Nexus between students’ attitude towards self-learning, Tencent APP usability, mobile-learning, and innovative performance

The effects of Covid-19 pandemic on the education system in Nigeria: The role of competency-based education

Covid-19 revealed the strengths and weaknesses in the global education atmosphere in both developed and developing countries. To that effect, this current study explored the impacts of the covid-19 pandemic on Nigeria's education system and in the process provided a distinctive solution to the challenges facing the sustainability of education in the country. However, the closure of schools for over six months at the onset of the covid -19 pandemic, and the inability of schools to engage learners in educational activities while at home also revealed the poor state of the education system in the country, which led to the discovery of the unavailability of distance online education, web-based learning system and ICT infrastructure in the Nigerian education environment. Covid-19 incidence impacted the stability of the academic calendar, caused teachers attrition, increased the rate of students dropout, and lack of interest in digital education. These outcomes resulted in the exploration of students' and teachers' perceptions, attitudes, literacy, competency, and willingness to engage in distance online education. A cross-sectional approach was applied through an online survey to obtain data from n = 82 learners across the three levels of institutions. And SPSS was used to analyze the demography data, while SMART PLS was used for structural equation modeling (SEM). The study outcome satisfied the objectives of the study that the lack of student-teacher digital competencies influences their perception and acceptability of web-based learning approach and use of smart learning and teaching devices.

Academic expectations among international students from North-Western China: A case of technology use during and post COVID-19

This study examines the influence of the COVID-19 crisis on academic expectations among international students from north-western China. According to past studies, academic expectations are multifaceted, making it critical to test the methods employed to assess this fundamental trait. The outbreak of COVID-19 has resulted in various significant changes in education, which have shifted from traditional to online or mixed formats. As a result, examining international students' academic expectations along with their interactions with adopted technologies is a topic that addresses the current situation and issues. A mixed approach, comprising two different instruments (questionnaire and interview), was followed to achieve this primary objective. While a survey with a questionnaire was undertaken with 551 international students, divided into two groups, ten students were interviewed during and after the lockdowns. The findings revealed that COVID-19 had a significant impact on the academic expectations of students as well as many elements such as training for employment, personal and social development, international student mobility, motivation, social pressure, and social interaction with the help of supporting technologies. In terms of gender, men outperformed women in motivation, social interaction, training for employment, and personal and social development factors. Similarly, as per the grade variable (undergraduate, postgraduate, doctoral), the same higher trend was seen in postgraduates. Based on these findings, a set of recommendations was put forward. In the future, technology will be helpful in China's educational sector, such as online group collaboration, open education, managing student retention, and supervising teachers' recruitment.

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

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.