A Method for Cheating Indication in Unproctored On-Line Exams

Abstract

COVID-19 has disrupted every field of life and education is not immune to it. Student learning and examinations moved on-line on a few weeks notice, which has created a large workload for academics to grade the assessments and manually detect students’ dishonesty. In this paper, we propose a method to automatically indicate cheating in unproctored on-line exams, when somebody else other than the legitimate student takes the exam. The method is based on the analysis of the student’s on-line traces, which are logged by distance education systems. We work with customized IP geolocation and other data to derive the student’s cheating risk score. We apply the method to approx. 3600 students in 22 courses, where the partial or final on-line exams were unproctored. The found cheating risk scores are presented along with examples of indicated cheatings. The method can be used to select students for knowledge re-validation, or to compare student cheating across courses, age groups, countries, and universities. We compared student cheating risk scores between four academic terms, including two terms of university closure due to COVID-19.

Effectiveness of Pre-Trained CNN Networks for Detecting Abnormal Activities in Online Exams

“…A rule-based inference system that has ascertained whether exam fraud occurred was produced by the integration of these models. The suggested [13] detection of laughter, eye gaze tracking to establish the applicant's direction of glance, eyes blinking/close duration, and head activity/head position detection are all included in the testing monitoring approach. Artificial intelligence models have been used in the task to categorize applicant activities.…”