Effectiveness Level of Online Plagiarism Detection Tools in Arabic
Internet of Things and Cloud Computing
Volume 7, Issue 1, March 2019, Pages: 19-24
Received: Apr. 10, 2019; Published: May 23, 2019
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Authors
Ghadah Mohammed Abdullah Adel, Computer Science and Technology, Xidian University, Xi’an, China
Yuping Wang, Computer Science and Technology, Xidian University, Xi’an, China
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Abstract
Plagiarism affects education quality, academic research results and publishers reputation. Consequently, many online plagiarism tools have been developed to detect and reduce such affects. However, most of these tools were evaluated according to their abilities to reveal different rates of plagiarism in English text. While evaluating their capability in detecting different plagiarism rates from different patterns in Arabic text is still vague. This paper aims to evaluate the efficiency level of online academic plagiarism detection tools (PlagScan, iThenticate and CheckForPlagiarism.net) in detecting different plagiarism patterns’ amounts in Arabic language. A comparison was made between, PlagScan, iThenticate and CheckForPlagiarism.net, detection capabilities by merging university theses and dissertations with eight plagiarism patterns (whole document, some parts, insertion, sentence split or join, phrase reordering, syntax, lexical and morpho-syntactic) with the ratio between 90% , 30% and 10% respectively. Experiment’s results showed that iThenticate is the most efficient online plagiarism detection tool in Arabic for eight plagiarism patterns between 90% and 80% ratio Arabic language. While none of the three online plagiarism detection tools are efficient for less than 80% plagiarized text from any of the eight plagiarism patterns. Hence, mechanism enhancements and consideration to the Arabic anguage structure are recommended for online plagiarism detection tool in Arabic.
Keywords
Academic Plagiarism, Plagiarism Levels and Patterns, Online Plagiarism Detection Tools, Arabic Plagiarism Detection, Effectiveness of Plagiarism Detection Tools
To cite this article
Ghadah Mohammed Abdullah Adel, Yuping Wang, Effectiveness Level of Online Plagiarism Detection Tools in Arabic, Internet of Things and Cloud Computing. Vol. 7, No. 1, 2019, pp. 19-24. doi: 10.11648/j.iotcc.20190701.13
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