In the digital era, detection of plagiarism often depends on text-matching software similarity reports. Although such reports have the potential to facilitate the detection of plagiarism, they should be approached with caution. All text-matching software aims to reveal plagiarism concerns by highlighting matches between the submitted text and other sources included in its database. To contribute to the interpretation of similarity reports, it is also a common practice for text-matching software to attach a similarity ratio. Although the ratio is intended to provide supportive statistical information about the matches, in practice such statistics are often treated as the main concern, and it is quite a common practice either for lecturers or students simply to depend on the statistics rather than check whether the highlighted expressions are due to plagiarism infringements or not. Within this respect, several hard-copy similarity report samples will be delivered to the audience of this session to exemplify how isolated statistics from highlighted matches may mislead both lecturers and students. These samples will illustrate why academic practices that merely depend on pure statistics follow a flawed methodology in the detection of plagiarism. The audience will be provided with an ideal similarity report interpretation procedure which is expected to contribute to the development of academic integrity policies, especially in institutions where plagiarism is wrongly acceptable until a certain threshold similarity ratio.
Foltýnek, T., Dlabolová, D., Anohina-Naumeca, A., Razı, S., Kravjar, J., Kamzola, L., Guerrero- Dib, J., Çelik, Ö., & Weber-Wulff, D. (2020). Testing of support tools for plagiarism detection. International Journal of Educational Technology in Higher Education, 17(46), 1-31. Razı, S. (2015). Development of a rubric to assess academic writing incorporating plagiarism detectors. SAGE Open, 5(2), 1-13. Razı, S., Glendinning, I, & Foltýnek, T. (Eds.). (2018). Towards consistency and transparency in academic integrity. Frankfurt: Peter Lang. Tauginienė, L., Gaižauskaitė, I., Razı, S., Glendinning, I., Sivasubramaniam, S., Marino, F., Cosentino, M., Anohina-Naumeca, A., & Kravjar, J. (2019). Enhancing the taxonomies relating to academic integrity and misconduct. Journal of Academic Ethics, 17(4), 345-361. Tauginienė, L., Ojsteršek, M., Foltýnek, T., Marino, M., Cosentino, M., Gaižauskaitė, I., Glendinning, I., Sivasubramaniam, S., Razı, S., Ribeiro, L., Odiņeca, T., & Trevisiol, O. (2018). General guidelines for academic integrity. Brno: ENAI report 3A [online].
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Salim Razı is an associate professor at the English Language Teaching Department of Çanakkale Onsekiz Mart University, Turkey. He trains English as a foreign language teachers and offers graduate courses. He is also a founder Board member of the European Network for Academic Integrity. He has been teaching academic writing for more than a decade and developed ‘Transparent academic writing rubric’ to enable more reliable assessment in academic writing. His recent research focuses on detecting and preventing plagiarism in academic writing through his ‘anonymous multi-mediated writing model’ that he was awarded the Turnitin Global Innovation Award in 2015 and the model was listed as a good practice by Australian Government Tertiary Education Quality and Standards Agency in 2017. He is the convenor of the ‘4th Plagiarism across Europe and Beyond Conference’ and one of the editors of the book entitled ‘Towards consistency and transparency in academic integrity’. Most of his publications are freely available on his personal webpage at www.salimrazi.com. He can be contacted at firstname.lastname@example.org or email@example.com
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