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All Journal International Journal of Electrical and Computer Engineering International Journal of Informatics and Communication Technology (IJ-ICT)
Mohammed Al Achhab
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.
Neural network dealing with Arabic language Adnan Souri; Mohammed Al Achhab; Badr Eddine Elmohajir; Abdelali Zbakh
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 9, No 2: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (411.072 KB) | DOI: 10.11591/ijict.v9i2.pp73-82

Abstract

Artificial Neural Networks have proved their efficiency in a large number of research domains. In this paper, we have applied Artificial Neural Networks on Arabic text to prove correct language modeling, text generation, and missing text prediction. In one hand, we have adapted Recurrent Neural Networks architectures to model Arabic language in order to generate correct Arabic sequences. In the other hand, Convolutional Neural Networks have been parameterized, basing on some specific features of Arabic, to predict missing text in Arabic documents. We have demonstrated the power of our adapted models in generating and predicting correct Arabic text comparing to the standard model. The model had been trained and tested on known free Arabic datasets. Results have been promising with sufficient accuracy.
Co-Authors Abdelali Zbakh Adnan Souri Badr Eddine EL Mohajir Badr Eddine Elmohajir Inssaf El Guabassi Ismail Jellouli Zakaria Bousalem