Automated Reading Detection in an Online Exam

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dc.contributor.authorBakhitzhan K.
dc.contributor.authorKadyrov Sh.
dc.contributor.authorMakhmutova A.
dc.date.accessioned2025-08-04T06:37:49Z
dc.date.available2025-08-04T06:37:49Z
dc.date.issued2022
dc.description.abstractIn this article we study a deep learning-based reading detection problem in an online exam proctoring. Pandemia-related restrictions and lockdowns lead many educational institutions to go online learning environment. It brought the exam integrity challenge to an online test-taking process. While various commercial exam proctoring solutions were developed, the online proctoring challenge is far from being fully addressed. This article is devoted to making a contribution to the exam proctoring system by proposing an automated test-taker reading detection method. To this end, we obtain our own dataset of short video clips that resemble a real online examination environment and different video augmentation methods utilized to increase the training dataset. Two different deep learning techniques are adapted for training. The experiments show quite satisfactory results with model accuracy varying from 98.46% to 100%. The findings of the article can help educational institutions to improve their online exam proctoring solutions, especially in language speaking tests.
dc.identifier.citationBakhitzhan K , Kadyrov Sh , Makhmutova A / Automated Reading Detection in an Online Exam / International Journal of Emerging Technologies in Learning (iJET) / 2022
dc.identifier.urihttps://repository.sdu.edu.kz/handle/123456789/1848
dc.language.isoen
dc.publisherInternational Journal of Emerging Technologies in Learning (iJET)
dc.subjectexam proctoring
dc.subjectreading detection
dc.subjectvideo recognition
dc.titleAutomated Reading Detection in an Online Exam
dc.typeArticle

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