<![CDATA[The implementation of online exams differs significantly from exams supervised directly by proctors. Participants' body movements and interactions can be easily read. Even with camera monitoring, participants can still easily switch browser tabs or open applications on their devices. On the other hand, camera-based proctoring solutions can also raise invasive privacy concerns. Furthermore, device limitations can hinder someone from taking the exam due to the lack of a camera, and bandwidth limitations can result in unstable connections during the exam. Sample log activity data from test participants for log_id ranges from 44790 to 46883, while the actions consist of fullscreen_exit, context_switch, standby_log, focus_return, fullscreen_enter, and devtools_open. The results and rules of this test have scores, with each result having an average value of 1, and the rules having an average value of 1-10. In this process, experts assess each detection result using a binary scheme: a score of 1 is given if the expert system's conclusion matches the expert's decision, and a score of 0 is given if the expert system's decision does not match the expert's decision. This study proposes an expert system framework with a cameraless forward chaining method running on the Google Chrome browser (desktop and Android) as decision support. The system utilizes browser event telemetry (visibility changes, fullscreen, standby) that aligns with existing policies, such as mandatory fullscreen mode, copy/paste blocking, and question blurring when exiting fullscreen or a visibility change occurs. This can generate an audit trail that can be audited by decision makers.]]>
