The following guidance is directed to administrators of schools from kindergarten to grade 12 (K-12) and local public health authorities (PHAs) in jurisdictions where these schools exist. The guidance is not prescriptive in nature, rather, it supports administrators and PHA's to consider potential risks and mitigation strategies associated with the resumption of in-school classes during the COVID-19 pandemic.

On March 2, 2022 DC Health announced the District’s new COVID-19 Community Level key metrics and reporting. COVID-19 cases are now reported on a weekly basis. District of Columbia Public Schools testing for the number of positive tests and quarantined. Due to rapidly changing nature of COVID-19, data for March 2020 is limited.General Guidelines for Interpreting Disease Surveillance DataDuring a disease outbreak, the health department will collect, process, and analyze large amounts of information to understand and respond to the health impacts of the disease and its transmission in the community. The sources of disease surveillance information include contact tracing, medical record review, and laboratory information, and are considered protected health information. When interpreting the results of these analyses, it is important to keep in mind that the disease surveillance system may not capture the full picture of the outbreak, and that previously reported data may change over time as it undergoes data quality review or as additional information is added. These analyses, especially within populations with small samples, may be subject to large amounts of variation from day to day. Despite these limitations, data from disease surveillance is a valuable source of information to understand how to stop the spread of COVID19.

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 school year 2020/2021 was started by parents in Poland with concerns about the conditions of teaching in schools during the ongoing COVID-19 pandemic. More than 48 percent of parents do not know what the teaching rules will be, and more than 62 percent said that the conditions of education in schools are not safe enough.

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

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).

Social-emotional education and the relational competence of school staff and leaders are emphasized in research since they strongly impact childrens’ social, emotional, and cognitive development. In a longitudinal project—Empathie macht Schule (EmS)—we aim at evaluating the outcome and process of an empathy training for the whole school staff, including leaders. We compare three treatments to three control elementary schools via a mixed-methods approach employing qualitative and quantitative research methods targeting both, the school staff and the schoolchildren. Since the start of the project in 2019, the COVID-19 pandemic has disrupted the global education process, that is, the range of training activities for school staff in an unprecedented manner. First the lockdown and then the hygienic measures impact the habits and certainties in schools on multiple levels, including artifacts (e.g., physical distancing measures and virtual platforms), processes (e.g., virtual learning and home-schooling), social structures (e.g., separation of a high-risk group), and values (e.g., difficulties in building relations and showing empathy due to physical distance). Leaders and staff are facing an uncertain situation, while their actions and decisions may—also unintentionally—shape the social reality that will be inhabited to a significant extent. In this context, a number of questions become salient. How does the disruption of the pandemic affect interpersonal relationships, interactions, and the social field—the sum of relationships within the system of a school—as a whole? And specifically, how do the actors reflect on changes in the social field, their relationships, and the schools’ and classrooms’ overall relationship quality due to the crisis? The assessment combines qualitative interviews with leaders and teachers (N = 10) along with a self-report survey (N = 80) addressing the effects of the pandemic on interpersonal aspects in schools. Surprisingly, a number of positive effects were mentioned regarding the learning environment in the smaller-sized classes, which were caused by hygienic measures, as well as increased cohesion among faculty. The potential influence of these effects by consciously shaping relationships and cultivating empathy is discussed in the article.

Experimental statistics from the Student Experiences Insights Survey (SEIS) in England. Includes information on the mental health and well-being, behaviours, plans, and opinions of first year higher education students in the context of guidance on the coronavirus (COVID-19) pandemic.

In September 2020, the school year begins in Poland. The government has decided not to impose extraordinary obligations on schools such as wearing masks. As part of the recommendations, schools are obliged to observe hygiene, airing the rooms, or changing the classes' organization. Every second Polish respondent assessed this decision negatively. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

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).

DPH note about change from 7-day to 14-day metrics: As of 10/15/2020, this dataset is no longer being updated. Starting on 10/15/2020, the school learning model indicator metrics will be calculated using a 14-day average rather than a 7-day average. The new school learning model indicators dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/CT-School-Learning-Model-Indicators-by-County-14-d/e4bh-ax24 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). 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 each week 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; the previous week period for each dataset is the previous Sunday-Saturday, known as an MMWR week (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). The date listed is the date the dataset was last updated and corresponds to a reporting period of the previous MMWR week. For instance, the data for 8/20/2020 corresponds to a reporting period of 8/9/2020-8/15/2020. 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 Notes: 9/25/2020: Data for Mansfield and Middletown for the week of Sept 13-19 were unavailable at the time of reporting due to delays in lab reporting.

This dataset contains daily and cumulative information on positive PCR in students and teachers, as well as quarantined classrooms and centers.

The data corresponds to the day before its publication.

Total active quarantined classrooms refers to the number of currently quarantined classrooms.

At the moment 16,117 classrooms have started the course.

The positive cases detected by PCR through the COVID-College Teams (tests carried out by the prevention services contracted by the Ministry of Education) are counted.

In the new cases, the cases detected on the day are counted.

Cases accumulated since 9 September.

The classrooms and centers communicated in the day are counted. The educational spaces affected are under the criteria established by the Ministry of Health for the educational field.

Centres supported by public funds are counted as quarantined centres.

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).

The COVID-19 pandemic brought many disruptions to children’s education, including the education of children with intellectual (learning) disability and/or autism. We investigated the educational experiences of autistic children and children with an intellectual disability about a year after the COVID-19 pandemic started in the UK.

An online survey collected data during the summer/autumn of 2021 from 1,234 parents of 5 to 15 year-old children across all 4 UK countries. The study investigated school attendance and home learning experiences of children with intellectual disability and/or autistic children who were registered to attend school in 2021. The study also investigated the experience of Elective Home Education in families of children with a neurodevelopmental condition whose child was de-registered from school before and after the pandemic started in the UK in March 2020.

The study provided evidence on the impact of COVID-19 on school attendance and home education for children with a neurodevelopmental condition.

Education changed dramatically due to the COVID-19 pandemic. Schools closed in 2019/20. There was compulsory return to school in September 2020 with measures in place to control infection and new regulations about COVID-19-related absences. School attendance in the first term of 2020-21 was lower compared to other years. Many children were de-registered from school. In early 2020-21, there was a second prolonged period of national school closures. The pandemic has caused many disruptions to children's education.

Children with neurodevelopmental conditions (NDCs), in particular intellectual disability and autism, are the most vulnerable of vulnerable groups. Among children with special educational needs and disabilities (SEND), children with intellectual disability and/or autism consistently struggle to meet the required standards in education. Our study will focus on these two groups of children.

Before the pandemic, many children with NDCs missed school. Then the pandemic disrupted everyone's education. Approximately one year after the pandemic started, we will investigate the educational experiences of children with NDCs.

Our project will investigate: - School absence and reasons for absence among children with intellectual disability and/or autism - Child, family, and school factors associated with school absence - Barriers and facilitators of school attendance - Parents' experiences of home schooling

An online survey will collect data from approximately 1,500 parents of 5 to 17 year-old children with NDCs across all 4 UK countries. We will recruit parents of: (i) children registered with a school in spring/summer 2021; (ii) children not registered with a school in spring/summer 2021 but who were registered with a school at the start of the pandemic in March 2020; and (iii) children not registered with a school on either date. We will collect data on school attendance for those registered with a school, and data on home learning experiences for those not registered with a school. For all children, we will collect data on their mental health.

The first analysis will investigate school absence with a focus on children registered with a school. We will summarise school absence data as well as reasons for absence as reported by the parents. The second analysis will investigate school attendance: attending school or home schooling. We will describe the children currently registered to attend school (group 1), those not currently registered who were registered in March 2020 at the start of the pandemic (group 2), and those not registered on either point (group 3). We will summarise the reasons parents give for de-registering their child from school. Our final analysis will focus on home learning support during home schooling. We will describe the types of support schools offer to school-registered students during remote learning (when students are self-isolating/shielding, or schools are closed because of lockdown). We will describe the home learning experiences of school de-registered children and parents' satisfaction with these arrangements.

We will work closely with parents of children with NDCs, seeking their advice on the study. Our team includes the Council for Disabled Children, the largest umbrella organization in the UK bringing together many charities supporting disabled children and their families. We will share the study findings widely, including key messages for policies related to the education of children with special educational needs and disabilities.