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

Fairouz Hussein (), Ayat Al-Ahmad, Subhieh El-Salhi, Esra’a Alshdaifat and Mo’taz Al-Hami
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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

Data, 2022, vol. 7, issue 9, 1-13

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.

Keywords: action recognition; machine learning; cheating; computer vision; feature extraction; video surveillance (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:7:y:2022:i:9:p:122-:d:902383

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