Continuous user identification in distance learning: a recent technology perspective

David Portugal, José N. Faria, Marios Belk, Pedro Martins, Argyris Constantinides, Anna Pietron, Andreas Pitsillides, Nikolaos Avouris, Christos A. Fidas

Research output: Contribution to journalReview articlepeer-review

3 Citations (Scopus)

Abstract

The worldwide shift to distance learning at Higher Education Institutions (HEIs) during the COVID-19 global pandemic has raised several concerns about the credibility of online academic activities, especially regarding student identity management. Traditional online frameworks cannot guarantee the authenticity of the enrolled student, which requires instructors to manually verify their identities, a time-consuming task that compromises academic quality. This article presents a comprehensive review of existing efforts around continuous user identification, focusing on intelligent proctoring systems and automatic identification methods, as well as their applicability in this domain. We conclude that there is a clear need for continuous user identification technology by HEIs, but existing systems lack agile system integration models that combine many inputs, such as face, voice and behavioural data in a practical manner, and encounter numerous barriers related to data protection during implementation.

Original languageEnglish
Article number38
JournalSmart Learning Environments
Volume10
Issue number1
DOIs
Publication statusPublished - Dec 2023
Externally publishedYes

Keywords

  • Biometrics
  • Continuous user identification
  • Data privacy-preservation
  • Distance learning
  • Image-based identification
  • Intelligent proctoring systems
  • Voice-based identification

ASJC Scopus subject areas

  • Education
  • Computer Science Applications

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