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Badr Eddine EL Mohajir
Abdelmalek Essaadi University
Computer Science & IT Electrical & Electronics Engineering

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Identifying learning style through eye tracking technology in adaptive learning systems Inssaf El Guabassi; Zakaria Bousalem; Mohammed Al Achhab; Ismail Jellouli; Badr Eddine EL Mohajir
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 5: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (666.159 KB) | DOI: 10.11591/ijece.v9i5.pp4408-4416

Abstract

Learner learning style represents a key principle and core value of the adaptive learning systems (ALS). Moreover, understanding individual learner learning styles is a very good condition for having the best services of resource adaptation. However, the majority of the ALS, which consider learning styles, use questionnaires in order to detect it, whereas this method has a various disadvantages, For example, it is unsuitable for some kinds of respondents, time-consuming to complete, it may be misunderstood by respondent, etc. In the present paper, we propose an approach for automatically detecting learning styles in ALS based on eye tracking technology, because it represents one of the most informative characteristics of gaze behavior. The experimental results showed a high relationship among the Felder-Silverman Learning Style and the eye movements recorded whilst learning.
Co-Authors Inssaf El Guabassi Ismail Jellouli Mohammed Al Achhab Zakaria Bousalem