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iChecker: An Efficient Plagiarism Detection Tool for Learning Management Systems

iChecker: An Efficient Plagiarism Detection Tool for Learning Management Systems

Samuel P. M. Choi, Sze Sing Lam
Copyright: © 2016 |Volume: 6 |Issue: 3 |Pages: 16
ISSN: 1947-3052|EISSN: 1947-3060|EISBN13: 9781466691827|DOI: 10.4018/IJSSOE.2016070102
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MLA

Choi, Samuel P. M., and Sze Sing Lam. "iChecker: An Efficient Plagiarism Detection Tool for Learning Management Systems." IJSSOE vol.6, no.3 2016: pp.16-31. http://doi.org/10.4018/IJSSOE.2016070102

APA

Choi, S. P. & Lam, S. S. (2016). iChecker: An Efficient Plagiarism Detection Tool for Learning Management Systems. International Journal of Systems and Service-Oriented Engineering (IJSSOE), 6(3), 16-31. http://doi.org/10.4018/IJSSOE.2016070102

Chicago

Choi, Samuel P. M., and Sze Sing Lam. "iChecker: An Efficient Plagiarism Detection Tool for Learning Management Systems," International Journal of Systems and Service-Oriented Engineering (IJSSOE) 6, no.3: 16-31. http://doi.org/10.4018/IJSSOE.2016070102

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Abstract

Academic plagiarism is regarded as a serious offense and much effort in the past has been devoted to build stand-alone plagiarism detection systems for a specific language. This paper proposes a new information retrieval-based plagiarism detection algorithm that handles multilingual documents and enables seamless integration with learning management systems. The proposed algorithm employs information retrieval and sequence matching techniques to identify suspected plagiarized sentences and permits parametric control to reduce both false-positive and false-negative results. The full-featured implementation, called iChecker, not only could quickly identify suspected plagiarized works but also ease academics' effort to evaluate the severity of the offence by a quantified measure. Currently iChecker is adopted by over 300 courses (with some having several hundred of students) and has obtained satisfactory results. During 2012 to 2016, iChecker has processed and verified a total of 276,943 documents in English, Traditional Chinese and Simplified Chinese text.

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