We know a lot about cheating in higher education, but maybe not the most important stuff.
We know that cheating (including plagiarism) happens more often than we would like and more often than most of us would guess. On anonymous surveys, more than half of university students in developed countries acknowledge some kind of cheating. Those rates can be higher in other parts of the world.
We know that university students are more likely to have misunderstandings about plagiarism and the related expectations of citations and references than they are to have misunderstandings about other kinds of cheating. In this context, universities often do not do a good job of teaching new students about the expectations of academic integrity in those institutions and higher education in general.
We know that personal characteristics are weak predictors of which students are most likely to cheat. Studies looking at characteristics like age, major, whether or not students are on scholarships, sex, race and ethnicity, and year in school typically find weak relationships between these characteristics and rates of cheating or no relationships at all.
We know that students’ experiences and context are stronger predictors of which students are more likely to cheat. For example, the more students have seen their peers cheat, the more likely it is that they have cheated. Even students’ general beliefs about how often they think their peers are cheating is a good predictor of whether they have cheated. A third example is evidence that international students who study in developed countries are more likely to cheat. This difference may be related to differences in academic cultures and expectations regarding academic integrity across cultures.
There’s also a lot we don’t know about cheating in higher education.
We don’t know what works to reduce cheating. The quantity and quality of research on reducing cheating in higher education have been criticized. Most studies are quite small and are based on relatively weak research methods. As a result, while we see some patterns in the results, they may not generalize to other places, and they may be spurious results because of the weak research methods.
We don’t know how Artificial Intelligence (AI) will affect cheating in higher education. For example, ChatGPT was just released in November of 2022. Students have already been using, and have been encouraged to use, other digital writing tools, such as Grammarly, Google Translate, and others, for their academic work. There is no consensus about when the use of these digital tools is considered inappropriate, and most universities have not yet developed clear guidelines about the use of these tools. The lack of guidelines about when and how these tools should be used, as well as technical details, like when and how they should be included in citations and references, leaves students vulnerable to accusations of cheating when they had no intention of doing so, and also leaves universities with limited tools and authority for managing the use of these digital tools in ethical ways.
Despite concerns about the quality of research on interventions to reduce cheating, we can make some recommendations.
University students should receive deliberate and explicit training on expectations for academic integrity, especially as it applies to avoiding plagiarism.
Universities should develop guidelines for the use of digital tools for academic work, especially tools that incorporate AI, with the recognition that these guidelines will need to be frequently reviewed in the face of continually evolving technology and evaluations of the effectiveness of those guidelines. Students should be included in this process. There is emerging evidence that students want to do their academic work ethically and recognize the challenges these emerging technologies pose with respect to academic integrity.
Universities that have adopted full Honor Codes have lower rates of cheating than universities that have not. A full Honor Code includes a judicial process that is run by the students themselves.
Text matching software, such as Turnitin and SafeAssign, are applications that compare written work with large databases of documents to determine how the text in that written work matches the text on other documents. Consistent use of text matching software does reduce the amount of matching text found in student work. This software can be especially useful as a teaching tool to teach students about avoiding plagiarism.
About the professor.
Bob Ives received his Ph.D. in special education from the University of Georgia and joined the University of Nevada, Reno, in 2002. He teaches classes in assessment, research and disabilities in mathematics. He was awarded a Fulbright Fellowship in 2008 and is cofounder and codirector of the Research in Romania program. During the pandemic, he was recognized with the Judith S. Bible Teaching Excellence in Education Award in 2020 and the Donald Tibbitts Distinguished Teacher Award in 2021.