Object-oriented online course recommendation systems based on deep neural networks

In the era of widespread online learning platforms, students commonly face the challenge of navigating an extensive array of available courses. Identifying relevant and fitting options aligned with students' educational objectives and interests is highly complex. The impact of system maintainab...

Full description

Bibliographic Details
Main Authors: Luo, Hao, Husin, Nor Azura, Abdipoor, Sina, Mohd Aris, Teh Noranis, Sharum, Mohd Yunus, Zolkepli, Maslina
Format: Article
Published: Little Lion Scientific 2024
_version_ 1825939322245742592
author Luo, Hao
Husin, Nor Azura
Abdipoor, Sina
Mohd Aris, Teh Noranis
Sharum, Mohd Yunus
Zolkepli, Maslina
author_facet Luo, Hao
Husin, Nor Azura
Abdipoor, Sina
Mohd Aris, Teh Noranis
Sharum, Mohd Yunus
Zolkepli, Maslina
author_sort Luo, Hao
collection UPM
description In the era of widespread online learning platforms, students commonly face the challenge of navigating an extensive array of available courses. Identifying relevant and fitting options aligned with students' educational objectives and interests is highly complex. The impact of system maintainability and scalability on escalated development costs is often neglected in the literature. To tackle these issues, this paper introduces a comprehensive analysis and design of an object-oriented online course recommendation system. Employing a deep neural network algorithm for course recommendation, our system adeptly captures user preferences, course attributes, and intricate relationships between them. This methodology facilitates the delivery of personalized course recommendations precisely tailored to individual needs and preferences. The incorporation of object-oriented design principles such as encapsulation, inheritance, and polymorphism ensure modularity, maintainability, and extensibility, thereby easing future system enhancements and adaptations. The main contribution of this paper is to propose a new idea of an adaptive learning system that combines deep learning for personalized recommendations with object-oriented design for scalability and continuous improvement. This practical solution demonstrably enhances online learning experiences by tailoring recommendations to individual needs and evolving trends. Evaluation of the proposed system's performance utilizes real-world online course datasets, demonstrating its efficacy in furnishing accurate and personalized course recommendations, ultimately enhancing the overall learning experience for students.
first_indexed 2024-12-09T02:17:24Z
format Article
id upm.eprints-106148
institution Universiti Putra Malaysia
last_indexed 2024-12-09T02:17:24Z
publishDate 2024
publisher Little Lion Scientific
record_format dspace
spelling upm.eprints-1061482024-10-08T06:42:51Z http://psasir.upm.edu.my/id/eprint/106148/ Object-oriented online course recommendation systems based on deep neural networks Luo, Hao Husin, Nor Azura Abdipoor, Sina Mohd Aris, Teh Noranis Sharum, Mohd Yunus Zolkepli, Maslina In the era of widespread online learning platforms, students commonly face the challenge of navigating an extensive array of available courses. Identifying relevant and fitting options aligned with students' educational objectives and interests is highly complex. The impact of system maintainability and scalability on escalated development costs is often neglected in the literature. To tackle these issues, this paper introduces a comprehensive analysis and design of an object-oriented online course recommendation system. Employing a deep neural network algorithm for course recommendation, our system adeptly captures user preferences, course attributes, and intricate relationships between them. This methodology facilitates the delivery of personalized course recommendations precisely tailored to individual needs and preferences. The incorporation of object-oriented design principles such as encapsulation, inheritance, and polymorphism ensure modularity, maintainability, and extensibility, thereby easing future system enhancements and adaptations. The main contribution of this paper is to propose a new idea of an adaptive learning system that combines deep learning for personalized recommendations with object-oriented design for scalability and continuous improvement. This practical solution demonstrably enhances online learning experiences by tailoring recommendations to individual needs and evolving trends. Evaluation of the proposed system's performance utilizes real-world online course datasets, demonstrating its efficacy in furnishing accurate and personalized course recommendations, ultimately enhancing the overall learning experience for students. Little Lion Scientific 2024-02 Article PeerReviewed Luo, Hao and Husin, Nor Azura and Abdipoor, Sina and Mohd Aris, Teh Noranis and Sharum, Mohd Yunus and Zolkepli, Maslina (2024) Object-oriented online course recommendation systems based on deep neural networks. Journal of Theoretical and Applied Information Technology, 102 (3). pp. 1276-1287. ISSN 1992-8645; eISSN: 1817-3195 https://www.jatit.org/volumes/hundredtwo3.php
spellingShingle Luo, Hao
Husin, Nor Azura
Abdipoor, Sina
Mohd Aris, Teh Noranis
Sharum, Mohd Yunus
Zolkepli, Maslina
Object-oriented online course recommendation systems based on deep neural networks
title Object-oriented online course recommendation systems based on deep neural networks
title_full Object-oriented online course recommendation systems based on deep neural networks
title_fullStr Object-oriented online course recommendation systems based on deep neural networks
title_full_unstemmed Object-oriented online course recommendation systems based on deep neural networks
title_short Object-oriented online course recommendation systems based on deep neural networks
title_sort object oriented online course recommendation systems based on deep neural networks
work_keys_str_mv AT luohao objectorientedonlinecourserecommendationsystemsbasedondeepneuralnetworks
AT husinnorazura objectorientedonlinecourserecommendationsystemsbasedondeepneuralnetworks
AT abdipoorsina objectorientedonlinecourserecommendationsystemsbasedondeepneuralnetworks
AT mohdaristehnoranis objectorientedonlinecourserecommendationsystemsbasedondeepneuralnetworks
AT sharummohdyunus objectorientedonlinecourserecommendationsystemsbasedondeepneuralnetworks
AT zolkeplimaslina objectorientedonlinecourserecommendationsystemsbasedondeepneuralnetworks