International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 11, Issue 7 (July 2024), Pages: 166-175

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 Original Research Paper

Enhancing personalized learning with deep learning in Saudi Arabian universities

 Author(s): 

 Lassaad K. Smirani 1, 2, *, Hanaa A. Yamani 3

 Affiliation(s):

 1Deanship of Information Technology and Elearning, Umm Al-Qura University, Makkah, Saudi Arabia
 2InnovCom, SUPCOM, Carthage University, Carthage, Tunisia
 3College of Computers, Umm Al-Qura University, Makkah, Saudi Arabia

 Full text

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-3195-6278

 Digital Object Identifier (DOI)

 https://doi.org/10.21833/ijaas.2024.07.018

 Abstract

This study explores the use of deep learning methods in personalized learning environments to improve educational outcomes. We collaborated with four major universities in Saudi Arabia and used data from the Blackboard Learning Management System to gather insights on various personalized learning approaches. This helped us develop a flexible model that is suitable for different learning environments, guided by the VARK model. We used a hybrid deep learning model combining Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Recurrent Neural Networks (RNNs) to classify students based on their learning preferences and engagement patterns. Our analysis showed significant improvements in student motivation and engagement with personalized learning materials. The results indicated high satisfaction levels among students and faculty, with the model achieving 85% accuracy in predicting student engagement and recommending personalized learning paths. Training the model on a dataset of 10,000 student records took about 12 hours, with 80% GPU utilization during training and 30% during inference. Precision and recall rates were 82% and 88%, respectively, with an F1-score of 0.85. Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were low at 0.15 and 0.20, respectively. Integrating deep learning methods into personalized learning environments represents a significant shift in education, enabling educators to enhance student engagement and performance effectively. Collaboration with faculty members highlights the importance of interdisciplinary approaches in advancing educational technology and pedagogy, ensuring stakeholder satisfaction and success.

 © 2024 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Deep learning, Personalized learning, VARK model, Student engagement, Educational technology

 Article history

 Received 19 March 2024, Received in revised form 7 July 2024, Accepted 10 July 2024

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Ethical considerations

This study was approved by the Institutional Review Boards of the participating universities. Informed consent was obtained from all participants, and their confidentiality was maintained. The research adhered to the ethical principles of the Declaration of Helsinki and relevant local regulations.

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Smirani LK and Yamani HA (2024). Enhancing personalized learning with deep learning in Saudi Arabian universities. International Journal of Advanced and Applied Sciences, 11(7): 166-175

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 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 

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