Nexus between Students’ Attitude Towards Self-Learning, Tencent App Usability, Mobile-Learning, and Innovative Performance

Muhideen, Sayibu; Chu, Jianxun; Akintunde, Tosin Yinka; Olayemi, Rufai Hafeez; Koroma, Jonathan; Amosum, Tosin Tunde; Shahani, Riffat

doi:10.2139/ssrn.3926083

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“…Significantly, the method employed for data analysis, Tencent meeting room allowed interactivity, flexibility, accessibility and convenient of the sample population to expedite action on the data. 41 The method employed was to overcome crowd-gathering of high transmission risk of COVID-19 and to abide by social distancing demands in the lives of social settings.…”

Section: Discussionmentioning

confidence: 99%

COVID-19 smart surveillance: Examination of Knowledge of Apps and mobile thermometer detectors (MTDs) in a high-risk society

et al. 2022

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Background Technological innovations gained momentum and supported COVID-19 intelligence surveillance among high-risk populations globally. We examined technology surveillance using mobile thermometer detectors (MTDs), knowledge of App, and self-efficacy as a means of sensing body temperature as a measure of COVID-19 risk mitigation. In a cross-sectional survey, we explored COVID-19 risk mitigation, mobile temperature detectable by network syndromic surveillance mobility, detachable from clinicians, and laboratory diagnoses to elucidate the magnitude of community monitoring. Materials and Methods In a cross-sectional survey, we create in-depth comprehension of risk mitigation, mobile temperature Thermometer detector, and other variables for surveillance and monitoring among 850 university students and healthcare workers. An applied structural equation model was adopted for analysis with Amos v.24. We established that mobile usability knowledge of APP could effectively aid in COVID-19 intelligence risk mitigation. Moreover, both self-efficacy and mobile temperature positively strengthened data visualization for public health decision-making. Results The algorithms utilize a validated point-of-center test to ascertain the HealthCode scanning system for a positive or negative COVID-19 notification. The MTD is an alternative personal self-testing procedure used to verify temperature rates based on previous SARS-CoV-2 and future mobility digital health. Personal self-care of MTD mobility and knowledge of mHealth apps can specifically manage COVID-19 mitigation in high or low terrestrial areas. We found mobile usability, mobile self-efficacy, and app knowledge were statistically significant to COVID-19 mitigation. Additionally, interaction strengthened the positive relationship between self-efficacy and COVID-19. Data aggregation is entrusted with government database agencies, using natural language processing and machine learning mechanisms to validate and analyze. Conclusion The study shows that temperature thermometer detectors, mobile usability, and knowledge of App enhanced COVID-19 risk mitigation in a high or low-risk environment. The standardizing dataset is necessary to ensure privacy and security preservation of data ethics.

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Section: Discussionmentioning

confidence: 99%