Open AccessIdentifying learning style through eye tracking technology in adaptive learning systems
Author(s) -
Inssaf El Guabassi,
Zakaria Bousalem,
Mohammed Al Achhab,
Ismail Jellouli,
Badr Eddine El Mohajir
Publication year - 2019
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v9i5.pp4408-4416
Subject(s) - respondent , adaptation (eye) , style (visual arts) , computer science , learning styles , artificial intelligence , eye tracking , adaptive learning , tracking (education) , machine learning , human–computer interaction , psychology , mathematics education , geography , pedagogy , archaeology , neuroscience , political science , law
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.