According to data from September 2020, 1.5 million students all over the world were affected by the coronavirus pandemic. Not only has education been disrupted for the student population, also 630 million primary and secondary school teachers were affected by COVID-19.

In connection with the emergence of the coronavirus in Poland, Internet education was introduced in 2020. The opinions of parents of primary school students were more favorable than those of parents of high school students. As for primary school, 38 percent of parents rated Internet classes as good or very good. Regarding secondary schools, only 30 percent of parents were satisfied.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

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.

As of August 1, 2020, close to 33.2 million children and teenagers had been affected by school closures in Mexico in the context of the COVID-19 pandemic. More than 28 million of the students were enrolled in primary and secondary schools at the time, while close to five million were enrolled in pre-primary schools. The national closure of schools in the country was implemented on March 20, 2020. By the first semester of the 2020/2021 school year, educational institutions still remained closed.

Secondary education around the world has been significantly disrupted by covid-19. Students have been forced into new ways of independent learning, often using remote technologies, but without the social nuances and direct teacher interactions of a normal classroom environment. Using data from the School Attitudes Survey—which surveys students regarding the perceived level of difficulty, anxiety level, self-efficacy, enjoyability, subject relevance, and opportunities for creativity with regards to each of their school subjects—this study examines students' responses to this disruption from two very different schools with two very different experiences of the pandemic. This paper reports on the composite attitudinal profiles of students in the senior secondary levels at each school (Years 10–12, n = 834). The findings challenged our expectation that the increased difficulty and anxiety caused by the disruption would reduce perceived opportunities for creativity. Indeed, our analyses showed that the students at both schools demonstrated generally positive attitudes toward their learning and strongly associated opportunities for creativity with other attitudinal constructs including enjoyability, subject relevance, and self-efficacy. These complex associations made by the students appear to have buffered the impacts of the disruption, and they may even have supported creative resilience.

Dataset

This dataset was created by Muhammad Gusanwa Akbar

Contents

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).

As of August 1, 2020, more than 10.4 million children and teenagers had been affected by school closures in Colombia in the context of the COVID-19 pandemic. Close to nine million of the students were enrolled in primary and secondary schools at the time, while around one million were enrolled in pre-primary schools. The national closure of schools in the country was implemented on March 15, 2020, and had lasted 139 days by the date of the study.

Background: Adequate understanding and precautionary behaviors are of vital importance to contain the spread of coronavirus disease 2019 (COVID-19). To date, the knowledge, attitudes, and practices (KAP) toward COVID-19 among different populations have been reported, whereas such information is unavailable in teachers. We aimed to investigate the KAP of teachers associated with COVID-19 during the global outbreak.Methods: A large-scale population-based survey was conducted to gather information on COVID-19-related KAP among Chinese teachers using a self-administered questionnaire. We received 10,658 responses in April 2020, out of which 8,248 were enrolled in the final analysis. Participants responded to a self-administered questionnaire concerning demographic characteristics and KAP associated with COVID-19.Results: This work included 4,252 (51.6%) teachers in kindergartens, 2,644 (32.1%) teachers in primary schools, and 1,352 (16.4%) teachers in secondary schools. The knowledge level (mean: 4.46 out of seven points) was relatively lower than the levels of attitudes (mean: 3.27 out of four points) and practices (mean: 4.29 out of five points) toward COVID-19. Knowledge scores significantly varied by the collected demographic variables except education worksite (p < 0.05), whereas practice scores significantly differed in age groups (p < 0.05), education level (p < 0.001), education worksite (p < 0.001), and years of teaching (p < 0.001). The multivariate logistic analysis indicated that poor knowledge related to COVID-19 was common among men, younger, and less-educated teachers. In contrast, female teachers and those with higher education levels tend to have good practices against COVID-19.Conclusion: The present work suggested the knowledge gaps regarding COVID-19 were needed to be corrected immediately in teachers. Given the critical role of teachers in the education system, health authorities should take gender, age, and education level into account when developing suitable health interventions.

NOTE: This dataset pertains only to the 2020-2021 school year and is no longer being updated. For additional data on COVID-19, visit data.ct.gov/coronavirus.

This dataset includes the leading and secondary metrics identified by the Connecticut Department of Health (DPH) and the Department of Education (CSDE) to support local district decision-making on the level of in-person, hybrid (blended), and remote learning model for Pre K-12 education.

Data represent daily averages for two-week periods by date of specimen collection (cases and positivity), date of hospital admission, or date of ED visit. Hospitalization data come from the Connecticut Hospital Association and are based on hospital location, not county of patient residence. COVID-19-like illness includes fever and cough or shortness of breath or difficulty breathing or the presence of coronavirus diagnosis code and excludes patients with influenza-like illness. All data are preliminary.

These data are updated weekly and reflect the previous two full Sunday-Saturday (MMWR) weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf).

These metrics were adapted from recommendations by the Harvard Global Institute and supplemented by existing DPH measures.

For national data on COVID-19, see COVID View, the national weekly surveillance summary of U.S. COVID-19 activity, at https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html

DPH note about change from 7-day to 14-day metrics: Prior to 10/15/2020, these metrics were calculated using a 7-day average rather than a 14-day average. The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: https://data.ct.gov/Health-and-Human-Services/CT-School-Learning-Model-Indicators-by-County/rpph-4ysy

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

Abstract copyright UK Data Service and data collection copyright owner.

In response to the Covid-19 pandemic, schools in England closed their buildings to all but vulnerable pupils and the children of key workers on 20 March 2020, representing an unprecedented disruption to the education of children and young people. This project explores schools' responses to the Covid-19 emergency and the impact this is having on pupils and teachers. Data will be collected via two school surveys, each administered to the National Foundation for Educational Research (NFER) Teacher Voice panel, and all remaining publicly-funded mainstream primary and secondary schools in England. The survey is offered for completion by a senior leader and a number of teachers within each school. The first survey (Wave 1) was administered in schools between 7 and 17 May 2020. The second (Wave 2), focused on the challenges schools would face from September, and was administered between 8 and 15 July.

Further information and research findings may be found on the NFER Schools' responses to Covid-19 webpage.

Latest edition information
For the second edition (December 2020), data and documentation for Wave 2 were added to the study.

Senior Leaders' survey:

Wave 1:

• Mechanisms for supporting remote learning
• Curriculum, teaching and learning and assessment via remote learning
• In-school provision for vulnerable children and the children of keyworkers
• Remote support for vulnerable pupils who are not attending school
• Job satisfaction
• Preparing schools for opening more fully
• Personal characteristics

Wave 2:

• The school’s provision during the Covid-19 crisis
• 'Catch-up' arrangements
• Preparing for the new school year
• Staffing in the school
• Personal characteristics

Teachers' survey:

Wave 1:

• Mechanisms for supporting remote learning
• Curriculum, teaching and learning
• Provision for vulnerable children and the children of keyworkers
• Job satisfaction
• Personal characteristics

Wave 2:

• Questions about teaching and learning during the Covid-19 crisis
• Identifying pupils' needs and deciding where support is most needed
• Personal characteristics

Initial estimates of staff and pupils testing positive for coronavirus (COVID-19) from the COVID-19 Schools Infection Survey across a sample of schools, within high and low prevalence local authority areas in England.

This study sought to assess the impact of COVID-19 and social isolation on young people's experiences of online sexual risks and gendered harms during a period of increased reliance on screens. Through surveys, and focus group interviews with young people (ages 13-21) and parents/carers, and teachers, the study addressed gaps in knowledge by exploring young people's differing experiences of online sexual harassment during Covid-19, in relation to gender (girls, boys, gender non-conforming), sexuality (LGBTQI+) and other intersecting identities. Survey: We administered an online survey to 551 teens of all genders (aged 13-18), 72 parents/carers, and 47 teachers, safeguarding leads and/or school staff across schools in England. These surveys were disseminated between May and September 2021 by our charitable partner, School of Sexuality Education (SSE). The survey for teens asked participants about their experiences of online sexual and gendered risk and harm during COVID-19, and the survey for parents/carers asked participants about their understanding of social media platforms (e.g. TikTok, WhatsApp, Instagram, Snapchat, etc.), and awareness of their children’s experiences of online sexual and gendered risk and harm online during COVID-19. The survey for teachers asked questions around their students’ experiences with a range of digital harassment and abuse (including technology facilitated gender-based violence), any training they received, and if their schools have policies dealing with these issues. Focus Groups and Interviews: Enacting a rigorous mixed methodology we simultaneously used a combination of focus groups and individual interviews with teens, school staff/safeguards, and parents/carers from May-July 2021 immediately following three major UK lockdowns. We conducted 17 focus groups with 65 teens and 29 individual follow-up interviews with this sample in five comprehensive secondary schools across England. The youth focus groups were arranged according to year group and self-identified gender and included two to six participants. Most groups were either all girls or all boys with one mixed gender group aligning to a pre-existing friendship group. Focus groups used arts-based methodologies and began with an ice-breaker activity where participants were asked to write down or draw something positive and negative about social media (including gaming platforms), using templates we provided. Template options included blank display screens of Instagram, Snapchat, TikTok, Yubo, WhatsApp, YouTube, Twitter, and PS5. After 5 to 10 minutes, participants took turns describing to the group what they wrote down. The researchers then used a focus group guide to ask questions, covering topics related to teens’ online experiences of risk and harm during COVID-19, as well as the gendered dynamics of these experiences. Following the focus groups, we provided teens with the opportunity to participate in follow-up individual interviews, where we elicited more detailed accounts of topics discussed in the focus groups. In addition, we conducted a total of 17 interviews with teachers, safeguarding leads and/or school staff in the five research schools. Interviews were designed to inform policy guidance for teachers and education associations on how to improve safety procedures and reporting practices for young people. We also conducted four online focus groups with parents/carers, with a total of nine parents/carers using a convenience sample. They were not parents of children from the schools in our study. Focus groups explored parents/carers’ knowledge and awareness of social media platforms, and the extent to which parents/carers felt equipped to support their children around sexually abusive or threatening online experiences they may have had on these popular platforms. After obtaining informed consent, discussions and interviews with students, teachers, and parents/carers were digitally recorded and transcribed verbatim. To ensure confidentiality, participants used pseudonyms, and transcripts were anonymized. The study's central aim is to take this data and develop a set of interactive digital resources that provide accessible and tailored advice and information for young people, teachers, and parents, on how to stay safe online during the pandemic and beyond.

We investigate the effects of school reopening on the evolution of COVID-19 infections during the second wave in Spain studying both regional and age-group variation within an interrupted time-series design. Spain's 17 Autonomous Communities reopened schools at different moments in time during September 2020. We find that in-person school reopening correlates with a burst in infections in almost all those regions. Data from Spanish regions gives a further leverage: in some cases, pre-secondary and secondary education started at different dates. The analysis of those cases does not allow to conclude whether reopening one educational stage had an overall stronger impact than the other. To provide a plausible mechanism connecting school reopening with the burst in contagion, we study the Catalan case in more detail, scrutinizing the interrupted time-series patterns of infections among age-groups and the possible connections between them. The stark and sudden increase in contagion among older children (10–19) just after in-person school reopening appears to drag the evolution of other age-groups according to Granger causality. This might be taken as an indirect indication of household transmission from offspring to parents with important societal implications for the aggregate dynamics of infections.

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 does not resume until fall 2021, that there will be an additional 1.1 million high-school dropouts in the U.S.

Dataset

This dataset was created by landlord

Contents

It contains the following files:

Dataset gathered during the lockdown among primary and secondary school heads in Lithuania and Slovenia in April and May 2020. The data show how the school heads organized remote education, what challenges they faced, what examples of good practice they developed, and how these could be used to deal with similar situations in the future. The study was conducted in the respective national languages through an online questionnaire containing 12 single-answer and multiple-choice questions, a Likert scale with seven items, and two open-ended questions.

The 2021-2022 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 2021-2022 school year and the Fall 2022 semester, from August 2021 – December 2022.

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.

• 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.

• Data from August 1, 2021 to June 24, 2022 correspond to the 2021-2022 school year. During this time frame, data from the AEI/Return to Learn Tracker and most state dashboards were not available. Inferred modalities with a probability below 0.6 were deemed inconclusive and were omitted. During the Fall 2022 semester, modalities for districts with a school closure reported by Burbio were updated to either “Remote”, if the closure spanned the entire week, or “Hybrid”, if the closure spanned 1-4 days of the week.
• Data from August 1, 2022 to December 31, 2022 correspond to the 2022-2023 school year and were processed in a similar manner to data from the 2021-2022 school year.
• 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.

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).