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Cited by 75Online Learning and AnalyticsArtificial Intelligence in EducationAcademic integrity and plagiarism

A systematic review on machine learning models for online learning and examination systems

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Kaddoura, Sanaa, Popescu, Daniela Elena, Hemanth, Jude D.·2022·PeerJ Computer Science
DOI 10.7717/peerj-cs.986PMC PMC9137850
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Abstract

Examinations or assessments play a vital role in every student’s life; they determine their future and career paths. The COVID pandemic has left adverse impacts in all areas, including the academic field. The regularized classroom learning and face-to-face real-time examinations were not feasible to avoid widespread infection and ensure safety. During these desperate times, technological advancements stepped in to aid students in continuing their education without any academic breaks. Machine learning is a key to this digital transformation of schools or colleges from real-time to online mode. Online learning and examination during lockdown were made possible by Machine learning methods. In this article, a systematic review of the role of Machine learning in Lockdown Exam Management Systems was conducted by evaluating 135 studies over the last five years. The significance of Machine learning in the entire exam cycle from pre-exam preparation, conduction of examination, and evaluation were studied and discussed. The unsupervised or supervised Machine learning algorithms were identified and categorized in each process. The primary aspects of examinations, such as authentication, scheduling, proctoring, and cheat or fraud detection, are investigated in detail with Machine learning perspectives. The main attributes, such as prediction of at-risk students, adaptive learning, and monitoring of students, are integrated for more understanding of the role of machine learning in exam preparation, followed by its management of the post-examination process. Finally, this review concludes with issues and challenges that machine learning imposes on the examination system, and these issues are discussed with solutions.

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  • Proctoring software utilising facial recognition and keystroke analysis aims to deter cheating in online assessments, though ethical concerns regarding privacy and surveillance remain.

    “Proctoring software utilising facial recognition and key -stroke analysis aims to deter cheating in online assessments, though ethical concerns regarding privacy and surveillance remain [ 40, 41 ].”

    From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [40]

    The ‘Proctoring (supervision) of examinations’ and ‘Fraud or cheat detection’ sections describe facial recognition and monitoring intended to protect exam integrity. Table 2 in ‘Authentication of students’ lists keystroke dynamics, and ‘Threats to using ML in the examination’ raises privacy concerns about webcam monitoring. The paper does not specifically describe keystroke analysis as a feature of the proctoring software used to deter cheating. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Authors
Kaddoura, S., Popescu, D. E., & Hemanth, J. D.
Affiliations
  • Zayed University (AE)
  • University of Oradea (RO)
  • Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India
ORCID
Venue
PeerJ Computer Science
Publisher
PeerJ
Published
2022-05-18
Volume
8
Pages
e986
ISSN
2376-5992 (ISSN-L)
DOI
10.7717/peerj-cs.986
PMC
PMC9137850
OpenAlex
W4280589899
Cited by
75
References
4
Citations by year
2026: 7 · 2025: 20 · 2024: 17 · 2023: 23 · 2022: 8
FWCI
2.41
Type
journal article
Language
en
Open access
Licence
Locations
  • PeerJ Computer Science (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
Zayed University Research Incentive Fund (R20128)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Safety Research › Academic integrity and plagiarism
Keywords
online examinations, proctoring, student authentication, test scheduling, adaptive learning, student monitoring, online learning, COVID-19 pandemic impact on education

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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