Designation statuses for individual NCTL members and schools, academies, such as teaching schools, National Leaders of Education, Local Leaders of Education, Specialist Leaders of education and National Leaders of Governance. data are also held in relation to unsuccessful applications for designation, specific specialisms, former designation statuses and reasons for de designations

Dataset description

A collection of higher education (HE) and further education (FE) establishments in the UK, dated May 2021.

Further education (Sixth form) includes colleges. Higher education includes universities and post-graduate establishments (e.g. research institutions).

Education establishments are able to be filtered by Local Authority and output areas and include their statistical (ONS) code. These are the most-used statistical area codes for UK statistics.

For more info on the UK education system: Education System in the UK (UK Government document).

Acknowledgements

Search query was performed using https://get-information-schools.service.gov.uk/ Data shared under the Open Government License 3.0 (UK). More info: https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

The dataset gathers historical series on the funding and enrolment in the UK public education system from 1833 to 2019. Funding and enrolment are distributed by level of education, funders and economic categories. It is based on the method of quantitative history which follows the principles of national accounting and provides a stable frame to integrate financial and other data, and allow comparisons across time and space

Records of where and when National Leaders of Education, Local Leaders of Education and National Leaders of Governance have been deployed

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.

Transcripts of interviews with UK policy advisors on Hong Kong education policy. Recently England has engaged heavily in external policy referencing to drive its educational reforms. Hong Kong has been a major source of such referencing by virtue of its strong performance on international tests of pupil achievement. Using Hong Kong as a case study; the project will analyse external policy referencing, with England as the ‘borrower’ and Hong Kong the ‘lender’. The aim is to cast a light on the role of external policy referencing in the policy making process, and how policy referencing is operationalised in the England context. The study provides an insight into the contemporary patterns of external policy referencing, and its manifestation in the West and East Asia, and examines the evidence used to inform the process. The study will undertake a literature review and interviews with stakeholders in both contexts to address the following research questions: (1) What have been the critical features of the patterns of external policy referencing in England since the 1990s? (2) How have policy makers in England interpreted the sources of success of Hong Kong’s education system, and how does this compare with the views of key stakeholders in Hong Kong?In 2007 the Principal Investigator returned to London after working for 31 years in Faculties / Institutes of Education in Hong Kong and specialising in East Asian education systems. As political parties in England competed to promote their vision of schooling, he was constantly bemused as to the extent to which their plans for reform were based on the claim that what they were proposing was a feature of one or all of the high performing East Asian societies that do well on international tests of pupil achievement e.g. the Programme for International Student Assessment (PISA), and Trends in International Mathematics and Science Study (TIMSS). The 2010 Schools White Paper in England and the ongoing review of the National Curriculum extensively cite practices in Hong Kong to support their policies. Also, agencies now bidding to get contracts to examine the New Baccalaureate have to demonstrate that they will follow the best practices of high performing nations. Some of these claims seem far removed from the reality that the Principal Investigator had experienced both as an academic, and as someone heavily engaged in policy making in Hong Kong. What is more worrying is that these claims are largely unchallenged in England. The claims are accepted partly because people generally have limited knowledge of foreign education systems, and comparative educators have tended to avoid engagement in the public debates relating to ongoing policy making about how schools should be reformed. The purpose of this study is to help address that situation. We plan to focus on how policy makers in England portray features of Hong Kong's education system to promote domestic reforms. We examine the nature of these features in Hong Kong by finding out what the relevant laws or rules are, and by interviewing people who are directly involved with these education features. This will allow us to find out the extent to which the claims made in England are valid and accurate. It will also allow us to contribute to the ongoing debates in comparative education as to the influence of global and local factors on education reform. The UK and Hong Kong team carried out a single-case study of England and Hong Kong because the two societies provide a powerful exemplar of the emerging patterns of policy transfer. For the first part of the project, we examined external policy referencing in England historically and currently, and located this within the broader literature on external policy referencing. In the second part of the project, we reviewed the academic literature on external policy referencing with specific reference to England. We carried out analysis of policy and related documents in England (e.g. key government announcements, speeches, and publications), between 1990 and the present, including authoritative sources and references made within policy documents or by policy makers (e.g. the McKinsey Report 2007, 2010). In the third part of the project, we provided an in-depth understanding of the policy making process. This was the part where the main empirical data collection took place. We undertook semi-structured, in-depth interviews with key policy makers involved in developing and implementing education reforms in England (N=10) and Hong Kong (N=15).

Abstract copyright UK Data Service and data collection copyright owner. The purpose of this study was to collect follow-up data on the children included in the national sample interviewed in the 1964 survey of primary school children for the Plowden Central Advisory Council for Education in England and Wales, using questionnaires to gather information on school characteristics, teachers' assessments and educational achievement for each child. Main Topics: There were five questionnaires. These were: 1. (administered to teachers of children still in primary school) Class: sex of pupils; size of class; whether streamed and, if so, at what level, age range. Child: friendship network; leadership behaviour; popularity; punctuality and attendance record; reasons for absences; discussions with parents on child's work and at whose instigation; extent of parental interest; child's attitude to school work and reaction to criticism/punishment; child's temperament (e.g. nervous, shy, aggressive, etc.); participation in sports and school events; general level of involvement in school life; any handicaps and type. 2. (administered to teachers of children in secondary schools). Class: sex of pupils; number of children; whether streamed and at what level; age range. Child: assessment of child's ability (whether would obtain 'O' and `A' levels or CSE in various subjects, whether would benefit from extra year beyond school leaving age). Further questions as Questionnaire 1. 3. (administered to all pupils). Whether homework set, how much time spent on it and whether helped by anyone; intended school leaving age, preferred age of leaving if free choice available, intended post-school activity, book borrowing activity from library, three favourite subjects, spare-time activities, whether intend to sit examination and which type, intended career or job, agreement or disagreement (on a five point scale) with a number of statements about child's school experiences, attitudes to school in general, friendship networks, own personality. 4. (administered to heads of primary schools attended by children in sample in 1968). Facts about School: age range and sex of pupils; type of funding; denomination; number of junior pupils on roll; age of school and school buildings; occupational background of pupil's father in percentages; number of top juniors eligible for academic secondary education; whether school has boarders. Continuity from Primary to Secondary School: information supplied to secondary schools about pupils; attempts to acclimatise juniors to future secondary schools; school involvement in parental choice of secondary school. School-Parent Interaction: number of parental invitations to school and for what purpose; how invitations made; parental attendance records. Whether school provides (and in what form): booklet or newsletter on school for parents; information on kinds of educational principles and practices of school; advice on suitable reading and television viewing for parents. Whether school has parent-teacher association, steps taken to encourage parental involvement with school work at home, visits by staff to pupils' homes. Curriculum and Pupil Activities: how curriculum developed; type of teaching used (e.g. short lessons on specific sections, large blocks allocated to projects etc.); any reference to local community in curriculum; extent of day trips outside school. Aspects of School Order: school uniform; corporal punishment; other punishments. Accommodation and Equipment: specifically equipped rooms for activities, library provision; audio-visual equipment available; outdoor games; accommodation for gymnastics; dining & school assembly. Head: length of tenure; previous appointments; length and type of teaching service; educational background; age; sex. Teaching Staff: number full and part-time; number of male, graduate, unqualified and graded-post staff; age distribution of full-time staff; length of service of full-time staff. 5. (administered to heads of secondary schools attended by children in sample). As above (4) with following additions: percentages of pupils staying on at school for 5th year and 6th year. Pupil Activities: activities provided for out of school hours; support of such activities and reasons; any joint activities with other schools; social welfare work in community by students; day trips; longer trips. Aspects of School Order: attitude to alternative dress and make-up; schools council; prefect system. Accommodation and equipment: swimming pool and playing fields on site.

OECD Education statistics database includes the UNESCO/OECD/EUROSTAT (UOE) database on education covering the outputs of educational institutions, the policy levers that shape educational outputs, the human and financial resources invested in education, structural characteristics of education systems, and the economic and social outcomes of education, learning and training throughout life, including on employment and unemployment. Also included in the database are the PISA 2015 dataset, Teaching and Learning International Survey (TALIS) data, the annual Education at a Glance data and data relating to Gender equality in education.

This system is used by applicants to apply to schools for School Direct places in initial teacher training. It contains information about the applicant and schools delivering School Direct places.

The research sets out to compare how British and German staff and students are changing in response to neoliberal influences in higher education. In the past, these two countries had a reasonably synoptic vision of values in higher education endorsing personal development, collegial community, pursuit of knowledge and academic freedom. Currently, a market forces model based on competition and choice is relativising some of these traditional values, and has penetrated much more deeply in the UK than in Germany. The research investigates whether expectations, academic values, work satisfaction levels and conceptions of human relationships now actually differ across the two systems: it finds, for example, that high study satisfaction on the part of UK students is ‘paid for’ by low job satisfaction on the part of staff. Methodologically, it is based upon surveys and interviews conducted among staff and students in 12 universities in each country. The data reveal participants’ perceptions of the strengths and weaknesses of each system, specifically in relation to Education. It highlights which features of modern-day academic life are accepted or rejected by staff, and what attitudes they take towards market-oriented reform. The UK staff feel over-worked, underpaid and downwardly mobile in terms of status in comparison with their German counterparts, but there is a love of the job that overrides all these negative feelings. Semi-structured interviews with staff in twelve HE institutions in the UK and twelve in the Federal Republic of Germany (FRG), and during the course of these interviews staff were asked to fill in questionnaires. Students too were given questionnaires, normally distributed during or at the end of class so as to avoid non-response rates. A sample of 90 staff was aimed for in each country; 87 in the UK and 82 in the FRG completed both questionnaires and interviews. 1489 UK students and 986 FRG students completed the questionnaire.

Measuring the E, I and XE indices for the secondary schools that educated those people who became cabinet ministers in the UK between 1922 and 2022

Abstract

Education Quality Improvement Programme in Tanzania (EQUIP-T) is a Government of Tanzania programme, funded by UK DIFD, which seeks to improve the quality of primary education, especially for girls, in seven regions of Tanzania. It focuses on strengthening professional capacity and performance of teachers, school leadership and management, systems which support district management of education, and community participation in education.

The independent Impact Evaluation (IE) of EQUIP-T is a four-year study funded by the United Kingdom Department for International Development (DFID). It is designed to: i) generate evidence on the impact of EQUIP-T on primary pupil learning outcomes, including any differential effects for boys and girls; ii) examine perceptions of effectiveness of different EQUIP-T components; iii) provide evidence on the fiscal affordability of scaling up EQUIP-T post-2018; and iv) communicate evidence generated by the impact evaluation to policy-makers and key education stakeholders.

The research priorities for the midline IE are captured in a comprehensive midline evaluation matrix (see Annex B in the 'EQUIP-Tanzania Impact Evaluation. Midline Technical Report, Volume I: Results and Discussion' under Reports and policy notes). The matrix sets out evaluation questions linked to the programme theory of change, and identifies sources of evidence to answer each question-either the quantitative survey or qualitative research, or both. It asks questions related to the expected results at each stage along the results chain (from the receipt of inputs to delivery of outputs, and contributions to outcomes and impact) under each of the programme's components. The aim is to establish: (i) whether changes have happened as expected; (ii) why they happened or did not happen (i.e. whether key assumptions in the theory of change hold or not); (iii) whether there are any important unanticipated changes; and (iv) what links there are between the components in driving changes.

The main IE research areas are: - Impact of EQUIP-T on standard 3 pupil learning in Kiswahili and mathematics. - Impact of EQUIP-T on teacher absence from school and from classrooms. - Impact of EQUIP-T on selected aspects of school leadership and management.

The IE uses a mixed methods approach that includes: - A quantitative survey of 100 government primary schools in 17 programme treatment districts and 100 schools in 8 control districts in 2014, 2016 and 2018 covering: - Standard three pupils and their parents/caregivers; - Teachers who teach standards 1-3 Kiswahili; - Teachers who teach standards 1-3 mathematics; - Teachers who teach standards 4-7 mathematics; - Head teachers; and - Standard two lesson observations in Kiswahili and mathematics.

• Qualitative fieldwork in nine research sites that overlap with a sub-set of the quantitative survey schools, in 2014, 2016 and 2018, consisting of key informant interviews (KIIs) and focus group discussions (FGDs) with head teachers, teachers, pupils, parents, school committee (SC) members, region, district and ward education officials and EQUIP-T programme staff.

The midline data available in the World Bank Microdata Catalog are from the EQUIP-T IE quantitative midline survey conducted in 2016. For the qualitative research findings and methods see 'EQUIP-Tanzania Impact Evaluation. Midline Technical Report, Volume I: Results and Discussion' and 'EQUIP-Tanzania Impact Evaluation. Midline Technical Report, Volume II: Methods and Supplementary Evidence' under Reports and policy notes.

Geographic coverage

The survey is representative of the 17 EQUIP-T programme treatment districts. The survey is NOT representative of the 8 control districts. For more details see the section on Representativeness in 'EQUIP-Tanzania Impact Evaluation. Final Baseline Technical Report, Volume I: Results and Discussion' and 'EQUIP-Tanzania Impact Evaluation. Final Baseline Technical Report, Volume II: Methods and Technical Annexes' under Reports.

The 17 treatment districts are: - Dodoma Region: Bahi DC, Chamwino DC, Kongwa DC, Mpwapwa DC - Kigoma Region: Kakonko DC, Kibondo DC - Shinyanga Region: Kishapu DC, Shinyanga DC - Simiyu Region: Bariadi DC, Bariadi TC, Itilima DC, Maswa DC, Meatu DC - Tabora Region: Igunga DC, Nzega DC, Sikonge DC, Uyui DC

The 8 control districts are: - Arusha Region: Ngorongoro DC - Mwanza Region: Misungwi DC - Pwani Region: Rufiji DC
- Rukwa Region: Nkasi DC - Ruvuma Region: Tunduru DC - Singida Region: Ikungi DC, Singida DC - Tanga Region: Kilindi DC

Analysis unit

• School
• Teacher
• Pupil
• Lesson (not sampled)

Kind of data

Sample survey data [ssd]

Sampling procedure

Because the EQUIP-T regions and districts were purposively selected (see 'EQUIP-Tanzania Impact Evaluation. Final Baseline Technical Report, Volume I: Results and Discussion' under Reports and policy notes), the IE sampling strategy used propensity score matching (PSM) to: (i) match eligible control districts to the pre-selected and eligible EQUIP-T districts (see below), and (ii) match schools from the control districts to a sample of randomly sampled treatment schools in the treatment districts. The same schools are surveyed for each round of the IE (panel of schools) and standard 3 pupils will be interviewed at each round of the survey (no pupil panel).

Identifying districts eligible for matching

Eligible control and treatment districts were those not participating in any other education programme or project that may confound the measurement of EQUIP-T impact. To generate the list of eligible control and treatment districts, all districts that are contaminated because of other education programmes or projects or may be affected by programme spill-over were excluded as follows:

• All districts located in Lindi and Mara regions as these are part of the EQUIP-T programme but implementation started later in these two regions (the IE does not cover these two regions);
• Districts that will receive partial EQUIP-T programme treatment or will be subject to potential EQUIP-T programme spillovers;
• Districts that are receiving other education programmes/projects that aim to influence the same outcomes as the EQUIP-T programme and would confound measurement of EQUIP-T impact;
• Districts that were part of pre-test 1 (two districts); and
• Districts that were part of pre-test 2 (one district).

Sampling frame

To be able to select an appropriate sample of pupils and teachers within schools and districts, the sampling frame consisted of information at three levels:

• District;
• School; and
• Within school.

The sampling frame data at the district and school levels was compiled from the following sources: the 2002 and 2012 Tanzania Population Censuses, Education Management Information System (EMIS) data from the Ministry of Education and Vocational Training (MoEVT) and the Prime Minister's Office for Regional and Local Government (PMO-RALG), and the UWEZO 2011 student learning assessment survey. For within school level sampling, the frames were constructed upon arrival at the selected schools and was used to sample pupils and teachers on the day of the school visit.

Sampling stages

Stage 1: Selection of control districts Because the treatment districts were known, the first step was to find sufficiently similar control districts that could serve as the counterfactual. PSM was used to match eligible control districts to the pre-selected, eligible treatment districts using the following matching variables: Population density, proportion of male headed households, household size, number of children per household, proportion of households that speak an ethnic language at home, and district level averages for household assets, infrastructure, education spending, parental education, school remoteness, pupil learning levels and pupil drop out.

Stage 2: Selection of treatment schools In the second stage, schools in the treatment districts were selected using stratified systematic random sampling. The schools were selected using a probability proportional to size approach, where the measure of school size was the standard two enrolment of pupils. This means that schools with more pupils had a higher probability of being selected into the sample. To obtain a representative sample of programme treatment schools, the sample was implicitly stratified along four dimensions:

• District;
• PSLE scores for Kiswahili;
• PSLE scores for mathematics; and
• Total number of teachers per school.

Stage 3: Selection of control schools As in stage one, a non-random PSM approach was used to match eligible control schools to the sample of treatment schools. The matching variables were similar to the ones used as stratification criteria: Standard two enrolment, PSLE scores for Kiswahili and mathematics, and the total number of teachers per school.

The midline survey was conducted for the same schools as the baseline survey (a panel of schools) and the endline survey in 2018 will cover the same sample of schools. However, the IE does not have a panel of pupils as a pupil only attends standard three once (unless repeating). Thus, the IE sample is a repeated cross-section of pupils in a panel of schools.

Stage 4: Selection of pupils and teachers within schools Pupils and teachers were sampled within schools using systematic random sampling based on school registers. The within-school sampling was assisted by selection tables automatically generated within the computer assisted survey instruments.

Per school, 15 standard 3 pupils were sampled. For the teacher development needs assessment (TDNA), in the sample treatment schools, up to three teachers of standards 1 to 3 Kiswahili, up to three teachers of standards 1 to 3 mathematics; and up to three

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Artificial Intelligence (AI) Market In Education Sector Size 2025-2029

The artificial intelligence (ai) market in education sector size is forecast to increase by USD 4.03 billion at a CAGR of 59.2% between 2024 and 2029.

The Artificial Intelligence (AI) market in the education sector is experiencing significant growth due to the increasing demand for personalized learning experiences. Schools and universities are increasingly adopting AI technologies to create customized learning paths for students, enabling them to progress at their own pace and receive targeted instruction. Furthermore, the integration of AI-powered chatbots in educational institutions is streamlining administrative tasks, providing instant support to students, and enhancing overall campus engagement. However, the high cost associated with implementing AI solutions remains a significant challenge for many educational institutions, particularly those with limited budgets. Despite this hurdle, the long-term benefits of AI in education, such as improved student outcomes, increased operational efficiency, and enhanced learning experiences, make it a worthwhile investment for forward-thinking educational institutions. Companies seeking to capitalize on this market opportunity should focus on developing cost-effective AI solutions that cater to the unique needs of educational institutions while delivering measurable results. By addressing the cost challenge and providing tangible value, these companies can help educational institutions navigate the complex landscape of AI adoption and unlock the full potential of this transformative technology in education.

What will be the Size of the Artificial Intelligence (AI) Market In Education Sector during the forecast period?

Request Free SampleArtificial Intelligence (AI) is revolutionizing the education sector by enhancing teaching experiences and delivering personalized learning. AI technologies, including deep learning and machine learning, power adaptive learning platforms and intelligent tutoring systems. These systems create learner models to provide personalized recommendations and instructional activities based on individual students' needs. AI is transforming traditional educational models, enabling intelligent systems to handle administrative tasks and data analysis. The integration of AI in education is leading to the development of intelligent training software for skilled professionals. Furthermore, AI is improving knowledge delivery through data-driven insights and enhancing the learning experience with interactive and engaging pedagogical models. AI technologies are also being used to analyze training formats and optimize domain models for more effective instruction. Overall, AI is streamlining administrative tasks and providing personalized learning experiences for students and professionals alike.

How is this Artificial Intelligence (AI) In Education Sector Industry segmented?

The artificial intelligence (ai) in education sector industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. End-userHigher educationK-12Learning MethodLearner modelPedagogical modelDomain modelComponentSolutionsServicesApplicationLearning platform and virtual facilitatorsIntelligent tutoring system (ITS)Smart contentFraud and risk managementOthersTechnologyMachine LearningNatural Language ProcessingComputer VisionSpeech RecognitionGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyItalySpainUKAPACChinaIndiaJapanSouth KoreaSouth AmericaBrazilMiddle East and AfricaUAE

By End-user Insights

The higher education segment is estimated to witness significant growth during the forecast period.The global education sector is witnessing significant advancements with the integration of Artificial Intelligence (AI). AI technologies, including Machine Learning (ML), are revolutionizing various aspects of education, from K-12 schools to higher education and corporate training. Intelligent Tutoring Systems and Adaptive Learning Platforms are increasingly popular, offering Individualized Instruction and Personalized Learning Experiences based on each student's Learning Pathways and Skills Gap. AI-enabled solutions are enhancing Student Engagement by providing Interactive Learning Tools and Real-time communication, while AI platforms and startups are developing Smart Content and Tailored Content for Remote Learning environments. AI is also transforming administrative tasks, such as Assessment processes and Data Management, by providing Personalized Recommendations and Automated Grading. Universities and educational institutions are leveraging AI for Pedagogical model development and Virtual Classrooms, offering Educational Experiences and Virtual support. AI is also being used f

This data shows Education Attainment at Key Stage 4. Numbers and percentages of pupils attaining at Key Stage 4 are shown by gender. Points to be aware of: • In 2016-2017, children were assessed under new school accountability standards with a new grading system of grades 9 to 1 instead of A* to G. This means data for the academic year ending in 2017 is not comparable with previous years' data. Analysis and comparisons between groups of pupils, types of schools and pupil characteristics are more likely to provide more meaningful information than comparisons over time. • Two new headline standards are shown in this dataset: English and maths strong passes at grades 9-5, and the English Baccalaureate with strong passes at grades 9 to 5 in English and maths. In addition, we have also provided both statistics based on standard passes at grade 9 to 4, as these statistics should be comparable with historical A*-C measures. More information: see the Secondary Curriculum, key stage 3 and key stage 4 (GCSEs) website (link to this included as Resource accompanying these datasets). Data is included for Wards, Lower Super Output Areas (LSOA), Districts, and Lincolnshire. The data has been aggregated based on pupil postcode and only includes those pupils living and educated within Lincolnshire. If you want Lincolnshire and District aggregations based on those pupils that are educated within Lincolnshire, irrespective of where they live; then please see the Department for Education Statistics website and School Performance Tables (links to these included as Resources accompanying these datasets). Data is suppressed where appropriate 5 persons and below (this may be shown by missing data). That and any unmatched postcodes may mean numbers for small areas might not add up exactly to figures shown for larger areas. This data is updated annually. Data source: Lincolnshire County Council, Performance Services – Schools Performance. For any enquiries about this publication contact schoolperformancedata@lincolnshire.gov.uk Please note: National data for Key Stage 4 results are published via: https://explore-education-statistics.service.gov.uk/find-statistics/key-stage-4-performance – GOV.UK (explore-education-statistics.service.gov.uk) There have been methodological changes since 2019 to cater for the issues seen during the pandemic. The DfE offer the following commentary via the link above: “Last academic year saw the return of the summer exam series, after they had been cancelled in 2020 and 2021 due to the impact of the COVID-19 pandemic, where alternative processes were set up to award grades (centre assessment grades, known as CAGs, and teacher assessed grades, known as TAGs). As part of the transition back to the summer exam series adaptations were made to the exams (including advance information) and the approach to grading for 2022 exams broadly reflected a midpoint between results in 2019 and 2021. More information on these changes can be seen in the Guide to GCSE results for England, summer 2022. Given the unprecedented change in the way GCSE results were awarded in the summers of 2020 and 2021, as well as the changes to grade boundaries and methods of assessment for 2021/22, users need to exercise caution when considering comparisons over time, as they may not reflect changes in pupil performance alone.”

This project had interrelated substantive and methodological foci. The substantive aim was to investigate the link between social background and educational experience, using a variety of methods and comparing England and Germany. This link is well established, and there are various theories to explain it, including rational choice theory, i.e. the notion that people undertake an analysis of perceived costs and benefits of courses of action, and habitus theory, which regards educational pathways and outcomes as shaped by behaviours and dispositions reflecting familial class origin. Methodologically, since the balance of rational and habitual behaviour may vary in complex ways by social origin, I used an analytic method orientated to such causal complexity, Qualitative Comparative Analysis (QCA), to analyse large datasets in combination with in-depth interviews. I studied various outcomes, including GCSE results, A-level subject choice, the differences between comprehensive and selective Local Education Authorities in England and Wales, which type of school someone attends at age 17 in Germany, moving up and down in the German secondary school system, and entry to Higher Education in Germany. Analysing large datasets with QCA, I found evidence of complex interactions of social background factors with other factors such as ability in producing social inequalities in education. In process-tracing interviews conducted with 15 to 18 year olds in both countries, I found evidence of behaviour in line both with rational action theory and with habitus theory, but also of how differing habituses across social classes shape the boundaries within which rational decisions are taken.An individual's adult economic and social status is influenced by the pathway he or she follows through the educational system. It is therefore important to understand the factors and processes acting on the individual, social and systemic levels to produce the distribution of students across these pathways. Employing existing datasets and new interview data, the research focuses on these substantive issues. The other, equally important focus of the research is methodological, exploring the use of case-based approaches with large n datasets. Ragin's Qualitative Comparative Analysis (QCA) attempts to bridge the supposed divide between quantitative and qualitative research methods. Based on Boolean algebra, QCA identifies necessary and sufficient conditions for some outcome, providing a configurational account of potentially causal conditions. In this work, QCA, in its crisp and fuzzy forms, is combined with process-tracing interviews. This approach with its capacity to explore the ways in which conjunctions of factors explain educational success and failure may offer insights over and above those available from either conventional quantitative methods or from small scale qualitative work. Studies in England and Germany, two countries whose educational systems differ widely in many ways, will aim to produce configurational accounts of transitions during students' educational careers. Semi-structured interviews with 43 German and 36 English young people between 15 and 18. Face-to-face interviews. Purposive selection/case studies

Abstract copyright UK Data Service and data collection copyright owner. This study is comprised by the data collected for a wider project exploring the historical relationship between higher education and the UK economy. The project sought to provide a long-term explanation of the relationships between funding, widening access and socio-economic aspects of higher education. Three main areas were considered: -The provision of an in-depth historical account and analysis of the numbers and extent of students and staff for the purposes of evaluating the main characteristics of UK higher education development back the 1920s. -The provision of an in-depth historical account and evaluation of levels and structures of income and expenditure in higher education -The interpretation of these data with reference to major socio-economic indicators. Main Topics: This study is a collation and analysis of statistics on UK higher education which refers to pre-1992 universities and includes all institutions delivering degrees afterwards. The dataset, which gathers historical series on funding and development of universities from the early 1920s, is the result of research into primary and secondary governmental and institutional sources. Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research. No sampling (total universe) Compilation or synthesis of existing material

Abstract copyright UK Data Service and data collection copyright owner. This project studied the consequences of potentially misrecorded educational attainment for the estimation of returns to education. The focus was on the British educational system, in which educational investments are best summarised by categorical qualifications rather than continuous years of education. The aims of the study were to explore the consequences of misclassification for returns to education from a general methodological point of view, and to explore empirically how the biases from measurement error and from omitted variables interact in the estimation of returns to educational qualifications in the UK. Data for male respondents to the fifth wave (conducted in 1991) of the National Child Development Study (NCDS) (held at the UK Data Archive (UKDA) under GN 33004) were used to explore the interaction of measurement error and ability biases, and to assess the plausibility for the two biases to cancel out in the estimation of returns. See documentation for further details. Main Topics: The data files include variables covering type of school, educational tests, parents' social class and educational background, social class, occupational status, qualifications and measures to assess bias. No sampling (total universe) For details of original sampling see the NCDS documentation. Compilation or synthesis of existing material For details of original data collection see the NCDS documentation.