According to a survey conducted in 2024, 50 percent of parents who chose to send their youngest child to a private school in the United States said that a safe environment was the main reason why they chose this type of school, followed by 40 percent who cited academic quality or reputation as the main reason. In comparison, parents who sent their youngest child to a public school, either inside or outside their school district, were most likely to say that location was the main reason behind their choice of school, at 57 percent.

The School Quality Reports share information about school performance, set expectations for schools, and promote school improvement. Due to size constraints only partial data is reflected, to view entire data open up the excel file that shown with data set name. These reports include information from multiple sources, including Quality Reviews, the NYC School Survey, and student performance. The School Quality Reports are organized around the Framework for Great Schools, which include six elements Rigorous Instruction, Collaborative Teachers, Supportive Environment, Effective School Leadership, Strong FamilyCommunity Ties, and Trust—that drive student achievement and school improvement.

According to a survey conducted in 2024, 32 percent of female parents and 31 percent of male parents agreed that if given the option, they would send their child to a public school inside their school district to obtain the best education. Male respondents were also slightly more likely than female respondents to say that they would send their child to a non-religious, secular private school to obtain the best education, at 23 percent.

Number of pupils with Special Educational Needs in Calderdale Primary Schools (Academies and Local Authority Maintained Schools); the data source is the termly school census. Academy data is only available from October 2015 onwards. The dataset for Academies and Local Authority Maintained Schools is being published as one dataset from October 2016. May 2020 is missing because this census was cancelled by the government due to the Covid-19 pandemic.

Also see - Other schools data

DfE Special Educational Needs (SEND) Report on LG Inform - report presents the statistics on SEND available for Calderdale, compared to comparison group of All English metropolitan boroughs.

This dataset provides, for each public NYC middle school, the number of students who participated in High School Admissions, the number of those students who took the Specialized High Schools Admissions Test (SHSAT) and the number who received an offer to one of the 8 testing Specialized High Schools.

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.

As of 2024, 33.1 percent of Americans said that they would send their child to a private school to obtain the best education if they were able to choose any type of school, a significant decrease from 44.8 percent who shared this belief in 2023. In comparison, the share of Americans who said that they would choose to send their child to a regular public school within their school district to obtain the best education rose to 32.3 percent from 27.8 percent in the previous year.

In a column published in the newspaper Le Monde at the beginning of September 2022, some left-wing deputies expressed their concern about the situation of the school, which, according to them, contravenes the commitments of the United Nations Convention on the Rights of the Child (UNCRC). Interviewed at the same time, nearly one French person in ten was willing to take to the streets to demonstrate over the current state of the school system. This inclination to mobilize was much higher among younger people. Indeed, the proportion of French people aged between 18 and 24 stating that they were ready to demonstrate was 4.5 times higher than that of people aged over 65, and twice as high as the national average. Concerns among the French population The education system ranks among the most worrying issues according to the population, and the level of concern has even increased over the past few months. While most French people feel that the early childhood education system is working quite well, or even very well, more than a third considered that the primary education system was functioning poorly in 2022, and nearly three out of five felt the same way about middle and high schools. When surveyed shortly before the start of the school year, they also seemed rather pessimistic about the perspectives for improvement in the French education system: for about 70 percent of them, the quality of education was worsening in secondary schools, and more than half held the same opinion about elementary schools. A crisis of the school system? Of the approximately 717,800 public sector teaching positions in France in 2021, resignations represent only a small portion, but their gradual increase over the past several years send a negative signal. In the 2012-2013 school year, 491 teachers chose to leave the French education system permanently. In 2020-2021, there were 2,978, over six times more. Moreover, according to the Ministry's figures, out of the 27,332 positions opened in 2022, only 19,838 had been filled in the public sector, and 3,482 in the private sector. It thus seems that articles 28 and 29 of the UNCRC, devoted to the right to education and personal development of children, are not applied, insofar as the lack of teachers and AESH (accompanying persons for students with disabilities) does not allow children to benefit from this right. In addition, this teacher shortage implies that the number of students per class is high. Indeed, despite its progress in reducing class sizes in schools, France is still behind other OECD countries. In 2019, a French public elementary school teacher had an average of about 18 students per class, compared to about ten in Norway, between 12 and 13 in Belgium and Spain, with the OECD average at 14.5.
Addressing the deterioration of the education system Among the measures that could improve the performance of the education system, improving teacher training and evaluation is perceived as a priority by the population. In addition, a large proportion of those surveyed before the start of the 2022 school year, and particularly sympathizers of left-wing parties, consider that it is urgent to recruit more teachers. Yet, while there is no single explanation for the recruitment crisis, which is multifactorial, the issue of salary is central. A Senate report published at the end of 2021 estimates that, in constant euros, i.e. after taking into account the impact of inflation, French teachers have lost between 15 and 25 percent of their salary over the past two decades. Their salary level is also among the lowest of the OECD countries: in 2019, a French elementary school teacher had a starting salary of 31,300 U.S. dollars per year, placing France far behind other European countries like Luxembourg (70,295 U.S. dollars), Germany (63,257 U.S. dollars), or Spain (42,215 U.S. dollars). Increasing teachers' salaries would thus be one of the measures that could make the profession more attractive.

Analysis of ‘2016 - 2017 School Quality Report Results for High School Transfer’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/8f4ae7d0-2fd7-4cfd-af6e-eb952772bb52 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

New York City Department of Education 2016 - 2017 School Quality Report Results for High School Transfer. The Quality Review is a process that evaluates how well schools are organized to support student learning and teacher practice. It was developed to assist New York City Department of Education (NYCDOE) schools in raising student achievement by looking behind a school’s performance statistics to ensure that the school is engaged in effective methods of accelerating student learning.

--- Original source retains full ownership of the source dataset ---

Internationally, teaching assistants (TA) support children with special needs and/or disabilities in completing classroom tasks while teachers manage whole-class instruction. Given the limited training available for TAs, influential researchers recently developed a framework to help TAs effectively design such a relevant practice. This ‘scaffolding’ framework encourages TAs to offer minor, neutral support, such as prompting, to students encountering difficulties completing tasks. This way, TAs maximise children’s thinking and learning.

To further inform the scaffolding framework, this research explored TAs’ views in England. Four primary-school TAs participated in a focus group (Focus Group 1) to discuss the scaffolding framework and examples of effective teaching practices across multiple task contexts, such as open and closed tasks. To facilitate the discussion around effective practices, two videos were illustrated to the participants including a TA supporting a child with SEND; the TAs were particularly invited to use the teaching context in the videos to describe effective strategies that they might adopt in comparable circumstances. Video 1 illustrates a TA supporting the child with SEND completing an open task, namely describing a picture, while Video 2 shows the dyad dealing with a closed task: identifying a grammar mistake in a sentence.

The data drawn from Focus Group 1 were finally thematically analysed. To this end, the author transcribed the data verbatim and then interrogated the transcript in relation to scaffolding theories and practices. This process resulted in a list of thematic codes identifying the co-constructed meaning of effective TA scaffolding practices along with practical examples and the factors (such as types of tasks) influencing the applicability of the scaffolding framework. These research findings drawn from Focus Group 1 were finally shared with the participants in a second round of focus group discussion, Focus Group 2, for confirmation and further elaboration.

Drawing from this research, this dataset includes pseudonymised transcriptions of the two focus group discussions. Video 1 and 2 and their transcripts are excluded from the dataset. Additionally, the dataset contains demographic information of the participating TAs gathered through a questionnaire.

This research programme aims to increase awareness of my PhD and its use amongst multiple audiences (e.g., politicians and scholars). To this end, academic publications, conferences, and podcast talks are used. Before describing the next project’s aims, an abstract of my PhD is produced. My PhD explored the classwork of teaching practitioners (TAs) internationally playing a crucial role in the mainstream education of children with SEND while teachers manage whole-class education. Whilst much of the existing research has targeted countries using TAs with limited training and a role focused on only assisting children with SEND, my study was carried out in a context (Italy) providing TAs with wealthy training and whole-class responsibilities equally to teachers. Drawing from classroom observations of a TA and interviews with 31 other TAs in Italian primary schools, the study suggested that: a) The TAs instructed children with SEND and infrequently managed whole-class instruction. b) Regardless of being well trained, they did not effectively scaffold the thinking of children with SEND – namely, they supplied children with answers to solve tasks, limiting their thinking and learning. Also, the TAs demonstrated a lack of awareness of a key sociocultural principle as to how children best learn, such as fostering their thinking by transferring them the responsibility of task completion. Hence, the project’s plan of sharing this PhD contributes to existing knowledge of a relatively unexplored research context. Moreover, the dissemination produces guidelines for TAs as to how to design effective instructions, known as ‘scaffolding’, in Italy and beyond according to the sociocultural tenet above. Despite being based on the experience of a few TAs, sharing the PhD findings might also have important implications for Italian policymakers due to uniform employment conditions and training of Italian TAs, whereby negatively impacting the teaching of highly trained TAs like the PhD participants. Among these is the seeming need to include more training on sociocultural principles of child development in the training of TAs, alongside its existing ample provision of courses on teaching methods. This might improve TAs’ awareness of the effect of their practice on children's learning and their teaching in practice. Though this policy implication is germane to the Italian context, countries reviewing the training of teachers and TAs might also benefit from this (e.g., the UK). Finally, this research programme includes new research addressing this question: ‘What do primary-school TAs perceive as effective scaffolding practices?’....

Restorative practices (RP) offer a means to establish positive and caring relationships and could thus foster the mental and scholastic development of students by improving classroom climate. This could benefit both students with and without special educational needs and disabilities (SEND), yet to date no studies evaluated these practices in inclusive educational settings. Here we report the findings of two consecutive studies: a pilot single-group pre-post (Study 1) and a non-randomised controlled study of RP training vs no-intervention control condition (Study 2). Across both studies, 531 students (46.5% female) with a mean age of 11.43 years (SD = 1.27) enrolled in the study at pre-test, of which 13.9% had a confirmed diagnosis of SEND and a further 5.7% were considered by teachers to likely have SEND. School and classroom climate, as well as victimisation experiences, emotional well-being and social inclusion of students were assessed using self-report questionnaires. Easy enrolment of schools and students at pre-test indicated that studies investigating the effects of RP training could be feasible. However, in part due to COVID-19 related school closures, student attrition rates of 90 and 77% were observed, for Study 1 and Study 2 respectively. In spite of observed improvements in classroom climate for the intervention group in Study 2, statistical analyses yielded no significant effects of the intervention and there were no moderation effects of students’ perceived inclusion and victimisation experiences. Together, these studies provide the first quantitative student data on implementing RP in an inclusive educational setting. We discuss our findings in light of the need for ideas on how to reduce attrition and also consider longer school-wide and single-class implementations of RP.

Overall attendance data include students in Districts 1-32 and 75 (Special Education). Students in District 79 (Alternative Schools & Programs), charter schools, home schooling, and home and hospital instruction are excluded. Pre-K data do not include NYC Early Education Centers or District Pre-K Centers; therefore, Pre-K data are limited to those who attend K-12 schools that offer Pre-K. Transfer schools are included in citywide, borough, and district counts but removed from school-level files. Attendance is attributed to the school the student attended at the time. If a student attends multiple schools in a school year, the student will contribute data towards multiple schools. Starting in 2020-21, the NYC DOE transitioned to NYSED's definition of chronic absenteeism. Students are considered chronically absent if they have an attendance of 90 percent or less (i.e. students who are absent 10 percent or more of the total days). In order to be included in chronic absenteeism calculations, students must be enrolled for at least 10 days (regardless of whether present or absent) and must have been present for at least 1 day. The NYSED chronic absenteeism definition is applied to all prior years in the report. School-level chronic absenteeism data reflect chronic absenteeism at a particular school. In order to eliminate double-counting students in chronic absenteeism counts, calculations at the district, borough, and citywide levels include all attendance data that contribute to the given geographic category. For example, if a student was chronically absent at one school but not at another, the student would only be counted once in the citywide calculation. For this reason, chronic absenteeism counts will not align across files. All demographic data are based on a student's most recent record in a given year. Students With Disabilities (SWD) data do not include Pre-K students since Pre-K students are screened for IEPs only at the parents' request. English language learner (ELL) data do not include Pre-K students since the New York State Education Department only begins administering assessments to be identified as an ELL in Kindergarten. Only grades PK-12 are shown, but calculations for "All Grades" also include students missing a grade level, so PK-12 may not add up to "All Grades". Data include students missing a gender, but are not shown due to small cell counts. Data for Asian students include Native Hawaiian or Other Pacific Islanders . Multi-racial and Native American students, as well as students missing ethnicity/race data are included in the "Other" ethnicity category. In order to comply with the Family Educational Rights and Privacy Act (FERPA) regulations on public reporting of education outcomes, rows with five or fewer students are suppressed, and have been replaced with an "s". Using total days of attendance as a proxy , rows with 900 or fewer total days are suppressed. In addition, other rows have been replaced with an "s" when they could reveal, through addition or subtraction, the underlying numbers that have been redacted. Chronic absenteeism values are suppressed, regardless of total days, if the number of students who contribute at least 20 days is five or fewer. Due to the COVID-19 pandemic and resulting shift to remote learning in March 2020, 2019-20 attendance data was only available for September 2019 through March 13, 2020. Interactions data from the spring of 2020 are reported on a separate tab. Interactions were reported by schools during remote learning, from April 6 2020 through June 26 2020 (a total of 57 instructional days, excluding special professional development days of June 4 and June 9). Schools were required to indicate any student from their roster that did not have an interaction on a given day. Schools were able to define interactions in a way that made sense for their students and families. Definitions of an interaction included: • Student submission of an assignment or completion of an

SHSAT Admissions

Daily Attendance figures are accurate as of 4:00pm, but are not final as schools continue to submit data after we generate this preliminary report.

This dataset is about books and is filtered where the book is Making the move : a guide for schools and parents on the transfer of pupils with autism spectrum disorders (ASDs) from primary to secondary school, featuring 7 columns including author, BNB id, book, book publisher, and ISBN. The preview is ordered by publication date (descending).

Note: This version supersedes version 1: https://doi.org/10.15482/USDA.ADC/1522654. In Fall of 2019 the USDA Food and Nutrition Service (FNS) conducted the third Farm to School Census. The 2019 Census was sent via email to 18,832 school food authorities (SFAs) including all public, private, and charter SFAs, as well as residential care institutions, participating in the National School Lunch Program. The questionnaire collected data on local food purchasing, edible school gardens, other farm to school activities and policies, and evidence of economic and nutritional impacts of participating in farm to school activities. A total of 12,634 SFAs completed usable responses to the 2019 Census. Version 2 adds the weight variable, “nrweight”, which is the Non-response weight. Processing methods and equipment used The 2019 Census was administered solely via the web. The study team cleaned the raw data to ensure the data were as correct, complete, and consistent as possible. This process involved examining the data for logical errors, contacting SFAs and consulting official records to update some implausible values, and setting the remaining implausible values to missing. The study team linked the 2019 Census data to information from the National Center of Education Statistics (NCES) Common Core of Data (CCD). Records from the CCD were used to construct a measure of urbanicity, which classifies the area in which schools are located. Study date(s) and duration Data collection occurred from September 9 to December 31, 2019. Questions asked about activities prior to, during and after SY 2018-19. The 2019 Census asked SFAs whether they currently participated in, had ever participated in or planned to participate in any of 30 farm to school activities. An SFA that participated in any of the defined activities in the 2018-19 school year received further questions. Study spatial scale (size of replicates and spatial scale of study area) Respondents to the survey included SFAs from all 50 States as well as American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and Washington, DC. Level of true replication Unknown Sampling precision (within-replicate sampling or pseudoreplication) No sampling was involved in the collection of this data. Level of subsampling (number and repeat or within-replicate sampling) No sampling was involved in the collection of this data. Study design (before–after, control–impacts, time series, before–after-control–impacts) None – Non-experimental Description of any data manipulation, modeling, or statistical analysis undertaken Each entry in the dataset contains SFA-level responses to the Census questionnaire for SFAs that responded. This file includes information from only SFAs that clicked “Submit” on the questionnaire. (The dataset used to create the 2019 Farm to School Census Report includes additional SFAs that answered enough questions for their response to be considered usable.) In addition, the file contains constructed variables used for analytic purposes. The file does not include weights created to produce national estimates for the 2019 Farm to School Census Report. The dataset identified SFAs, but to protect individual privacy the file does not include any information for the individual who completed the questionnaire. Description of any gaps in the data or other limiting factors See the full 2019 Farm to School Census Report [https://www.fns.usda.gov/cfs/farm-school-census-and-comprehensive-review] for a detailed explanation of the study’s limitations. Outcome measurement methods and equipment used None Resources in this dataset:Resource Title: 2019 Farm to School Codebook with Weights. File Name: Codebook_Update_02SEP21.xlsxResource Description: 2019 Farm to School Codebook with WeightsResource Title: 2019 Farm to School Data with Weights CSV. File Name: census2019_public_use_with_weight.csvResource Description: 2019 Farm to School Data with Weights CSVResource Title: 2019 Farm to School Data with Weights SAS R Stata and SPSS Datasets. File Name: Farm_to_School_Data_AgDataCommons_SAS_SPSS_R_STATA_with_weight.zipResource Description: 2019 Farm to School Data with Weights SAS R Stata and SPSS Datasets