As a result of the coronavirus outbreak in 2020, many parents have been hesitant to send their kids back to school. However, 61 percent of parents in a survey said they were not at all likely to seek alternative, full-time education in the fall if schools reopened.

More than 70 percent of parents believed that final classes such as the VIII and XII grade should reopen before summer holidays to help students who will have their final exams. Nevertheless, 22 percent of respondents underlined that nurseries should also open before summer. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

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.

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.

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.

According to a survey among Indian parents in March 2020, a majority of respondents favored schools remaining closed for two months until end of May. Over 81 percent of respondents wanted schools to reopen on June 1, 2020. Many Indian families that considered the novel coronavirus a health issue stated that they are staying alert and taking precautionary measures.

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.

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 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.

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 July 2020, 62 percent of U.S. respondents said that they do not support cutting federal funding for schools that do not fully reopen for in-person classes in the fall, while 19 percent of respondents supported cutting federal funding.

As of July 2020, 84 percent of Democratic respondents said that they do not support cutting federal funding for schools that don't fully open for in-person classes, while 41 percent of Republican respondents supported cutting federal funding.

This article provides the findings of an exploratory, qualitative study on distance learning policies and practices from a purposeful sample of five California school districts and 25 district and school leaders with large numbers and/or larger percentages of current or former English Learners. To understand the extent to which leaders address English Learners’/Emergent Bilinguals’ (EL/EM) needs during the pandemic, we posed the following research question: What are leaders’ local policies and practices in designing and implementing distance learning to promote equity for English Learners? We gathered three key district policy documents across three moments during the pandemic: (1) COVID-19 Operations Written Reports (Spring 2020), (2) School Reopening Plans (Summer 2020), and (3) Learning Continuity and Attendance Plans (Fall 2020). We also conducted interviews and triangulated data sources using grounded theory to analyze and understand how equity is framed and implemented. Data triangulation and iterative rounds of coding allowed us to identify three inter-related findings: (1) leading in the crisis of connectivity and bridging the digital divide; (2) maximizing diverse ELs’ learning experiences; and, (3) building from collaborative leadership cultures to collaborative virtual leadership cultures. Using these key findings, we conceptualized the framework for equity leadership for English Learners to address the needs of this underserved population. We conclude with a call for further examination, in both leadership preparation as well as in policy implementation research.

For fall 2020, when buildings reopened and students were able to return on-site, schools were advised to review any requests from families submitted via the remote learning device form and conduct an assessment of all of their devices (including recent purchases) in order to assign available devices to families in need . For any families where the school could not meet the need, schools confirmed that a device was needed by requesting that a device be prepared for that student after the City ordered the additional 100K ipads. Once receiving this confirmation, the central office prepared and shipped the device.

Parents that selected one of the below factors as the top 5 priorities for reopening schools and resuming in-person classes.

Abstract copyright UK Data Service and data collection copyright owner.

The Active Lives Children and Young People Survey, which was established in September 2017, provides a world-leading approach to gathering data on how children engage with sport and physical activity. This school-based survey is the first and largest established physical activity survey with children and young people in England. It gives anyone working with children aged 5-16 key insight to help understand children's attitudes and behaviours around sport and physical activity. The results will shape and influence local decision-making as well as inform government policy on the PE and Sport Premium, Childhood Obesity Plan and other cross-departmental programmes. More general information about the study can be found on the Sport England Active Lives Survey webpage and the Active Lives Online website, including reports and data tables.

The Active Lives Children and Young People Survey, 2019-2020 began as the usual school-based survey (i.e. completed at school as part of lessons). From 20 March 2020, schools, colleges and nurseries were closed in the UK due to the COVID-19 pandemic and remained closed until 1 June 2020, when there was a phased reopening for reception, and Years 1 and 6. The Active Lives survey fieldwork in Spring term finished two weeks early before the end of term, in line with the school closures.

Due to the closure of schools, the survey had to be adapted for at home completion. The adaptions involved minor questionnaire changes (e.g. to ensure the wording was appropriate for both the new lockdown situation and to account for the new survey completion method at home) and communication changes. For further details on the changes, please see the accompanying technical report. The circumstances and adaptations resulted in a delay to survey fieldwork re-starting. This means that the data does not cover the full lockdown period, and instead re-starts from mid-May 2020 (when the survey was relaunched). Sample targets were also reduced as a result of the pandemic, resulting in a smaller proportion of summer term responses for 2019-20 when compared to previous years. As part of Sport England’s official publication, an additional Coronavirus report was produced, which outlines changes during the ‘easing restrictions’ phase of lockdown from mid-May to the end of July, comparing the summer term in 2020 with summer 2019. Due to the reduced summer term sample, it is recommended to analyse within term and/or school phase for academic year 2019-20.

The survey identifies how participation varies across different activities and sports, by regions of England, between school types and terms, and between different demographic groups in the population. The survey measures levels of activity (active, fairly active and less active), attitudes towards sport and physical activity, swimming capability, the proportion of children and young people that volunteer in sport, sports spectating, and wellbeing measures such as happiness and life satisfaction. The questionnaire was designed to enable analysis of the findings by a broad range of variables, such as gender, family affluence and school year.

The following datasets have been provided:

1. Main dataset: includes responses from children and young people from school years 3 to 11, as well as responses from parents of children in years 1-2. The parents of children in years 1-2 provide behavioural answers about their child’s activity levels, they do not provide attitudinal information. Using this main dataset, full analyses can be carried out into sports and physical activity participation, levels of activity, volunteering (years 5 to 11), etc. Weighting is required when using this dataset (wt_gross / wt_gross.csplan files are available for SPSS users who can utilise them).
2. Year 1-2 dataset: includes responses from children in school years 1-2 directly, providing their attitudinal responses (e.g. whether they like playing sport and find it easy). Analysis can be carried out into feelings towards swimming, enjoyment for being active, happiness etc. Weighting is required when using this dataset (wt_gross / wt_gross.csplan files are available for SPSS users who can utilise them).
3. Teacher dataset – this .sav file includes response from the teachers at schools selected for the survey. Analysis can be carried out into school facilities available, length of PE lessons, whether swimming lessons are offered, etc. Weighting was formerly not available, however, as Sport England have started to publish the Teacher data, from December 2023 we decide to apply weighting to the data. The Teacher dataset now includes weighting by applying the ‘wt_teacher’ weighting variable.

For further information about the variables available for analysis, and the relevant school years...

{"references": ["Ploj Virti\u010d, M., Dolenc, K., & \u0160orgo, A. (2021). Changes in online distance learning behaviour of university students during the Coronavirus disease 2019 outbreak, and development of the model of forced distance online learning preferences. European Journal of Educational Research, 10(1), 393-411. https://doi.org/10.12973/eu-jer.10.1.393", "\u0160orgo, A., Ploj Virti\u010d, M., Dolenc, K. (2021). Differences in personal innovativeness in the domain of information technology among university students and teachers [submitted manuscript]", "Dolenc, K., \u0160orgo, A., Ploj Virti\u010d, M. (2021). The difference in views of educators and students on Forced Online Distance Education can lead to unintentional side effects. Education and Information Technologies [accepted manuscript], https://www.10.1007/s10639-021-10558-4", "Ploj Virti\u010d, M., Dolenc, K., \u0160orgo, A. (2020). \u0160tudij na daljavo na Univerzi v Mariboru v \u010dasu izbruha CoVID-19 in napovedni model \u0161tudija na daljavo za \u010das po ponovnem odprtju univerze. V: Inovativna uporaba IKT v visokem \u0161olstvu: izzivi in prilo\u017enosti: konferenca IKTVVIS, online, 24. - 25. september 2020. Ljubljana: IKTVVIS. 2020, http://iktvvis.si/SekcijaCC12.html#povzetek50", "\u0160orgo, A., Ploj Virti\u010d, M., Dolenc, K. (2020). Razlike med univerzitetnimi u\u010ditelji in \u0161tudenti v osebni inovativnosti na podro\u010dju informacijskih tehnologij. V: Inovativna uporaba IKT v visokem \u0161olstvu: izzivi in prilo\u017enosti: konferenca IKTVVIS, online, 24. - 25. september 2020. Ljubljana: IKTVVIS. 2020, http://iktvvis.si/SekcijaC21.html#povzetek10"]} The outbreak of COVID -19 forced most universities into distance education. Three didacticians and researchers from the University of Maribor, Slovenia: Kosta Dolenc, Mateja Ploj Virti�� and Andrej ��orgo formed a self-initiated initiative project group during the COVID -19 epidemic and started the first project with the working title: The Side Effects of Forced Online Distance Education (FODE). The aim of the second study, conducted during the first wave of the epidemic in March 2020, was to investigate the response of university students to the new situation. The project documentation provided for the Forced Online Distance Learning (FODL) consists of: abstract, instrument, copy of the descriptive statistics, and SPSS dataset. This work was supported by the Slovenian Research Agency under the core projects: "Information systems", grant no. P2-0057 (��orgo Andrej) and "Computationally intensive complex systems", grant no. P1-0403 (Ploj Virti�� Mateja). The authors declared that there were no conflicts of interest and that the opinions expressed were those of the authors. For the English translation of the instrument, see the articles cited in the Introduction (see Appendix).

The market of online supplementary education for children in Russia was measured at 93.6 million academic hours in 2020. The figure increased rapidly in 2020 due to the COVID-19 pandemic and a consecutive demand for online educational services.

Distance education during COVID-19 in Russia Due to the COVID-19 pandemic, schools and universities closed in Russia in March 2020, reopening at a different pace depending on the regional epidemiological situation. Prior to the lockdown, 60 percent of Russian higher education institutions were prepared for remote learning. To facilitate the public education system, state authorities set up platforms like the Russian Electronic School or the Moscow Electronic School; the latter saw a 630 percent growth in search queries in the capital in March 2020. Moreover, several EdTech companies offered free courses during the lockdown. However, approximately one third of Russians living in medium-income households stated that they did not possess the necessary resources for distance education, such as a workplace, electronic devices, or an internet connection.

Results from multivariable logistic regression models estimating unadjusted and adjusted odds ratio for demographic characteristics associated with parents’ plans to not have their child return to school.

Seroprevalence of SARS -CoV-2 (seroprevalence = n/N*100, N = Total participants and n = SARS-COV-2 positive for IgG, Where N = 1884 and n = 485 for the first round and N = 1721 and n = 588 for second round respectively).

In the fiscal year 2020, the Ministry of Education, Culture, Sports, Science and Technology (MEXT) in Japan planned to utilize around 58 billion Japanese yen for an emergency aid package supporting sports and cultural activities following the spread of the coronavirus disease (COVID-19). About 40.5 billion yen were prepared for the support of schools to reopen with a safe learning environment after the countrywide closing down due to the spread of COVID-19 infections. Following the accelerated impact of COVID-19, the government announced the second supplementary budget in June 2020 as an addition to the initial annual budget and the first supplementary budget stated by the end of April 2020.

Univariable and multivariable logistic regression analysis for preparedness to combat the spread of COVID-19 in DBU, Northeast Ethiopia, 2020.

University of California campus COVID-19 dashboards.