Cheating Detection with a Cheating Trap in Online Education

The recent global COVID-19 pandemic has proven that today education could no longer solely rely on traditional methods and reside within the walls of school buildings. With this unexpected shock, most higher education institutions have rapidly reacted by switching to remote forms of education. However, online education is not without limitations. When it comes to student evaluation, academic integrity becomes a significant concern, since distant forms of exams are categorically different from in-class assessments. Previous research illustrated that cheating rates rise when students have the opportunity to take online exams. Even though technological tools could be helpful for cheating detection, the downside is that these tools are often utilized at the expense of students’ privacy. In this paper, we tested the effectiveness of cheating traps in cheating detection and attempted to understand better the conditions and students’ motivations for online cheating. Using an experimental method, we presented easy and difficult tests with the expectation that students would fall into the traps during the difficult tests. Our findings indicate that when questions are perceived as difficult by students, they are more likely to cheat and rely on external sources irrespective of the credibility of the source consulted. To our knowledge, this is the first experimental study that investigates cheating in an online education setting by using a cheating trap. With this study, we also argue that setting cheating traps can be a practical solution for educators to detect online cheating without raising any concern and discomfort caused by digital surveillance.