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.

This dataset is about books and is filtered where the book subjects is School boards-Law and legislation-England, featuring 9 columns including author, BNB id, book, book publisher, and book subjects. The preview is ordered by publication date (descending).

Forecast: Total Tertiary Education Graduates in Social Sciences, Business and Law in the UK 2024 - 2028 Discover more data with ReportLinker!

Data on class sizes in primary schools are collected each year as part of the pupil census. The data gives the number of pupils in each class in September 2023."Class type" gives the stage of pupils in the class or, where more than one stage is present "Co" denotes a composite class.In a class where there are two or more teachers then 'Two or More Teachers' will have a value of '1' class size maxima for P1 classes is 25 and for single stage P2 or P3 classes is 30. This is set out in The Education (Lower Class Sizes) (Scotland) Amendment Regulations 2010.These regulations allow certain exceptions such as pupils who join a class after the end of a placing round and Additional Support Needs pupils who only join a class for part of the time.For P4-P7 class size maxima are set out in teachers terms and conditions of service. For these years there is a normal maximum of 33. Composite classes throughout primary have a class size maximum of 25.Excepted pupils in class-size legislation are-(a) children whose record of additional support needs specify that they should be educated at the school concerned, and who are placed in the school outside a normal placing round;(b) children initially refused a place at a school, but subsequently on appeal offered a place outside a normal placing round or because the education authority recognise that an error was made in implementing their placing arrangements for the school;(c) children who cannot gain a place at any other suitable school within a reasonable distance of their home because they move into an area outside a normal placing round;(d) children who are pupils at special schools, but who receive part of their education at a mainstream school; and(e) children with additional support needs who are normally educated in a special unit in a mainstream school, but who receive part of their lessons in a non-special class.These are National Statistics background data. National Statistics are produced to high professional standards set out in the National Statistics Code of Practice. They undergo regular quality assurance reviews to ensure that they meet customer needs. They are produced free from any political interference.

Forecast: Female Tertiary Education Graduates in Social Sciences, Business and Law in the UK 2024 - 2028 Discover more data with ReportLinker!

The Department for Education is committed to continuous improvement in its handling of complaints about schools. The completed report covers complaints that relate to state funded schools including academies and free schools, received by the department in the period 1st August 2012 to July 2013. Prior to July 2012 some complaints about schools were handled by the Secretary of State for Education, some were handled as a pilot by the Local Government Ombudsman(LGO). The Education Act 2011 rationalised the LGO arrangements . In July 2012 the powers of the LGO to consider school complaints were repealed, so that all complainants in England could complain to the Secretary of State about a school. During the passage of the Education Act 2011, the department commissioned independent research about its handling of complaints about schools. This report covers those findings and can be located here:- https://www.gov.uk/government/publications/complaints-about-schools-customer-satisfaction-survey-2013

Abstract copyright UK Data Service and data collection copyright owner.The National Pupil Database (NPD) is one of the richest education datasets in the world. It is a longitudinal database which links pupil characteristics to information about attainment for those who attend schools and colleges in England. There are a range of data sources in the NPD providing detailed information about children's education at different stages (pre-school, primary and secondary education and further education). Pupil level information was first collected in January 2002 as part of the Pupil Level Annual Schools Census (PLASC). The School Census replaced the PLASC in 2006 for secondary schools and in 2007 for nursery, primary and special schools. The School Census is carried out three times a year in the spring, summer and autumn terms (January, May and October respectively) and provides the Department for Education with both pupil and school-level data. The NPD is available through the UK Data Archive in three tiers. Tiers two and three are the most sensitive and must be accessed via the Archive's safe room, whereas tier four can be accessed remotely through the Archive's Secure Lab. Tier two contains individual pupil level data which is identifiable and sensitive. Individual pupil level extracts include sensitive information about pupils and their characteristics, including items described as 'sensitive personal data' within the UK Data Protection Act 1998 which have been recoded to become less sensitive. Examples of sensitive data items include ethnic group major, ethnic group minor, language group major, language group minor, Special Educational Needs and eligibility for Free School Meals. Tier three represents aggregated school level data which is identifiable and sensitive. Included are aggregated extracts of school level data from the Department of Education's School Level Database which include items described as 'sensitive personal data' within the Data Protection Act 1998 and could include small numbers and single counts. For example, there is 1 white boy eligible for Free School Meals in school x who did not achieve level 4 in English and maths at Key Stage 2. Tier four represents less sensitive data than tiers two and three. Included are individual pupil level extracts that do not contain information about pupils and their characteristics which are considered to be identifying or described as sensitive personal data within the Data Protection Act 1998. For example, the extracts may include information about pupil attainment, prior attainment, progression and pupil absences but do not include any identifying data items like names and addresses and any information about pupil characteristics other than gender. Extracts from the NPD are also available directly from the Department of Education through GOV.UK's National pupil database: apply for a data extract web page. The fourth edition (September 2017) includes a data file and documentation for the year 2016.

This statistic displays the frequency of lessons about legal and illegal drugs in each school year in England in 2023, by school year. In this year, eight percent of year nine pupils received lessons about legal and illegal drugs more than once a term.

Class size data in publicly funded primary schools is collected each year as part of the annual pupil census. The data presented here is an extract of data published by the Scottish Government. The data provides a count of pupils in each class in the Glasgow local authority area only and is graduated to school and class type level. 'Class type' gives the stage of pupils in the class or, where more than one stage is present. 'Co' denotes a composite class. Data forms part of a time series and covers the years 2003 - 2013. The class size maxima (2014-03-31T12:00:00) for P1 pupils is 25 and 30 for single stage class P2 or P3 is set out in 'The Education (Lower Class Sizes) (Scotland) Amendment Regulations 2010'. These regulations allow certain exceptions such as pupils who join a class after the end of a placing round and Additional Support Needs pupils who only join a class for part of the time. For P4-P7 class size maxima are set out in teachers terms and conditions of service. For these years there is a normal maximum of 33. Composite classes throughout primary have a class size maximum of 25 These are National Statistics background data. National Statistics are produced to high professional standards set out in the National Statistics Code of Practice. They undergo regular quality assurance reviews to ensure that they meet customer needs. They are produced free from any political interference. Analysis of class size at a national level is available through the following link. Licence: None class-size-2003-13.zip - https://dataservices.open.glasgow.gov.uk/Download/Organisation/728522f0-86da-48c6-8f75-1649934eb8a4/Dataset/aa88cabd-ede8-448f-b8ea-314c852c29fb/File/fd985540-f242-4c1a-a6e4-54fb3f8c4455/Version/d1cdf392-0304-43f3-92de-33a3af8edd19

14 semi-structured interviews conducted with legal services professionals in England over the period 2019-20. Interviewees were drawn from law firms, legal technology companies, law libraries, and legal data providers. The interviews explored in what ways does a lack of appropriate educational provision currently present a barrier to (a) law firms in adopting new technologies, and (b) computer scientists in proceeding efficiently within the rule of law; and how might this need best be addressed to allow those working in these sectors to interact innovatively and efficiently?

The proposed research will explore the potential and limitations of using artificial intelligence (AI) in support of legal services. AI's capabilities have made enormous recent leaps; many expect it to transform how the economy operates. In particular, activities relying on human knowledge to create value, insulated until now from mechanisation, are facing dramatic change. Amongst these are professional services, such as law.

Like other professions, legal services contribute to the economy both through revenues of service providers and through benefits provided to clients. For large business clients, who can choose which legal regime will govern their affairs, UK legal services are an export good. For small businesses and citizens, working within the domestic legal system, UK legal services affect costs directly. Yet unlike other professions, the legal system has a dual role in society. Beyond the law's role in governing economic order, the legal system is more fundamentally a structure for social order. It sets out rules agreed on by society, and also the limits of politicians' ability to enact these rules.

Consequently, the stakes for AI's implementation in UK legal services are high. If mishandled, it could threaten both economic success and governance more generally. Yet if executed effectively, it is an opportunity to improve legal services not only for export but also for citizens and domestic small businesses. Our research seeks to identify how constraints on the implementation of AI in legal services can be relaxed to unlock its potential for good.

One major challenge is the need for 'complementary' adjustments. Adopting a disruptive new technology like AI requires changes in skills, training, and working practices, without which the productivity gains will be muted. We will investigate training and educational needs for lawyers' engagement with technology and programmers' engagement with law. With private sector partners, we will develop education and training packages that respond to these needs for delivery by both universities and private-sector firms. We will investigate emerging business models deploying AI in law, and identify best practice in governance and strategy. Finally, we will compare skills training and technology transfer in the UK with countries such as the US, Hong Kong and Singapore, and ask what UK policymakers can learn from these competitors. To the extent that these issues are also faced by other high-value professional services, these parts of our results will also have relevance for them.

However, the dual role of the legal system poses unique challenges that justify a research package focusing primarily on this sector. There are constitutional limits to how far law's operation can be adjusted for economic reasons: we term this second constraint 'legitimacy'. We will map how automation in dispute resolution might trigger constitutional legal challenges, how these challenges relate to types of dispute resolution technology and types of claim, and use the resulting matrix to identify opportunities for maximum benefit from automation in dispute resolution.

A third constraint is the limits of technological possibility. AI systems rely on machine learning, which reaches answers by identifying patterns in very large amounts of data. Its limitations are the size of the datasets needed, and its inability to provide an explanation for how the answer was reached. This poses particular difficulties for law, where many applications require or benefit from reasons being given. We will explore the possibility for frontier AI technologies to deliver legal reasoning.

The research will involve a mix of disciplinary inputs, reflecting the multi-faceted nature of the problem: Law, Computer Science, Economics, Education, Management and Political Economy. Working closely with private-sector partners will ensure our research benefits from insights into, and testing against, real requirements.

Data on the top universities for Law in 2025.

(:unav)...........................................

As industry-university collaborations are promoted to commercialize university research and foster economic growth, it is important to understand how companies benefit from these collaborations, and to ensure that resulting academic discoveries are developed for the benefit of all stakeholders: companies, universities and public. Lock up of inventions, and censoring of academic publications, should be avoided if feasible. This case-study analysis of interviews with 90 companies in Canada, Japan, the UK and USA assesses the scope of this challenge and suggests possible resolutions. The participating companies were asked to describe an important interaction with universities, and most described collaborative research. The most frequently cited tensions concerned intellectual property management and publication freedom. IP disagreements were most frequent in the context of narrowly-focused collaborations with American universities. However, in the case of exploratory research, companies accepted the IP management practices of US universities. It might make sense to let companies have an automatic exclusive license to IP from narrowly defined collaborations, but to encourage universities to manage inventions from exploratory collaborations to ensure development incentives. Although Canada, the UK and US have strong publication freedom guarantees, tensions over this issue arose frequently in focused collaborations, though were rare in exploratory collaborations. The UK Lambert Agreements give sponsors the option to control publications in return for paying the full economic cost of a project. This may offer a model for the other three countries. Uniquely among the four countries, Japan enables companies to control exclusively most collaborative inventions and to censor academic publications. Despite this high degree of control, the interviews suggest many companies do not develop university discoveries to their full potential. The steps suggested above may rebalance the situation in Japan. Overall, the interviews reveal the complexity of these issues and the need for flexibility on the part of universities and companies.

37 of the United States' 45 presidents (officially 46 as Grover Cleveland is counted as both the 22nd and 24th president) attended a university, college or other institution of higher education; 34 of these completed their studies and graduated. After completing their undergraduate studies, twenty U.S. presidents attended a graduate school, with eleven attaining a qualification (seven of which were law degrees). Only eight U.S. presidents, including two of the most highly regarded, George Washington and Abraham Lincoln, did not attend college, while all presidents since Dwight D. Eisenhower have attained some form of degree or equivalent qualification.

Institutions Harvard University, the oldest institution of higher education in the U.S., has the highest number of presidential alumni, with eight in total. Of the eight Ivy League schools, widely regarded as the most prestigious universities in the United States, five include U.S. presidents among their alumni, and fifteen U.S. presidents have attained a qualification these universities. Only two U.S. presidents have studied abroad; they were John Quincy Adams, who studied at Leiden University in the Netherlands while his father was stationed in Europe, and Bill Clinton, who studied at Oxford University in England. John F. Kennedy had planned to study at the London School of Economics, but fell ill after enrolling and transferred stateside to Princeton, before illness again forced his withdrawal a few months later. Two U.S. presidents founded universities; the University of Virginia was founded by Thomas Jefferson (and attended by Woodrow Wilson), and the State University of New York at Buffalo was founded by Millard Fillmore; one of the eight U.S. presidents who never attended college. Donald Trump did establish a company called "Trump University" in 2004, however this provided training for potential property realtors, and was not an educational institution (in 2016, Trump paid 25 million U.S. dollars to settle a lawsuit with the State of New York, as Trump University was deemed to have defrauded customers and made false statements).

Most educated presidents

In 1751, John Adams was the first future-president to go to college, entering Harvard at the age of sixteen, and graduating with a Bachelor of Arts in 1755. The most recent presidential graduate is Barack Obama, who attended Occidental College from 1979 to 1981, before transferring to Columbia University where he majored in political science, graduating in 1983; Obama later obtained his law degree from Harvard Law School in 1991. Woodrow Wilson is the only U.S. president to have obtained a Ph.D., which he received from Johns Hopkins University in 1886 for his work titled "Congressional Government: A Study in American Politics", and George W. Bush is the only U.S. president to have attained an MBA degree. Three U.S. presidents attended military universities, with both Ulysses S. Grant and Dwight D. Eisenhower graduating from West Point Military Academy, and Jimmy Carter graduating from the U.S. Naval Academy (Eisenhower also attended three other U.S. Army colleges during his military career, which began in 1915 and ended in 1969). Incumbent President Donald Trump obtained a B.S. in economics from the Wharton School of the University of Pennsylvania in 1968.

Abstract copyright UK Data Service and data collection copyright owner.The National Pupil Database (NPD) is one of the richest education datasets in the world. It is a longitudinal database which links pupil characteristics to information about attainment for those who attend schools and colleges in England. There are a range of data sources in the NPD providing detailed information about children's education at different stages (pre-school, primary and secondary education and further education). Pupil level information was first collected in January 2002 as part of the Pupil Level Annual Schools Census (PLASC). The School Census replaced the PLASC in 2006 for secondary schools and in 2007 for nursery, primary and special schools. The School Census is carried out three times a year in the spring, summer and autumn terms (January, May and October respectively) and provides the Department for Education with both pupil and school-level data. The NPD is available through the UK Data Archive in three tiers. Tiers two and three are the most sensitive and must be accessed via the Archive's safe room, whereas tier four can be accessed remotely through the Archive's Secure Lab. Tier two contains individual pupil level data which is identifiable and sensitive. Individual pupil level extracts include sensitive information about pupils and their characteristics, including items described as 'sensitive personal data' within the UK Data Protection Act 1998 which have been recoded to become less sensitive. Examples of sensitive data items include ethnic group major, ethnic group minor, language group major, language group minor, Special Educational Needs and eligibility for Free School Meals. Tier three represents aggregated school level data which is identifiable and sensitive. Included are aggregated extracts of school level data from the Department of Education's School Level Database which include items described as 'sensitive personal data' within the Data Protection Act 1998 and could include small numbers and single counts. For example, there is 1 white boy eligible for Free School Meals in school x who did not achieve level 4 in English and maths at Key Stage 2. Tier four represents less sensitive data than tiers two and three. Included are individual pupil level extracts that do not contain information about pupils and their characteristics which are considered to be identifying or described as sensitive personal data within the Data Protection Act 1998. For example, the extracts may include information about pupil attainment, prior attainment, progression and pupil absences but do not include any identifying data items like names and addresses and any information about pupil characteristics other than gender. Extracts from the NPD are also available directly from the Department of Education through GOV.UK's National pupil database: apply for a data extract web page. The fourth edition (September 2017) includes a data file and documentation for the year 2016.

(:unav)...........................................

Abstract copyright UK Data Service and data collection copyright owner.

Background to the PACE study
The PACE (Primary Assessment, Curriculum and Experience) project aimed to study the impact on English primary schools of the introduction, following the 1988 Education Reform Act, of the National Curriculum and its associated assessment procedures. Questionnaires and interviews with heads and infant teachers in forty-eight schools in eight varied areas, provided background data for a closer focus on nine schools. Children as well as teachers in primary school classes were interviewed. These randomly selected pupils formed a cohort who have taken part in the subsequent years of the study. New assessment procedures were also observed and analysed as they were introduced.
The project consisted of three phases: phase 1 (SN:3486) was carried out in 1990-1992, phase 2 (SN:3470) in 1993-1994 and phase 3(SN:3969) in 1995-1996.
Pupils of the original cohort, continued to be observed and interviewed, as did their teachers into year 6 (phase 3). In addition, teachers and heads in the wider sample of schools have again been interviewed; in this phase both Key Stage 1 and Key Stage 2 teachers provide material on the degree and kind of change experienced in primary schools during a period of unprecedented innovation.

Phase Two of the PACE Study (PACE 2)
This dataset consists of four files:
PACE Year 3 and 4 Teachers Interviews 1993 (9T INT 93)
PACE Headteacher Interviews 1994 (9HT INT 94)
PACE Advance Questionnaire to Teachers and Headmasters 1994 (AQ 3 94)
PACE Year 1 and Year 2 Teacher Interviews 1994 (10T INT 94)
The aims of the survey are:
1. To monitor the progressive implementation in primary schools of the National Curriculum and associated assessment procedures, following the Education Reform Act (1988) by building on Stage 1 of the PACE (Primary Assessment, Curriculum and Experience) project (PACE 1). In Stage 2 of the PACE project (PACE 2), this will focus on:
Continuity and progression in the Key Stage 1 to Key Stage 2 transfer: the research will analyse curriculum provision, assessment practice, classroom pedagogy and pupil experience.
Teachers' changing perceptions of innovation: the project will consider the degree to which such innovations become routinised and the differences in response of infant and junior teachers and headteachers.
2. To advance theoretical understanding of the sustained effects of multiple innovations on processes of teaching and learning in primary classrooms. This work will focus on:
Curriculum, pedagogy, and pupil experiences, assessment practices, and teacher professionalism.
3. To provide a basis for the final projected stage of the PACE project (PACE 3) which will continue to focus on the above issues as pupils move through the later years of Key Stage 2 and are subject to the National Curriculum and assessment procedures at age 11.
Main Topics:

Year 3 and 4 teachers Interviews 1993 (9T INT 93): Teacher background characteristics; pedagogy and pupil experience; curriculum change and continuity; assessment practices; impact of the National Curriculum on teaching and learning; changes in teachers' roles; changes in planning and management within the school; teacher/pupil relationships; staff relationships.
Headteacher Interviews 1994 (9HT INT 94): Headteacher perspectives; school relationships; impact of recent policy changes; curriculum and pedagogy; assessment policy; pupil experience.
Year 1 and Year 2 Teacher Interviews 1994 (10T INT 94): Teacher background characteristics; pedagogy and pupil experience; curriculum change and continuity; assessment practices; impact of the National Curriculum on teaching and learning; changes in teachers' roles; changes in planning and management within the school; teacher/pupil relationships; staff relationships.
Advance Questionnaire to Teachers and Headmasters 1994 (AQ 3 94): Characteristics of teachers and headteachers, e.g. age, sex, years of experience, socio-economic catchment area of school; educational objectives for the children; accountability; meaning of professional responsibility.