Cheating Detection in Browser-based Online Exams through Eye Gaze Tracking

Abstract

Eye-tracking can detect and examine human visual attention, emotional conditions, latent cognitive processes such as efforts to recall a concept or the fear of running out of time, and so on. Hence, we can use eye-tracking to identify deviant behavior patterns in learning and problem-solving. At present, given the existence of a global pandemic, online exams are widely used by educational institutions to evaluate students' performance. However, identifying cheating is challenging due to the absence of a human (invigilator) monitoring students' behavior as done in exams held in a physical location. In an online environment, students' behavior, and attempts to cheat, can only be captured via a computer, thus requiring a mechanism for online proctoring with capabilities for cheating detection. In this research paper, we present a browser based cheating detection approach in online examinations through eye gaze tracking. We developed a browser plugin to track the eye gaze movements through the in-built web camera. Using the plugin, we generate an eye gaze dataset while a student faces an online examination. We then process and analyze this dataset to detect any misbehavior during an online examination. The underlying research work of this paper identifies different eye gaze patterns during online examinations and present a cheating detection mechanism. For anomaly detection in the eye gaze data, we use a One-Class Support Vector Machine (OCSVM). We then use these identified anomalies to predict cheating behaviors of the test takers. The given approach can be used for any web-based quiz examination such as academic institutions' exams, company recruitment exams, and overseas testing exams to detect any anomalous behaviors of the test takers during the examination period. The given eye tracking approach can also be applied to other research domains such as online gaming, and web usability studies to capture information related to user behaviors.