Smart Online Exam Proctoring Assist for Cheating Detection

Mohammad M. Masud, Kadhim Hayawi, Sujith Samuel Mathew, Temesgen Michael, Mai El Barachi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)


Online exams are the most preferred mode of exams in online learning environment. This mode of exam has been even more prevalent and a necessity in the event of a forced closure of face-to-face teaching such as the recent Covid-19 pandemic. Naturally, conducting online exams poses much greater challenge to preserving academic integrity compared to conducting on-site face-to-face exams. As there is no human proctor for policing the examinee on site, the chances of cheating are high. Various online exam proctoring tools are being used by educational institutes worldwide, which offer different solutions to reduce the chances of cheating. The most common technique followed by these tools is recording of video and audio of the examinee during the whole duration of exam. These videos can be analyzed later by human examiner to detect possible cheating case. However, viewing hours of exam videos for each student can be impractical for a large class and thus detecting cheating would be next to impossible. Although some AI-based tools are being used by some proctoring software to raise flags, they are not always very useful. In this paper we propose a cheating detection technique that analyzes an exam video to extract four types of event data, which are then fed to a pre-trained classification model for detecting cheating activity. We formulate the cheating detection problem as a multivariate time-series classification problem by transforming each video into a multivariate time-series representing the time-varying event data extracted from each frame of the video. We have developed a real dataset of cheating videos and conduct extensive experiments with varying video lengths, different deep learning and traditional machine learning models and feature sets, achieving prediction accuracy as high as 97.7%.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications - 17th International Conference, ADMA 2021, Proceedings
EditorsBohan Li, Lin Yue, Jing Jiang, Weitong Chen, Xue Li, Guodong Long, Fei Fang, Han Yu
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
ISBN (Print)9783030954048
Publication statusPublished - 2022
Event17th International Conference on Advanced Data Mining and Applications, ADMA 2021 - Sydney, Australia
Duration: Feb 2 2022Feb 4 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13087 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th International Conference on Advanced Data Mining and Applications, ADMA 2021


  • Cheating detection
  • Deep learning
  • Online exam
  • Video analysis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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