Institutions and proctoring services use a multi-layered approach combining technology and analytics to detect cheating in online exams. This process relies on identifying anomalous behaviors and digital footprints that deviate from expected honest test-taking patterns.
Primary methods to detect cheating in online exams include:
- Behavioral Analysis via AI Proctoring: Software flags unusual eye movements (e.g., persistent looking away from screen), excessive background noise, or the presence of another person in the video feed.
- Browser and Activity Monitoring: Tools lock down the testing browser and monitor for unauthorized actions, such as switching tabs, opening new applications, or copying text.
- Forensic Question Analysis: Statistical tools compare performance data, flagging students with suspiciously similar answer patterns or implausible speed on complex questions.
- Plagiarism and Content Detection: Submitted answers are cross-referenced against online sources, course materials, and other student submissions for matches.
A robust strategy to detect cheating in online exams integrates several of these layers. No single method is foolproof, but combined they create a strong deterrent and identification system. The goal is to uphold academic integrity by making unauthorized collaboration or resource use a measurable and risky behavior.