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.

The student sample for this research was selected from YouthSight’s Student Panel. Based on HESA statistics, the sample comprises national representation of gender, course year, and university type. The data is weighted on these factors. After fieldwork, the sample collected was checked for quality, and any ‘straight-liners’ were removed from the final total. The total student sample size is 2,153 respondents.Fieldwork was carried out between 29th July and 2nd August 2019.The survey instrument was developed by reviewing the limited number of studies and surveys on freedom of expression, consultations with colleagues and informed by our own experience. This resulted in the inclusion of seven comparative statements that are routinely used in surveys on freedom of expression in US universities, and a 15-item Moral Foundations Questionnaire, which enables the data to be interrogated by underlying moral profile. The definition of freedom of expression uses the framing adopted by King’s College London, which was developed through extensive consultation with the Students’ Union.

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

This dataset is about universities in the United Kingdom. It has 92 rows. It features 5 columns: country, total students, domain, and graduate students.

This dataset is about universities in the United Kingdom. It has 92 rows. It features 6 columns including country, city, total students, and latitude.

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 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.

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.

We have previously found that the psychoeducational 'Science of Happiness' course has a beneficial effect on participant well-being (Hood et al, 2021; Hobbs et al, 2022). In this study, we examine whether these benefits are sustained 1-2 years post course.

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.

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.

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.

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

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 ...

The datasets provided by UK based online learning university "Open University". More about the dataset: https://analyse.kmi.open.ac.uk/open_dataset

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.

Context

I am working on a Learning Analytics Program Evaluation Framework for a doctoral program that takes into account student course interaction behaviors, performance and satisfaction. Behavior information is gathered from LMS and other ed tech tool analytics. Performance data is gathered from student scores. Satisfaction data is gathered from surveys.

This old slide deck was the earliest iteration of this research project: https://www.slideshare.net/rockirussell/d-min-programeval

I was happy to find this dataset provided by Open University that will allow me to play around with anonymized data from outside of my academic context.

Content

This dataset offers two of the elements in the framework: behavior and performance. It contains information about 22 courses, 32,593 students, their assessment results, and logs of their interactions with the VLE represented by daily summaries of student clicks (10,655,280 entries).

A thorough description of this data is provided here: https://analyse.kmi.open.ac.uk/open_dataset#description

Acknowledgements

This dataset is provided by: Kuzilek J., Hlosta M., Zdrahal Z. Open University Learning Analytics dataset Sci. Data 4:170171 doi: 10.1038/sdata.2017.171 (2017).

Inspiration

If you are interested in learning analytics or educational data, please spend some time exploring and manipulating this data.

This survey aimed to establish what foods are typically eaten by students in the United Kingdom as preliminary information for later studies of gender identity and diet. Specifically, we aimed to uncover what foods were typically consumed by students as snacks, breakfast, lunch and dinner. Students were sent a link to an online survey via Facebook and through student email at a university in the United Kingdom. Students were asked to record demographic information, then list three foods that they typically ate for breakfast, lunch and dinner, and up to five typical snacks. In this dataset, we have stored demographics as coded variables and food choices as string/free text variables. The survey of 116 students was conducted via Google Docs.

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.