Office for National Statistics' national and subnational Census 2021. Schoolchildren and full-time studentsThis dataset provides Census 2021 estimates that classify all usual residents aged 5 years and over in England and Wales. The estimates are as at Census Day, 21 March 2021. Schoolchild or full-time student indicator definition: Indicates whether a person aged 5 years and over was in full-time education on Census Day, 21 March 2021. This includes schoolchildren and adults in full-time education.Schoolchildren and students in full-time education studying away from home are treated as usually resident at their term-time address.Comparability with 2011: Broadly comparable.We have removed the category Schoolchild or full-time student for Census 2021 and replaced it with Student. In the 2011 Census people aged 4 years and over were asked to answer the question, in Census 2021 people aged 5 years and over were asked to answer the question. This data is issued at (BGC) Generalised (20m) boundary type for:Country - England and WalesRegion - EnglandUTLA - England and WalesLTLA - England and WalesWard - England and WalesMSOA - England and WalesLSOA - England and WalesOA - England and WalesIf you require the data at full resolution boundaries, or if you are interested in the range of statistical data that Esri UK make available in ArcGIS Online please enquire at content@esriuk.com.The data services available from this page are derived from the National Data Service. The NDS delivers thousands of open national statistical indicators for the UK as data-as-a-service. Data are sourced from major providers such as the Office for National Statistics, Public Health England and Police UK and made available for your area at standard geographies such as counties, districts and wards and census output areas. This premium service can be consumed as online web services or on-premise for use throughout the ArcGIS system.Read more about the NDS.

The Health Survey for England (HSE), 2002: Teaching Dataset has been prepared solely for the purpose of teaching and student use. The dataset will help class tutors to incorporate empirical data into their courses and thus to develop students’ skills in quantitative methods of analysis.

All the variables and value labels are those used in the original HSE files, with one exception (New-wt) which is a new weighting variable.

Users may be interested in the Guide to using SPSS for Windows available from Online statistical guides and which explores this dataset.

The original HSE 2002 dataset is held at the UK Data Archive under SN 4912.

Experimental statistics from the Student Experiences Insights Survey (SEIS) in England. Includes information on the behaviours, plans, opinions and well-being of higher education students in their third year or higher in the context of guidance on the coronavirus (COVID-19) pandemic. The period covered in this dataset is 29 November to 20 December 2021.

This dataset provides Census 2022 estimates for Economic activity of full-time students aged 16 and over in Scotland.

Economic activity

Economic activity relates to whether or not a person aged 16 and over was working or looking for work in the week before census. Rather than a simple indicator of whether or not someone was currently in employment, it provides a measure of whether or not a person was an active participant in the labour market.

A person's economic activity is derived from their 'activity last week'. This is an indicator of their status or availability for employment - whether employed, unemployed, or their status if not employed and not seeking employment. Additional information included in the economic activity classification is also derived from information about the number of hours a person works and their type of employment - whether employed or self-employed.

The census concept of economic activity is compatible with the standard for economic status defined by the International Labour Organisation (ILO). It is one of a number of definitions used internationally to produce accurate and comparable statistics on employment, unemployment and economic status.

Details of classification can be found here

Full-time students

A student is a person who is in full-time education either at school or in higher or further education.

Details of classification can be found here

The quality assurance report can be found here

Full-time equivalent enrolments of students with a verified disability by education setting, collected from 2012 as part of the annual enrolment data collection in Term 3. Students with a disability …Show full descriptionFull-time equivalent enrolments of students with a verified disability by education setting, collected from 2012 as part of the annual enrolment data collection in Term 3. Students with a disability are those students who are verified by a Department for Education psychologist or speech pathologist as eligible for the Department for Education Disability Support Program. Education settings include Mainstream Classes, Special Classes, Special Units and Special Schools. Special Classes are located within some junior primary, primary and secondary schools. They provide a setting for learners with a disability who need extensive curriculum support, for a short or long-term placement. Special Units are located within some primary and secondary schools. They provide long-term educational options in a mainstream school for learners with very significant or multiple disabilities. Special Units and Special Schools both cater for a similar range of learner needs. The difference is that Special Units provide an option within a mainstream school, while Special Schools provide the option in a separate setting.

The purpose of the study: to collect opinions of schoolchildren and students on the influence of science culture and scientific self-efficacy on futures consciousness in Finland, Italy, Lithuania and the United Kingdom. Dataset "FEDORA. Limits and Potential of the Current Organization of Knowledge in Disciplines: Finish, Italian, Lithuanian and United Kingdom Schoolchildren and Students Survey, February - April 2022" was published implementing project "Future-oriented Science Education to enhance Responsibility and Engagement in the society of acceleration and uncertainty" from 2020 to 2023. Project is funded by the EU Framework Programme for Research and Innovation “Horizon 2020” (No. 872841)". Temporary accessibility restrictions apply for this dataset. Data will be made available without restrictions from 2026-12-31.

Dataset population: Full-time students aged 16 and over

Age

Age is derived from the date of birth question and is a person's age at their last birthday, at 27 March 2011. Dates of birth that imply an age over 115 are treated as invalid and the person's age is imputed. Infants less than one year old are classified as 0 years of age.

Economic activity

Economic activity relates to whether or not a person who was aged 16 and over was working or looking for work in the week before census. Rather than a simple indicator of whether or not someone was currently in employment, it provides a measure of whether or not a person was an active participant in the labour market.

A person's economic activity is derived from their 'activity last week'. This is an indicator of their status or availability for employment - whether employed, unemployed, or their status if not employed and not seeking employment. Additional information included in the economic activity classification is also derived from information about the number of hours a person works and their type of employment - whether employed or self-employed.

The census concept of economic activity is compatible with the standard for economic status defined by the International Labour Organisation (ILO). It is one of a number of definitions used internationally to produce accurate and comparable statistics on employment, unemployment and economic status.

Student accommodation

Student term-time accommodation for all students (i.e. schoolchildren and students aged 4 and over in full-time education) living in households and communal establishments. For students in households, the classification is derived by looking at the other members of the household, their student status and their generation in family.

Student accommodation defines the type of accommodation a schoolchild or student lives in during term-time and therefore relates only to their term-time address.

These datasets were generated as part of a study investigating whether participation in an international, collaborative learning experience—specifically the “Global Classroom Project”—would lead to increases in students’ Cultural Intelligence (CQ) and their satisfaction with the learning experience. The main hypothesis was that students involved in this project would show significant increases in overall CQ and its sub-dimensions (motivational, cognitive, metacognitive, and behavioural), and that higher levels of CQ would be positively associated with greater student satisfaction.

Two datasets are included:

Intervention Group Dataset: This dataset contains matched pre- and post-intervention data from 56 undergraduate psychology students who participated in the Global Classroom Project across three academic years (2021/2022, 2022/2023, and 2023/2024) at three institutions: Opole University (Poland), Leeds Trinity University (UK), and Taylors University (Malaysia). Students worked in intercultural teams and completed an online presentation comparing psychological concepts across the three cultures.

Control Group Dataset: This smaller dataset includes matched pre- and post-data from 6 students in a control group at the UK university who did not participate in the international project.

In both datasets, participants completed two instruments:

Expanded Cultural Intelligence Scale (E-CQS) (Van Dyne et al., 2012), a validated 39-item self-report tool measuring four CQ dimensions on a 7-point Likert scale: motivational, cognitive, metacognitive, and behavioural.

Student Satisfaction Scale, a self-developed 18-item measure assessing satisfaction with the international learning experience (administered only in the intervention group), using a 5-point Likert scale.

Statistical analyses showed that participants in the intervention group experienced significant gains in overall CQ and various sub-dimensions, as well as a positive relationship between CQ and satisfaction. No significant changes were found in the control group, except a small increase in behavioural CQ.

These datasets can be used to explore the development of Cultural Intelligence through international education. The intervention dataset is particularly suited for secondary analyses of trends across cohorts, countries, and CQ dimensions, while the control dataset supports comparison of outcomes for students without intercultural exposure. Both datasets are anonymised, clearly labelled, and formatted for compatibility with common statistical software.

This dataset records responses from 306 public primary school children during data collection in Rwanda in 2018. Specifically, each pupil completed two assessments to measure their cognitive flexibility (the Dimension Change Card Sort (DCCS) and Flexible Item Selection Task (FIST)), one measure of their non-verbal reasoning (the Object Pattern-Based Reasoning Assessment (OPRA)) and one measure of their wider executive function and problem-solving skills (the Tower of Hanoi (TOH)). The participants also completed up to four tasks to ascertain their basic reading skills and answered questions from a survey examining different family and background characteristics. All children were assessed and surveyed one-to-one in their mother tongue, Kinyarwanda. Data are only provided for pupils who consented to take part in the research.

Doctoral Training Partnerships: a range of postgraduate training is funded by the Research Councils. For information on current funding routes, see the common terminology at www.rcuk.ac.uk/StudentshipTerminology. Training grants may be to one organisation or to a consortia of research organisations. This portal will show the lead organisation only.

This dataset is based on original survey data from 224 Chinese undergraduate students who are studying in China and the UK. This dataset is to explore how UK-based and China-based Chinese students’ smoking behaviours are affected by their social networks, particularly their friends and family members, to address an empirical gap by testing the validity of three social network mechanisms: person-to-person contact, social support, and social stress, and to identify how studying abroad influences smoking behaviours by comparing these smoking behaviours for Chinese students in China.

International students’ mental health has become an increasing concern in recent years, as more students leave their country for better education. They experience a wide range of challenges while studying abroad that have an impact on their psychological well-being. These challenges can include language obstacles, cultural differences, homesickness, financial issues and other elements that could severely impact the mental health of international students. Given the limited research on the demographic, cultural, and psychosocial variables that influence international students’ mental health, and the scarcity of studies on the use of machine learning algorithms in this area, this study aimed to analyse data to understand the demographic, cultural factors, and psychosocial factors that impact mental health of international students. Additionally, this paper aimed to build a machine learning-based model for predicting depression among international students in the United Kingdom. This study utilized both primary data gathered through an online survey questionnaire targeted at international students and secondary data was sourced from the ’A Dataset of Students’ Mental Health and Help-Seeking Behaviors in a Multicultural Environment,’ focusing exclusively on international student data within this dataset. We conducted data analysis on the primary data and constructed models using the secondary data for predicting depression among international students. The secondary dataset is divided into training (70%) and testing (30%) sets for analysis, employing four machine learning models: Logistic Regression, Decision Tree, Random Forest, and K Nearest Neighbor. To assess each algorithm’s performance, we considered metrics such as Accuracy, Sensitivity, Specificity, Precision and AU-ROC curve. This study identifies significant demographic variables (e.g., loan status, gender, age, marital status) and psychosocial factors (financial difficulties, academic stress, homesickness, loneliness) contributing to international students’ mental health. Among the machine learning models, the Random Forest model demonstrated the highest accuracy, achieving an 80% accuracy rate in predicting depression.

Achievements of young people in GCE/VCE A/AS examinations. The dataset includes the average GCE/VCE A/AS point score per student and the average GCE/VCE A/AS point score per entry. For the same set of students, the data also include the percentage achieving 2 or more GCE/VCE A level passes or the AS level equivalent; and the percentage achieving 3 or more GCE/VCE A grades at A level. A breakdown by pupil gender for these variables is also included. This data is based on pupil residence rather than where they go to school Source: Department for Children Schools and Families (DCSF) Publisher: Neighbourhood Statistics Geographies: Local Authority District (LAD), Government Office Region (GOR), National Geographic coverage: England Time coverage: 2004/05 to 2007/08 Type of data: Administrative data Notes: The dataset covers 16 to 18 year old students at the end of their second (and final) year of post-16 study. To determine whether a student belongs to this group, a set of criteria known as 'trigger criteria' has been established. These criteria state that students must be aged 16, 17 or 18 (at the start of the academic year), and they must have been entered for a GCE/VCE A level, or a VCE Double Award in the summer session of the academic year. Data based on school location rather than pupil residence and is available for maintained schools only.

Background The high prevalence of mental distress among university students is gaining academic, policy, and public attention. As research efforts mount, it is important to involve students to ensure that work in this field translates into meaningful improvements. The aim of this study was to consult students in the UK on their priorities for future research into student mental health.

Methods In this cross-sectional priority setting exercise, current UK university students were asked to submit three research questions relating to student mental health. Responses were aggregated into themes through content analysis and considered in the context of existing research. Outcomes UK university students (N = 385) submitted 991 questions, categorised into the following themes: (1) epidemiology and trends, (2) causes and risk factors, (3) academic factors and work-life balance, (4) sense of belonging, (5) intervention and services, (6) mental health literacy, and (7) consequences. Across themes, respondents highlighted the importance of understanding the experience of minority groups.

Interpretation Students’ research interests are mostly unmet in the existing literature. In line with student priorities, future research should identify how mental health problems vary across the student population and investigate risk and protective factors. Students are interested in how academic and social cultures impact mental health at university; in particular, repeated reference to pressure and loneliness was striking. Future research should take a broad lens to evaluate interventions; considering how services are designed and delivered and investigating institutional and behavioural barriers to accessibility.

A cross-sectional, mixed methods online survey was deployed to students in five universities in the NE. The survey explored whether students changed behaviour at university, what they changed and why they changed. Their engagement in physical activity, smoking, diet (consumption of fruit and vegetables) and alcohol consumption was assessed.

This dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in England and Wales by NS-SEC and by sex. The estimates are as at Census Day, 21 March 2021.

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

National Statistics Socio-economic Classification (NS-SeC)

The National Statistics Socio-economic Classification (NS-SEC) indicates a person's socio-economic position based on their occupation and other job characteristics.

It is an Office for National Statistics standard classification. NS-SEC categories are assigned based on a person's occupation, whether employed, self-employed, or supervising other employees.

Full-time students are recorded in the "full-time students" category regardless of whether they are economically active.

Sex

This is the sex recorded by the person completing the census. The options were “Female” and “Male”.

This dataset provides Census 2022 estimates for Economic activity of Household Reference Person in Scotland.

Economic activity

Economic activity relates to whether or not a person aged 16 and over was working or looking for work in the week before census. Rather than a simple indicator of whether or not someone was currently in employment, it provides a measure of whether or not a person was an active participant in the labour market.

A person's economic activity is derived from their 'activity last week'. This is an indicator of their status or availability for employment - whether employed, unemployed, or their status if not employed and not seeking employment. Additional information included in the economic activity classification is also derived from information about the number of hours a person works and their type of employment - whether employed or self-employed.

The census concept of economic activity is compatible with the standard for economic status defined by the International Labour Organisation (ILO). It is one of a number of definitions used internationally to produce accurate and comparable statistics on employment, unemployment and economic status.

Details of classification can be found here

Household reference person

The concept of a Household Reference Person (HRP) was introduced in the 2001 Census (in common with other government surveys in 2001/2) to replace the traditional concept of the 'head of the household'. HRPs provide an individual person within a household to act as a reference point for producing further derived statistics and for characterising a whole household according to characteristics of the chosen reference person.

For a person living alone, it follows that this person is the HRP.

If a household contains only one family (with or without ungrouped individuals) then the HRP is the same as the Family Reference Person (FRP).

The Family Reference Person (FRP) is identified by criteria based on the family make up:

In a lone parent family it is taken to be the lone parent.

In a couple family, the FRP is chosen from the two people in the couple on the basis of their economic activity (in the priority order: full-time job, part-time job, unemployed, retired, other). If both people have the same economic activity, the FRP is identified as the elder of the two or, if they are the same age, the first member of the couple on the form.

If there is more than one family in a household the HRP is chosen from among the FRPs using the same criteria used to choose the FRP. This means the HRP will be selected from the FRPs on the basis of their economic activity, in the priority order:

• Economically active, employed, full-time, non-student
• Economically active, employed, full-time, student
• Economically active, employed, part-time, non-student
• Economically active, employed, part-time, student
• Economically active, unemployed, non-student
• Economically active, unemployed, student
• Economically inactive, retired
• Economically inactive, other

If some or all FRPs have the same economic activity, the HRP is the eldest of the FRPs. If some or all are the same age, the HRP is the first of the FRPs from the order in which they were listed on the questionnaire.

For families in which there is generational divide between family members that cannot be determined (Other related family), there is no FRP. Members of these families are treated the same as ungrouped individuals.

If a household is made up entirely of any combination of ungrouped individuals and other related families, the HRP is chosen from among all people in the household, using the same criteria used to choose between FRPs. Students at their non term-time address cannot be the HRP.

Details of classification can be found here

The quality assurance report can be found here

This dataset provides Census 2022 estimates for Economic activity of Household Reference Person by age (in 16 categories) in Scotland.

Age

A person's age on Census Day, 20 March 2022. Infants aged under 1 year are classified as 0 years of age.

Economic activity

Economic activity relates to whether or not a person aged 16 and over was working or looking for work in the week before census. Rather than a simple indicator of whether or not someone was currently in employment, it provides a measure of whether or not a person was an active participant in the labour market.

A person's economic activity is derived from their 'activity last week'. This is an indicator of their status or availability for employment - whether employed, unemployed, or their status if not employed and not seeking employment. Additional information included in the economic activity classification is also derived from information about the number of hours a person works and their type of employment - whether employed or self-employed.

The census concept of economic activity is compatible with the standard for economic status defined by the International Labour Organisation (ILO). It is one of a number of definitions used internationally to produce accurate and comparable statistics on employment, unemployment and economic status.

Details of classification can be found here

Household reference person

The concept of a Household Reference Person (HRP) was introduced in the 2001 Census (in common with other government surveys in 2001/2) to replace the traditional concept of the 'head of the household'. HRPs provide an individual person within a household to act as a reference point for producing further derived statistics and for characterising a whole household according to characteristics of the chosen reference person.

For a person living alone, it follows that this person is the HRP.

If a household contains only one family (with or without ungrouped individuals) then the HRP is the same as the Family Reference Person (FRP).

The Family Reference Person (FRP) is identified by criteria based on the family make up:

In a lone parent family it is taken to be the lone parent.

In a couple family, the FRP is chosen from the two people in the couple on the basis of their economic activity (in the priority order: full-time job, part-time job, unemployed, retired, other). If both people have the same economic activity, the FRP is identified as the elder of the two or, if they are the same age, the first member of the couple on the form.

If there is more than one family in a household the HRP is chosen from among the FRPs using the same criteria used to choose the FRP. This means the HRP will be selected from the FRPs on the basis of their economic activity, in the priority order:

• Economically active, employed, full-time, non-student
• Economically active, employed, full-time, student
• Economically active, employed, part-time, non-student
• Economically active, employed, part-time, student
• Economically active, unemployed, non-student
• Economically active, unemployed, student
• Economically inactive, retired
• Economically inactive, other

If some or all FRPs have the same economic activity, the HRP is the eldest of the FRPs. If some or all are the same age, the HRP is the first of the FRPs from the order in which they were listed on the questionnaire.

For families in which there is generational divide between family members that cannot be determined (Other related family), there is no FRP. Members of these families are treated the same as ungrouped individuals.

If a household is made up entirely of any combination of ungrouped individuals and other related families, the HRP is chosen from among all people in the household, using the same criteria used to choose between FRPs. Students at their non term-time address cannot be the HRP.

Details of classification can be found here

The quality assurance report can be found here

This dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in England and Wales by general health and by NS-SEC. The estimates are as at Census Day, 21 March 2021.

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

General health

A person's assessment of the general state of their health from very good to very bad. This assessment is not based on a person's health over any specified period of time.

National Statistics Socio-economic Classification (NS-SeC)

The National Statistics Socio-economic Classification (NS-SEC) indicates a person's socio-economic position based on their occupation and other job characteristics.

It is an Office for National Statistics standard classification. NS-SEC categories are assigned based on a person's occupation, whether employed, self-employed, or supervising other employees.

Full-time students are recorded in the "full-time students" category regardless of whether they are economically active.

This file set is the basis of a project in which Stephanie Pywell from The Open University Law School created and evaluated some online teaching materials – Fundamentals of Law (FoLs) – to fill a gap in the knowledge of graduate entrants to the Bachelor of Laws (LLB) programme. These students are granted exemption from the Level 1 law modules, from which they would normally acquire the basic knowledge of legal principles and methods that is essential to success in higher-level study. The materials consisted of 12 sessions of learning, each covering one key topic from a Level 1 law module.
The dataset includes a Word document that consists of the text of a five-question, multiple-choice Moodle poll, together with the coding for each response option.
The rest of the dataset consists of spreadsheets and outputs from SPSS and Excel showing the analyses that were conducted on the cleaned and anonymised data to ascertain students' use of, and views on, the teaching materials, and to explore any statistical association between students' studying of the materials and their academic success on Level 2 law modules, W202 and W203.

Students were asked to complete the Moodle poll at the end of every session of study, of which there were 1,013. Only one answer from each of the 240 respondents was retained for Questions 3, 4 and 5, to avoid skewing the data. Some data are presented as percentages of the number of sessions studied; some are presented as percentages of the number of respondents, and some are presented as percentage of the number of respondents who meet specific criteria.
Student identifiers, which have been removed to ensure anonymity, are as follows: Open University Computer User code (OUCU) and Personal Identifier (PI). These were used to collate the output from the Moodle poll with students' Level 2 module results.

International comparative study on the influence of types of school on the attitudes of the schoolchildren to mathematics and their achievements in this subject.

Topics: The data set contains three parts: 1. Schoolchildren data: mathematics tests: solving algebraic and trigonometric tasks; attitude to school and learning; attitude to mathematics; learning difficulties in mathematics; attitude to mathematics instruction and description of the mathematics instruction; significance of mathematics in society; attitude to surroundings; occupational goal; class size; tutor help; learning progress.

1. Teacher survey: manner of qualification for mathematics instruction; place of education; judgement on the significance of mathematics for the education of schoolchildren.

2. School principals: type and size of school; number of mathematics teachers; average class size; equipment of the school.

Demography: age; sex; school education; social origins; city size.