Detection of contract cheating in pen-and-paper exams through the analysis of handwriting style

Abstract

Contract cheating, i.e., when a student employs another person to participate in an exam, appears to become a growing problem in academia. Cases of paid test takers are repeatedly reported in the media, but the number of unreported cases is unclear. Proctoring systems as a countermeasure are typically not appreciated by students and teachers because they may violate the students' privacy and can be imprecise and nontransparent. In this work, we propose to use automatic handwriting analysis based on digital ballpoint pens to identify individuals during exams unobtrusively. We implement a system that enables continuous authentication of the user during exams. We use a deep neural network architecture to model a user's handwriting style. An evaluation based on the large Deepwriting dataset shows that our system can successfully differentiate between the handwriting styles of different authors and hence detect simulated cases of contract cheating. In addition, we conducted a small validation study using digital ballpoint pens to assess the system's reliability in a more realistic environment.