A National Study of Zoom Fatigue and Mental Health During the COVID-19 Pandemic: Implications for Future Remote Work

Factors influencing online learning fatigue among blended learners in higher education

“…Surprisingly, it was obtained that there was no significant correlation between LMSs Fatigue, and the amount of time spent in online learning settings. This finding contradicts the results of various studies conducted during the pandemic period (for a review please see, Bailenson, 2021;Elbogen et al, 2022;Oducado et al, 2021b;Shockley et al, 2021;Tufvesson, 2020). Contrarily, research demonstrates that meeting size, duration, and the presence of the supervisor have no effect on fatigue levels (Cohen et al, 2011;Shosan & Wehrt, 2021), even when there is fatigue, appear to be consistent with the current findings.…”

“…On the other hand, interest in online learning environments is not limited just to academic, so it is crucial to consider the possibility that it may become ingrained in other professions or professional fields (Durak et al, 2020;Mays, 2021). This issue will continue to be significant in the future even if social distancing policies are progressively eased since hybrid and face-to-face meeting formats are expected to be embraced in many professional domains (Elbogen et al, 2022). In brief, the current study has the potential to make significant and multiplexed contributions to the determination of online learning fatigue levels in higher education, the development of preventive and improvement studies, and the advancement of research understanding and instructional innovations concerning online learning fatigue.…”

“…To sum up, during the COVID-19 pandemic, the rapid and relatively mandatory integration of various LMSs into distance learning environments or online learning-based programs has catalyzed the need to assess the potential challenges and risk factors faced by learners who participate in distance education (Hisey et al, 2022;Tugtekin, 2022a). The fundamental cause of this is that, in pandemic settings, online learning has replaced traditional classroom instruction as a need rather than an option (Bozkurt & Sharma, 2020;Elbogen et al, 2022;Fırat & Bozkurt, 2020;Xie et al, 2021). Although there are various practical benefits, such as urgent crisis management, guaranteeing academic continuity, and being the most practical answer available, some drawbacks of distance education and ERT should not be overlooked by the field expert, practitioners, and policymakers.…”

Clinical characteristics and analysis of risk factors for disease progression of patients with SARS-CoV-2 Omicron variant infection: A retrospective study of 25207 cases in a Fangcang hospital

“…This is most likely due to the mutation at E484A, which leads to the reduced recognition and neutralization ( Kupcewicz et al., 2022 ). According to clinical data, unvaccinated or partly vaccinated cases have an higher risk of developing severity, which indicates that the vaccines are still effective to prevent severe disease ( Elbogen et al., 2022 ). Chinese anti-epidemic measures have proved that effective treatment of non-critical patients is a powerful tool to reduce the deterioration rate of non-critical cases turning into critical or even death cases.…”

A Machine Learning-supported Analysis of University Students' Mental Health

“…The loss function uses category cross-entropy, with y and y^ as the actual output and labelled result respectively, which is calculated as follows The structure of each weak classifier neural network is shown above [15][16], and the structure of the integrated sampling neural network used by the model is shown in the following figure. The model uses the adam (Adaptive Moment Estimation) optimization algorithm, which is a method that will calculate an adaptive learning rate for each of its parameters [17][18]. The algorithmic idea is equivalent to the combination of RMSprop + Momentum algorithm [19][20].…”