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Examining Monitoring System: Detecting Abnormal Behavior In Online Examinations

arXiv 2402.12179 ↗First posted 19 Feb 2024cs.CV cs.AI cs.CYcited by 0

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Abstract (arXiv)

Cheating in online exams has become a prevalent issue over the past decade, especially during the COVID-19 pandemic. To address this issue of academic dishonesty, our "Exam Monitoring System: Detecting Abnormal Behavior in Online Examinations" is designed to assist proctors in identifying unusual student behavior. Our system demonstrates high accuracy and speed in detecting cheating in real-time scenarios, providing valuable information, and aiding proctors in decision-making. This article outlines our methodology and the effectiveness of our system in mitigating the widespread problem of cheating in online exams.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 18 references that are in the map. First posted on arXiv 19 Feb 2024.

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References · 8

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
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  6. 2019MediaPipe: A Framework for Building Perception Pipelinescited by 583
  7. 2019Real-time Facial Surface Geometry from Monocular Video on Mobile GPUscited by 73
  8. 2020A Systematic Review of Online Exams Solutions in E-learning: Techniques, Tools and Global Adoptioncited by 1

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