Automatic paraphrasing tools: an unexpected consequence of addressing student plagiarism and the impact of COVID in distance education settings

Abstract

Text matching tools employed to detect plagiarism are widely used in universities, but their availability may have pushed students to find ways to evade detection. One such method is the use of automatic paraphrasing software, where assignments can be rewritten with little effort required by students. This paper uses the search engine analytics methodology with data from SEMrush and Google Trends to estimate the level of interest in online automatic paraphrasing tools, focusing on the period 2016 to 2020 and the four countries: the USA, UK, Canada and Australia. The results show a concerning trend, with the number of searches for such tools growing during the period, especially during COVID-19, and notable increases observed during the months where assessment periods take place in universities. The method employed in this study opens up a new avenue of analysis to enrich and supplement the existing knowledge in the field of academic integrity research. The data obtained demonstrates that faculty should be alert for student use of automatic paraphrasing tools and that academic integrity interventions need to be in place across the sector to address this problem.

Unmasking deceptive journals in education sciences: a study on prevalence, characteristics and visibility

“…The research methodology based on the analysis of internet search metrics has several advantages, such as providing real-time data, covering a wide range of topics and geographical locations, and offering insights into user behavior that might not be available through traditional research methods (Comas-Forgas et al, 2021). It has been used in studies around many different topics and from many disciplines, for instance: the study and monitoring of online information for public health purposes also known as infodemiology that has many examples and was widely used during the COVID-19 pandemic (Mavragani, 2020), the prediction of electoral results (Digrazia, 2017), Práxis Educativa, Ponta Grossa, v. 19, e22563, p. 1-17, 2024 Available at: <https://revistas2.uepg.br/index.php/praxiseducativa> 6 conduct macroeconomy analyses (Donadelli, 2015), to study marketing strategies and campaigns (Ghose & Yang, 2009), for tourism forecasting studies (Li et al, 2021) and even it has been used for analyzing academic dishonest behaviors (Comas-Forgas et al, 2023).…”