Search

This rapid systematic review aims to examine emerging evidence on the effects of COVID-19 on educational institutions and assess the prevalence of e-learning changes in the sector. This paper reviews literature on learning, teaching, and assessment approaches adopted since the COVID-19 outbreak, and assesses the impact on the sector, staff, and students, summarizing findings from peer-reviewed articles. It categorizes these into five key themes: (1) digital learning; (2) e-learning challenges; (3) digital transition to emergency virtual assessment (EVA); (4) psychological impact of COVID-19; and (5) creating collaborative cultures. This represents the first systematic review of COVID-19's impact on education, clarifying current themes being investigated. The author suggests that the term 'emergency virtual assessment' (EVA) is now added for future research discussion. Finally, the paper identifies research gaps, including researching the impact on lesser developed countries, the psychological impact of transition, and the important role of leadership and leadership styles during the transition and handling of the pandemic.

The genesis and spread of COVID-19 around the world since 2020 have caused severe impacts in every aspect of people's lives, from work life to recreation, social activities to physical health. Higher education has not been excluded. Universities have altered curriculum, changed delivery methods, provided more counseling, purchased new technology, and altered attendance policy for classroom, athletic, social and artistic events (Hamlin, 2021). To assess the impacts of these changes on college students, the authors created a questionnaire to ask students about their perceptions of these COVID-related impacts on their own personal lives. The survey had 56 questions about how the virus affected their academic, social, financial, physical and emotional lives. Over 800 students responded with objective input and subjective comments. Due to the volume of data, the authors have split the study into two parts. The survey results for the first part, academic and social aspects of the survey, were published in "Understanding the Impact of Covid-19 on College Student Academic and Social Lives," Research in Higher Education Journal Volume 41 (see http://www.aabri.com/manuscripts/213347.pdf). It will sometimes be referred to herein to provide clarity to the reader. The actual survey itself can also be found at that site. This paper focuses on the impact of the coronavirus on student financial and physical well-being, which have become major stressors to this age group and have contributed to higher levels of anxiety and depression. It also examines how the virus has affected their social and emotional well-being. Lastly, recommendations are made to help educators understand the severity of the problem, and to take action to provide assistance for those students who have been adversely affected.

Acknowledging the extreme risk COVID-19 poses to humans, this paper attempted to analyze and compare case fatality rates, identify the existence of learning curves for COVID-19 medical treatments, and examine the impact of vaccination on fatality rate reduction. Confirmed cases and deaths were extracted from the "Daily Situation Report" provided by the World Health Organization. The results showed that low registration and low viral test rates resulted in low fatality rates, and the learning curve was significant for all countries except China. Treatment for COVID-19 can be improved through repeated experience. Vaccinations in the U.K. and U.S.A. are highly effective in reducing fatality rates, but not in other countries. The positive impact of vaccines may be attributed to higher vaccination rates. In addition to China, this study identified the existence of learning curves for the medical treatment of COVID-19 that can explain the effect of vaccination rates on fatalities. [ABSTRACT FROM AUTHOR]

This paper investigates the possibility of the implementation of Genetic Programming (GP) algorithm on a publicly available COVID-19 data set, in order to obtain mathematical models which could be used for estimation of confirmed, deceased, and recovered cases and the estimation of epidemiology curve for specific countries, with a high number of cases, such as China, Italy, Spain, and USA and as well as on the global scale. The conducted investigation shows that the best mathematical models produced for estimating confirmed and deceased cases achieved R2 scores of 0.999, while the models developed for estimation of recovered cases achieved the R2 score of 0.998. The equations generated for confirmed, deceased, and recovered cases were combined in order to estimate the epidemiology curve of specific countries and on the global scale. The estimated epidemiology curve for each country obtained from these equations is almost identical to the real data contained within the data set [ABSTRACT FROM AUTHOR]

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak. [ABSTRACT FROM AUTHOR]