DOI - Mendel University Press

DOI identifiers

DOI: 10.11118/978-80-7509-772-9-0050

DE-EMPHASIZING PLAGIARISM WITH BIG DATA: MODERN APPROACHES AND ETHICAL CONSIDERATIONS

Daniel Dusza
Kanda Institute of Foreign Languages, Odawara, Japan

This article introduces present trends in Big Data analytics and how they might be implemented to de-emphasize plagiarism. Regrettably, academic institutions have continued to rely on essay-based coursework and written reports as a basis of assessment. As a result of the COVID-19 pandemic, there has been a mass migration into online learning, and with it, a further increase in the reliance on textual content. With more writers now composing online in the absence of face-to-face accountability to peers and teachers, the risks to academic integrity through plagiarism and contract cheating should be expected to increase. Three empirical research studies were used to investigate how the writing process might be automatically and covertly monitored to measure the dynamics of compositions. The areas of interest include the equipment, the method of data management, and the information that could be gleaned from the recorded data. Each study is progressively more complex, and presented in a manner to support the future formulation of a framework for implementation into education. The results indicate that the equipment that is readily available to most students is capable much more than composing and transmitting a written manuscript. Currently, technology is capable of identifying writing problems and providing assistance to help writers navigate even the most difficult tasks in composition. The technological solutions suggested in this paper provides far more than plagiarism detection. The results in this paper indicate that future writing will be supported through process verification, semantic network authentications, and other certifications that will form part of the future requirements of assessment and academic integrity.

Keywords: plagiarism, TPACK, Big Data, AI, academic-integrity

pages: 50-63



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