This large, international dataset contains survey responses from N = 12,570 students from 100 universities in 35 countries, collected in 21 languages. We measured anxieties (statistics, mathematics, test, trait, social interaction, performance, creativity, intolerance of uncertainty, and fear of negative evaluation), self-efficacy, persistence, and the cognitive reflection test, and collected demographics, previous mathematics grades, self-reported and official statistics grades, and statistics module details. Data reuse potential is broad, including testing links between anxieties and statistics/mathematics education factors, and examining instruments’ psychometric properties across different languages and contexts. Note that the pre-registration can be found here: https://osf.io/xs5wf

Presents statistics on student support paid to students in the form of loans and grants or to their University/College in the form of tution fees. The students are English domiciles studying anywhere in the UK or EU students studying in England. Source agency: Student Loans Company Designation: National Statistics Language: English Alternative title: Student Support for Higher Education in England Data and Resources 2012/13 (Final) and 2013/14 (Provisional)HTML 2012/13 (Final) and 2013/14 (Provisional)

Presents statistics on student support paid to students in the form of loans and grants or to their University/College in the form of tution fees. The students are Northern Ireland domiciles studying anywhere in the UK, ROI or EU students studying in Northern Ireland. Source agency: Student Loans Company Designation: Official Statistics not designated as National Statistics Language: English Alternative title: Student Support for Higher Education in Northern Ireland

This dataset shows the location of Higher Education (HE) and Further Education (FE) institutes in the Great Britain. This should cover Universities and Colleges. Many institutes have more than one campus and where possible this is refelcted in the data so a University may have more than one entry. Postcodes have also been included for instities where possible. This data was collected from various sources connected with HEFE in the UK including JISC and EDINA. This represents the fullest list that the author could compile from various sources. If you spot a missing institution, please contact the author and they will add it to the dataset. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2011-02-01 and migrated to Edinburgh DataShare on 2017-02-21.

Presents statistics on student support paid to students in the form of loans and grants or to their University/College in the form of tution fees. The students are Welsh domiciles studying anywhere in the UK or EU students studying in Wales. Source agency: Student Loans Company Designation: Official Statistics not designated as National Statistics Language: English Alternative title: Student Support for Higher Education in Wales Data and Resources Academic Year 2012/13 (Final) and 2013/14 (Provisional)HTML Academic Year 2012/13 (Final) and 2013/14 (Provisional)

This dataset provides Census 2021 estimates that classify schoolchildren and full-time students aged 5 years and over in England and Wales by student accommodation and by age. The estimates are as at Census Day, 21 March 2021.

Estimates for single year of age between ages 90 and 100+ are less reliable than other ages. Estimation and adjustment at these ages was based on the age range 90+ rather than five-year age bands. 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.

Coverage

Census 2021 statistics are published for the whole of England and Wales. Data are also available in these geographic types:

• 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

Student accommodation type

Combines the living situation of students and school children in full-time education, whether they are living:

• alone
• in a student household
• with parents
• in a communal establishment, split by university and other communal establishment type
• in other household types

It also includes whether these households contain one or multiple families.

This variable is comparable with the student accommodation variable but splits the communal establishment type into “university” and “other” categories.

Age

A person’s age on Census Day, 21 March 2021 in England and Wales. Infants aged under 1 year are classified as 0 years of age.

These are the grants that HEFCE will be providing to each university and college that we fund. It includes the student number control.

This is the dataset derived from the sistematic review describes at https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=330361

The information refers to NI domiciled students gaining higher education qualifications from UK higher education institutions. The dataset is collected annually and is based on students obtaining a qualification at UK higher education institutions. The dataset is collected by the Higher Education Statistics Agency from higher education institutions throughout the UK and provided to the Department for Employment and Learning, Northern Ireland, for analysis. For 2013/14, NI Domiciled enrolments and qualifications at Open University are available. In previous years, these figures were included in NI students studying in England, as the administrative centre of the Open University is located in England.

DOI Abstract copyright UK Data Service and data collection copyright owner.The USR consists of records of undergraduate students on courses of one academic year or more; postgraduate students on courses of one academic year or more; academic and related staff holding regular salaried appointments, and finance data for all UK universities. The Finance dataset contains details of income and expenditure for all of the UK universities. These data are contained in a series of files for each year. For detailed information on structure and content of these files users should refer to the documentation that accompanies this dataset. Also included in the Finance dataset is the Student Load data. Student Load is, in the USR context, a reallocation of student-head count numbers, by apportioning them as a percentage to the departmental cost centres where they are taught, thus enabling student load, staff and financial data to be brought together. Main Topics: Finance: income and expenditure; university; cost centre. Student load: undergraduate, postgraduate (taught course or research); cost centre. No information recorded Annual returns from each university.

The information refers to NI domiciled students enrolled at higher education institutions in the UK. The dataset is collected annually and is based on enrolments in higher education institutions in the UK on 1st December each year. The dataset is collected by the Higher Education Statistics Agency from higher education institutions throughout the UK and provided to the Department for Employment and Learning, Northern Ireland, for analysis. For 2013/14, NI Domiciled enrolments and qualifications at Open University are available. In previous years, these figures were included in NI students studying in England, as the administrative centre of the Open University is located in England. The specification of the HESA Standard Registration Population has changed for 2007/08 enrolments onwards. Writing up and sabbatical students are now excluded from this population where they were previously included in published enrolment data and therefore 2007/08 data onwards cannot be directly compared to previous years.

The data article presents the relationship between university league tables and teaching qualification in the UK. Data were collected from the university and subject league tables (Complete University Guide) and teaching qualification (The Higher Education Academy - HEA), and Higher Education Funding Council for England - Hefce), UK.

Data product is provided by ASL Marketing. It contains current college students who are attending colleges and universities nationwide. Connect with this market by: Class Year Field of Study Home/School address College Attending Ethnicity School Type Region Sports Conference Gender eSports Email

This dataset is a compilation of processed data on citation and references for research papers including their author, institution and open access info for a selected sample of academics analysed using Microsoft Academic Graph (MAG) data and CORE. The data for this dataset was collected during December 2019 to January 2020.Six countries (Austria, Brazil, Germany, India, Portugal, United Kingdom and United States) were the focus of the six questions which make up this dataset. There is one csv file per country and per question (36 files in total). More details about the creation of this dataset are available on the public ON-MERRIT D3.1 deliverable report.The dataset is a combination of two different data sources, one part is a dataset created on analysing promotion policies across the target countries, while the second part is a set of data points available to understand the publishing behaviour. To facilitate the analysis the dataset is organised in the following seven folders:PRTThe dataset with the file name "PRT_policies.csv" contains the related information as this was extracted from promotion, review and tenure (PRT) policies. Q1: What % of papers coming from a university are Open Access?- Dataset Name format: oa_status_countryname_papers.csv- Dataset Contents: Open Access (OA) status of all papers of all the universities listed in Times Higher Education World University Rankings (THEWUR) for the given country. A paper is marked OA if there is at least an OA link available. OA links are collected using the CORE Discovery API.- Important considerations about this dataset: - Papers with multiple authorship are preserved only once towards each of the distinct institutions their authors may belong to. - The service we used to recognise if a paper is OA, CORE Discovery, does not contain entries for all paperids in MAG. This implies that some of the records in the dataset extracted will not have either a true or false value for the _is_OA_ field. - Only those records marked as true for _is_OA_ field can be said to be OA. Others with false or no value for is_OA field are unknown status (i.e. not necessarily closed access).Q2: How are papers, published by the selected universities, distributed across the three scientific disciplines of our choice?- Dataset Name format: fsid_countryname_papers.csv- Dataset Contents: For the given country, all papers for all the universities listed in THEWUR with the information of fieldofstudy they belong to.- Important considerations about this dataset: * MAG can associate a paper to multiple fieldofstudyid. If a paper belongs to more than one of our fieldofstudyid, separate records were created for the paper with each of those _fieldofstudyid_s.- MAG assigns fieldofstudyid to every paper with a score. We preserve only those records whose score is more than 0.5 for any fieldofstudyid it belongs to.- Papers with multiple authorship are preserved only once towards each of the distinct institutions their authors may belong to. Papers with authorship from multiple universities are counted once towards each of the universities concerned.Q3: What is the gender distribution in authorship of papers published by the universities?- Dataset Name format: author_gender_countryname_papers.csv- Dataset Contents: All papers with their author names for all the universities listed in THEWUR.- Important considerations about this dataset :- When there are multiple collaborators(authors) for the same paper, this dataset makes sure that only the records for collaborators from within selected universities are preserved.- An external script was executed to determine the gender of the authors. The script is available here.Q4: Distribution of staff seniority (= number of years from their first publication until the last publication) in the given university.- Dataset Name format: author_ids_countryname_papers.csv- Dataset Contents: For a given country, all papers for authors with their publication year for all the universities listed in THEWUR.- Important considerations about this work :- When there are multiple collaborators(authors) for the same paper, this dataset makes sure that only the records for collaborators from within selected universities are preserved.- Calculating staff seniority can be achieved in various ways. The most straightforward option is to calculate it as _academic_age = MAX(year) - MIN(year) _for each authorid.Q5: Citation counts (incoming) for OA vs Non-OA papers published by the university.- Dataset Name format: cc_oa_countryname_papers.csv- Dataset Contents: OA status and OA links for all papers of all the universities listed in THEWUR and for each of those papers, count of incoming citations available in MAG.- Important considerations about this dataset :- CORE Discovery was used to establish the OA status of papers.- Papers with multiple authorship are preserved only once towards each of the distinct institutions their authors may belong to.- Only those records marked as true for _is_OA_ field can be said to be OA. Others with false or no value for is_OA field are unknown status (i.e. not necessarily closed access).Q6: Count of OA vs Non-OA references (outgoing) for all papers published by universities.- Dataset Name format: rc_oa_countryname_-papers.csv- Dataset Contents: Counts of all OA and unknown papers referenced by all papers published by all the universities listed in THEWUR.- Important considerations about this dataset :- CORE Discovery was used to establish the OA status of papers being referenced.- Papers with multiple authorship are preserved only once towards each of the distinct institutions their authors may belong to. Papers with authorship from multiple universities are counted once towards each of the universities concerned.Additional files:- _fieldsofstudy_mag_.csv: this file contains a dump of fieldsofstudy table of MAG mapping each of the ids to their actual field of study name.

Abstract copyright UK Data Service and data collection copyright owner. The purpose of this study was the construction and analysis of a database of the records relating to students who attended the University of Aberdeen from 1860 to 1920, in order to create a comprehensive textual dossier of information about individual students which could be accessed easily by the University Archivist in answering frequent enquiries about past students; to facilitate the researches of scholars preparing publications on different aspects of University life for the institution's quincentenary in 1995; to demonstrate trends in the geographical and social mobility of the student population, as well as the impact on academic life of major changes in the curriculum. Main Topics: Three broad categories of data have been sought in constructing a database of students at the University of Aberdeen from 1860-1920. Out of a possible total of 52 fields, approximately 16 are devoted to biographical and background information, including dates of birth and death, place of origin, schools attended and father's occupation. A further 21 fields are concerned with the student's university education and experiences, including actual and total periods of study, classes attended, examinations passed (where applicable), degree(s) obtained, both at Aberdeen and at other universities, bursaries, prizes and medals awarded, the location of lodgings (where applicable), and membership of university societies. The third major area of investigation was the student's post-university life, incorporating information on locations, careers, and civil, military and academic honours and awards. Several further fields have been allocated to identifying sources of information. The database has been designed to allow not only minute documentation of the backgrounds and careers of individual students, but also global analysis of changing patterns of geographical and social mobility among the student population as a whole. Data on students' (and parents') occupations have been classified in conformity with the Registrar General's classification. No sampling (total universe)

This dataset presents a cluster analysis of UK universities based on four synthetic environments: social, cultural, physical and economic. These were developed based on variables that represented an educational ecosystem of well-being. The cluster analysis was initially linked to the LSYPE-Secure dataset using the UKPRNs (i.e. higher education institutional number) and hence the cluster analysis used data from around 2009-2012 to represent Wave 6 and Wave 7 of the LSYPE-Secure dataset. The cluster analysis was based on using a variety of variables available from HESA and the Office for Students (OfS) to represent these environments, for example: Social: had demographics of students and staff including ethnicity and sex Cultural: had data on research and teaching scores Economic: had data on student: staff ratio and expenditure Physical: had data related to the built and natural environment including residential sites, blue and green spacesEarlier last year (April 2018), the UK Office for Students (OfS) noted that students from underrepresented groups such as black and minority ethnic (BME) students and those from disadvantaged backgrounds were less likely to succeed at university. Coupled with this, research has shown that students from these groups are also more likely to have poorer mental health and wellbeing. However, there is substantial social and political pressure on universities to act to improve student mental health. For example, the Telegraph ran the headline "Do British universities have a suicide problem?" Thus, in June 2018, the Hon. Sam Gyimah, the then UK universities minister, informed university vice-chancellors that student mental health and wellbeing has to be one of their top priorities. Universities are investing substantive sums in activities to tackle student mental health but doing so with no evidence base to guide strategic policy and practice. These activities may potentially be ineffective, financially wasteful, and possibly, counter-productive. Therefore, we need a better evidence base which this project intends to fulfil. Currently, there is a lack of evidence and understanding about which groups of young people going to universities may have poorer life outcomes (such as education, employment, and mental health and well-being) as a result of their mental health and wellbeing during their adolescent years. These life outcomes and their mental health and wellbeing, however, are important for understanding the context of the complex social identities of the young people, such as the intersections between their gender, ethnicity, sexuality, religion and socio-economic status. Otherwise, these young people may feel misunderstood or judged. Most of the large body of quantitative research on life outcomes tend to focus on one social characteristic/identity of the student, such as the young person's gender or ethnicity or socio-economic status, but not the combination of all of these, i.e. the intersectionalities. Primarily, the reason for this has been the lack of sufficient data. This research draws on data from the Longitudinal Study of Young People in England (LSYPE), which tracked over 15,000 adolescents' education and health over 7 years between 2004-2010 (from when they were 13-19 years old), and the Next Steps Survey, which collected data from the same individuals in 2015 when they were 25 years and in the job market. This dataset also had an ethnic boost, which thus allows for the exploratory analysis of intersectionalities. Currently, there are a number of interventions being implemented to improve the university environment. However, there is a lack of evidence on how the university environment (such as their its size, amount of academic support available, availability of sports activities, students' sense of belonging, etc.) can affect the young person'students' mental health and wellbeing life outcomes. This evidence can be determined through by using the LSYPE data supplemented and by university environment data supplemented from the National Student Survey (NSS) and the Higher Education Statistics Agency (HESA). Thus this research uses an intersectional approach to investigate the extent to which the life outcomes of young persons who go to university are affected by their social inequality groupings and mental health and well-being during adolescence. Additionally, this research also aims to determine the characteristics of university environments that can improve the life outcomes of these young people depending on their social and mental health/wellbeing background. We use secondary data analysis of mainly HESA and OfS variables and created derived variables.

This dataset provides Census 2022 estimates for distance travelled to place of study of people aged 4 and over studying by age (in 4 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.

Distance travelled to work or place of study

The distance between a person’s home address and their main place of work or study (Grouped).

Address of place of work or study is used (along with home address) to explore the relationship between where people live and where they work or study. Used in conjunction with information from the method of travel question, the data helps to identify commuter patterns and routes and provide a reliable indicator for the demands placed on public and private transport.

It is used to inform the balance of housing and jobs in particular areas and assess the need for services such as new schools. Information on where people live and work is used by government departments to define “Travel to Work Areas” - these are approximations of self-contained labour markets and are the smallest areas for which unemployment rates are published. Collecting information on both work and study address enables a more accurate count of daytime populations to be obtained, which is particularly useful for areas accommodating universities and businesses. It also allows the differences in travel patterns between these groups to be compared.

Details of classification can be found here

The quality assurance report can be found here

Percentage of students, who entered an A Level or equivalent qualification, going to, or remaining in, an education destination or employment. The percentage of students progressing to further learning in a school, Further Education or Sixth Form College, Apprenticeship, work based learning provider or Higher Education Institution. To be included in the Measure, young people have to show sustained participation in an education destination in all of the first two terms of the year after they completed KS4 or took A level or equivalent qualifications. The first two terms is defined as October to March. The statistics are published as "Experimental Statistics" and do not display the National Statistics Logo. They are still being evaluated and remain subject to further testing to determine their reliability and ability to meet customer needs. The figures should be treated with caution as this is the first year for which such data have been produced. As improvements are made to the methodology, data quality will be assessed to establish whether the statistics meet the quality standards for National Statistics. “x“ means the data has been suppressed as the school or college has fewer than 6 students in a particular denominator, or small numbers for the numerator (1’s and 2’s). Results are not shown because of the risk of an individual student being identified. All totals have been rounded to the nearest 10. Zeros are shown as zeros. See Technical Note from Department of Education for further detail. https://www.gov.uk/government/collections/statistics-destinations

Abstract copyright UK Data Service and data collection copyright owner.The USR consists of records of undergraduate students on courses of one academic year or more; postgraduate students on courses of one academic year or more; academic and related staff holding regular salaried appointments, and finance data for all UK universities. Main Topics: (i) Personal information: date of birth; sex; marital status; country/county of domicile; country of birth; whether home or overseas student for fee purposes; occupation of parent or guardian. (ii) Academic history: last full-time school attended; other full-time/part-time post secondary educational institution attended; GCE `A' level or Scottish Certificate of Education higher grade results; other entrance qualifications; course for which admitted. (iii) Annual information: university; subject of course; normal duration of course; type of course; year of course; date of enrolment; method of study (full-time, part-time, sandwich, etc.); qualification aimed for; source of fees; accommodation (hall. lodgings, home, etc.). (iv) Leavers' details: qualification obtained; class of degree; date of leaving; reason for leaving; first destination. No information recorded Annual returns from each university.

The Open University (OU) dataset is an open database containing student demographic and click-stream interaction with the virtual learning platform. The available data are structured in different CSV files. You can find more information about the original dataset at the following link: https://analyse.kmi.open.ac.uk/open_dataset.

We extracted a subset of the original dataset that focuses on student information. 25,819 records were collected referring to a specific student, course and semester. Each record is described by the following 20 attributes: code_module, code_presentation, gender, highest_education, imd_band, age_band, num_of_prev_attempts, studies_credits, disability, resource, homepage, forum, glossary, outcontent, subpage, url, outcollaborate, quiz, AvgScore, count.

Two target classes were considered, namely Fail and Pass, combining the original four classes (Fail and Withdrawn and Pass and Distinction, respectively). The final_result attribute contains the target values.

All features have been converted to numbers for automatic processing.

Below is the mapping used to convert categorical values to numeric:

code_module: 'AAA'=0, 'BBB'=1, 'CCC'=2, 'DDD'=3, 'EEE'=4, 'FFF'=5, 'GGG'=6

code_presentation: '2013B'=0, '2013J'=1, '2014B'=2, '2014J'=3

gender: 'F'=0, 'M'=1

highest_education: 'No_Formal_quals'=0, 'Post_Graduate_Qualification'=1, 'HE_Qualification'=2, 'Lower_Than_A_Level'=3, 'A_level_or_Equivalent'=4

IMBD_band: 'unknown'=0, 'between_0_and_10_percent'=1, 'between_10_and_20_percent'=2, 'between_20_and_30_percent'=3, 'between_30_and_40_percent'=4, 'between_40_and_50_percent'=5, 'between_50_and_60_percent'=6, 'between_60_and_70_percent'=7, 'between_70_and_80_percent'=8, 'between_80_and_90_percent'=9, 'between_90_and_100_percent'=10

age_band: 'between_0_and_35'=0, 'between_35_and_55'=1, 'higher_than_55'=2

disability: 'N'=0, 'Y'=1

student's outcome: 'Fail'=0, 'Pass'=1

For more detailed information, please refer to:

Casalino G., Castellano G., Vessio G. (2021) Exploiting Time in Adaptive Learning from Educational Data. In: Agrati L.S. et al. (eds) Bridges and Mediation in Higher Distance Education. HELMeTO 2020. Communications in Computer and Information Science, vol 1344. Springer, Cham. https://doi.org/10.1007/978-3-030-67435-9_1