Factors Affecting Students' Acceptance of e-Learning System in Higher Education

Ahmad, Nurhafizah; Umar, Norazah; Kadar, Rozita; Othman, Jamal

doi:10.24191/jcrinn.v5i2.134

Cited by 5 publications

(7 citation statements)

References 26 publications

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“…Self-efficacy is the ability to use an e-learning system while carrying out specific learning tasks (Pituch, Lee, 2006) or user-perceived trust in their own ability to use a PC effectively (Compeau, Higgins, 1995).. Low selfefficacy levels can result in a user's inability to handle problems, especially when systems are complex, and discourage users from continued use of the device. Many previous studies confirmed that computer selfefficacy significantly affected the student's intention to use an e-learning system (Zardari et al, 2021;Ahmad et al, 2020;Ameen et al, 2019). Based on the findings by Al-Fraihat et al (2020), computer self-efficacy was one of the primary determinants of student learning satisfaction.…”

Section: Computer Self-efficacy Vs Satisfactionmentioning

confidence: 74%

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Factors Influencing Satisfaction and Future Intention to Use E-Learning at the University Level

Ejdys¹

2022

Foresight and STI Governance

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With the growing interest in e-education, particularly in the context of the pandemic, more scientific studies have been undertaken recently to analyze and identify factors influencing e-learning acceptance. Indeed, e-learning acceptance depends on many different factors, but no consensus has been reached on factors that contribute most to the acceptance of e-learning solutions. Consequently, this article ascertains the factors and their relationships behind the satisfaction and the future intention to use e-learning among Polish university students. From among the factors analyzed in the literature, the author examined the relationship between computer self-efficacy (CSE), facilitating conditions (FC), satisfaction (S) and the future intention to use e-learning (FI). Data were gathered using structured questionnaires and computer-assisted web interviewing (CAWI). Students at Bialystok University of Technology (Poland) were sent an electronic link to the questionnaires using the internal e-mail system. Eight hundred three forms were returned fully filled out. Aiming to ascertain the extent to which measured variables describe the number of constructs, the author made the Confirmatory Factor Analysis (CFA). The Generalized Least Squares (GLS) estimator was used to calculate the values of model parameters.The results confirmed that higher computer self-efficacy and better facilitation conditions result in greater user satisfaction with e-learning. However, facilitating conditions impact user satisfaction more than computer self-efficacy construct variables. Based on the findings, user satisfaction is a strong anticedents of the future intention to use e-learning.

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Section: Computer Self-efficacy Vs Satisfactionmentioning

confidence: 74%

“… Zardari et al (2021). andAhmad et al (2020) proved that computer self-efficacy had a significant effect on students' intention to use the e-learning system. Also, the findings of the study conducted byAl-Fraihat et al (2020) indicated that computer self-efficacy was one of the primary determinants of student learning satisfaction.…”

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confidence: 93%

Factors Influencing Satisfaction and Future Intention to Use E-Learning at the University Level

Ejdys¹

2022

Foresight and STI Governance

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“…https://er.educause.edu/articles/2020/3/the-difference-between-emergency-remote-teaching-and-online-learning to the Internet/Wi-Fi in addition to the technological tools used for learning online, such as mobile phones, laptops and computers. Technology accessibility has been found to be an important factor that affects student online learning (Ahmad et al, 2020, Salloum, 2018. In addition, according to Dogruera et al (2011), access to the Internet in the educational setting has facilitated information sharing via online resources.…”

Section: Technology Accessibilitymentioning

confidence: 99%

Exploring University Students’ Online Learning Readiness: A Mixed Methods Study of Forced Online Learning

Loi,

Lim,

Mohd Suki

et al. 2024

JLE

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Background: Despite the advancement achieved in previous research into online learning, few studies have used both quantitative and qualitative data to examine how students’ readiness to learn online is affected by three different external factors, comprising (i) the degrees to which technology is available to students, (ii) the support provided by the institutions of learning, and (iii) the social influence affecting the students engaged in forced online learning in a pandemic situation. Purpose: To fill this research gap, this study explored university students’ forced online learning readiness in relation to technological accessibility, institutional support and social influence during a pandemic, in an attempt to furnish insights into how educators can maximize the benefits of adopting online learning methods. Method: A mixed methods research design was employed in this study. Quantitative data, elicited via self-administered questionnaires completed by 211 participants, was analyzed using the frequencies, means, standard deviations and Pearson correlation analysis involving the Statistical Package for the Social Sciences (SPSS) software version 27. Qualitative data, elicited via 11 open-ended questions posed to 41 students through in-depth interviews, was then studied using a thematic analysis of the participants’ feedback concerning the three constructs in online learning. Results: Our quantitative analysis showed that institutional support had the strongest positive correlation with online learning readiness, and this was followed by technology accessibility and social influence in relation to students’ readiness to learn online. Qualitative findings further indicated that students were largely concerned about Internet accessibility and the setting where their roles were restricted to being mere listeners in online sessions. Apart from being apprehensive about excessive online assignments, students also acknowledged that their online interactions were influenced by their friends and family members, and they would prefer practical work that could inspire them to reflect and engage actively with the course material given during the pandemic. Conclusion: While lecturers can make online classes more interactive and discussion-generative, university administrators need to aptly facilitate their institution’s transition to the forced online learning mode, moderate social influence, improve the learning management system, and provide training to teachers and students on the use of emerging technology.

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“…An analysis of prior research that has used the proposed e-learning systems success model (ELS) since it was published in 2017 shows that part of this research used the model's accompanied factors and dimensions to extend new models [8][9][10][11][12][13][14]. The other part of these researches used some aspects of the model to compare it with other models [15][16][17].…”

Section: E-learning Systems Success Model In the Literaturementioning

confidence: 99%

Adaptive E-Learning Systems Success Model

Romi

2023

Int. J. Emerg. Technol. Learn.

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E-learning implementation is growing alongside the e-learning market’s growth. As a result, competing models and approaches were proposed to improve e-learning and learner performance; one of these models is the e-learning systems success model. In order to expand this model, an analysis of the published e-learning models and frameworks in the period 2017–2022 was conducted using the aggregative review method. 37 studies that match the current study interests have been selected and analyzed. The main results support the e-learning system success model. In addition to further constructs other than those used in the model that have been used to enhance it, an adaptive e-learning success model has been proposed, which integrates the e-learning success model with the other models and frameworks proposed in the literature. This study recommends that achieving e-learning success requires an analysis of the internal and external environment and crafting an adaptation plan to determine the e-learning methods depending on the pedagogical approaches and tutors’ and learners’ abilities and characteristics, which in turn determine the instructional material required and assessment methods, besides the required ICT. Besides the required enhancements, such as tutor and learner training, as well as the institution’s policy and e-learning management, in addition to the required adaptations to cope with environmental factors in order to embrace learners in education policies and future strategies.

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Factors Affecting Students' Acceptance of e-Learning System in Higher Education

Cited by 5 publications

References 26 publications

Factors Influencing Satisfaction and Future Intention to Use E-Learning at the University Level

Factors Influencing Satisfaction and Future Intention to Use E-Learning at the University Level

Exploring University Students’ Online Learning Readiness: A Mixed Methods Study of Forced Online Learning

Adaptive E-Learning Systems Success Model

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