Extending the Technology Acceptance Model for Use of e-Learning Systems by Digital Learners

Hanif, Aamer; Jamal, F; Imran, Muhammad

doi:10.1109/access.2018.2881384

Cited by 70 publications

(68 citation statements)

References 54 publications

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“…Finally, TTF and the behavioral intentions of students to utilize social media for learning purposes enriched the academic activity of higher education students. These provide an important theoretical contribution to prior TAM research in which the impact of using social media for learning had not been taken fully into account [4], [8], [24], [30], [57], [77], [96]. The following conclusions are based on the results of this research: Using social media (e.g.…”

Section: Discussionmentioning

confidence: 60%

Task-Technology Fit and Technology Acceptance Model Application to Structure and Evaluate the Adoption of Social Media in Academia

et al. 2020

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The purpose of this article was to reduce the dissimilarities in the literature regarding the use of social media for training and its impact on students' academic performance in higher education institutions. The main method of data collection for task-technology fit (TTF) and the technology acceptance model (TAM) was a questionnaire survey. This research hypothesizes that TTF applied to social media for learning will affect technology, task, and social characteristics that in turn improve students' satisfaction and students' academic performance. It also posits that the behavioral intent to use social media for learning will affect comprehension efficiency, ease of use, and enjoyment, all of which also improve students' satisfaction and students' academic performance. The data collection questionnaire was conducted with 162 students familiar with social media. Quantitative structural equation modeling was employed to analyze the results. A significant relationship was found between technology, task, and social features with TTF for utilizing social media for academic purposes, all of which fostered student enjoyment and improved outcomes. Similarly, a clear relationship was found between comprehension efficiency, ease of use, and enjoyment with behavioral intentions to utilize social media for academic purposes that positively affected satisfaction and achievement. Therefore, the study indicates that TTF and behavioral intentions to use social media improve the active learning of students and enable them to efficiently share knowledge, information, and discussions. We recommend that students utilize social media in pursuit of their educational goals. Educators should also be persuaded to incorporate social media into their classes at higher education institutions. INDEX TERMS Task-technology fit, technology acceptance model, social media.

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Section: Discussionmentioning

confidence: 60%

Task-Technology Fit and Technology Acceptance Model Application to Structure and Evaluate the Adoption of Social Media in Academia

et al. 2020

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“…Many studies have demonstrated that Enjoyment from using any system affected users’ PEOU. Researchers also observe a strong positive relation between the Enjoyment and Perceived Usefulness of online learning systems, which increases students’ intention to use (Actual Use) of these systems [ 39 , 40 ]. Referring to these conclusions, the authors propose two research hypotheses as for the Enjoyment of distance learning tool during the pandemic.…”

Section: Methodsmentioning

confidence: 99%

“…Computer Self-Efficacy (SE) is defined as a user’s belief about his/her ability to conduct a particular task using a computer. Findings also suggest that students who have higher e-learning SE are more likely to use e-learning and computer-supported education [ 39 , 43 ]. SE is considered to have high influence especially on the Perceived Ease of Use [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

“…For Anxiety, as the only construct with negative sentiment, the hypotheses were put forward to prove or disprove the CA negative influence on students' perception of technology [42]. It is also necessary to mention that some research, like [39], has shown that CA does not have a significant relationship with Perceived Usefulness and Perceived Ease of Use.…”

Section: Computer Anxietymentioning

confidence: 99%

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Students’ Acceptance of the COVID-19 Impact on Shifting Higher Education to Distance Learning in Poland

Rizun

Strzelecki

2020

IJERPH

246

174

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This paper is dedicated to the higher education institutions shifting towards distance learning processes due to the global pandemic situation caused by COVID-19 in 2020. The paper covers the pandemic situation in Poland generally, analyzing governmental ordinances and tracking the gradual extension of restrictions for educational institutions. The purpose of this study is to investigate the influence of Experience, Enjoyment, Computer Anxiety, and Self-Efficacy on students’ acceptance of shifting education to distance learning. The study tested and used the adapted General Extended Technology Acceptance Model for E-Learning (GETAMEL) in the context of coronavirus pandemic. The partial least squares method of structural equation modeling was employed to test the proposed research model. The study utilizes an online survey to obtain data from 1692 Polish undergraduate and graduate students in both full- and part-time study. The dataset was analyzed using SmartPLS 3 software. Results showed that the best predictor of student’s acceptance of shifting education to distance learning is Enjoyment, followed by Self-Efficacy. Both Perceived Ease of Use and Perceived Usefulness predict student’s Attitude Towards Using and Intention to Use the distance learning. The findings improve understanding regarding the acceptance of distance learning and this work is therefore of particular interest to teachers and practitioners of education.

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“…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. Hanif et al [40] also extended TAM where subjective norm, perception of external control, system accessibility, enjoyment, and result demonstrability have a positive influence on undergraduate students' use of e-learning systems. Kimathi and Zhang [41] conducted a study in Tanzania and concluded that subjective norm, experience, enjoyment, and computer anxiety are the factors positively influencing perceived usefulness or perceived ease of use that further determine the usage intention of e-learning, while computer anxiety was found to negatively affect students' behavioral intention to use e-learning in Chang et al's research in Azerbaijan [42].…”

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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Extending the Technology Acceptance Model for Use of e-Learning Systems by Digital Learners

Cited by 70 publications

References 54 publications

Task-Technology Fit and Technology Acceptance Model Application to Structure and Evaluate the Adoption of Social Media in Academia

Task-Technology Fit and Technology Acceptance Model Application to Structure and Evaluate the Adoption of Social Media in Academia

Students’ Acceptance of the COVID-19 Impact on Shifting Higher Education to Distance Learning in Poland

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

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