Employing Technology Acceptance Model (TAM): An Analysis on Students’ Reception on Online Learning Platforms During Covid-19 Pandemic

Hernandez, Rowell M.

doi:10.1109/i2cacis52118.2021.9495865

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…Less popular than LMS, pedagogically focused technologies such as online learning [87][88][89][90][91], mobile learning [15,44,73,[92][93][94][95], online education [81,84,96], online teaching [97][98][99][100], and emergency remote teaching [78,101,102] were investigated to a substantial degree. This illustrates an emerging trend to understand the acceptance of tools (such as LMS) rather than approaches.…”

Section: The Technologies Studiedmentioning

confidence: 99%

A Systematic Review of the Technology Acceptance Model for the Sustainability of Higher Education during the COVID-19 Pandemic and Identified Research Gaps

et al. 2022

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Over the past three decades, the Technology Acceptance model (TAM) has garnered considerable attention in higher education. COVID-19 boosted the development of TAM as multiple studies were rapidly undertaken during the pandemic. This, however, created a gap in our current understanding of the directions and trends of TAM advancement. The purpose of this study is to obtain insight into the advancement of TAM throughout the pandemic. It would assist researchers in comprehending the advancement and direction of TAM studies in higher education, such as gaining an understanding of the prevalent external variables for TAM, the statistical analysis employed, research methodologies, the technologies studied, and the geographic location of the research conducted. Finally, research gaps and future directions for TAM studies are presented. A systematic review utilizing PRISMA was conducted on 104 sampled publications. It was found that self-efficacy, subjective norms, experience, and enjoyment were the external variables most frequently used in TAM, while internal motivation received minimal attention. The existing studies have focused mainly on student samples, so further investigation is needed into lecturers, higher education personnel, and mixed groups. Further study is also required on qualitative and mixed methods, with the partial least square structural equation model currently dominating statistical analysis. Future technologies such as 5G, AI, cloud computing, augmented reality, virtual reality, and BYOD represent new TAM-related research gaps. The majority of studies have been undertaken in Asian countries, such as China and those in southeast Asia. This new systematic literature review provides insight into the trend of TAM advancement in the sustainability of higher education during the pandemic, the identified research gaps, and recommendations for future research directions. These findings also serve as a reference for future research by enhancing the foundation established by previous reviews and research on TAM, thereby facilitating the model’s ongoing expansion.

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Section: The Technologies Studiedmentioning

confidence: 99%

A Systematic Review of the Technology Acceptance Model for the Sustainability of Higher Education during the COVID-19 Pandemic and Identified Research Gaps

et al. 2022

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“…A Technology Acceptance Model (TAM) questionnaire (Davis 1989;Hernandez, 2021;Weng et al, 2018) was developed to elicit information about students' perceived usefulness, perceived ease of use, student satisfaction, and intention to use. The questionnaire included 16 items, with four items for each component.…”

Section: Instruments Tam Questionnairementioning

confidence: 99%

“…The 63 first-year students from two intact sections of Intensive English were used as participants of the study. A technology acceptance model (TAM) questionnaire (Davis, 1989;Hernandez, 2021) was adapted to assess the effectiveness of the instructional program. The results of the study revealed that all of the participants achieved the overall objective of the course and the target CEFR's A2 level.…”

mentioning

confidence: 99%

Optimizing the virtual classroom: A case of intensive English course in the Next Normal

Kaoropthai

2021

Engl. Lang. Teach. Educational. Journal

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Due to the COVID-19 pandemic, online learning has automatically become a normal practice of the “Next Normal”. Intensive English is a compact three-week course offered to get first-year students ready for university study. Online learning was something new for both students and instructors. A key challenge in online learning was how to make virtual classroom as lively and interactive as possible. The aim of this study was thus to design an instructional program which integrated various technological tools and applications to optimize the virtual classroom. The 63 first-year students from two intact sections of Intensive English were used as participants of the study. A technology acceptance model (TAM) questionnaire (Davis, 1989; Hernandez, 2021) was adapted to assess the effectiveness of the instructional program. The results of the study revealed that all of the participants achieved the overall objective of the course and the target CEFR’s A2 level. They found their online learning experience rewarding. They were satisfied with this online learning experience and were willing to participate in similar online learning in the future. Additionally, recommendations for further research were discussed.

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Network Data Feature Selection in Detecting Network Intrusion using Supervised Machine Learning Techniques

Mendoza

Hernandez

Amorado

et al. 2022

2022 2nd Asian Conference on Innovation in Technology (ASIANCON)

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Employing Technology Acceptance Model (TAM): An Analysis on Students’ Reception on Online Learning Platforms During Covid-19 Pandemic

Cited by 5 publications

References 24 publications

A Systematic Review of the Technology Acceptance Model for the Sustainability of Higher Education during the COVID-19 Pandemic and Identified Research Gaps

A Systematic Review of the Technology Acceptance Model for the Sustainability of Higher Education during the COVID-19 Pandemic and Identified Research Gaps

Optimizing the virtual classroom: A case of intensive English course in the Next Normal

Network Data Feature Selection in Detecting Network Intrusion using Supervised Machine Learning Techniques

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