As of June 14, 2021, 34 states in the United States had no school closure order in effect due to the COVID-19 pandemic. Meanwhile, 13 states had ordered schools open.

School closure during COVID-19

The debate on whether to close schools during the COVID-19 pandemic has been highly contentious in the United States. As experts are unsure about the long-term effects of COVID-19 on individuals, the call to close schools has been rapidly growing. The evidence of children being asymptomatic spreaders helps support the case to close schools to decrease the risk of spreading to their parents and grandparents. Those in favor of closing schools argue that closing schools hinders children’s emotional, mental, and educational development. However, a majority of voters agree that schools should wait to reopen until all teachers are vaccinated.

Parents' worry

Regardless of income level or working status, most parents in the United States share the same fears and concerns about their children’s education and safety during COVID-19. A majority of parents were concerned about their children falling behind as a result of online schooling, as an online school does not provide the same academic and social benefits as an in-person school.

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

Due to the coronavirus (COVID-19) outbreak, institutions in the United States have had to react in order to try to contain the virus. As of March 17, 2020, 489 schools or school districts in the U.S. have closed in response to COVID-19. 22 schools or school districts are scheduled to close.

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 June 30, 2022 is the last report for the 2021 – 2022 academic school year. 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

This dataset provides the number of weekly COVID-19 cases for staff and students in CT PK-12 schools by school during the 2022-2023 school year.

The number of children, youth and adults not attending schools or universities because of COVID-19 is soaring. Governments all around the world have closed educational institutions in an attempt to contain the global pandemic.

According to UNESCO monitoring, over 100 countries have implemented nationwide closures, impacting over half of world’s student population. Several other countries have implemented localized school closures and, should these closures become nationwide, millions of additional learners will experience education disruption.

This dataset include two .csv files containing the integrated dataset used by the COVID-19 School Dashboard website to report and maps confirmed school-related cases of COVID-19 in publicly funded elementary and secondary schools in Ontario, Canada, and connects this to data on school social background characteristics. One csv file reports cases from 2020-09-10 to 2021-04-14 (2020 school year) while the other csv file reports cases from 2021-09-13 to 2021-12-22 (2021 school year). Two accompanying .doc files are included to describe the variables in the .csv files.

Initial estimates of staff and pupils testing positive for the coronavirus (COVID-19) across a sample of schools within selected local authority areas in England.

This project aims to build a model that is able to generate risk scores for schools in different areas of San Diego and provide insights for schools to take the appropriate precautionary measures when reopening for in-person instructions. We plan to utilize the 2020 synthetic population data for simulating transportation from and to schools. Combining the trips data with school information and case rates in individual census tracts, we can then assign weights to various factors and compute the final risk score for schools in each census tract. The final result can also serve as a baseline for agent-based model to simulate COVID-19 spread on campus.Notable Modules Used:Matplotlib We used matplotlib to plot some of our data into graph to better view them in a visualized way.Geopandas We used geopandas to read in the shape files in our data.Pandas We used pandas to handle dataframe and have done some preprocessing using it.Numpy We used numpy for some arithmetic operations.ArcGIS Feature Module It is mainly used for feature summarization. Using the summarize_within function provided in this module, we are able to turn our zip code based COVID data into MGRA based COVID data.

Across South Asian countries, the average length of partial or full school closures during the coronavirus (COVID-19) pandemic amounted to 84 weeks as of April 2022. In comparison, schools across East Asia and the Pacific were partially or fully closed for 43 weeks on average.

The 2020-2021 School Learning Modalities dataset provides weekly estimates of school learning modality (including in-person, remote, or hybrid learning) for U.S. K-12 public and independent charter school districts for the 2020-2021 school year, from August 2020 – June 2021.

These data were modeled using multiple sources of input data (see below) to infer the most likely learning modality of a school district for a given week. These data should be considered district-level estimates and may not always reflect true learning modality, particularly for districts in which data are unavailable. If a district reports multiple modality types within the same week, the modality offered for the majority of those days is reflected in the weekly estimate. All school district metadata are sourced from the https://nces.ed.gov/ccd/files.asp#Fiscal:2,LevelId:5,SchoolYearId:35,Page:1">National Center for Educational Statistics (NCES) for 2020-2021.

School learning modality types are defined as follows:

• In-Person: All schools within the district offer face-to-face instruction 5 days per week to all students at all available grade levels.
• Remote: Schools within the district do not offer face-to-face instruction; all learning is conducted online/remotely to all students at all available grade levels.
• Hybrid: Schools within the district offer a combination of in-person and remote learning; face-to-face instruction is offered less than 5 days per week, or only to a subset of students.

Data Information

• School learning modality data provided here are model estimates using combined input data and are not guaranteed to be 100% accurate. This learning modality dataset was generated by combining data from four different sources: Burbio [1], MCH Strategic Data [2], the AEI/Return to Learn Tracker [3], and state dashboards [4-20]. These data were combined using a Hidden Markov model which infers the sequence of learning modalities (In-Person, Hybrid, or Remote) for each district that is most likely to produce the modalities reported by these sources. This model was trained using data from the 2020-2021 school year. Metadata describing the location, number of schools and number of students in each district comes from NCES [21].
• You can read more about the model in the CDC MMWR: https://www.cdc.gov/mmwr/volumes/70/wr/mm7039e2.htm" target="_blank">COVID-19–Related School Closures and Learning Modality Changes — United States, August 1–September 17, 2021.
• The metrics listed for each school learning modality reflect totals by district and the number of enrolled students per district for which data are available. School districts represented here exclude private schools and include the following NCES subtypes:
  • Public school district that is NOT a component of a supervisory union
  • Public school district that is a component of a supervisory union
  • Independent charter district
• “BI” in the state column refers to school districts funded by the Bureau of Indian Education.

Technical Notes

• Data from September 1, 2020 to June 25, 2021 correspond to the 2020-2021 school year. During this timeframe, all four sources of data were available. Inferred modalities with a probability below 0.75 were deemed inconclusive and were omitted.
• Data for the month of July may show “In Person” status although most school districts are effectively closed during this time for summer break. Users may wish to exclude July data from use for this reason where applicable.

Sources

1. K-12 School Opening Tracker. Burbio 2021; https://cai.burbio.com/school-opening-tracker/. June 2021.
2. COVID-19 IMPACT: School District Operational Status. MCH Strategic Data: 2021; https://www.mchdata.com/covid19/schoolclosings. (30 June 2021, date last accessed).
3. Return to Learn Tracker. American Enterprise Institute 2021; https://www.returntolearntracker.net/. (30 June 2021, date last accessed).
4. Colorado School District Reopening Plans. Colorado Department of Education 2021; https://docs.google.com/spreadsheets/d/e/2PACX-1vQipdjO8QWhilhhJ4bX0FBebnHEzK1G3LEDQbE_S-xRvs2t0oHNm--acHwMRFmL9uKw4cXcOUqy1V66/pubhtml. (30 June 2021, date last accessed).
5. Connecticut Report Cards. Connecticut State Department of Education 2021; http://edsight.ct.gov/SASPortal/main.do. (30 June 2021, date last accessed).
6. COVID Planning Tool. Hawaii Department of Education 2021; https://public.tableau.com/views/COVIDPlanningTool-Embed700px/SimpleDashboard. (30 June 2021, date last accessed).
7. Idaho School District Status. Idaho State Department of Education 2021; https://ioem.maps.arcgis.com/apps/opsdashboard/index.html#/379299e56f234fa89cff3c8c6acf3ac8. (30 June 2021, date last accessed).
8. How Illinois Districts Are Providing Instruction: Virtual, In-Person, and Blended Learning. Illinois State Board of Education 2021; https://isp.maps.arcgis.com/apps/opsdashboard/index.html#/fceeacb37da04de4b237ed941dd7d5c4. (30 June 2021, date last accessed).
9. Digital Learning Model Schools. Louisiana Department of Education 2021; https://analytics.la.gov/t/DOE/views/DigitalLearningModel/SchoolLevelModeofLearning. (30 June 2021, date last accessed).
10. Safe Learning Model Dashboard. Minnesota Department of Education 2021; https://analytics.education.state.mn.us/t/MDEPublic/views/MN_Safe_Learning_Model/Dashboard. (30 June 2021, date last accessed).
11. Coronavirus (COVID-19) information. Missouri Department of Elementary & Secondary Education 2021; https://dese.mo.gov/communications/coronavirus-covid-19-information. (30 June 2021, date last accessed).
12. North Carolina School Dashboard. North Carolina Department of Public Instruction 2021. https://docs.google.com/spreadsheets/d/1We8gDpa4Do5NR83Nf8niGE_YxzLDf-KZh-tVWifStxE/edit#gid=0. (30 June 2021, date last accessed).
13. Ring the Bell: Return to In-Person Learning. New Mexico Public Education Department 2021; https://webnew.ped.state.nm.us/reentry-district-and-school-guidance/. (30 June 2021, date last accessed).
14. Reset and Restart Education. Ohio Department of Education 2021; http://education.ohio.gov/Topics/Reset-and-Restart. (30 June 2021, date last accessed).
15. 2020-21 School Status. Oregon Department of Education 2021; https://www.oregon.gov/ode/students-and-family/healthsafety/Pages/2020-21-School-Status.aspx. (30 June 2021, date last accessed).
16. Current Operational Status. South Carolina Department of Education 2021; https://ed.sc.gov/districts-schools/schools/district-and-school-closures/operational-status/#blk2102. (30 June 2021, date last accessed).
17. Tennessee Department of Education COVID-19 District Information. Tennessee Department of Education 2021; https://districtinformation.tnedu.gov/covid-information/search/-1. (30 June 2021, date last accessed).
18. State Snapshot: Virginia School Operational Status. Virginia Department of Education 2021; https://www.doe.virginia.gov/support/health_medical/office/reopen-status.shtml. (30 June 2021, date last accessed).
19. Current Reopening Status of Vermont's K-12 Public Schools. Vermont Department of Financial Regulation 2021; https://dfr.vermont.gov/about-us/covid-19/school-reopening#public-school. (30 June 2021, date last accessed).
20. School Reopening Data. Washington Office of Superintendent of Public Instruction 2021; https://www.k12.wa.us/about-ospi/press-releases/novel-coronavirus-covid-19-guidance-resources/school-reopening-data. (30 June 2021, date last accessed).
21. Public school districts. National Center for Education Statistics 2021; https://nces.ed.gov/ccd/districtsearch/. (30 June 2021, date last accessed).

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 ---

This dataset includes information on school reported COVID-19 testing and case positive data from the 2021-2022 academic year. Data was collected from K-12 public schools on each operational day using the daily school survey form, which school administrators access by logging in to the NYSDOH school survey website.

The primary goal of publishing this dataset is to provide users timely information about disease spread and reporting of positive cases within schools. The data will be updated daily, reflecting data submitted by school administrators the previous day.

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

At the start of the 2020 school year, some colleges chose to reopen in person while others offered primarily online classes. We find that colleges responded to financial and other incentives largely as one might expect. Larger shares of revenue attributed to in-person activities, such as dorms and dining halls, led schools to reopen in person. In general, the share of revenue due to tuition and fees had little association with reopening in-person, which is consistent with the idea that the effect of the mode of reopening on enrollment was ambiguous. However, private schools experiencing financial distress due to tuition and fees were more likely to reopen in-person while public schools were less likely. Public colleges were influenced by political pressures and the fraction of students from out of state, while private schools responded to the severity of COVID in their local community.

Number of COVID-19 cases (PCR test) in schools by county A graphical representation of the numbers is available on the website Covid19 Location Picture Schools. Character separator is comma, character set is UTF-8

A substantial fraction of k-12 schools in the United States closed their in-person operations during the COVID-19 pandemic. These closures may have altered the labor supply decisions of parents of affected children due to a need to be at home with children during the school day. In this paper, we examine the impact of school closures on parental labor market outcomes. We test whether COVID-19 school closures have a disproportionate impact on parents of school-age children (ages 5-17 years old). Our results show that both women’s and men’s work lives were affected by school closures, with both groups seeing a reduction in work hours and the likelihood of working full-time but only women being less likely to work at all. We also find that closures had a corresponding negative effect on the earnings of parents of school-aged children. These effects are concentrated among parents without a college degree and parents working in occupations that do not lend themselves to telework, suggesting that such individuals had a more difficult time adjusting their work lives to school closures.

The survey examined the perceptions of well-being, well-being work, and working conditions in comprehensive schools in Central Finland during the exceptional circumstances caused by the coronavirus during the distance teaching and learning period (March 18 - May 13, 2020). It is a continuation of the FSD3611 Well-being Work and Personnel Well-being in Schools in Central Finland 2020 data. The survey was conducted as part of the Yhteistyöllä hyvinvointia kouluyhteisöön project, which is part of the Central Finland Health and Wellbeing Ecosystem (KeHO) network. The dataset also contains the open-ended responses. First, the respondents' various concerns related to the school, pupils, and staff were surveyed. In addition, several questions were asked about the respondents' working conditions, coping, and well-being. Some of the statements were also presented in the FSD3611 data. The scales and inventories used in the data include e.g. the BBI-9 (Bergen Burnout Inventory) and the UWES-3 (Ultra-Short Measure for Work Engagement). For more information on the construction of the questionnaire, see the section Related Materials. Background variables included the respondent's gender, work role, work experience, job description during the distance education period, and school type.

Unplanned public K-12 school district and individual school closures due to COVID-19 in the United States from February 18–June 30, 2020.