Continued Intention to Use UCOM: Four Factors for Integrating With a Technology Acceptance Model to Moderate the Satisfaction of Learning

Tawafak, Ragad M; Romli, Awanis; Arshah, Ruzaini Abdullah

doi:10.1109/access.2018.2877760

Cited by 58 publications

(79 citation statements)

References 73 publications

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“…Furthermore, E-Learning allows students to study and learn freely using cuttingedge new technology without conventional teaching approaches like direct supervision and control mechanisms [8][9][10]. The fast development in IT with new software and high configuration of hardware have made E-Learning more easy and useful, which then improves university learning outcomes [11]. It is therefore indisputable that E-Learning has become an indispensable tool in educational technology [12].…”

Section: Introductionmentioning

confidence: 99%

Evaluating critical success factors in implementing E-learning system using multi-criteria decision-making

et al. 2020

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Learning using the Internet or training through E-Learning is growing rapidly and is increasingly favored over the traditional methods of learning and teaching. This radical shift is directly linked to the revolution in digital computer technology. The revolution propelled by innovation in computer technology has widened the scope of E-Learning and teaching, whereby the process of exchanging information has been made simple, transparent, and effective. The E-Learning system depends on different success factors from diverse points of view such as system, support from the institution, instructor, and student. Thus, the effect of critical success factors (CSFs) on the E-Learning system must be critically analyzed to make it more effective and successful. This current paper employed the analytic hierarchy process (AHP) with group decision-making (GDM) and Fuzzy AHP (FAHP) to study the diversified factors from different dimensions of the web-based E-Learning system. The present paper quantified the CSFs along with its dimensions. Five different dimensions and 25 factors associated with the web-based E-Learning system were revealed through the literature review and were analyzed further. Furthermore, the influence of each factor was derived successfully. Knowing the impact of each E-Learning factor will help stakeholders to construct education policies, manage the E-Learning system, perform asset management, and keep pace with global changes in knowledge acquisition and management.

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Section: Introductionmentioning

confidence: 99%

Evaluating critical success factors in implementing E-learning system using multi-criteria decision-making

et al. 2020

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“…The result considered good since many participants reported that they learned new skills and concepts during the time. (Tawafak et al, 2018c).…”

Section: E-learning Systemsmentioning

confidence: 99%

“…A hybrid method defined as uses structural properties of raster lines to improve the efficiency of multi-point line generation. A quadruple-step algorithm developed requires fewer decision tests than other multi-point algorithms while retaining the multi-points advantage in pixel output efficiency, mainly when implemented in hardware (Tawafak et al, 2018c) visualization of Bresenham's Algorithm done using flash. The visualization used to explain how a line produced in a display computer for education.…”

Section: Bresenham's Line Algorithmmentioning

confidence: 99%

Innovative Smart Phone Learning System for Graphical Systems within COVID-19 Pandemic

2021

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With restrictions on face-to-face clinical consultations within the COVID-19 pandemic and the challenges faced by healthcare systems in delivering patient care, alternative information technologies like telemedicine and smartphones play a crucial role. A wide variety of smartphone applications employs high-tech mathematical and programming approaches to enhance computer-assisted communication and learning processes' pedagogical efficiency. Accordingly, this study's main objective is to develop a model system that can function as smartphone computer graphics. This paper used the Technology Acceptance Model (TAM) as an m-learning model, and Bresenham's line algorithm is a calculation system implemented by applications. The study method applies technology to validate the accuracy of the contents' acceptance method of use. The results reveal significant positive effects of the proposed model on generating reasonable, fast, and accurate solutions for the presented problems and developing a more interactive platform of m-learning.

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“…Park et al [37] recently extended TAM with perceived playfulness and perceived risk for the adoption of digital devices for children education in Korean cases where buyers and users are different entities. Tawafak et al [38] have integrated academic performance, student satisfaction, support assessment, and effectiveness with TAM to explain the continuance of intention to use the universities' learning management systems. Herrador-Alcaide et al's study [39] targeted at students of financial accounting and revealed that students' perception of both e-learning environment and their own skill have effect on their overall feelings of satisfaction.…”

Section: Motivational Modeling In E-learning Systems and Technology Amentioning

confidence: 99%

Modeling Dyslexic Students’ Motivation for Enhanced Learning in E-learning Systems

Wang

Chen

Solheim

2020

ACM Trans. Interact. Intell. Syst.

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E-Learning systems can support real-time monitoring of learners' learning desires and effects, thus offering opportunities for enhanced personalized learning. Recognition of the determinants of dyslexic users' motivation to use e-learning systems is important to help developers improve the design of e-learning systems and educators direct their efforts to relevant factors to enhance dyslexic students' motivation. Existing research has rarely attempted to model dyslexic users' motivation in e-learning context from a comprehensive perspective. The present work has conceived a hybrid approach, namely, combining the strengths of qualitative and quantitative analysis methods, to motivation modeling. It examines a variety of factors that affect dyslexic students' motivation to engage in e-learning systems from psychological, behavioral, and technical perspectives, and establishes their interrelationships. Specifically, the study collects data from a multi-item Likert-style questionnaire to measure relevant factors for conceptual motivation modeling. It then applies both covariance-based (CB-SEM) and variance-based structural equation modeling (PLS-SEM) approaches to determine the quantitative mapping between dyslexic students' continued use intention and motivational factors, followed by discussions about theoretical findings and design instructions according to our motivation model. Our research has led to a novel motivation model with new constructs of Learning Experience, Reading Experience, Perceived Control, and Perceived Privacy. From both the CB-SEM and PLS-SEM analyses, results on the total effects have indicated consistently that Visual Attractiveness, Reading Experience, and Feedback have the strongest effects on continued use intention.

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Continued Intention to Use UCOM: Four Factors for Integrating With a Technology Acceptance Model to Moderate the Satisfaction of Learning

Cited by 58 publications

References 73 publications

Evaluating critical success factors in implementing E-learning system using multi-criteria decision-making

Evaluating critical success factors in implementing E-learning system using multi-criteria decision-making

Innovative Smart Phone Learning System for Graphical Systems within COVID-19 Pandemic

Modeling Dyslexic Students’ Motivation for Enhanced Learning in E-learning Systems

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