This dataset is about books. It has 5 rows and is filtered where the book subjects is Special education-Law and legislation-England. It features 9 columns including author, publication date, language, and book publisher.

A list of all independent schools and special post-16 institutions for children with special educational needs or disabilities (SEND) approved under section 41 of the Children and Families Act 2014 in England and Wales.

You can filter the list by local authority or by type of setting.

Our guide for independent special schools and special post-16 institutions explains how to apply for approval under section 41.

Voluntary removal

Contact hns.sos@education.gov.uk to request removal from the approved list, stating your reason. We will remove your institution in the next update and notify local authorities. The published list includes all removed institutions.

Once removed, you cannot re-apply for one full academic year.

Other special schools

Details of all special schools in England are available on the https://www.get-information-schools.service.gov.uk/Search">Department for Education’s Get Information about Schools system. This includes:

• maintained schools
• academies
• independent schools
• non-maintained schools

The SEND guide for parents and carers explains how parents can ask for one of these schools or special post-16 institutions to be named in their child’s education, health and care plan.

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. Interviews conducted primarily face-to-face with a smaller number remotely. Interviewees were sent a list of topics for discussion in advance of the interview. Interviews focused on these topics but were only semi-structured so as to permit discussion of other topics raised by the subjects.

This dataset was created through an anonymous survey of solicitors in England and Wales, conducted between 12 November 2019 and 13 January 2020. Respondents answered a series of questions regarding their use of AI technology, as well as their training for and attitudes to the use of technology in their work. After discarding partial responses, the dataset comprises a total of 353 valid responses.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. The survey was run anonymously using the Qualtrics platform. Invitations to participate were distributed by email to 10,000 randomly-selected solicitors. In order to increase survey participation, subsequent survey invitations were sent to under-represented groups of respondents. Further details of survey methodology, participant information, and the survey questions are included in the data documentation.

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

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

The main objective of this research was to develop a multi-disciplinary understanding of the political economies and consequences of school exclusion across the UK through a home-international comparison.

The motivation for the study was the need to understand the great differences in the rates of permanent school exclusions and suspensions in different parts of the UK. with numbers rising rapidly in England but remaining relatively low or falling in Northern Ireland, Scotland and Wales.

The research was undertaken by the multi-disciplinary (criminology, economics, education, law, psychology, psychiatry, sociology) and multi-site (the universities of Oxford, Cardiff, Edinburgh, Queen’s Belfast, and the LSE) Excluded Lives Research Team. The research was organised into two work strands: A. Landscapes of Exclusion; and B. Experiences of Exclusion. In Strand A work packages examined: the ways in which policies and legal frameworks shape interventions designed to prevent exclusions; the financial costs associated with exclusion; and patterns and characteristics of exclusion. Strand B work packages focussed on families’, pupils’ and professionals’ experiences of the risks and consequences of exclusion.

The data were collected from representative local educational authorities (4 in England, 2 in both Scotland and Wales) and across NI. Our sampling strategy for schools used modelled data, whereby we calculated the rates of exclusions for schools after controlling for pupil characteristics to estimate whether schools had above or below expected levels of exclusion based on their pupil characteristics. For the purposes of sampling, we used the number of temporary exclusions officially recorded over a five-to-seven-year period (depending on the availability of national data in each of the UK jurisdictions). School and local authority staff were selected on the basis of their roles. This data set comprises of interviews from across the UK with Headteachers, Alternative Provision providers in England and Scotland, and national policymakers in Scotland.

Security guidance for schools, colleges, and universities to safeguard students, staff, and premises.

This file set is the basis of a project in which Stephanie Pywell from The Open University Law School created and evaluated some online teaching materials – Fundamentals of Law (FoLs) – to fill a gap in the knowledge of graduate entrants to the Bachelor of Laws (LLB) programme. These students are granted exemption from the Level 1 law modules, from which they would normally acquire the basic knowledge of legal principles and methods that is essential to success in higher-level study. The materials consisted of 12 sessions of learning, each covering one key topic from a Level 1 law module.The dataset includes a Word document that consists of the text of a five-question, multiple-choice Moodle poll, together with the coding for each response option.The rest of the dataset consists of spreadsheets and outputs from SPSS and Excel showing the analyses that were conducted on the cleaned and anonymised data to ascertain students' use of, and views on, the teaching materials, and to explore any statistical association between students' studying of the materials and their academic success on Level 2 law modules, W202 and W203.Students were asked to complete the Moodle poll at the end of every session of study, of which there were 1,013. Only one answer from each of the 240 respondents was retained for Questions 3, 4 and 5, to avoid skewing the data. Some data are presented as percentages of the number of sessions studied; some are presented as percentages of the number of respondents, and some are presented as percentage of the number of respondents who meet specific criteria.Student identifiers, which have been removed to ensure anonymity, are as follows: Open University Computer User code (OUCU) and Personal Identifier (PI). These were used to collate the output from the Moodle poll with students' Level 2 module results.

Health Poverty Index - Root Causes: Educational resourcing: Average expenditure per pupil Source: Department of Health (DoH): Budget and Outturn Statements, 2001-2002, Pupil Level Annual School Census (PLASC) - 2001-2002, Department for Education and Skills (DfES) Publisher: Health Poverty Index Geographies: Local Authority District (LAD), National Geographic coverage: England Time coverage: 2001/02 Type of data: Administrative data Notes: The Section 52 Budget and Outturn Statements are produced by Local Education Authorities at the beginning and end of each financial year for every school maintained by the authority. They are available to the general public on the DfES website: PLASC is completed by maintained state schools (primary, middle and secondary) and maintained special schools every January and is a statutory requirement under the Education Act 1996.

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. An investigation of the response to the 1981 Education Act in local education authorities (in England and Wales), regarding the identification of and provision for children with special (remedial) needs in ordinary primary schools. Main Topics: Variables Method of identification; organisation of screening; development; types of provision available; staffing; training. Help available in school; teachers' views. No sampling (total universe) Local education authorities; Total sample of teachers in primary/junior schools in six LEAS (two metropolition, three county council areas and one London borough). The LEAS were selected to cover a range of type, geographical area, presence/absence of a screening programme and type of support service. The schools were chosen by the local authority to represent a range of take-up/use of provision available to meet special needs Postal survey LEAS; Self completion: Teachers

UK Parliament petition with 120,776 signatures

The Secretary of State designates these courses as eligible for tuition fee loans under the Higher Education Short Course Loans Regulations 2022. This is under the powers of The Teaching and Higher Education Act 1998, section 22.

Information for HESC learners is available.

Data in support of BIS research paper no.66. The research paper assesses the impact of the Further Education Teachers’ Qualifications (England) Regulations to date. It explores unexpected outcomes and barriers to the achieving the aims of the Regulations. It identifies examples of good practice among providers to overcome barriers and mitigate risk, and makes recommendations about how to further support and facilitate progress.

Data on the top universities for Law in 2025.