This dataset shows the results of the SARS-CoV-2 tests, which were carried out on pupils in baselstadt secondary schools. Individual tests are carried out at this school level. More information about testing in schools: https://www.coronavirus.bs.ch/testen/testen-in-schulen.htmlDieser Dataset has not been updated since the end of February 2022. Since mid-March 2022, data on tests in Basler schools will be published in a new dataset: https://data.bs.ch/explore/dataset/100183

This dataset shows the class pools tested on SARS-CoV-2 from Primary and Secondary Basel Urban Schools, indicating the number of pools tested and the test positivity rate per week. More information about testing in schools: https://www.coronavirus.bs.ch/testen/testen-in-schulen.htmlDieser Dataset has not been updated since the end of February 2022. Since mid-March 2022, data on tests in Basler schools will be published in a new dataset: https://data.bs.ch/explore/dataset/100183

Test-to-stay modified quarantine programs implemented in elementary and secondary schools increased participation in in-person learning during the Covid-19 pandemic . Little is known about the impact of other types of testing programs, such as surveillance testing, or immunity and vaccination, on cases of COVID-19 in elementary and secondary school settings. This retrospective cohort study, which was conducted in the state of Massachusetts during the 2021- 22 academic year, found that high vaccination uptake and community immunity acquired via prior infection mitigated COVID-19 cases in elementary and secondary schools. Testing strategies, including surveillance testing programs and test-to-stay modified quarantine programs, for supporting in-person learning were safe and effective but feasibility challenges are important considerations. These data can be used to inform policy about in-school mitigation measures during future respiratory virus pandemics.

School testing data were provided by Shield Illinois (ShieldIL), which conducted weekly in-school testing on behalf of the Illinois Department of Public Health (IDPH) for all participating schools in the state excluding Chicago Public Schools. The populations and proportions of students and employees in the studied school districts are reported by Elementary/Secondary Information System (ElSi) database.

There has been a lot of discussion about the role of schools in the transmission of severe acute respiratory coronavirus 2 (SARS-CoV-2) during the coronavirus 2019 (COVID-19) pandemic, where many countries responded with school closures in 2020. Reopening of primary schools in the Netherlands in February 2021 was sustained by various non-pharmaceutical interventions (NPIs) following national recommendations. Our study attempted to assess the degree of regional implementation and effectiveness of these NPIs in South Limburg, Netherlands. We approached 150 primary schools with a structured questionnaire containing items on the implementation of NPIs, including items on ventilation. Based on our registry of cases, we determined the number of COVID-19 cases linked to each school, classifying cases by their source of transmission. We calculated a crude secondary attack rate by dividing the number of cases of within-school transmission by the total number of children and staff members. Two-sample proportion tests were performed to compare these rates between schools stratified by the presence of a ventilation system and mask mandates for staff members. A total of 69 schools responded. Most implemented NPIs were aimed at students, except for masking mandates, which preferentially targeted teachers over students (63% versus 22%). We observed lower crude secondary attack rates in schools with a ventilation system compared to schools without a ventilation system (1.2% versus 2.8%, p

BackgroundU.S. school closures due to the coronavirus disease 2019 (COVID-19) pandemic led to extended periods of remote learning and social and economic impact on families. Uncertainty about virus dynamics made it difficult for school districts to develop mitigation plans that all stakeholders consider to be safe.MethodsWe developed an agent-based model of infection dynamics and preventive mitigation designed as a conceptual tool to give school districts basic insights into their options, and to provide optimal flexibility and computational ease as COVID-19 science rapidly evolved early in the pandemic. Elements included distancing, health behaviors, surveillance and symptomatic testing, daily symptom and exposure screening, quarantine policies, and vaccination. Model elements were designed to be updated as the pandemic and scientific knowledge evolve. An online interface enables school districts and their implementation partners to explore the effects of interventions on outcomes of interest to states and localities, under a variety of plausible epidemiological and policy assumptions.ResultsThe model shows infection dynamics that school districts should consider. For example, under default assumptions, secondary infection rates and school attendance are substantially affected by surveillance testing protocols, vaccination rates, class sizes, and effectiveness of safety education.ConclusionsOur model helps policymakers consider how mitigation options and the dynamics of school infection risks affect outcomes of interest. The model was designed in a period of considerable uncertainty and rapidly evolving science. It had practical use early in the pandemic to surface dynamics for school districts and to enable manipulation of parameters as well as rapid update in response to changes in epidemiological conditions and scientific information about COVID-19 transmission dynamics, testing and vaccination resources, and reliability of mitigation strategies.

To all,

This dataset was generated in order to fullfill a requirement for a graduate class in applied econometrics. I originally wanted to collect data on the effect of COVID-19 on student performance from a school district, but was unable to given that our local district was already conducting their own research.

The set contains a panel dataset, meant to emulate 6 semesters/trimesters with the first three taking place before the COVID-19 lockdowns, and the final three coming after the lockdowns. It also contains a cross-sectional dataset that is meant to be a single semester/trimester after the COVID-19 lockdowns. Variables were included and manipulated to model real world trends, or local demographics in Portland Oregon. There is a full list of variables at the end of this markdown.

It should be noted that student performance has greatly been diminished as a result of online education.

Feel free to reach out about the Stata code. It ended up being about 1500 lines to generate and manipulate, but I'm happy to share it with the same Public Domain license.

// VARIABLES used in the program // // NAME DATATYPE PURPOSE // // PERSONAL INFORMATION // // studentID into Number assigned to student. // school dummy 0/1, bool 1=school B (poor), 0= school A (wealthy) // gradelevel int Determine grade level of child. // gender dummy 0/1, bool 1=male, 0=female // covidpos dummy 0/1 1=child had Covid, 0=null // freelunch dummy 0/1 1=takes free and reduced lunch, 0=null // timeperiod categorical {0,1,2}=in-person learning, {3,4,5}=online learning // numcomputers into Defines number of computers in child's home. // familysize int Defines size of family, parents and siblings // householdincome float Household income for child. // fathereduc categorical System of values for highest level of father education // /* // no HS diploma 0 -- // High School diploma 1 Highest level of education is High School. // Bachelor degre 2 " " bachelors degree. // Master's Degree 3 " " masters degree. // Doctoral Degree 4 " " PhD. // \ // \ // then, if fathereduc=0, father did not finish High School. // / // mothereduc categorical System of values for highest level of mother education // / // no HS diploma 0 -- // High School diploma 1 Highest level of education is High School. // Bachelor degre 2 " " bachelors degree. // Master's Degree 3 " " masters degree. // Doctoral Degree 4 " " PhD. // \ // \ // then, if mothereduc=0, mother did not finish High School. // */ // // SCHOOL PERFORMANCE INFORMATION // // readingscore float Score for "reading" test in school. // writingscore float Score for "writing" test in school. // mathscore float Score for "math" test in school.
// // STATE PERFORMANCE INFORMATION // // readingscoreSL float Score for "reading" test at state level. // writingscoreSL float Score for "writing" test at state level. // mathscoreSL float Score for "math" test at state level. */

IntroductionWith the reopening of schools during the coronavirus disease 2019 (COVID-19) pandemic, it was imperative to understand the role of students and education professionals in the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this paper, we determined the seroprevalence of the SARS-CoV-2 anti-nucleocapsid antibodies in the school community in Campo Grande, the capital and most populous city of the state of Mato Grosso do Sul (Brazil) and evaluated its association with sex, school level, and school type.Materials and methodsThe survey was carried out in 20 public and private schools in the urban region of Campo Grande using the TR DPP® COVID-19 immunoglobulin M/immunoglobulin G (IgM/IgG) kit from the Immunobiological Technology Institute (Bio-Manguinhos, Rio de Janeiro, Brazil). Testing was carried out in three periods: from October to December 2021; from March to July 2022; and from August to November 2022. The participants were students aged 6–17 years enrolled in primary or secondary schools and professionals of different ages and roles.ResultsDuring the first testing period, 162 participants were seropositive for the IgM and/or IgG anti-nucleocapsid SARS-CoV-2 antibodies, with an estimated seroprevalence of 19.6% using Bayesian multilevel regression. In the second period, 251 participants were seropositive (estimated seroprevalence, 34.6%), while in the third period, 393 participants were seroconverted (estimated seroprevalence, 56.7%). In 2022, there was an increase in the seroconversion rate compared to that in 2021. The most frequently described acute manifestations in the three periods were fever, headache, sore throat, and runny nose. In terms of the demographic profile, there was no predominance of seropositivity between the sexes, although women represented approximately 70% of the study population. There were also no differences between students and school staff.DiscussionThe results made it possible to evaluate the extent of SARS-CoV-2 transmission in the school community through immunity developed against the virus, in addition to providing information about COVID-19 symptoms in children, adolescents, and adults.

BackgroundCOVID-19 testing is critical for identifying cases to prevent transmission. COVID-19 self-testing has the potential to increase diagnostic testing capacity and to expand access to hard-to-reach areas in low-and-middle-income countries. We investigated the feasibility and acceptability of COVID-19 self-sampling and self-testing using SARS-CoV-2 Antigen-Rapid Diagnostic Tests (Ag-RDTs).MethodsFrom July 2021 to February 2022, we conducted a mixed-methods cross-sectional study examining self-sampling and self-testing using Standard Q and Panbio COVID-19 Ag Rapid Test Device in Urban and rural Blantyre, Malawi. Health care workers and adults (18y+) in the general population were non-randomly sampled.ResultsOverall, 1,330 participants were enrolled of whom 674 (56.0%) were female and 656 (54.0%) were male with 664 for self-sampling and 666 for self-testing. Mean age was 30.7y (standard deviation [SD] 9.6). Self-sampling usability threshold for Standard Q was 273/333 (82.0%: 95% CI 77.4% to 86.0%) and 261/331 (78.8%: 95% CI 74.1% to 83.1%) for Panbio. Self-testing threshold was 276/335 (82.4%: 95% CI 77.9% to 86.3%) and 300/332 (90.4%: 95% CI 86.7% to 93.3%) for Standard Q and Panbio, respectively. Agreement between self-sample results and professional test results was 325/325 (100%) and 322/322 (100%) for Standard Q and Panbio, respectively. For self-testing, agreement was 332/333 (99.7%: 95% CI 98.3 to 100%) for Standard Q and 330/330 (100%: 95% CI 99.8 to 100%) for Panbio. Odds of achieving self-sampling threshold increased if the participant was recruited from an urban site (odds ratio [OR] 2.15 95% CI 1.44 to 3.23, P < .01. Compared to participants with primary school education those with secondary and tertiary achieved higher self-testing threshold OR 1.88 (95% CI 1.17 to 3.01), P = .01 and 4.05 (95% CI 1.20 to13.63), P = .02, respectively.ConclusionsOne of the first studies to demonstrate high feasibility and acceptability of self-testing using SARS-CoV-2 Ag-RDTs among general and health-care worker populations in low- and middle-income countries potentially supporting large scale-up. Further research is warranted to provide optimal delivery strategies of self-testing.

After the fifth and final year of high school, students in Italy must take a school-leaving examination, in Italian Esame di Maturità. Since 2019, it consists of two written tests and an oral one, scored on a scale of zero to 100, with 60 being the pass mark. Until 2018, there were three written exams. Between 2015 and 2025, about 60 percent of the students concluded their high school studies with a grade between 61 and 80. Around 20 percent of the candidates earned a mark from 81 to 90 each year, while the share of students scoring 100 or 100 with honors progressively increased. Grades between 2020 and 2022 were generally higher than in previous years due to different evaluation criteria brought on by the COVID-19 pandemic. In particular, the 2020 session comprised only a single oral test.

The Education Endowment Foundation (EEF) has been leading the management of the Tuition Partners (TP) pillar of the National Tutoring Programme (NTP) in 2020/2021, funded as part of the government coronavirus catch-up package. The TP programme allows schools to access subsidised tuition from a list of 33 tuition partners, quality approved by the EEF, to support pupils who have missed out the most as a result of school closures due to the COVID-19 pandemic. The focus is on supporting disadvantaged pupils, in particular those eligible for Pupil Premium, but with flexibility for schools to select those pupils who they feel were most in need of the support. The EEF commissioned the National Foundation for Educational Research (NFER) to run a reach and engagement nimble randomised controlled trial (RCT) with EM Tuition, an approved NTP Tuition Partner. The RCT explored the impact of two distinctive types of recruitment emails on school sign-up to the TP programme provided by EM Tuition: one email included a testimonial from a headteacher on the benefits of tutoring, the other included a summary of the research evidence of the benefits of tutoring. EM Tuition sent recruitment emails during February and March 2021 to 1,949 primary, secondary, and special schools in areas of England where they offer tutoring provision, including Hertfordshire, Essex, North London, the East of England, and Suffolk. Schools were randomly allocated to receive one of the two types of email messages. A team from NFER analysed the impact of the different recruitment emails on the proportion of schools signing a Memorandum of Understanding (MoU) or providing an Expression of Interest (EoI) for their pupils to receive tutoring from EM Tuition as part of the TP programme.

The Education Endowment Foundation (EEF) has been leading the management of the Tuition Partners (TP) pillar of the National Tutoring Programme (NTP) in 2020/2021, funded as part of the government coronavirus catch-up package. The TP programme allows schools to access subsidised tuition from a list of 33 tuition partners, quality approved by the EEF, to support pupils who have missed out the most as a result of school closures due to the COVID 19 pandemic. The focus is on supporting disadvantaged pupils, in particular those eligible for Pupil Premium, but with flexibility for schools to select those pupils who they feel were most in need of the support. The EEF commissioned the National Foundation for Educational Research (NFER) to run a reach and engagement nimble randomised controlled trial (RCT) with EM Tuition, an approved NTP Tuition Partner. The RCT explored the impact of two distinctive types of recruitment emails on school sign-up to the TP programme provided by EM Tuition: one email included a testimonial from a headteacher on the benefits of tutoring, the other included a summary of the research evidence of the benefits of tutoring. EM Tuition sent recruitment emails during February and March 2021 to 1,949 primary, secondary, and special schools in areas of England where they offer tutoring provision, including Hertfordshire, Essex, North London, the East of England, and Suffolk. Schools were randomly allocated to receive one of the two types of email messages. A team from NFER analysed the impact of the different recruitment emails on the proportion of schools signing a Memorandum of Understanding (MoU) or providing an Expression of Interest (EoI) for their pupils to receive tutoring from EM Tuition as part of the TP programme.

The Surrey Communication and Language in Education Study (SCALES) is the first UK population study of language development and disorder at school entry. The study is funded by Wellcome and the ESRC and involves more than 180 schools across Surrey UK.

This longitudinal study was initially established to determine (1) the extent to which 'Specific' Language Impairment (SLI) was prevalent in a population (as opposed to clinically ascertained) sample at school entry, and (2) the impact of language impairment on other aspects of development and how these patterns of development change over time. A second phase of SCALES aimed to test theoretical accounts of the developing relationship between language and social, emotional, and mental health during the transition to secondary school. Unfortunately, the final testing wave coincided with the global Covid-19 pandemic which impacted data collection due to school closures and lockdown.

The Surrey Communication and Language in Education Study: Intensive Data T2-T5, 2012-2020 concerns the intensive cohort who were assessed at four time points: Year 1, Year 3, Year 6, and Year 8. The dataset includes 528 variables assessing language, literacy, cognition, executive function and social, emotional and behavioural well-being.

Further information about the study can be found on the UCL Literacy, Language and Communication Laboratory SCALES project website.

Since the COVID-19 pandemic broke out in 2019, neuroticism has been proven a predictor of fear of COVID-19 infection. However, only few studies have been conducted on the factors affecting the relationship between neuroticism and this kind of fear. The present study is aimed at analyzing the role intolerance of uncertainty (IU) and sense of control (SOC) play in relation to neuroticism and the fear of COVID-19. We conducted a cross-sectional study in Guangdong and Guangxi provinces, China, and we collected complete datasets from 792 high school students. The main results can be described as follows: (a) individuals with high neuroticism tended to have higher intolerance of uncertainty (IU) and a lower sense of control (SOC); (b) IU and SOC played a mediating role between neuroticism and fear of COVID-19, and a serial mediation effect was found between these factors; (c) after controlling for both IU and SOC, the effect of neuroticism on fear was no longer significant. The results suggested a critical role of IU and sense of control in the causal relationship between neuroticism and fear.

Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students

Cumulation of the weekly release of COVID-19 data for Maricopa County by High School District. Includes PCR Test Percent Positivity as viewed on the Maricopa County School Reopening Dashboard map by week. For more information about the data, visit: https://www.maricopa.gov/5594/School-Metrics.

Study district and participants’ socio-demographic characteristics by study rounds, base line (December, 2020) and follow up (April, 2021).