How to use academic and digital fingerprints to catch and eliminate contract cheating during online multiple-choice examinations: a case study

Abstract

Over four semesters of a large introductory statistics course the authors found students were engaging in contract cheating on Chegg.com during multiple choice examinations. In this paper we describe our methodology for identifying, addressing and eventually eliminating cheating. We successfully identified 23 out of 25 students using a combination of unique academic and digital fingerprints, and identified students who used virtual private networks (VPNs) to protect their online identity. There were two forms of cheating–posting questions and waiting for responses from tutors, and looking for questions that had already been solved. We found that 165 questions from these examinations were posted by 10 different students, but that the most common form of cheating was searching for answers that had already been posted. This paper discusses these patterns of Chegg usage, the consequences of not catching cheating early on, and how students reacted to being caught. Also provided are R and Python code that readers may use to identify cheating students in their own courses.