This project investigated UK secondary school students’ views of inequality and their sense of agency concerning their occupational prospects, using questionnaire and interview data. The data came from 78 students from three secondary schools in England between Year 7 and Year 13 who were aged between 12 and 19. The three schools were in areas with different socioeconomic characteristics – an affluent town in the London commuter belt (School A), a city in the east of England (School B) and a town to the east of London (School C). School A had a lower than national average free school meals (FSM) rate, whereas both School B and School C had a higher than national average FSM rate. 18 participants were from School A, 38 from School B and 22 from School C. While all 18 students in School A and all 22 students in School C participated in both the questionnaire and follow-up interview stages, in School B 37 participants filled in the questionnaire and, of these, 22 took part in the interviews. One student from School B who did not fill in a questionnaire took part in the interview, making the total interviews from School B 23. One student from School C did not want to have their interview audio-recorded; therefore, their interview transcript does not exist. As a result, the dataset in total contains 77 questionnaires and 62 interview transcripts. The PDF files are questionnaire files and the word document files are interview transcripts. A file name (for both the pdf files and word document files) begins with ‘Y’ that is followed by a number which indicates a school year and this is followed by two letters that indicate a code for an individual participant, while the letter A, B or C immediately after a hyphen indicates School A, B or C respectively.

Number of pupils and classes and average class sizes for state-funded primary and secondary schools, including infant and key stage 2 class sizes

Parliamentary Constituency level information - time series of average class sizes in primary and secondary schools Source: Department for Education and Skills (DfES) Publisher: Department for Children Schools and Families (DCSF) Geographies: County/Unitary Authority, Government Office Region (GOR), National Geographic coverage: England Time coverage: 1997 to 2005 Type of data: Administrative data

United Kingdom UK: Secondary Education: Pupils: % Female data was reported at 49.596 % in 2015. This records a decrease from the previous number of 49.805 % for 2014. United Kingdom UK: Secondary Education: Pupils: % Female data is updated yearly, averaging 49.195 % from Dec 1971 (Median) to 2015, with 45 observations. The data reached an all-time high of 49.921 % in 2013 and a record low of 48.422 % in 1971. United Kingdom UK: Secondary Education: Pupils: % Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s UK – Table UK.World Bank: Education Statistics. Female pupils as a percentage of total pupils at secondary level includes enrollments in public and private schools.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

Number of pupils and average class sizes in classes taught by more than one teacher in maintained primary and secondary schools Source: Department for Education and Skills (DfES) Publisher: Department for Children Schools and Families (DCSF) Geographies: County/Unitary Authority, Government Office Region (GOR) Geographic coverage: England Time coverage: 2006 Type of data: Administrative data

United Kingdom UK: Adolescents Out of School: Female: % of Female Lower Secondary School Age data was reported at 1.329 % in 2015. This records a decrease from the previous number of 2.944 % for 2014. United Kingdom UK: Adolescents Out of School: Female: % of Female Lower Secondary School Age data is updated yearly, averaging 2.024 % from Dec 1971 (Median) to 2015, with 22 observations. The data reached an all-time high of 6.726 % in 1975 and a record low of 0.293 % in 2000. United Kingdom UK: Adolescents Out of School: Female: % of Female Lower Secondary School Age data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Education Statistics. Adolescents out of school are the percentage of lower secondary school age adolescents who are not enrolled in school.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

Context

The early identification of students facing learning difficulties is one of the most critical challenges in modern education. Intervening effectively requires leveraging data to understand the complex interplay between student demographics, engagement patterns, and academic performance.

This dataset was created to serve as a high-quality, pre-processed resource for building machine learning models to tackle this very problem. It is a unique hybrid dataset, meticulously crafted by unifying three distinct sources:

The Open University Learning Analytics Dataset (OULAD): A rich dataset detailing student interactions with a Virtual Learning Environment (VLE). We have aggregated the raw, granular data (over 10 million interaction logs) into powerful features, such as total clicks, average assessment scores, and distinct days of activity for each student registration.

The UCI Student Performance Dataset: A classic educational dataset containing demographic information and final grades in Portuguese and Math subjects from two Portuguese schools.

A Synthetic Data Component: A synthetically generated portion of the data, created to balance the dataset or represent specific student profiles.

Data Unification and Pre-processing

A direct merge of these sources was not possible as the student identifiers were not shared. Instead, a strategy of intelligent concatenation was employed. The final dataset has undergone a rigorous pre-processing pipeline to make it immediately usable for machine learning tasks:

• Advanced Imputation: Missing values were handled using a sophisticated iterative imputation method powered by Gaussian Mixture Models (GMM), ensuring the dataset's integrity.

• One-Hot Encoding: All categorical features have been converted to a numerical format.

• Feature Scaling: All numerical features have been standardized (using StandardScaler) to have a mean of 0 and a standard deviation of 1, preventing model bias from features with different scales.

The result is a clean, comprehensive dataset ready for modeling.

File Information

Instance

Each row represents a student profile, and the columns are the features and the target.

Feature

Features include aggregated online engagement metrics (e.g., clicks, distinct activities), academic performance (grades, scores), and student demographics (e.g., gender, age band). A key feature indicates the original data source (OULAD, UCI, Synthetic).

Sensitive Information

The dataset contains no Personally Identifiable Information (PII). Demographic information is presented in broad, anonymized categories.

Key Columns:

Target Variable:

  had_difficulty: The primary target for classification. This binary variable has been engineered from the original final_result column of the OULAD dataset.

    1: The student either failed (Fail) or withdrew (Withdrawn) from the course.

    0: The student passed (Pass or Distinction).

Feature Groups:

  OULAD Aggregated Features (e.g., oulad_total_cliques, oulad_media_notas): Quantitative metrics summarizing a student's engagement and performance within the VLE.

  Academic Performance Features (e.g., nota_matematica_harmonizada): Harmonized grades from different data sources.

  Demographic Features (e.g., gender_*, age_band_*): One-hot encoded columns representing student demographics.

  Origin Features (e.g., origem_dado_OULAD, origem_dado_UCI): One-hot encoded columns indicating the original source of the data for each row. This allows for source-specific analysis.

(Note: All numerical feature names are post-scaling and may not directly reflect their original names. Please refer to the complete column list for details.)

Acknowledgements

This dataset would not be possible without the original data providers. Please acknowledge them in any work that uses this data:

OULAD Dataset: Kuzilek, J., Hlosta, M., and Zdrahal, Z. (2017). Open University Learning Analytics dataset. Scientific Data, 4. https://analyse.kmi.open.ac.uk/open_dataset

UCI Student Performance Dataset: P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS. https://archive.ics.uci.edu/ml/datasets/student+performance

Inspiration

This dataset is perfect for a variety of predictive modeling tasks. Here are a few ideas to get you started:

Can you build a classification model to predict had_difficulty with high recall? (Minimizing the number of at-risk students we fail to identify).

• Which features are the most powerful predictors of student failure or withdrawal? (Feature Importance Analysis).

• Can you build separate models for each data origin (origem_dado_*) and compare ...

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.

This data contains school counts by school-level Progress 8 scores for White British disadvantaged pupils, for those secondary schools where more than 20% of the pupils at the end of Key Stage 4 (KS4) were disadvantaged White British.

Full 2023/24 academic year local authority, regional and national attendance since 11 September 2023, including reasons for absence. Figures are provided for state-funded primary, secondary and special schools broken down by Free School Meals eligibility. Totals for all schools are also included that include estimates for non-response.

Percentage of young people in England participating in sport. Young people are all those aged 5-19. All 5-16 year olds will have the chance to do 5 hours of high quality Physical Education (PE) and Sport.

Source: Department for Children Schools and Families (DCSF)

Publisher: DCLG Floor Targets Interactive

Geographies: County/Unitary Authority, Government Office Region (GOR), National

Geographic coverage: England

Time coverage: 2004/05 to 2008/09

Notes: For young people in schools sport will include any activity that requires physical skilfulness and is part of a schools planned formal, semi-formal, supervised or led provision. It will also include PE lessons and activities based in community sport and dance clubs. For young people not in schools sporting activities will be based in community sport and dance clubs.

Guidelines: Good performance will be typified by a high percentage, one that is above the national average.

The Growth in Grammar Corpus is a collection of texts written by children at schools in England as part of their regular school work. Texts were mainly sampled from children in years 2, 6, 9 and 11, covering the disciplines of English, Science and Humanities. There is also a small collection of texts from year 4. The corpus was collected as part of a project aiming develop the first systematic understanding of the distinctive uses of grammar which mark out student writing across the full range of ages, attainment levels and text types in English primary and secondary education to age sixteen. Through quantitative and qualitative analyses of a large, systematically-sampled collection of student writing, it aimed to identify key form-function combinations both in student writing as a whole and in writing at different levels of age and attainment. It explored how these form-function combinations develop across levels, how they are differentially deployed in the range of different text types which students need to produce, and how they relate to adults' use of grammar. A central aim of schooling is to help students become effective writers. Becoming an effective writer is critical both for individual and for economic well-being: through writing, we can learn about ourselves and our world, we can understand the past and imagine the future, we can share new knowledge and innovative ideas, and we can create revolutions. The emergence of new media for writing, such as email, blogs and Twitter have served to increase the prevalence of writing as a communication tool both socially and in the workplace. Young people whose education has equipped them to be confident and capable writers are socially and economically advantaged. Central to good writing is the meaningful and appropriate use of grammar. Research has shown that becoming a good writer requires learning both how to use grammar in purposeful ways to express meanings and how to adapt grammatical choices to meet the expectations of different communicative contexts (e.g. formal vs. informal) and text types (e.g. telling a story vs. constructing an argument). The importance of grammar as a tool for creating contextually appropriate meanings is recognized in the National Curriculum in England, which states that students should be taught how to write by "selecting appropriate grammar and vocabulary, understanding how such choices can change and enhance meaning" (DfE, 2013a). However, although this principle is well-established, translating it into practice through specific guidelines regarding what should be taught and when it should be assessed, as well as through teachers' day-to-day practices and choices, requires a more detailed understanding, which research has not yet provided. This has serious implications for the teaching of writing. In particular, current curricular guidance as to when particular grammatical forms should be taught and assessed is not based on a substantial, systematic evidence-base. Moreover, the presentation of grammar in the curriculum largely through the use of an Annex runs the risk of presenting forms as individual isolated items, divorced from context, leading to a teaching focus on labelling and identification, rather than enhancing learners' understanding of shaping written text for differing purposes and audiences. Given this picture, it is important to establish how students' control over grammatical forms develops throughout the course of their school careers. We need to know which forms are used to express which meanings at each stage of a typical student's development; we need to know how such uses differ across students at different levels of attainment in writing; we need to know how students at different stages and levels adapt their use of grammar to different communicative contexts; and we need to know how these trajectories of development compare to the uses of grammar which are typical of successful mature writers. This project aims to provide these understandings through computer-aided analysis of a large, systematically-collected, body of authentic student writing. The objectives of the study are fourfold. Firstly, it will establish the first full multi-dimensional analysis of the grammatical patterns which mark the broad spectrum of school-aged writing produced by students in England across the age and attainment range. Secondly, it will offer a more thorough understanding of grammatical development, along with its specific relationship to student writing as found within a particular educational system. Thirdly, it will generate an updatable and publicly accessible corpus of grammatically-annotated, educationally authentic student writing designed to support further studies of literacy development. Finally, it will form the basis for a set of recommendations to inform both national and international curriculum policies. Several hundred primary and secondary schools were contacted by email and/or phone and invited to participate in the study. Schools were identified both through the national Edubase database and through existing contacts of the project team. Our sampling frame aimed to include a balanced sample of schools according to the following categories: (1) School Location I: North vs. South England, (2) School Location II: Urban vs. rural areas, (3) School Demographics I: Greater vs. less than 10% of pupils qualifying for free school meals, and (4) School Demographics II: Below vs. above average ethnic diversity. To help achieve this, school contacts were first attempted via the Edubase database using a stratified random sampling technique. When particular category combinations were exhausted, schools were identified using the existing educational networks of the project team at the University of Exeter. Pupils within participating schools were invited to submit texts which they had already written as part of their normal school work. Specifically, we aimed to include pupils in equal numbers across: four age groups (Years 2, 6, 9, 11), three attainment groups (low, average, high) and boys vs. girls. We aimed to collect texts across disciplines in the following proportions: 2/3 English, 1/3 History, 1/3 Science. In the event, the sample was limited by two key factors: 1) Lack of uptake by schools, which forced us to deviate from the original sampling frame. 2) A lack of writing being done in some disciplines at some age groups (especially a lack of science writing by younger children). A summary of the sample collected in terms of demographics and pupil/text variables is provided as supplementary material with this submission.

Abstract copyright UK Data Service and data collection copyright owner. The surveys were commissioned by the Committee of Inquiry into Education and Training, set up by the Australian Government, in order to gather information from students and staff in all sectors of Australian post-secondary education. Areas covered include characteristics of students and staff, career choice, preparation and planning and attitudes towards issues of importance to post-secondary education in the seventies. Main Topics: Attitudinal/Behavioural Variables A. National Educational Survey, 1977 Date left secondary school, type and location, qualifications attained, whether specialised in any subjects, relative standard achieved (e.g. above/below average). Whether intended to enter university/college or follow a specific career. Whether entered a university/college, qualifications obtained. Whether worked full-time for more than six months. Whether currently enrolled in a university or college and details of courses, whether currently in employment and details. Whether recently changed or given up any courses - if so, reasons, future plans if not currently enrolled. Attributes of a typical teacher at respondent's college/university (e.g. inspires confidence, displays enthusiasm), whether agrees/disagrees with several statements concerning courses, reasons for current enrolment, whether present course and institution was first preference, overall evaluation of course and institution, relative standard achieved, whether committed to work for a particular employer when graduated, expected ease of obtaining a job, expected and preferred occupation. Time of choosing career, whether choice was restricted by subjects taken at secondary school, highest qualification would like to acquire, perceived differences between Universities, Colleges of Advanced Education and Technical Colleges. General comments were elicited concerning education and training and the effect of financial factors on career development. Background Variables Sex, age, country of birth of self and parents, no. of years lived in Australia, religion, marital status, whether has children, sources of financial support, comparison of own income (current and expected) and parents' income with the average, parents' occupations and highest level of education. B. National Survey of Post-Secondary Teaching Staff, 1977 Separate questionnaires were sent to the three sectors of tertiary education. Questions asked in each include: Position, field of teaching, length of time in university/college teaching/at present institution, positions/ qualifications held, publications, whether currently enrolled for a degree, details of teaching responsibilities, breakdown of activities each week. Attitude towards certain activities (e.g. research, teaching, administration), assessment of the goals of higher education, opinion of ability of incoming and whether affected by expansion in post-secondary education. Expected ease with which students will obtain jobs. Opinion of courses taught and student participation in decision-making, main reasons for students giving up courses, opinion of size of institution. Attributes of a typical teacher at own institution, ways in which university/college education could be improved, whether numbers in post-secondary education/in own discipline should continue to expand and by how much, whether admission standards should be relaxed/tightened, whether student transfers between institutions should be made easier/more difficult. Perceived differences between Universities, Colleges of Advanced Education and Technical Colleges, opinion of plans to amalgamate Universities and Colleges. Areas in which expenditure cutbacks should/should not fall. Expected date of leaving current institution, whether would support early retirement schemes, overall job satisfaction, whether would accept another position elsewhere, at what salary and for what reasons. General comments regarding education and training. Background Variables Age, sex, country of birth and of first degree, length of time lived in Australia, educational and occupational background of parents.

Abstract copyright UK Data Service and data collection copyright owner.The Family Resources Survey (FRS) has been running continuously since 1992 to meet the information needs of the Department for Work and Pensions (DWP). It is almost wholly funded by DWP. The FRS collects information from a large, and representative sample of private households in the United Kingdom (prior to 2002, it covered Great Britain only). The interview year runs from April to March.The focus of the survey is on income, and how much comes from the many possible sources (such as employee earnings, self-employed earnings or profits from businesses, and dividends; individual pensions; state benefits, including Universal Credit and the State Pension; and other sources such as savings and investments). Specific items of expenditure, such as rent or mortgage, Council Tax and water bills, are also covered.Many other topics are covered and the dataset has a very wide range of personal characteristics, at the adult or child, family and then household levels. These include education, caring, childcare and disability. The dataset also captures material deprivation, household food security and (new for 2021/22) household food bank usage. The FRS is a national statistic whose results are published on the gov.uk website. It is also possible to create your own tables from FRS data, using DWP’s Stat Xplore tool. Further information can be found on the gov.uk Family Resources Survey webpage. Safe Room Access FRS data In addition to the standard End User Licence (EUL) version, Safe Room access datasets, containing unrounded data and additional variables, are also available for FRS from 2005/06 onwards - see SN 7196, where the extra contents are listed. The Safe Room version also includes secure access versions of the Households Below Average Income (HBAI) and Pensioners' Incomes (PI) datasets. The Safe Room access data are currently only available to UK HE/FE applicants and for access at the UK Data Archive's Safe Room at the University of Essex, Colchester. Prospective users of the Safe Room access version of the FRS/HBAI/PI will need to fulfil additional requirements beyond those associated with the EUL datasets. Full details of the application requirements are available from Guidance on applying for the Family Resources Survey: Secure Access.FRS, HBAI and PIThe FRS underpins the related Households Below Average Income (HBAI) dataset, which focuses on poverty in the UK, and the related Pensioners' Incomes (PI) dataset. The EUL versions of HBAI and PI are held under SNs 5828 and 8503 respectively. The secure access versions are held within the Safe Room FRS study under SN 7196 (see above). The FRS aims to: support the monitoring of the social security programme; support the costing and modelling of changes to national insurance contributions and social security benefits; provide better information for the forecasting of benefit expenditure. From April 2002, the FRS was extended to include Northern Ireland. Detailed information regarding anonymisation within the FRS can be found in User Guide 2 of the dataset documentation. Edition History: For the second edition (July 2009), correction was made to variables TOTCAPBU and TOTCAPB2. Edits made to the PENPROV table were reviewed and new edits, based on revised criteria, applied to the dataset (see Penprov note for details). For the third edition (October 2014) the data have been re-grossed following revision of the FRS grossing methodology to take account of the 2011 Census mid-year population estimates. New variable GROSS4 has been added to the dataset. Main Topics: Household characteristics (composition, tenure type); tenure and housing costs including Council Tax, mortgages, insurance, water and sewage rates; welfare/school milk and meals; educational grants and loans; children in education; informal care (given and received); childcare; occupation and employment; health restrictions on work; travel to work; children's health; wage details; self-employed earnings; personal and occupational pension schemes; income and benefit receipt; income from pensions and trusts, royalties and allowances, maintenance and other sources; income tax payments and refunds; National Insurance contributions; earnings from odd jobs; children's earnings; interest and dividends; investments; National Savings products; assets. Standard Measures Standard Occupational Classification Multi-stage stratified random sample Face-to-face interview Computer Assisted Personal Interviewing 2006 2007 ABSENTEEISM ACADEMIC ACHIEVEMENT ADMINISTRATIVE AREAS AGE APARTMENTS APPLICATION FOR EMP... APPOINTMENT TO JOB ATTITUDES BANK ACCOUNTS BEDROOMS BONDS BUILDING SOCIETY AC... BUSES BUSINESS RECORDS CARE OF DEPENDANTS CARE OF THE DISABLED CARE OF THE ELDERLY CARS CHARITABLE ORGANIZA... CHILD BENEFITS CHILD CARE CHILD DAY CARE CHILD MINDERS CHILD MINDING CHILD SUPPORT PAYMENTS CHILD WORKERS CHILDREN CHRONIC ILLNESS CIVIL PARTNERSHIPS COHABITATION COLOUR TELEVISION R... COMMERCIAL BUILDINGS COMMUTING CONCESSIONARY TELEV... CONSUMPTION COUNCIL TAX CREDIT UNIONS Consumption and con... DAY NURSERIES DEBILITATIVE ILLNESS DEBTS DISABILITIES DISABILITY DISCRIMI... DISABLED CHILDREN DISABLED PERSONS DOMESTIC RESPONSIBI... ECONOMIC ACTIVITY ECONOMIC VALUE EDUCATION EDUCATIONAL BACKGROUND EDUCATIONAL FEES EDUCATIONAL GRANTS EDUCATIONAL INSTITU... EDUCATIONAL VOUCHERS ELDERLY EMPLOYEES EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ENDOWMENT ASSURANCE ETHNIC GROUPS EXPENDITURE EXTRACURRICULAR ACT... FAMILIES FAMILY MEMBERS FINANCIAL DIFFICULTIES FINANCIAL INSTITUTIONS FINANCIAL RESOURCES FINANCIAL SUPPORT FOOD FREE SCHOOL MEALS FRIENDS FRINGE BENEFITS FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... FURTHER EDUCATION Family life and mar... GENDER GIFTS GRANDPARENTS GRANTS HEADS OF HOUSEHOLD HEALTH HEALTH SERVICES HEARING IMPAIRED PE... HEARING IMPAIRMENTS HIGHER EDUCATION HOLIDAY LEAVE HOME BASED WORK HOME OWNERSHIP HOME SHARING HOURS OF WORK HOUSEHOLD BUDGETS HOUSEHOLD HEAD S OC... HOUSEHOLDS HOUSING HOUSING FACILITIES HOUSING FINANCE HOUSING TENURE INCOME INCOME TAX INDUSTRIES INSURANCE INSURANCE PREMIUMS INTEREST FINANCE INVESTMENT INVESTMENT RETURN Income JOB DESCRIPTION JOB HUNTING JOB SEEKER S ALLOWANCE LANDLORDS LEAVE LOANS LODGERS MANAGERS MARITAL STATUS MARRIED WOMEN MARRIED WOMEN WORKERS MATERNITY LEAVE MATERNITY PAY MEDICAL PRESCRIPTIONS MORTGAGE PROTECTION... MORTGAGES MOTORCYCLES NEIGHBOURS Northern Ireland OCCUPATIONAL PENSIONS OCCUPATIONAL QUALIF... OCCUPATIONS ONE PARENT FAMILIES ONLINE BANKING OVERTIME PARENTS PART TIME COURSES PART TIME EMPLOYMENT PARTNERSHIPS BUSINESS PASSENGERS PATERNITY LEAVE PENSION CONTRIBUTIONS PENSIONS PHYSICALLY DISABLED... PHYSICIANS POVERTY PRIVATE EDUCATION PRIVATE PERSONAL PE... PRIVATE SCHOOLS PROFITS QUALIFICATIONS RATES REBATES REDUNDANCY REDUNDANCY PAY REMOTE BANKING RENTED ACCOMMODATION RENTS RESIDENTIAL MOBILITY RETIREMENT ROOM SHARING ROOMS ROYALTIES SAVINGS SAVINGS ACCOUNTS AN... SCHOLARSHIPS SCHOOL MILK PROVISION SCHOOLCHILDREN SCHOOLS SEASONAL EMPLOYMENT SECONDARY EDUCATION SECONDARY SCHOOLS SELF EMPLOYED SEWAGE DISPOSAL AND... SHARES SHIFT WORK SICK LEAVE SICK PAY SICK PERSONS SOCIAL CLASS SOCIAL HOUSING SOCIAL SECURITY SOCIAL SECURITY BEN... SOCIAL SECURITY CON... SOCIAL SERVICES SOCIAL SUPPORT SOCIO ECONOMIC STATUS SPECIAL EDUCATION SPECTACLES SPOUSES STATE EDUCATION STATE HEALTH SERVICES STATE RETIREMENT PE... STUDENT HOUSING STUDENT LOANS STUDENTS STUDY SUBSIDIARY EMPLOYMENT SUPERVISORS SUPERVISORY STATUS Social stratificati... TAXATION TELEPHONES TELEVISION LICENCES TELEVISION RECEIVERS TEMPORARY EMPLOYMENT TENANCY AGREEMENTS TENANTS HOME PURCHA... TERMINATION OF SERVICE TIED HOUSING TIME TOP MANAGEMENT TRAINING TRANSPORT FARES TRAVEL CONCESSIONS TRAVEL PASSES UNEARNED INCOME UNEMPLOYED UNEMPLOYMENT BENEFITS UNFURNISHED ACCOMMO... UNWAGED WORKERS VISION IMPAIRMENTS VISUALLY IMPAIRED P... VOCATIONAL EDUCATIO... VOLUNTARY WORK WAGES WATER RATES WIDOWED WORKING MOTHERS WORKING WOMEN property and invest...

National level performance measures by establishment type and some pupil characteristics since 2018/19, includes an extended timeseries of headline performance measures since 2009/10.

United Kingdom UK: Secondary Education: General Pupils: % Female data was reported at 49.676 % in 2015. This records an increase from the previous number of 49.671 % for 2014. United Kingdom UK: Secondary Education: General Pupils: % Female data is updated yearly, averaging 49.131 % from Dec 1971 (Median) to 2015, with 45 observations. The data reached an all-time high of 49.676 % in 2015 and a record low of 48.519 % in 1971. United Kingdom UK: Secondary Education: General Pupils: % Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Education Statistics. Secondary general pupils are the number of secondary students enrolled in general education programs, including teacher training.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

After the challenges of the Covid-19 pandemic, the NEU (2021) report that one in three teachers plan to leave the profession in the next five years. As previous studies published by the DfE (2016, 2016a, 2016b and 2016c) focusing on workload have not affected the wastage rate of the profession, there is something deeper at work which needs to be explored.A critical theory, mixed method approach is used to gain a breadth and depth of understanding of the attitudes of 55 respondents to a survey and 17 participants in semi-structured interviews. All data collection was carried out in secondary schools in Lincolnshire, where teacher pay is good in comparison to the county average of workforce pay. These methods aim to test the assumption that concerns about workload and pay are causing teachers’ discontent. This data set is for the quantitative surevy second of thw work.

This is one of the 7 domains of the IMD, the indicators used in the latest update of this domain are; - Average test score of pupils at Key Stage 2 - Average test score of pupils at Key Stage 3 - Best of 8 average capped points score at Key Stage 4 (this includes results of GCSEs, GNVQs and other vocational equivalents) - Proportion of young people not staying on in school or non-advanced education above the age of 16 - Secondary school absence rate - Proportion of those aged under 21 not entering higher education - Proportion of working age adults with no or low qualifications The methodology for producing this domain changed between 2004 and 2007. The longer time series in Key Stage 2 results allowed weighted average of results to be taken in 2007; this reduced the variability in the results. More information about this domain can be found in Chapter 2, Section 5 of the English Indices of Deprivation 2007 report.

This is the average travel time in minutes to 8 key services by public transport/walking. The 8 key services are the average of minimum journey times to medium sized centres of employment (500-4999 jobs), primary schools, secondary schools, further education, GPs, hospitals, food stores and town centres.

It is part of the Journey Time Statistics produced by DfT which consists of theoretical journey times calculated by modelling journeys between known sets of origins and destinations, using information on the road network, traffic speeds and public transport timetables.

Statistics are designed to represent as far as possible the situation in October of the year to which they related. The origins, destinations and public transport timetables used are as far as possible for this date. The traffic data are averages for the preceding 12 months up to and including August. The road networks are those for the previous spring.

The origins used for all Access to Services calculations are the English Output Areas (OA). To provide the actual journey start point in each OA, the population weighted centroid of the OA was shifted to the nearest node (i.e junction) on the road network.

These statistics are very similar to those of the Accessibility Statistics, however, the methodology employed is different so the results are not directly comparable." Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.