Abstract:
The modern world is evolving and connecting to computers on a daily basis. In recent
years, e-learning has grown significantly. Maintaining the integrity of exams in the
context of online learning is a major challenge that calls for creative ways to prevent
cheating and protect academic justice. This problem is addressed by three different
methods: First, a Smart computer vision-based system proposes automatic video
summarization of abnormal behavior during online exams, aiding remote proctors in
post-exam reviews. By modeling normal and abnormal student behavior patterns, this
method offers promising results, potentially expanding to real-time alert generation.
Second, a survey-based study examines the impact of online webcam exam proctoring on
student anxiety and performance, particularly among non-white and socioeconomically
diverse populations. While anxiety over being wrongly flagged exists, it doesn't directly
impede exam performance, highlighting the need for nuanced support for students and
faculty navigating unfamiliar technologies. Lastly, a continuous authentication system for
online exams is proposed, leveraging machine learning algorithms to detect and prevent
fraud through modules like registration, identity verification, live video streams, and
session recording. These approaches collectively underscore the critical importance of
maintaining integrity, reliability, and security in online examinations, especially amidst
the unprecedented challenges posed by events like the COVID-19 pandemic..
