Essential Factors to Improve Student Performance Using an E-Learning Model: Review Study
Dublin Core
Title
Essential Factors to Improve Student Performance Using an E-Learning Model: Review Study
Subject
e-learning
Acceptance Model
HEI
TAM
Description
The e-learning system is one of the most common methods for improving student performance and the ongoing goal of using e-learning in higher education. The most popular kind of e-learning system is a big one that is used at the college level, including Moodle, MOOCs, and e-learning systems. A rigorous review of various e-learning tools that can be used to raise students' performance is presented in this paper. Based on a survey of the literature, a comparison was established between the key elements employed in e-learning. The method used was to extract the common elements used in more than one popular model, then to connect these factors to e-learning. In order to create a better model with full element constraints, this paper also includes extracting causal links between these aspects. The analysis's finding is that the most recent models accepted for use in raising student performance are TAM2, TAM3, and ECT. This study reveals the framework used to highlights the easy and popular factors according to the updated related studies.
Creator
Tawafak, Ragad M
Alyoussef, Ibrahim Yaussef
Al-Rahmi, Waleed Mugahed
Source
International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 03 (2023); pp. 160-176
1865-7923
Publisher
International Association of Online Engineering (IAOE), Vienna, Austria
Date
2023-02-06
Rights
Copyright (c) 2023 Ragad Tawafak
https://creativecommons.org/licenses/by/4.0
Relation
Format
application/pdf
Language
eng
Type
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
Identifier
Citation
Ragad Tawafak M, Ibrahim Alyoussef Yaussef and Al-Rahmi, Waleed Mugahed, Essential Factors to Improve Student Performance Using an E-Learning Model: Review Study, International Association of Online Engineering (IAOE), Vienna, Austria, 2023, accessed November 23, 2024, https://igi.indrastra.com/items/show/2428