Background: The COVID-19 pandemic has affected our society and human well-being in various ways. In this study, we investigate how the pandemic has influenced people's emotions and psychological states compared to a pre-pandemic period using real-world data from social media. Method: We collected Reddit social media data from 2019 (pre-pandemic) and 2020 (pandemic) from the subreddits communities associated with eight universities. We applied the pre-trained Robustly Optimized BERT pre-training approach (RoBERTa) to learn text embedding from the Reddit messages, and leveraged the relational information among posted messages to train a graph attention network (GAT) for sentiment classification. Finally, we applied model stacking to combine the prediction probabilities from RoBERTa and GAT to yield the final classification on sentiment. With the model-predicted sentiment labels on the collected data, we used a generalized linear mixed-effects model to estimate the effects of pandemic and in-person teaching during the pandemic on sentiment. Results: The results suggest that the odds of negative sentiments in 2020 (pandemic) were 25.7% higher than the odds in 2019 (pre-pandemic) with a $p$-value $<0.001$; and the odds of negative sentiments associated in-person learning were 48.3% higher than with remote learning in 2020 with a $p$-value of 0.029. Conclusions: Our study results are consistent with the findings in the literature on the negative impacts of the pandemic on people's emotions and psychological states. Our study contributes to the growing real-world evidence on the various negative impacts of the pandemic on our society; it also provides a good example of using both ML techniques and statistical modeling and inference to make better use of real-world data.

In this study, learning modalities offered by public schools across the United States were investigated to track changes in the proportion of schools offering fully in-person, hybrid and fully remote learning over time. Learning modalities from 14,688 unique school districts from September 2020 to June 2021 were reported by Burbio, MCH Strategic Data, the American Enterprise Institute's Return to Learn Tracker and individual state dashboards. A model was needed to combine and deconflict these data to provide a more complete description of modalities nationwide. A hidden Markov model (HMM) was used to infer the most likely learning modality for each district on a weekly basis. This method yielded higher spatiotemporal coverage than any individual data source and higher agreement with three of the four data sources than any other single source. The model output revealed that the percentage of districts offering fully in-person learning rose from 40.3% in September 2020 to 54.7% in June of 2021 with increases across 45 states and in both urban and rural districts. This type of probabilistic model can serve as a tool for fusion of incomplete and contradictory data sources in support of public health surveillance and research efforts.

As the metaverse industry is expected to be an $800 billion market by 2024, we continue to learn new ways this immersive, virtual environment might better enable us to connect with each other from anywhere in the world. This comes at a time when many are already participating in and benefitting from virtual activities that otherwise would not be possible due to constraints of distance, time or cost. In enabling new opportunities for virtual rather than in-person instruction, the metaverse has the power to transform access to education and the way we learn. The types of education that the metaverse can accommodate are varied, from school-based interactive learning and workplace training to professional accreditation. In so many ways, the metaverse is offering new chances for people to learn what they want by mitigating obstacles of accessibility.

The move reaffirms Touro's focus on in-person learning.

High school senior Isabell Diaz has a routine. She rolls out of bed half an hour before her 9 a.m. On breaks, she steps away from the screen to eat breakfast or complete chores. She has learned how to navigate online assignments and virtual club meetings. So when she learned that her school would open in late April, she had mixed emotions.

Parents file lawsuit against New York City; councilman Joe Borelli with insight. Sound science, like the coronavirus itself, is apolitical. Most everything else this year -- including decisions on whether to close schools -- is not. As the pandemic enters its deadliest phase to date, government leaders and school districts are having to make extraordinarily difficult decisions about whether to continue in-person learning amid record communitywide surges in cases, hospitalizations and deaths. New York City's decision to close schools indefinitely, and the decision in my home state of New Jersey to allow school districts to keep them open, offers a stark contrast in how the two states with the highest death rates for COVID-19 are managing this crisis.