Analysis of ‘COVID-19 Cases in CT Schools (Statewide), 2021-2022 School Year’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/eb4f35b9-f502-4811-b2f2-4ede33a3ca5d on 27 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset provides the number of weekly COVID-19 cases for staff and students in CT public and private PK-12 schools during the 2021-2022 school year. The following metrics are included:

Number of student cases - total Number of student cases - fully vaccinated Number of student cases - not vaccinated Number of student cases - no vaccine information

Number of staff cases - total Number of staff cases - fully vaccinated Number of staff cases - not vaccinated Number of staff cases - no vaccine information

Data for the 2020-2021 school year is available here: https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-in-CT-Schools-Statewide-2020-2021-S/ehua-hw73

--- Original source retains full ownership of the source dataset ---

Due to the COVID-19 pandemic in 2020, many schools in the United States had to make the switch to distance learning rather than in-person classes. Because of the switch to online learning, it is estimated that if in-classroom instruction resumes by January 2021, students will have lost about 6.8 months of learning.

Please cite the following paper when using this dataset:

N. Thakur, “A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave,” Journal of Data, vol. 7, no. 8, p. 109, Aug. 2022, doi: 10.3390/data7080109

Abstract

The COVID-19 Omicron variant, reported to be the most immune evasive variant of COVID-19, is resulting in a surge of COVID-19 cases globally. This has caused schools, colleges, and universities in different parts of the world to transition to online learning. As a result, social media platforms such as Twitter are seeing an increase in conversations, centered around information seeking and sharing, related to online learning. Mining such conversations, such as Tweets, to develop a dataset can serve as a data resource for interdisciplinary research related to the analysis of interest, views, opinions, perspectives, attitudes, and feedback towards online learning during the current surge of COVID-19 cases caused by the Omicron variant. Therefore this work presents a large-scale public Twitter dataset of conversations about online learning since the first detected case of the COVID-19 Omicron variant in November 2021. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter and the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.

Data Description

The dataset comprises a total of 52,984 Tweet IDs (that correspond to the same number of Tweets) about online learning that were posted on Twitter from 9th November 2021 to 13th July 2022. The earliest date was selected as 9th November 2021, as the Omicron variant was detected for the first time in a sample that was collected on this date. 13th July 2022 was the most recent date as per the time of data collection and publication of this dataset.

The dataset consists of 9 .txt files. An overview of these dataset files along with the number of Tweet IDs and the date range of the associated tweets is as follows. Table 1 shows the list of all the synonyms or terms that were used for the dataset development.

Filename: TweetIDs_November_2021.txt (No. of Tweet IDs: 1283, Date Range of the associated Tweet IDs: November 1, 2021 to November 30, 2021)

Filename: TweetIDs_December_2021.txt (No. of Tweet IDs: 10545, Date Range of the associated Tweet IDs: December 1, 2021 to December 31, 2021)

Filename: TweetIDs_January_2022.txt (No. of Tweet IDs: 23078, Date Range of the associated Tweet IDs: January 1, 2022 to January 31, 2022)

Filename: TweetIDs_February_2022.txt (No. of Tweet IDs: 4751, Date Range of the associated Tweet IDs: February 1, 2022 to February 28, 2022)

Filename: TweetIDs_March_2022.txt (No. of Tweet IDs: 3434, Date Range of the associated Tweet IDs: March 1, 2022 to March 31, 2022)

Filename: TweetIDs_April_2022.txt (No. of Tweet IDs: 3355, Date Range of the associated Tweet IDs: April 1, 2022 to April 30, 2022)

Filename: TweetIDs_May_2022.txt (No. of Tweet IDs: 3120, Date Range of the associated Tweet IDs: May 1, 2022 to May 31, 2022)

Filename: TweetIDs_June_2022.txt (No. of Tweet IDs: 2361, Date Range of the associated Tweet IDs: June 1, 2022 to June 30, 2022)

Filename: TweetIDs_July_2022.txt (No. of Tweet IDs: 1057, Date Range of the associated Tweet IDs: July 1, 2022 to July 13, 2022)

The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used. For hydrating this dataset the Hydrator application (link to download and a step-by-step tutorial on how to use Hydrator) may be used.

Table 1. List of commonly used synonyms, terms, and phrases for online learning and COVID-19 that were used for the dataset development

Terminology

List of synonyms and terms

COVID-19

Omicron, COVID, COVID19, coronavirus, coronaviruspandemic, COVID-19, corona, coronaoutbreak, omicron variant, SARS CoV-2, corona virus

online learning

online education, online learning, remote education, remote learning, e-learning, elearning, distance learning, distance education, virtual learning, virtual education, online teaching, remote teaching, virtual teaching, online class, online classes, remote class, remote classes, distance class, distance classes, virtual class, virtual classes, online course, online courses, remote course, remote courses, distance course, distance courses, virtual course, virtual courses, online school, virtual school, remote school, online college, online university, virtual college, virtual university, remote college, remote university, online lecture, virtual lecture, remote lecture, online lectures, virtual lectures, remote lectures

The share of in-class content covered by remote learning materials at schools in the United Kingdom increased significantly between December 2020 and January 2021, rising from 56 percent to 85 percent in Primary schools, and from 73 percent to 87 percent at Secondary schools. This increase was due to schools generally closing their doors to regular attendance in early 2021, as the Coronavirus pandemic situation worsened in the UK.

Characteristics of school-based persons with index cases of COVID-19 and close contacts from 103 K–12 schools, Greene and St. Louis Counties, Missouri, January 25–March 21, 2021.

Abstract copyright UK Data Service and data collection copyright owner.

The Understanding Society COVID-19 Study is a regular survey of households in the UK. The aim of the study is to enable research on the socio-economic and health consequences of the COVID-19 pandemic, in the short and long term. The surveys started in April 2020 and took place monthly until July 2020. From September 2020 they take place every other month. They complement the annual interviews in the Understanding Society study.

This dataset contains school code variables for the Understanding Society COVID-19 study (SN 8644).

A file is provided for the fifth web wave of the Understanding Society COVID-19 study, the only one that school information has currently been gathered for. For each child it contains: state school code, country of state school, private school name and private school town variables for both mother and father responses. A child personal identification serial number (pidp_c) is also provided for matching to the main data in SN 8644.

In addition, this dataset contains a file of school code variables that can be matched to a dataset released with the main Understanding Society COVID-19 study containing data taken from waves 10 and 11 of the main Understanding Society survey specifically for the respondents in the Understanding Society COVID-19 study. Child school codes are only available for Wave 11 as they are only collected in odd-numbered waves. For each child it contains the state school code and country of state school variables as well as a personal identification serial number (pidp) and a household identification serial number for wave 11 (jk_hidp). Further details on the files in this dataset can be found in the Understanding Society COVID-19 User Guide.

Additional information can be found on the Understanding Society COVID-19 website, including Data documentation. A list of Understanding Society COVID-19 Research Outputs (regularly updated) is also available.

New edition information
For the second edition (January 2021), both previously deposited files have been revised to include a significant number of additional school codes resulting from manual coding. For further details please refer to the UKHLS COVID-19: Data Changes document, included in the main COVID-19 study (SN 8644).

Analysis of ‘COVID-19 Data in Schools. Course 2020-2021’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-analisi-transparenciacatalunya-cat-api-views-jrf5-hj8d on 07 January 2022.

--- Dataset description provided by original source is as follows ---

Les dades dels casos covid-19 als centres educatius (Curs 2020-2021) s’obtenen del buidatge de les dades que cada centre educatiu introdueix a l’aplicació Traçacovid, per aquest motiu, les dades poden arribar a tenir alguna mena de desfasament temporal.

Aquesta aplicació dona suport als centres en el marc del Protocol de casos covid-19 als centres educatius.

Les dades referents a alumnes i personal confinat fan referència a la situació diària. Les dades referents a PCR es divideixen entre les que fan referència al total acumulat durant tot el període i les que només consideren els darrers 10 dies. Aquestes darreres només són disponibles des del 7 d’octubre.

Les dades de casos positius, publicades a partir del 7 d'abril del 2021, provenen de la incorporació automàtica dels resultats de les proves diagnòstiques del Departament de Salut.

--- Original source retains full ownership of the source dataset ---

Every day, schools, child care centres and licensed home child care agencies report to the Ministry of Education on children, students and staff that have positive cases of COVID-19.

If there is a discrepancy between numbers reported here and those reported publicly by a Public Health Unit, please consider the number reported by the Public Health Unit to be the most up-to-date.

Schools and school boards report when a school is closed to the Ministry of Education. Data is current as of 2:00 pm the previous day.

This dataset is subject to change.

Data is only updated on weekdays excluding provincial holidays

Effective June 15, 2022, board and school staff will not be expected to report student/staff absences and closures in the Absence Reporting Tool. The ministry will no longer report absence rates or school/child care closures on Ontario.ca for the remainder of the school year.

Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak.

Summary of school closures

This is a summary of school closures in Ontario.

Data includes:

• Number of schools closed
• Total number of schools
• Percentage of schools closed

School Absenteeism

This report provides a summary of schools and school boards that have reported staff and student absences.

Data includes:

• School board
• School
• City or Town
• Percentage of staff and students who are absent

Summary of cases in schools

This report provides a summary of COVID-19 activity in publicly-funded Ontario schools.

Data includes:

• School-related cases (total)
• School-related student cases
• School-related staff cases
• Current number of schools with a reported case
• Current number of schools closed

Note: In some instances the type of cases are not identified due to privacy considerations.

Schools with active COVID-19 cases

This report lists schools and school boards that have active cases of COVID-19.

Data includes :

• School Board
• School
• Municipality
• Confirmed Student Cases
• Confirmed Staff Cases
• Total Confirmed Cases

Cases in school board partners

This report lists confirmed active cases of COVID-19 for other school board partners (e.g. bus drivers, authorized health professionals etc.) and will group boards if there is a case that overlaps.

Data includes :

• School Board(s)
• School Municipality
• Confirmed cases – other school board partners

Summary of targeted testing conducted in schools

This data includes all tests that have been reported to the Ministry of Education since February 1, 2021. School boards and other testing partners will report to the Ministry every Wednesday based on data from the previous seven days.

Data includes : * School boards or regions * Number of schools invited to participate in the last seven days * Total number of tests conducted in the last seven days * Cumulative number of tests conducted * Number of new cases identified in the last seven days * Cumulative number of cases identified

Summary of asymptomatic testing at conducted in pharmacies:

This is a summary of COVID-19 rapid antigen testing conducted at participating pharmacies in Ontario since March 27, 2021.

• Total number of tests conducted in the last seven days
• Cumulative number of tests conducted
• Number of new cases identified in the last seven days
• Cumulative number of cases identified

Analysis of ‘COVID-19 Cases in CT Schools by County, 2020-2021 School Year’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/38eb039f-ca7a-4abf-a51b-142949f62475 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Counts of COVID-19 Cases in CT Schools by County

As of 6/24/2021, COVID-19 school-based surveillance activities for the 2020 – 2021 academic year has ended. The Connecticut Department of Public Health along with the Connecticut State Department of Education are planning to resume these activities at the start of the 2021 – 2022 academic year.

--- Original source retains full ownership of the source dataset ---

Analysis of homeschooling in Great Britain during the coronavirus (COVID-19) pandemic from the Opinions and Lifestyle Survey (OPN). Data relate to homeschooling from the COVID-19 module of the OPN, collected between 13 January and February 2021

Before the outbreak of the coronavirus COVID-19 pandemic in December 2019 and January 2020, around 25.1 percent of the user attendance on the Tencent Classroom platform in China was related to IT and Internet courses. As the pandemic spread in China in February and March 2020, the user number of courses on exam preparation increased significantly, mainly driven by high school students who were searching for additional training during the lockdown.

On January 21, 2020, the U.S. Centers for Disease Control and Prevention (CDC) and Washington State Department of Health (DOH) announced the first case of 2019 Novel Coronavirus (COVID-19) in the United States, in Washington state. The link below provides access to DOH daily updates of confirmed Washington State COVID-19 cases and deaths, along with essential information about the virus and guidance on prevention and risk management. The link includes Frequently Asked Questions, as well as resources for specific groups such as parents, caregivers, employers, schools and health care providers.

Abstract copyright UK Data Service and data collection copyright owner.The COVID Social Mobility and Opportunities Study (COSMO) is a longitudinal cohort study, a collaboration between the UCL Centre for Education Policy and Equalising Opportunities (CEPEO), the UCL Centre for Longitudinal Studies (CLS), and the Sutton Trust. The overarching aim of COSMO is to provide a representative data resource to support research into how the COVID-19 pandemic affected the life chances of pupils with different characteristics, in terms of short-term effects on educational attainment, and long-term educational and career outcomes.The topics covered by COSMO include, but are not limited to, young people's education experiences during the pandemic, cancelled assessments and education and career aspirations. They have also been asked for consent for linking their survey data to their administrative data held by organisations such as the UK Department for Education (DfE). Linked data is planned to be made available to researchers through the ONS Secure Research Service.Young people who were in Year 11 in the 2020-2021 academic year were drawn as a clustered and stratified random sample from the National Pupil Database held by the DfE, as well as from a separate sample of independent schools from DfE's Get Information about Schools database. The parents/guardians of the sampled young people were also invited to take part in COSMO. Data from parents/guardians complement the data collected from young people.Further information about the study may be found on the COVID Social Mobility and Opportunities Study (COSMO) webpage. COSMO Wave 1, 2021-2022Data collection in Wave 1 was carried out between September 2021 and April 2022. Young people and parents/guardians were first invited to a web survey. In addition to receiving online reminders, some non-respondents were followed up via face-to-face visits over the winter and throughout spring. Latest edition information: The fourth edition (April 2024) follows the release of Wave 2 data. For this edition, a longitudinal parents dataset has been deposited, to help data users find core background information from parents who took part in either Wave 1 or Wave 2, in one place. A new version of the young person data file (version 2.1) has also been deposited. This file now includes weight variables for researchers who wish to analyse complete households, where, in addition to a young person taking part at Wave 1, a parent had taken part at either Wave (1 or Wave 2). The COSMO Wave 1 Data User Guide Version 2.1 explains these updates in detail. Further information about the study may be found on the COSMO website. Main Topics: For young people, Wave 1 covered: a household gridcurrent statusqualifications studied towardseducation during the pandemic (on the basis of three main time periods: Lockdown 1: from April to July 2020, and Lockdown 3: from January to March 2021, and when most schools were open, September to December 2020)catch-up activitiescancelled assessmentseducation and career aspirationsextra-curricular activities pre- and post-pandemicattitudes to educationhealth and wellbeingfriends, peers and family support; and health-related behaviours. For parents, Wave 2 covered: demographicsattitudes to educationparenting, home learning, tuition and catch-upworking status across the pandemicparental tenure, HRP and occupation detailsparental educationparental incomeCOVID-19 history and vaccinationpandemic impact on family lifeparent health and well-being; and disadvantage. Multi-stage stratified random sample Face-to-face interview: Computer-assisted (CAPI/CAMI)

BackgroundDue to the COVID-19 pandemic, schools were closed, teachers had to teach from home and after a while, they had to return to the classroom while the pandemic was still on-going. Even before the pandemic, teachers were already more at risk for burn-out syndrome compared to the general population. Furthermore, not much research pertaining to this population has been carried out during the pandemic and so the impact of the pandemic on teachers' risk of burn-out syndrome and recovery need remains unclear. The aim of the current study was to fill this knowledge gap and map out the impact on risk of burn-out syndrome and recovery need at different time points during the pandemic.Methods and findingsAt baseline, 2,167 secondary school teachers in Flanders were included in this prospective study. Questionnaire data were obtained at ten different time points between September 2019 and August 2021. To assess risk of burn-out syndrome and its dimensions, the Utrecht Burn-out Scale for Teachers was administered. Need for recovery was assessed using questions adopted from the Short Inventory to Monitor Psychosocial Hazards. The results revealed an initial positive effect of the first lockdown (Mar/Apr 2020) with a decrease in risk of burn-out syndrome [Odds ratio (OR) Jan/Feb 2020–Mar/Apr 2020 = 0.33, p < 0.001], emotional exhaustion (EMM Jan/Feb 2020–Mar/Apr 2020 = −0.51, p < 0.001), depersonalization (EMM Jan/Feb 2020–Mar/Apr 2020 = −0.13, p < 0.001) and recovery need [Estimated marginal mean (EMM) Jan/Feb 2020–Mar/Apr 2020 = −0.79, p < 0.001]. No significant effect on personal accomplishment was found (p = 0.410). However, as the pandemic went on, higher risk of burn-out syndrome, emotional exhaustion, depersonalization and recovery need, and lower personal accomplishment were observed.ConclusionsDespite the initial positive impact on risk of burn-out syndrome, its dimensions and recovery need, a negative long-term impact of the COVID-19 pandemic became visible. This study highlights once again the importance for interventions to reduce teachers' risk of burn-out syndrome, especially in such difficult times as a pandemic.