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Asian Journal of University Education ; 19(2):282-293, 2023.
Article in English | Scopus | ID: covidwho-2325373


Online learning has become compulsory when the world was facing the Covid 19 outbreak. Accordingly, students' engagement and participation during online learning have been a major concern among teachers. By adapting the Technology Acceptance Model (TAM), the present study is carried out to examine influential factors towards the intention to use gamification during online classes. With the use of judgmental sampling, 283 usable responses have been gathered from undergraduate students in Malaysia. Results revealed that ‘authentic' positively affects three mediating variables of perceived value (PEU), perceived usefulness (PU) and perceived enjoyment (PE). Additionally, PEU, PU and PE posit positive responses towards the intention to use gamification in online learning. Furthermore, all three mediators also present positive effects in the relationship between authentic and intention to use. Thus, this study affirms the usability of TAM in the online learning context with the extension of authenticity as the external factor and perceived enjoyment as the mediating factor. The results give implications for educators and higher learning institutions to modify their learning outcomes and course content to be more interesting with the usage of online gamification tools. Perhaps, this study gives further insight for future research to apply other external factors, such as knowledge and trust to enrich the study in gamification context © 2023, Asian Journal of University Education.All Rights Reserved.

2023 International Conference on Smart Computing and Application, ICSCA 2023 ; 2023.
Article in English | Scopus | ID: covidwho-2312468
History of Medicine ; 8(2):35-42, 2022.
Article in English | Scopus | ID: covidwho-2302880
Malaysian Journal of Public Health Medicine ; 22(3):90-97, 2022.
Article in English | Scopus | ID: covidwho-2264137
7th International Conference on Soft Computing in Data Science, SCDS 2023 ; 1771 CCIS:291-302, 2023.
Article in English | Scopus | ID: covidwho-2264117
4th International Conference Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2262156
International Journal of Social Ecology and Sustainable Development ; 13(8), 2022.
Article in English | Scopus | ID: covidwho-2282885
19th International Bhurban Conference on Applied Sciences and Technology, IBCAST 2022 ; : 364-369, 2022.
Article in English | Scopus | ID: covidwho-2213200
Journal of Design and Built Environment ; 22(3):140-186, 2022.
Article in English | Scopus | ID: covidwho-2207425
Journal of Tropical Resources and Sustainable Science ; 9(2):93-102, 2021.
Article in English | CAB Abstracts | ID: covidwho-2206596
International Journal of Advanced Computer Science and Applications ; 13(11):139-147, 2022.
Article in English | Scopus | ID: covidwho-2203972
2022 International Conference on Recent Advances in Electrical Engineering and Computer Sciences, RAEE and CS 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2192051
Critical Care Medicine ; 51(1 Supplement):604, 2023.
Article in English | EMBASE | ID: covidwho-2190686
Cureus ; 14(11): e31897, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2203348


BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has disrupted the world since 2019, causing significant morbidity and mortality in developed and developing countries alike. Although substantial resources have been diverted to developing diagnostic, preventative, and treatment measures, disparities in the availability and efficacy of these tools vary across countries. We seek to assess the ability of commercial artificial intelligence (AI) technology to diagnose COVID-19 by analyzing chest radiographs. MATERIALS AND METHODS: Chest radiographs taken from symptomatic patients within two days of polymerase chain reaction (PCR) tests were assessed for COVID-19 infection by board-certified radiologists and commercially available AI software. Sixty patients with negative and 60 with positive COVID reverse transcription-polymerase chain reaction (RT-PCR) tests were chosen. Results were compared against results of the PCR test for accuracy and statistically analyzed by receiver operating characteristic (ROC) curves along with area under the curve (AUC) values. RESULTS: A total of 120 chest radiographs (60 positive and 60 negative RT-PCR tests) radiographs were analyzed. The AI software performed significantly better than chance (p = 0.001) and did not differ significantly from the radiologist ROC curve (p = 0.78). CONCLUSION: Commercially available AI software was not inferior compared with trained radiologists in accurately identifying COVID-19 cases by analyzing radiographs. While RT-PCR testing remains the standard, current advances in AI help correctly analyze chest radiographs to diagnose COVID-19 infection.

International Journal of Biomedicine ; 12(4):627-630, 2022.
Article in English | Scopus | ID: covidwho-2164457