Cargando…

Enhancing the e-learning system based on a novel tasks’ classification load-balancing algorithm

In the educational field, the system performance, as well as the stakeholders’ satisfaction, are considered a bottleneck in the e-learning system due to the high number of users who are represented in the educational system’s stakeholders including instructors and students. On the other hand, succes...

Descripción completa

Detalles Bibliográficos
Autores principales: Khedr, Ayman E., Idrees, Amira M., Salem, Rashed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8444070/
https://www.ncbi.nlm.nih.gov/pubmed/34604515
http://dx.doi.org/10.7717/peerj-cs.669
_version_ 1784568415893061632
author Khedr, Ayman E.
Idrees, Amira M.
Salem, Rashed
author_facet Khedr, Ayman E.
Idrees, Amira M.
Salem, Rashed
author_sort Khedr, Ayman E.
collection PubMed
description In the educational field, the system performance, as well as the stakeholders’ satisfaction, are considered a bottleneck in the e-learning system due to the high number of users who are represented in the educational system’s stakeholders including instructors and students. On the other hand, successful resource utilization in cloud systems is one of the key factors for increasing system performance which is strongly related to the ability for the optimal load distribution. In this study, a novel load-balancing algorithm is proposed. The proposed algorithm aims to optimize the educational system’s performance and, consequently, the users’ satisfaction in the educational field represented by the students. The proposed enhancement in the e-learning system has been evaluated by two methods, first, a simulation experiment for confirming the applicability of the proposed algorithm. Then a real-case experiment has been applied to the e-learning system at Helwan University. The results revealed the advantages of the proposed algorithm over other well-known load balancing algorithms. A questionnaire was also developed to measure the users’ satisfaction with the system’s performance. A total of 3,670 thousand out of 5,000 students have responded, and the results have revealed a satisfaction percentage of 95.4% in the e-learning field represented by the students.
format Online
Article
Text
id pubmed-8444070
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher PeerJ Inc.
record_format MEDLINE/PubMed
spelling pubmed-84440702021-09-30 Enhancing the e-learning system based on a novel tasks’ classification load-balancing algorithm Khedr, Ayman E. Idrees, Amira M. Salem, Rashed PeerJ Comput Sci Algorithms and Analysis of Algorithms In the educational field, the system performance, as well as the stakeholders’ satisfaction, are considered a bottleneck in the e-learning system due to the high number of users who are represented in the educational system’s stakeholders including instructors and students. On the other hand, successful resource utilization in cloud systems is one of the key factors for increasing system performance which is strongly related to the ability for the optimal load distribution. In this study, a novel load-balancing algorithm is proposed. The proposed algorithm aims to optimize the educational system’s performance and, consequently, the users’ satisfaction in the educational field represented by the students. The proposed enhancement in the e-learning system has been evaluated by two methods, first, a simulation experiment for confirming the applicability of the proposed algorithm. Then a real-case experiment has been applied to the e-learning system at Helwan University. The results revealed the advantages of the proposed algorithm over other well-known load balancing algorithms. A questionnaire was also developed to measure the users’ satisfaction with the system’s performance. A total of 3,670 thousand out of 5,000 students have responded, and the results have revealed a satisfaction percentage of 95.4% in the e-learning field represented by the students. PeerJ Inc. 2021-09-09 /pmc/articles/PMC8444070/ /pubmed/34604515 http://dx.doi.org/10.7717/peerj-cs.669 Text en © 2021 Khedr et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Algorithms and Analysis of Algorithms
Khedr, Ayman E.
Idrees, Amira M.
Salem, Rashed
Enhancing the e-learning system based on a novel tasks’ classification load-balancing algorithm
title Enhancing the e-learning system based on a novel tasks’ classification load-balancing algorithm
title_full Enhancing the e-learning system based on a novel tasks’ classification load-balancing algorithm
title_fullStr Enhancing the e-learning system based on a novel tasks’ classification load-balancing algorithm
title_full_unstemmed Enhancing the e-learning system based on a novel tasks’ classification load-balancing algorithm
title_short Enhancing the e-learning system based on a novel tasks’ classification load-balancing algorithm
title_sort enhancing the e-learning system based on a novel tasks’ classification load-balancing algorithm
topic Algorithms and Analysis of Algorithms
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8444070/
https://www.ncbi.nlm.nih.gov/pubmed/34604515
http://dx.doi.org/10.7717/peerj-cs.669
work_keys_str_mv AT khedraymane enhancingtheelearningsystembasedonanoveltasksclassificationloadbalancingalgorithm
AT idreesamiram enhancingtheelearningsystembasedonanoveltasksclassificationloadbalancingalgorithm
AT salemrashed enhancingtheelearningsystembasedonanoveltasksclassificationloadbalancingalgorithm