100+ datasets found
  1. šŸŒ World Education Dataset šŸ“š

    • kaggle.com
    zip
    Updated Nov 22, 2024
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    Bushra Qurban (2024). šŸŒ World Education Dataset šŸ“š [Dataset]. https://www.kaggle.com/datasets/bushraqurban/world-education-dataset
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    zip(248507 bytes)Available download formats
    Dataset updated
    Nov 22, 2024
    Authors
    Bushra Qurban
    License

    https://www.worldbank.org/en/about/legal/terms-of-use-for-datasetshttps://www.worldbank.org/en/about/legal/terms-of-use-for-datasets

    Area covered
    World
    Description

    Dataset Overview šŸ“

    The dataset includes the following key indicators, collected for over 200 countries:

    • Government Expenditure on Education (% of GDP): Shows the percentage of a country’s GDP allocated to education.
    • Literacy Rate (Adult Total): Represents the percentage of the population aged 15 and above who can read and write.
    • Primary Completion Rate: The percentage of children who complete their primary education within the official age group.
    • Pupil-Teacher Ratio (Primary and Secondary Education): Indicates the average number of students per teacher at the primary and secondary levels.
    • School Enrollment Rates (Primary, Secondary, Tertiary): Reflects the percentage of the relevant age group enrolled in schools across different education levels.

    Data Source 🌐

    World Bank: This dataset is compiled from the World Bank's educational database, providing reliable, updated statistics on educational progress worldwide.

    Potential Use Cases šŸ” This dataset is ideal for anyone interested in:

    Educational Research: Understanding how education spending and policies impact literacy, enrollment, and overall educational outcomes. Predictive Modeling: Building models to predict educational success factors, such as completion rates and literacy. Global Education Analysis: Analyzing trends in global education systems and how different countries allocate resources to education. Policy Development: Helping governments and organizations make data-driven decisions regarding educational reforms and funding.

    Key Questions You Can Explore šŸ¤”

    How does government expenditure on education correlate with literacy rates and school enrollment across different regions? What are the trends in pupil-teacher ratios over time, and how do they affect educational outcomes? How do education indicators differ between low-income and high-income countries? Can we predict which countries will achieve universal primary education based on current trends?

    Important Notes āš ļø - Missing Data: Some values may be missing for certain years or countries. Consider using techniques like forward filling or interpolation when working with time series models. - Data Limitations: This dataset provides global averages and may not capture regional disparities within countries.

  2. d

    Ministry of Public Administration and Security_Statistical Yearbook_Local...

    • data.go.kr
    xml
    Updated Jun 4, 2025
    + more versions
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    (2025). Ministry of Public Administration and Security_Statistical Yearbook_Local Government Human Resources Development Institute General Education [Dataset]. https://www.data.go.kr/en/data/15107448/openapi.do
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    xmlAvailable download formats
    Dataset updated
    Jun 4, 2025
    License

    https://data.go.kr/ugs/selectPortalPolicyView.dohttps://data.go.kr/ugs/selectPortalPolicyView.do

    Description
    • The Ministry of the Interior and Safety publishes the 'Administrative Safety Statistical Yearbook' every year by compiling statistical data from the headquarters of the Ministry of the Interior and Safety and its affiliated organizations in accordance with the 'Ministry of the Interior and Safety Statistics Management Regulations'. - The statistical information by field included in the 'Administrative Safety Statistical Yearbook' is provided as an open API so that it can be used in various fields in both the public and private sectors. - The open API in question is the statistics on 'Local Autonomy Human Resources Development Institute General Education' among 'Others' included in the 'Administrative Safety Statistical Yearbook'. It provides general education statistical information such as the number of courses, sessions, and participants for each long-term education, basic education, specialized policy education, and other education. - In addition, the 'Administrative Safety Statistical Yearbook' can be downloaded in PDF format from the Ministry of the Interior and Safety website at Policy Data > Statistics > Statistical Yearbook/Statistics by Subject.
  3. f

    Data from: From the specific actions to government mark: medicine in the...

    • datasetcatalog.nlm.nih.gov
    • scielo.figshare.com
    Updated Mar 26, 2021
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    Teixeira, Marcia; Dias, Henrique Sant’Anna; de Lima, Luciana Dias (2021). From the specific actions to government mark: medicine in the trajectory of the national policy of education and work regulation in health [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0000930588
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    Dataset updated
    Mar 26, 2021
    Authors
    Teixeira, Marcia; Dias, Henrique Sant’Anna; de Lima, Luciana Dias
    Description

    Abstract This article analyzes the trajectory of the Brazilian policy of education and work regulation considering institutional issues and forms of incorporation of medicine on the policy, comprising the period from 2003 to 2015. This study was based on contributions of historical institutionalism, involving the analysis of legal and normative documents, interviews with state personnel and data from federal financing reports of the policy. Five key moments were defined, considering political-institutional contexts, governmental initiatives, and the insertion of medicine into the agenda of governments. The emphasis on the medical profession evolved from one-off actions to the status of a landmark, in a context of loss of space of the health workforce agenda and increasing prioritization of education actions, associated with institutional gains and changes in funding. The More Doctors Program resulted from the dynamics of the trajectory, incorporating advances in the training and incorporation of physicians into the Unified Health System of Brazil. Challenges remain, however, related to precarious employment relationships, the privatization of medical education, and professional regulation that considers the exercise of medicine in the private sector.

  4. d

    Education Master Facilities Plan 2018

    • catalog.data.gov
    • opendata.dc.gov
    Updated Feb 4, 2025
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    City of Washington, DC (2025). Education Master Facilities Plan 2018 [Dataset]. https://catalog.data.gov/dataset/education-master-facilities-plan-2018
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    Dataset updated
    Feb 4, 2025
    Dataset provided by
    City of Washington, DC
    Description

    The Office of the Deputy Mayor for Education (DME) developed the DC Public Education Master Facilities Plan 2018 (MFP), a forward-looking, comprehensive study that provides the city, school leaders, stakeholders, and the community with the information needed to support current and future school facilities planning in Washington, DC. This web application contains a series of web maps that correspond to figures in the 2018 report available at https://dme.dc.gov/MFP2018.This report includes extensive information detailing facility utilization, facility condition assessments, facility modernization efforts, population forecasts, school-specific enrollment projections, and aspirational school enrollment plans that will allow stakeholders to better understand the current landscape of these facilities, as well as our public education facilities needs over the next decade. The analysis within the MFP will help the city to address the schools with over and under utilization, more efficiently prioritize and allocate capital funding, better utilize the DC Government’s real estate assets, and make better use of available resources in our growing public education system. Robust stakeholder engagement was an essential piece of the development of the MFP. The DME and its contractors met with parents, teachers, residents, and community leaders throughout the city through nine Districtwide community engagement meetings and a survey of over 500 public school parents to understand their priorities. Agency Website.

  5. d

    Data from: Lessons Learned: How Parents Respond to School Mandates and...

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 21, 2023
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    Lavery, Lesley (2023). Lessons Learned: How Parents Respond to School Mandates and Sanctions [Dataset]. http://doi.org/10.7910/DVN/YJYPFE
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    Dataset updated
    Nov 21, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Lavery, Lesley
    Description

    Over the past three decades a reform movement bent on improving schools and educational outcomes through standards-based accountability systems and market-like competitive pressures has dominated policy debates. Many have examined reform policies’ effects on academic outcomes, but few have explored these policies’ influence on citizens' political orientations. In this study, using data from an original survey, I examine whether and how No Child Left Behind’s (NCLB) accountability-based architecture influences parents’ attitudes toward government and federal involvement in education. I find little evidence that diversity in parents’ lived policy experiences shapes their political orientations. However, the results of a survey experiment suggest that information linking school experience to policy and government action may increase parents’ confidence in their ability to contribute to the political process. Understanding whether and under what conditions parents use public school experiences to inform orientations toward government can inform the design of future reforms.

  6. G

    Department of Education Policies

    • open.canada.ca
    html, pdf
    Updated Sep 17, 2025
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    Government of Yukon (2025). Department of Education Policies [Dataset]. https://open.canada.ca/data/dataset/d29fd0f4-dd63-4444-94ca-7a1476b76583
    Explore at:
    pdf, htmlAvailable download formats
    Dataset updated
    Sep 17, 2025
    Dataset provided by
    Government of Yukon
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Description

    Read the Department of Education policies.

  7. The Role of AI and Government Policies in Inclusive Education for...

    • figshare.com
    xlsx
    Updated Jun 23, 2025
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    Ritesh Karmaker (2025). The Role of AI and Government Policies in Inclusive Education for Marginalized Communities [Dataset]. http://doi.org/10.6084/m9.figshare.28329851.v1
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    xlsxAvailable download formats
    Dataset updated
    Jun 23, 2025
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Ritesh Karmaker
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This paper explores the role of Artificial Intelligence (AI) and government policies in advancing educational equity, particularly focusing on their impact on marginalized communities. It examines how AI-driven tools and platforms can help bridge educational gaps, improve learning outcomes, and promote inclusivity in education systems. Additionally, the paper discusses the critical role of government policies in fostering an environment that supports inclusive education, particularly for marginalized groups. By analyzing the intersection of technology and policy, the paper aims to provide insights into how these two forces can collaborate to ensure equal educational opportunities for all, regardless of socio-economic status or background.

  8. d

    Replication Data for: 'Building Social Cohesion in Ethnically Mixed Schools:...

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 19, 2023
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    Alan, Sule; Baysan, Ceren; Gumren, Mert; Kubilay, Elif (2023). Replication Data for: 'Building Social Cohesion in Ethnically Mixed Schools: An Intervention on Perspective Taking' [Dataset]. http://doi.org/10.7910/DVN/WUHIUG
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    Dataset updated
    Nov 19, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Alan, Sule; Baysan, Ceren; Gumren, Mert; Kubilay, Elif
    Description

    The data and programs replicate tables and figures from "Building Social Cohesion in Ethnically Mixed Schools: An Intervention on Perspective Taking", by Alan, Baysan, Gumren, and Kubilay. Please see the readme file for additional details.

  9. d

    Education - Arlington Public Schools Students Per Teacher

    • datasets.ai
    • s.cnmilf.com
    • +1more
    23, 53
    Updated Aug 7, 2021
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    Arlington County, VA (2021). Education - Arlington Public Schools Students Per Teacher [Dataset]. https://datasets.ai/datasets/education-arlington-public-schools-students-per-teacher-78c3c
    Explore at:
    53, 23Available download formats
    Dataset updated
    Aug 7, 2021
    Dataset authored and provided by
    Arlington County, VA
    Area covered
    Arlington County Public Schools, Arlington County
    Description

    The Arlington Profile combines countywide data sources and provides a comprehensive outlook of the most current data on population, housing, employment, development, transportation, and community services. These datasets are used to obtain an understanding of community, plan future services/needs, guide policy decisions, and secure grant funding. A PDF Version of the Arlington Profile can be accessed on the Arlington County website.

  10. o

    Impact of district education policies on learning: PPG Selectivity

    • portal.sds.ox.ac.uk
    xlsx
    Updated Feb 6, 2023
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    Asri Yusrina; Luhur Bima (2023). Impact of district education policies on learning: PPG Selectivity [Dataset]. http://doi.org/10.25446/oxford.21583029.v1
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    xlsxAvailable download formats
    Dataset updated
    Feb 6, 2023
    Dataset provided by
    University of Oxford
    Authors
    Asri Yusrina; Luhur Bima
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This study forms part of the teams Reform Area B workstream that focusses on decentralisation and district innovations through four specially selected innovative districts that will be used as "learning laboratories" to generate findings. In each district, the team studies a particular system innovation and its impact on learning outcomes. Some of these innovations are co-designed with the district government.

  11. R

    Russia Consolidated Government Expenditure: ytd: SC: Education: Youth Policy...

    • ceicdata.com
    Updated Oct 15, 2025
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    CEICdata.com (2025). Russia Consolidated Government Expenditure: ytd: SC: Education: Youth Policy & Children Health Improvement [Dataset]. https://www.ceicdata.com/en/russia/consolidated-government-expenditure-ytd/consolidated-government-expenditure-ytd-sc-education-youth-policy--children-health-improvement
    Explore at:
    Dataset updated
    Oct 15, 2025
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jun 1, 2021 - Jun 1, 2022
    Area covered
    Russia
    Variables measured
    Operating Statement
    Description

    Russia Consolidated Government Expenditure: Year to Date: SC: Education: Youth Policy & Children Health Improvement data was reported at 87.431 RUB bn in Jul 2022. This records an increase from the previous number of 71.819 RUB bn for Jun 2022. Russia Consolidated Government Expenditure: Year to Date: SC: Education: Youth Policy & Children Health Improvement data is updated monthly, averaging 21.642 RUB bn from Jan 2005 (Median) to Jul 2022, with 210 observations. The data reached an all-time high of 106.449 RUB bn in Dec 2021 and a record low of 0.100 RUB bn in Jan 2006. Russia Consolidated Government Expenditure: Year to Date: SC: Education: Youth Policy & Children Health Improvement data remains active status in CEIC and is reported by Federal Treasury. The data is categorized under Russia Premium Database’s Government and Public Finance – Table RU.FA004: Consolidated Government Expenditure: ytd.

  12. G

    Department of Education Off-Site Experiential Learning Policy

    • open.canada.ca
    • ouvert.canada.ca
    html, pdf
    Updated Sep 17, 2025
    + more versions
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    Government of Yukon (2025). Department of Education Off-Site Experiential Learning Policy [Dataset]. https://open.canada.ca/data/dataset/6bb61676-cd5f-4480-b89d-0b59dd4cef59
    Explore at:
    pdf, htmlAvailable download formats
    Dataset updated
    Sep 17, 2025
    Dataset provided by
    Government of Yukon
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Description

    Activity standards to assist educators to plan and lead field trips. Activity standards are appendixes to the Off-Site Experiential Learning Policy.

  13. R

    Russia Regional Government Expenditure: ytd: SC: Education: Youth Policy &...

    • ceicdata.com
    Updated Oct 15, 2025
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    CEICdata.com (2025). Russia Regional Government Expenditure: ytd: SC: Education: Youth Policy & Children Health Improvement [Dataset]. https://www.ceicdata.com/en/russia/regional-government-expenditure-ytd/regional-government-expenditure-ytd-sc-education-youth-policy--children-health-improvement
    Explore at:
    Dataset updated
    Oct 15, 2025
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jul 1, 2021 - Jul 1, 2022
    Area covered
    Russia
    Variables measured
    Operating Statement
    Description

    Russia Regional Government Expenditure: Year to Date: SC: Education: Youth Policy & Children Health Improvement data was reported at 53.816 RUB bn in Jul 2022. This records an increase from the previous number of 40.148 RUB bn for Jun 2022. Russia Regional Government Expenditure: Year to Date: SC: Education: Youth Policy & Children Health Improvement data is updated monthly, averaging 18.250 RUB bn from Jan 2005 (Median) to Jul 2022, with 210 observations. The data reached an all-time high of 79.094 RUB bn in Dec 2021 and a record low of 0.100 RUB bn in Jan 2006. Russia Regional Government Expenditure: Year to Date: SC: Education: Youth Policy & Children Health Improvement data remains active status in CEIC and is reported by Federal Treasury. The data is categorized under Russia Premium Database’s Government and Public Finance – Table RU.FC004: Regional Government Expenditure: ytd.

  14. How higher education (HE) statistics are used

    • gov.uk
    Updated Jul 31, 2009
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    Department for Business, Innovation & Skills (2009). How higher education (HE) statistics are used [Dataset]. https://www.gov.uk/government/statistics/how-he-statistics-are-used
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    Dataset updated
    Jul 31, 2009
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Department for Business, Innovation & Skills
    Description

    The Department for Business, Innovation and Skills (BIS)

    The key statistic in the ā€œParticipation Rates in Higher Educationā€ Statistical First Release (SFR) is the Higher Education Initial Participation Rate (HEIPR).

    HEIPR was used by BIS (and former Departments) and Her Majesty’s Treasury to track progress on the former Skills PSA target to ā€œIncrease participation in Higher Education towards 50 per cent of those aged 18 to 30, with growth of at least a percentage point every two years to the academic year 2010-11ā€. For example, it was reported in the http://www.bis.gov.uk/assets/biscore/corporate/migratedD/publications/D/DIUS-Annual%20Report-2009">Departmental annual report.

    HEIPR has been quoted in http://www.parliament.the-stationery-office.co.uk/pa/cm200809/cmselect/cmpubacc/226/22605.htm">Public Accounts Committees around increasing and widening participation in higher education

    HEIPR has been quoted extensively by the http://news.bbc.co.uk/1/hi/education/8596504.stm">Press

    BIS receives enquiries (including Freedom of Information (FoI) requests) from the public about HEIPR, including from the following groups:

    • academic researchers
    • higher education sector
    • local authorities - students
    • Members of Parliament (via Parliamentary Questions).

    The Higher Education Statistics Agency (HESA)

    Figures in the HESA SFRs are high profile and are frequently used in the press and other external publications to illustrate: trends in university entry and graduation, often in the context of current higher education policies; graduate employment/unemployment rates, average salaries, and job quality. Members of the public also often request these figures. Some examples of media coverage are included below:

    Higher Education student enrolments and qualifications

    Destinations of leavers from Higher Education

    These statistical outputs are not used to measure progress on any government targets, but the data that underpin them are of importance to funding bodies, Higher Education Institutions, and potential students:

    Potential Students – sources such as the http://unistats.direct.gov.uk/">Unistats website use qualifier and graduate employment information to inform students when they are making their choice of what course to study and at which university.

    Figures from the HESA statistical outputs are often u

  15. Widening participation in higher education: 2018

    • gov.uk
    Updated Nov 22, 2018
    + more versions
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    Department for Education (2018). Widening participation in higher education: 2018 [Dataset]. https://www.gov.uk/government/statistics/widening-participation-in-higher-education-2018
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    Dataset updated
    Nov 22, 2018
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Department for Education
    Description

    Statistics providing information on three measures of increasing participation in higher education:

    • estimated percentages of 15-year-olds eligible for free school meals (FSM) who progressed to higher education (HE) by age 19
    • estimated percentages of A level and equivalent students, by school or college type, who progressed to HE by age 19 with breakdowns for the most selective higher education institutions
    • estimated proportions of young people graduating from less advantaged backgrounds on entry to HE and estimated proportions in high-skilled jobs 6 months after graduating. These statistics use the https://www.ons.gov.uk/methodology/classificationsandstandards/standardoccupationalclassificationsoc/soc2010">Standard Occupation Classification (SOC) at both points

    A separate document sets out changes we have made to the publication this year and requests feedback on the changes and proposed new most selective HE measure.

    Additional experimental statistics have also been added and include breakdowns by additional pupil characteristics such as:

    • gender
    • ethnicity
    • first language
    • special educational need status

    Further breakdowns include POLAR disadvantage and Teaching Excellence and Student Outcomes Framework rating.

    Widening participation statistics

    Email mailto:HE.statistics@education.gov.uk">HE.statistics@education.gov.uk

    John Simes Telephone: 0370 000 2288

  16. Link to raw data collected

    • figshare.com
    pdf
    Updated Nov 26, 2021
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    Stephine Smith (2021). Link to raw data collected [Dataset]. http://doi.org/10.6084/m9.figshare.17086334.v1
    Explore at:
    pdfAvailable download formats
    Dataset updated
    Nov 26, 2021
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    Stephine Smith
    License

    https://www.apache.org/licenses/LICENSE-2.0.htmlhttps://www.apache.org/licenses/LICENSE-2.0.html

    Description

    My mission is to equip young Liberians from online and training with the right leadership skill(s) needed to drive the change in the public and private sector of Liberia by 2024.

  17. G

    Department of Education Off-Site Experiential Learning Policy (superseded /...

    • open.canada.ca
    html, pdf
    Updated Sep 17, 2025
    + more versions
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    Government of Yukon (2025). Department of Education Off-Site Experiential Learning Policy (superseded / obsolete) [Dataset]. https://open.canada.ca/data/dataset/c6946011-b933-4c93-a3f7-f9e2b7e02180
    Explore at:
    pdf, htmlAvailable download formats
    Dataset updated
    Sep 17, 2025
    Dataset provided by
    Government of Yukon
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Description

    Superseded / obsolete activity standards that assisted educators to plan and lead field trips. Activity standards are appendixes to the Off-Site Experiential Learning Policy. The policy and standards below are no longer in effect. See the current Off-Site Experiential Learning Policy.

  18. Government major projects portfolio data for the Department for Education...

    • gov.uk
    Updated May 24, 2013
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    Department for Education (2013). Government major projects portfolio data for the Department for Education 2013 [Dataset]. https://www.gov.uk/government/publications/government-major-projects-portfolio-data-for-the-department-for-education-2013--2
    Explore at:
    Dataset updated
    May 24, 2013
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Department for Education
    Description

    As part of the transparency policy for publication of major projects data the Major Projects Authority (MPA) has published its first annual report. It includes a set of combined data of the government’s major projects portfolio, progress and future priorities. In addition, each government department has published detailed information about their government major project portfolio (GMPP). This includes the MPA RAG rating, key project data including financial information (whole life cost, annual budget and forecast spend) and timetable. This data is six months in arrears and will be updated every twelve months.

    The publication of this data represents a fundamental step forward in the government’s drive for transparency.

  19. f

    Data from: Implementation of the national school nourishment program in the...

    • figshare.com
    • scielo.figshare.com
    jpeg
    Updated Sep 4, 2019
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    Katia de Fatima Vilela; Alair Ferreira de Freitas; Rodney Alves Barbosa; Rafael Junior dos Santos Figueiredo Salgado (2019). Implementation of the national school nourishment program in the Brazilian Federal institution of Education of Bahia State [Dataset]. http://doi.org/10.6084/m9.figshare.9765305.v1
    Explore at:
    jpegAvailable download formats
    Dataset updated
    Sep 4, 2019
    Dataset provided by
    SciELO journals
    Authors
    Katia de Fatima Vilela; Alair Ferreira de Freitas; Rodney Alves Barbosa; Rafael Junior dos Santos Figueiredo Salgado
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    State of Bahia
    Description

    ABSTRACT: The Brazilian National School Nourishment Program (PNAE) is one of the strongest public policies of food and nutrition in the world. Besides guaranteeing free and high-quality meals to students in the basic public educational system, the PNAE also establishes that at least 30% of the resources passed on to the municipalities by the federal government should be destined for the acquisition of foodstuffs from family agriculture. However, the budget execution of the PNAE by Federal Institutes of Education has been challenging and limiting regarding the practicability of the acquisition of food from family farmers. In this study, we provided empirical evidence that constitutes an essential analytical approach still little explored in the literature, but capable of revealing operational barriers to the implementation of the program. The objective of this study was to describe the implementation and to understand the difficulties perceived by the agents responsible for the operationalization of the PNAE in the campuses of the Federal Institute of Education, Science and Technology of Bahia State (IF Baiano). Therefore, we collected secondary data from institutional documents, whereas the primary data were obtained by semi-structured interviews performed in ten campuses of IF Baiano and the rectory. We observed that in some years, the budget destined to PNAE returned without implementation, evidencing flaws in the resources management. Thus, we concluded that some challenges need to be overcome before the implementation of the Program in these Federal Institutions.

  20. Department for Education business plan quarterly data summary

    • data.wu.ac.at
    • ckan.publishing.service.gov.uk
    • +1more
    html, xls
    Updated Sep 5, 2014
    + more versions
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    Cite
    Department for Education (2014). Department for Education business plan quarterly data summary [Dataset]. https://data.wu.ac.at/odso/data_gov_uk/NjhjNzg4NjItZjdiNy00MGQ1LTk0ZTMtMTg3YjkwY2QxNzMz
    Explore at:
    html, xlsAvailable download formats
    Dataset updated
    Sep 5, 2014
    Dataset provided by
    Department for Educationhttps://gov.uk/dfe
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    The Business Plan quarterly data summaries (QDS) provide the latest data on indicators included in Departmental business plans as well as other published data and management information. The QDS follows commitments made at Budget 2011 and the Written Ministerial Statement on business plans. Their primary purpose was to make more of the management information currently held by government available to members of the public on a regular basis. The last QDS was 2012. This page also incorporates the education priorities and spending plans produced by the Department for Education from 2011 -2015.

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Bushra Qurban (2024). šŸŒ World Education Dataset šŸ“š [Dataset]. https://www.kaggle.com/datasets/bushraqurban/world-education-dataset
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šŸŒ World Education Dataset šŸ“š

Global Insights into Educational Indicators

Explore at:
42 scholarly articles cite this dataset (View in Google Scholar)
zip(248507 bytes)Available download formats
Dataset updated
Nov 22, 2024
Authors
Bushra Qurban
License

https://www.worldbank.org/en/about/legal/terms-of-use-for-datasetshttps://www.worldbank.org/en/about/legal/terms-of-use-for-datasets

Area covered
World
Description

Dataset Overview šŸ“

The dataset includes the following key indicators, collected for over 200 countries:

  • Government Expenditure on Education (% of GDP): Shows the percentage of a country’s GDP allocated to education.
  • Literacy Rate (Adult Total): Represents the percentage of the population aged 15 and above who can read and write.
  • Primary Completion Rate: The percentage of children who complete their primary education within the official age group.
  • Pupil-Teacher Ratio (Primary and Secondary Education): Indicates the average number of students per teacher at the primary and secondary levels.
  • School Enrollment Rates (Primary, Secondary, Tertiary): Reflects the percentage of the relevant age group enrolled in schools across different education levels.

Data Source 🌐

World Bank: This dataset is compiled from the World Bank's educational database, providing reliable, updated statistics on educational progress worldwide.

Potential Use Cases šŸ” This dataset is ideal for anyone interested in:

Educational Research: Understanding how education spending and policies impact literacy, enrollment, and overall educational outcomes. Predictive Modeling: Building models to predict educational success factors, such as completion rates and literacy. Global Education Analysis: Analyzing trends in global education systems and how different countries allocate resources to education. Policy Development: Helping governments and organizations make data-driven decisions regarding educational reforms and funding.

Key Questions You Can Explore šŸ¤”

How does government expenditure on education correlate with literacy rates and school enrollment across different regions? What are the trends in pupil-teacher ratios over time, and how do they affect educational outcomes? How do education indicators differ between low-income and high-income countries? Can we predict which countries will achieve universal primary education based on current trends?

Important Notes āš ļø - Missing Data: Some values may be missing for certain years or countries. Consider using techniques like forward filling or interpolation when working with time series models. - Data Limitations: This dataset provides global averages and may not capture regional disparities within countries.

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