These statistics on student enrolments and qualifications obtained by higher education (HE) students at HE providers in the UK are produced by the Higher Education Statistics Agency (HESA). Information is available for:
Earlier higher education student statistics bulletins are available on the https://www.hesa.ac.uk/data-and-analysis/statistical-first-releases?date_filter%5Bvalue%5D%5Byear%5D=&topic%5B%5D=4" class="govuk-link">HESA website.
Statistics providing information on measures of widening participation in higher education.
These include estimates of progression to higher education (HE) by age 19 for state-funded pupils by personal characteristics, including:
The publication includes geographic breakdowns to enable comparisons of HE progression rates between local authorities and regions by personal characteristics.
Figures are also provided showing estimated percentages of A level and equivalent students, by school or college type, who progressed to HE by age 19 with breakdowns for high tariff higher education providers.
Further breakdowns include progression by POLAR disadvantage and Teaching Excellence and Student Outcomes Framework rating.
The latest data relates to HE entry in 2019 to 2020, so the figures presented will be unaffected by the impact of the coronavirus (COVID-19) pandemic.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
GENERAL INFORMATIONThe dataset represents supporting data for the research findings of the paper accepted for AIED'21 conference: http://oro.open.ac.uk/76042/ SHARING/ACCESS INFORMATIONLinks to publications that cite or use the data: Hlosta, Martin; Christothea, Herodotou; Miriam, Fernandez and Vaclav, Bayer Impact of Predictive Learning Analytics on Course Awarding Gap of Disadvantaged students in STEM. In: 22nd International Conference on Artificial Intelligence in Education, AIED 2021, Springer.Was data derived from another source? Yes - the data was derived from the internal OU data Recommended citation for this dataset: Hlosta, Martin; Christothea, Herodotou; Miriam, Fernandez and Vaclav, Bayer Impact of Predictive Learning Analytics on Course Awarding Gap of Disadvantaged students in STEM. In: 22nd International Conference on Artificial Intelligence in Education, AIED 2021, Springer.DATA & FILE OVERVIEWThe dataset contains coefficients of a logistic and linear regression that was used to model 3 student outcomes in 3 STEM courses - 1) completion, 2) passing and 3) overall score. The results are split into four tabs1. Regression BetasBets coefficients and the Standard Error for each variable student outcome , i.e. - completion: comp_B comp_SE - passing: pass_B pass_SE - overall score: score_B score_SE 2. LogReg Marginal Effectsthe marginal effect coefficients for the two dichotomous outcomes from the previous tab (completion and passing) More information about the marginal effects: https://www.statisticshowto.com/marginal-effects/3. Reg_BAME - These are the regression coefficients reported in the in the first tab, for the same outcomes (i.e. completion/passing/overall score), but disaggregated by whether the student is identified as BAME or not. Note that the analysis does not contain the 'BAME' coefficients, because it would be constant4. Red_IMDSimilarly as for BAME (point 3), these are regression coefficients disaggregated by IMD quintiles. IMD_Missing is a special category capturing the students without any IMD, i.e. international students.Regression coefficient variablesThe variables entering the regressions can be split into three categories and the intercept(1) Student level - age - banded into age_60, age_MISSING (reference category: age_[21-24]) - gender - gender_F (reference category Gender_M) - an indicator of linked qualification - linked_qual (reference category: linked_qual =False) - declared disability - disability (reference category: disability=False) - caring responsibility carer_NO, carer_YES (reference category: carer_MISSING) - flag whether the student is new at the OU - is_new (reference category: is_new=False) - highest previous education - ed_NoFormal, ed_HE_Qual, ed_PostGrad (reference category: ed_A Level/Equivalent) - average previous score - discretised into prev_score_LOW, prev_score_MOD, prev_score_VERY_HIGH (avg.prev.score=MISSING, i.e. the student did not study any previous course) these are banded into 4 quartiles (LOW, MOD, HIGH, VERY_HIGH), independently for each course - i.e. the specific values of these thresholds vary for the courses, as they will usually have values of the average score. - number of other credits studied - banded as credits_other_[1-60], credits_other_>=61 (reference category: credits_other=0) - number of previous attempts of the course - prev_attempt_=1, prev_attempt >1 (reference category: prev_attempt_0) - IMD (Index of Multiple Deprivation) - banded into quintiles, i.e. imd_=81 imd_MISSING (reference category: imd_[41-60]) - whether the student is identified as BAME - BAME_YES (reference category: BAME_NO) - Membership in the intervention group - group_INT (reference category: group_INT=0) (2) Teacher level - no. of students the teacher is responsible for - stud_in_group - avg. student pass rate in the previous years they were teaching - tut_pr_pass_LOW, tut_pr_pass_HIGH, tut_pr_pass_VERY_HIGH, tut_pr_pass_MISSING (reference category: tut_pr_pass_MOD) - these are banded into 4 quartiles (LOW, MOD, HIGH, VERY_HIGH), independently for each course - i.e. the specific values of these thresholds vary for the courses, as they will usually have different pass rates (3) Course level - dummy variable encoded as - course_1, course_2 (reference category: course_3)(4) intercept
In the academic year 2023/24, there were 331,602 international students from India studying in the United States. International students The majority of international students studying in the United States are originally from India and China, totaling 331,602 students and 277,398 students respectively in the 2023/24 school year. In 2022/23, there were 467,027 international graduate students , which accounted for over one third of the international students in the country. Typically, engineering and math & computer science programs were among the most common fields of study for these students. The United States is home to many world-renowned schools, most notably, the Ivy League Colleges which provide education that is sought after by both foreign and local students. International students and college Foreign students in the United States pay some of the highest fees in the United States, with an average of 24,914 U.S. dollars. American students attending a college in New England paid an average of 14,900 U.S. dollars for tuition alone and there were about 79,751 international students in Massachusetts . Among high-income families, U.S. students paid an average of 34,700 U.S. dollars for college, whereas the average for all U.S. families reached only 28,026 U.S. dollars. Typically, 40 percent of families paid for college tuition through parent income and savings, while 29 percent relied on grants and scholarships.
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...
School Survey:
A school survey was introduced into Young Lives in 2010, following the third round of the household survey, in order to capture detailed information about children's experiences of schooling, and to improve our understanding of:
Approximately 14.1 percent of people aged 16 to 24 were unemployed in the United Kingdom in the second quarter of 2025, the highest of any age group in that month. During this time period, older age groups have had much lower unemployment rates than younger ones, who have consistently had the highest unemployment rate. For almost all the age groups, the peak in the unemployment rate was recorded in 2011 when almost a quarter of young working age people were unemployed. Young adults in the labor market In the provided time period, youth unemployment was at its lowest rate in the third quarter of 2022, when it was 10.3 percent. Since then, there has been a noticeable uptick in youth unemployment, which was 14.8 percent towards the end of 2024. A more long-term trend among this age group is the increase in economic inactivity, with 40.8 percent of 16 to 24-year-old's not in work or actively looking for work in 2024. Although students or people in training account for a high share of this economic inactivity, there has also been a rise in the proportion of young adults who are not in education, employment or training (NEET), which reached a ten-year-high of 13.2 percent in late 2024. Unemployment up from low baseline in late 2024 In 2022, the UK labor market, had very low levels of unemployment along with a record number of job vacancies. Throughout 2023 and 2024, this very tight labor market began to loosen, although is still quite low by historic standards. One indicator that has stood out since the COVID-19 pandemic, however, has been the number of people economically inactive due to being on long-term sick leave, which reached 2.82 million in the first quarter of 2024, and has been the main reason for economic inactivity in the UK since late 2021.
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These statistics on student enrolments and qualifications obtained by higher education (HE) students at HE providers in the UK are produced by the Higher Education Statistics Agency (HESA). Information is available for:
Earlier higher education student statistics bulletins are available on the https://www.hesa.ac.uk/data-and-analysis/statistical-first-releases?date_filter%5Bvalue%5D%5Byear%5D=&topic%5B%5D=4" class="govuk-link">HESA website.