes a method to detect plagiarism by applying the core concept of text, which is semantic associations of words and their syntactic composition. Information on the browser was obtained through Request application programming interface by name Url.AbsoluteUri, and it is stored in a centralized Microsoft database Server. A total of 55,001 data samples were collected from 2015 to 2019. Furthermore, we assimilated data from a university website, specifically from the psau.edu.sa network, and arranged the data into students’ categories. Furthermore, we extracted words from source documents and student documents using the WordNet library. On a benchmark dataset consisting of 785 plagiarized text and 4,716 original text data, a significant accuracy of 90.2% was achieved. Therefore, the proposed framework demonstrated better performance than the other available tools. Many students mentioned that working on assignments using the framework was suitable because they were able to work on the assignments in harmony, as per their timeframe and from different network locations. The framework also recommends procedures that can be used to avoid plagiarism.
Keywords
NLP; information science; text data; semantic; syntactic analysis
Cite This Article
M. Kolhar and A. Alameen, "University learning and anti-plagiarism back-end services," Computers, Materials & Continua, vol. 66, no.2, pp. 1215–1226, 2021.
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