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Advances in Contextual Action Recognition: Automatic Cheating Detection Using Machine Learning Techniques

Author

Listed:
  • Fairouz Hussein

    (Department of Computer Information System, Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan)

  • Ayat Al-Ahmad

    (Department of Computer Science and Applications, Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan)

  • Subhieh El-Salhi

    (Department of Computer Information System, Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan)

  • Esra’a Alshdaifat

    (Department of Computer Information System, Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan)

  • Mo’taz Al-Hami

    (Department of Computer Information System, Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan)

Abstract

Teaching and exam proctoring represent key pillars of the education system. Human proctoring, which involves visually monitoring examinees throughout exams, is an important part of assessing the academic process. The capacity to proctor examinations is a critical component of educational scalability. However, such approaches are time-consuming and expensive. In this paper, we present a new framework for the learning and classification of cheating video sequences. This kind of study aids in the early detection of students’ cheating. Furthermore, we introduce a new dataset, “actions of student cheating in paper-based exams”. The dataset consists of suspicious actions in an exam environment. Five classes of cheating were performed by eight different actors. Each pair of subjects conducted five distinct cheating activities. To evaluate the performance of the proposed framework, we conducted experiments on action recognition tasks at the frame level using five types of well-known features. The findings from the experiments on the framework were impressive and substantial.

Suggested Citation

  • Fairouz Hussein & Ayat Al-Ahmad & Subhieh El-Salhi & Esra’a Alshdaifat & Mo’taz Al-Hami, 2022. "Advances in Contextual Action Recognition: Automatic Cheating Detection Using Machine Learning Techniques," Data, MDPI, vol. 7(9), pages 1-13, August.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:9:p:122-:d:902383
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    References listed on IDEAS

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    1. Esra’a Alshdaifat & Doa’a Alshdaifat & Ayoub Alsarhan & Fairouz Hussein & Subhieh Moh’d Faraj S. El-Salhi, 2021. "The Effect of Preprocessing Techniques, Applied to Numeric Features, on Classification Algorithms’ Performance," Data, MDPI, vol. 6(2), pages 1-23, January.
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