Investigation of Complexity Impact on Learning Behaviour for Personalised e-learning: Eye-Tracking Perspective

Khawla Alhasan, Liming Chen, Feng Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

To examine learning behaviour in an adaptive e-learning system, an experiment is designed employing eye-tracking to investigate learners' eye movements and their states. Gaze behaviour not only reveals the type of learning style, but also indicates the range of mental processes and feelings that can support learning. This paper outlines the experimentation strategy used in our smart lab. The experiment is conducted on postgraduate students on tracking eye gaze behaviour and fixation. Several test techniques are applied to eye-tracking data, such as boxplots and ANOVA. Studies and analyses of learning styles concerning the various courses and the complexity level are presented, along with the impact on eye behaviour, which led to the discovery of a key conclusion that the learning behaviour of the visual and verbal style learners is unaffected by the courses' complexity level.
Original languageEnglish
Title of host publication2023 IEEE Smart Word Congress (SWC)
PublisherIEEE
Pages1-7
Number of pages7
ISBN (Electronic)979-8-3503-1980-4
ISBN (Print)979-8-3503-1981-1
DOIs
Publication statusPublished online - 1 Mar 2024

Publication series

Name2023 IEEE Smart World Congress (SWC)
PublisherIEEE Control Society

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • e-learning
  • eye-tracking
  • learning style
  • eye gaze

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