The Colleges and Universities feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Integrated Post Secondary Education System (IPEDS, http://nces.ed.gov/ipeds/), National Center for Education Statistics (NCES, https://nces.ed.gov/), US Department of Education for the 2018-2019 school year. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories. This feature class contains all MEDS/MEDS+ as approved by the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program (HSIP) Team. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the "Place Keyword" section of the metadata. This feature class does not have a relationship class but is related to Supplemental Colleges. Colleges and Universities that are not included in the NCES IPEDS data are added to the Supplemental Colleges feature class when found. This release includes the addition of 175 new records, the removal of 468 no longer reported by NCES, and modifications to the spatial location and/or attribution of 6682 records.

Abstract

The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.

Geographic coverage

National

Analysis unit

Schools, teachers, students, public officials

Kind of data

Sample survey data [ssd]

Sampling procedure

The aim of the Global Education Policy Dashboard school survey is to produce nationally representative estimates, which will be able to detect changes in the indicators over time at a minimum power of 80% and with a 0.05 significance level. We also wish to detect differences by urban/rural location. For our school survey, we will employ a two-stage random sample design, where in the first stage a sample of typically around 200 schools, based on local conditions, is drawn, chosen in advance by the Bank staff. In the second stage, a sample of teachers and students will be drawn to answer questions from our survey modules, chosen in the field. A total of 10 teachers will be sampled for absenteeism. Five teachers will be interviewed and given a content knowledge exam. Three 1st grade students will be assessed at random, and a classroom of 4th grade students will be assessed at random. Stratification will be based on the school’s urban/rural classification and based on region. When stratifying by region, we will work with our partners within the country to make sure we include all relevant geographical divisions. For our Survey of Public Officials, we will sample a total of 200 public officials. Roughly 60 officials are typically surveyed at the federal level, while 140 officials will be surveyed at the regional/district level. For selection of officials at the regional and district level, we will employ a cluster sampling strategy, where roughly 10 regional offices (or whatever the secondary administrative unit is called) are chosen at random from among the regions in which schools were sampled. Then among these 10 regions, we also typically select around 10 districts (tertiary administrative level units) from among the districts in which schools werer sampled. The result of this sampling approach is that for 10 clusters we will have links from the school to the district office to the regional office to the central office. Within the regions/districts, five or six officials will be sampled, including the head of organization, HR director, two division directors from finance and planning, and one or two randomly selected professional employees among the finance, planning, and one other service related department chosen at random. At the federal level, we will interview the HR director, finance director, planning director, and three randomly selected service focused departments. In addition to the directors of each of these departments, a sample of 9 professional employees will be chosen in each department at random on the day of the interview.

Sampling deviation

The sample for the Global Education Policy Dashboard in SLE was based in part on a previous sample of 260 schools which were part of an early EGRA study. Details from the sampling for that study are quoted below. An additional booster sample of 40 schools was chosen to be representative of smaller schools of less than 30 learners.

EGRA Details:

"The sampling frame began with the 2019 Annual School Census (ASC) list of primary schools as provided by UNICEF/MBSSE where the sample of 260 schools for this study were obtained from an initial list of 7,154 primary schools. Only schools that meet a pre-defined selection criteria were eligible for sampling.

To achieve the recommended sample size of 10 learners per grade, schools that had an enrolment of at least 30 learners in Grade 2 in 2019 were considered. To achieve a high level of confidence in the findings and generate enough data for analysis, the selection criteria only considered schools that: • had an enrolment of at least 30 learners in grade 1; and • had an active grade 4 in 2019 (enrolment not zero)

The sample was taken from a population of 4,597 primary schools that met the eligibility criteria above, representing 64.3% of all the 7,154 primary schools in Sierra Leone (as per the 2019 school census). Schools with higher numbers of learners were purposefully selected to ensure the sample size could be met in each site.

As a result, a sample of 260 schools were drawn using proportional to size allocation with simple random sampling without replacement in each stratum. In the population, there were 16 districts and five school ownership categories (community, government, mission/religious, private and others). A total of 63 strata were made by forming combinations of the 16 districts and school ownership categories. In each stratum, a sample size was computed proportional to the total population and samples were drawn randomly without replacement. Drawing from other EGRA/EGMA studies conducted by Montrose in the past, a backup sample of up to 78 schools (30% of the sample population) with which enumerator teams can replace sample schools was also be drawn.

In the distribution of sampled schools by ownership, majority of the sampled schools are owned by mission/religious group (62.7%, n=163) followed by the government owned schools at 18.5% (n=48). Additionally, in school distribution by district, majority of the sampled schools (54%) were found in Bo, Kambia, Kenema, Kono, Port Loko and Kailahun districts. Refer to annex 9. for details on the population and sample distribution by district."

Because of the restriction that at least 30 learners were available in Grade 2, we chose to add an additional 40 schools to the sample from among smaller schools, with between 3 and 30 grade 2 students. The objective of this supplement was to make the sample more nationally representative, as the restriction reduced the sampling frame for the EGRA/EGMA sample by over 1,500 schools from 7,154 to 4,597.

The 40 schools were chosen in a manner consistent with the original set of EGRA/EGMA schools. The 16 districts formed the strata. In each stratum, the number of schools selected were proportional to the total population of the stratum, and within stratum schools were chosen with probability proportional to size.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.

More information pertaining to each of the three instruments can be found below: - School Survey: The School Survey collects data primarily on practices (the quality of service delivery in schools), but also on some de facto policy indicators. It consists of streamlined versions of existing instruments—including Service Delivery Surveys on teachers and inputs/infrastructure, Teach on pedagogical practice, Global Early Child Development Database (GECDD) on school readiness of young children, and the Development World Management Survey (DWMS) on management quality—together with new questions to fill gaps in those instruments. Though the number of modules is similar to the full version of the Service Delivery Indicators (SDI) Survey, the number of items and the complexity of the questions within each module is significantly lower. The School Survey includes 8 short modules: School Information, Teacher Presence, Teacher Survey, Classroom Observation, Teacher Assessment, Early Learner Direct Assessment, School Management Survey, and 4th-grade Student Assessment. For a team of two enumerators, it takes on average about 4 hours to collect all information in a given school. For more information, refer to the Frequently Asked Questions.

• Policy Survey: The Policy Survey collects information to feed into the policy de jure indicators. This survey is filled out by key informants in each country, drawing on their knowledge to identify key elements of the policy framework (as in the SABER approach to policy-data collection that the Bank has used over the past 7 years). The survey includes questions on policies related to teachers, school management, inputs and infrastructure, and learners. In total, there are 52 questions in the survey as of June 2020. The key informant is expected to spend 2-3 days gathering and analyzing the relavant information to answer the survey

Abstract

The Master datasets comprise of four datasets: on children, schools, teachers and households. These master datasets contain key variables and identifiers which will allow users of the data to determine the progression of sample sizes and attrition of children, households, schools and teachers across the four years of the LEAPS panel data.

The children dataset contains round-by-round status of children's grades, enrollment, promotion etc. It also has variables indicating the panel child belongs to (the first panel being grade 3 children LEAPS started following in 2003, and the second one being 3rd graders followed starting in 2005 i.e. round 3 of the survey) as well as whether child was randomly selected for child questionnaire in class. The school dataset contains information such as school type, survey status, construction date. Note that there is only one schoolid variable and it is constant across all rounds. To capture the fact that there is merging of some schools going on across the rounds, refer to the school_merged_into and school_merged_with variables. The school_merged_into variable only exists for the small schools that merged into a larger school whereas the school_merged_with variable exists for the larger schools that the smaller schools merged in to. The teachers dataset contains information such as their round-by-round school, teaching status. The household dataset contains a Mauza indicator, and a variable on whether the household was surveyed in a particular round.

Geographic coverage

Rural Punjab, Pakistan

Sampling procedure

The sample comprises 112 villages in 3 districts of Punjab-Attock, Faisalabad and Rahim Yar Khan. The districts represent an accepted stratification of the province into North (Attock), Central (Faisalabad) and South (Rahim Yar Khan). The 112 villages in these districts were chosen randomly from the list of all villages with an existing private school. This allows us to look at differences between private and public schools in the same village. Although these villages are thus bigger and richer than average villages in these districts, we believe this is a forward-looking strategy and the insights earned here will soon be applicable to a significant fraction of all villages in the country.

Sampling deviation

None

Response rate

The attrition has been remarkably small, averaging 3-4 percent in each year.

The number of children, youth, and adults not attending schools or universities because of COVID-19 is soaring. Governments all around the world have closed educational institutions in an attempt to contain the global pandemic. According to UNESCO monitoring, over 100 countries have implemented nationwide closures, impacting over half of the world’s student population. Several other countries have implemented localized school closures and, should these closures become nationwide, millions of additional learners will experience education disruption. Method Data taken from: UNESCO Caveats / Comments Note: Figures correspond to total number of learners enrolled at pre-primary, primary, lower-secondary, and upper-secondary levels of education [ISCED levels 0 to 3], as well as at tertiary education levels [ISCED levels 5 to 8] who could be affected should localized closures become countrywide. Enrollment figures based on latest UNESCO Institute of Statistics data.

An Taisce, in co-operation with the Local Authorities, run the national Green Schools 9-flag program. Schools carry out a number of tasks, run educational programs and environmental projects which are incorporated into everyday school-life. The themes are:Litter & Waste ,Energy, Water, Travel, Biodiversity, Global Citizenship Litter & Waste, Global Citizenship Energy ,Global Citizenship Marine Environment, Global Citizenship TravelThere are 145 schools in Fingal registered as active in the An Taisce Green Schools Program. Got to www.fingal.ie for more information on Green Schools Programme details.. Fingal County Council invests in a number of school projects every year.Each flag has to be maintained as the school progresses towards the new flags. Fingal County Council assists the schools by providing information and support and environmental presentations to the students. In Feb/ March we also carry out all the Green Flag Assessments.We congratulate all the schools on their efforts – the green schools coordinators and committees, the teachers and students and caretakers and all involved put in a lot of work to make this possible.

Educational Youth Indicators

School Enrollment, Attendance, Achievement, and Engagement

By City of Baltimore [source]

About this dataset

This dataset from the Baltimore Neighborhood Indicators Alliance-Jacob France Institute (BNIA-JFI) gathers information about education and youth across Baltimore. Through tracking 27 indicators grouped into seven categories - student enrollment and demographics, dropout rate and high school completion, student attendance, suspensions and expulsions, elementary and middle school student achievement, high school performance, youth labor force participation, and youth civic engagement - BNIA-JFI paints a comprehensive picture of education trends within the city limits. Data sourced from the Baltimore City Public School System (BCPSS), American Community Survey (ACS), as well as Maryland Department of Education allows for cross program comparison to better map connections between educational outcomes affected by neighborhood context. The 2009-2010 school year was used based on readily available data with an approximated 3.4% of address unable to be matched or geocoded and therefore not included in these calculations. Leveraging this data provides perspective to help guide decisions made at local government level that could impact thousands of lives in years ahead

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How to use the dataset

This dataset contains valuable information about the educational performance and youth engagement in Baltimore City. It provides data on 27 indicators, grouped into seven categories: student enrollment and demographics; dropout rate and high school completion; student attendance, suspensions and expulsions; elementary and middle school student achievement; high school performance; youth labor force participation; and youth civic engagement. This dataset can be used to answer important questions about education in Baltimore, such as examining the relationship between community conditions and educational outcomes.

Before using this dataset, it’s important to understand the source of data for each indicator (e.g., Baltimore City Public School System, American Community Survey) so you can understand potential limitations inherent in each data set. Additionally, keep in mind that this dataset does not include students whose home address cannot be geocoded or matched between datasets due to inconsistency of information or other issues - this means that comparisons between some of these indicators may not be as accurate as is achievable with other datasets available from sources such as the Maryland Department of Education or the Baltimore City Public Schools System.

Once you are familiar with where the data comes from you can use it to answer these questions by exploring different trends within Baltimore city over time:

• How have student enrollment numbers changed over time?
• What has been the overall trend in dropout rates across elementary schools?
• Are there any differences in student attendance based on school type?
• What correlations exist between neighborhood community characteristics (such as crime rates or poverty levels), and academic achievement scores?
• How have rates of labor force participation among adolescents shifted year-over-year?

And more! By looking at trends by geography within this diverse city we can gain valuable insight into what factors may play a role influencing educational outcomes for children growing up in different areas around Baltimore City - an essential step for developing methodologies for successful policy interventions targeting our most vulnerable populations!

Research Ideas

• Analyzing the correlation between student achievement and socio-economic status of the neighborhoods in which students live.
• Creating targeted policies that are tailored to address specific educational issues showcased in each Baltimore neighborhood demographic.
• Using data visualizations to demonstrate to residents and community leaders how their area is performing compared to other communities in terms of education, dropout rates, suspension rates, and more

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. [See Other Information](https://creativecommons.org/public...

United Arab Emirates Number of Schools data was reported at 1,226.000 Unit in 2017. This records a decrease from the previous number of 1,230.000 Unit for 2016. United Arab Emirates Number of Schools data is updated yearly, averaging 1,027.000 Unit from Jun 1976 (Median) to 2017, with 41 observations. The data reached an all-time high of 1,238.000 Unit in 2005 and a record low of 227.000 Unit in 1976. United Arab Emirates Number of Schools data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under Global Database’s United Arab Emirates – Table AE.G005: Education Statistics.

Interested parties can now request extracts of data from the NPD using an improved application process accessed through the following website; GOV.UK The first version of the NPD, including information from the first pupil level School Census matched to attainment information, was produced in 2002. The NPD is one of the richest education datasets in the world holding a wide range of information about pupils and students and has provided invaluable evidence on educational performance to inform independent research, as well as analysis carried out or commissioned by the department. There are a range of data sources in the NPD providing information about children’s education at different phases. The data includes detailed information about pupils’ test and exam results, prior attainment and progression at each key stage for all state schools in England. The department also holds attainment data for pupils and students in non-maintained special schools, sixth form and further education colleges and (where available) independent schools. The NPD also includes information about the characteristics of pupils in the state sector and non-maintained special schools such as their gender, ethnicity, first language, eligibility for free school meals, awarding of bursary funding for 16-19 year olds, information about special educational needs and detailed information about any absences and exclusions. Extracts of the data from NPD can be shared (under strict terms and conditions) with named bodies and third parties who, for the purpose of promoting the education or well-being of children in England, are:- • Conducting research or analysis • Producing statistics; or • Providing information, advice or guidance. The department wants to encourage more third parties to use the data for these purposes and produce secondary analysis of the data. All applications go through a robust approval process and those granted access are subject to strict terms and conditions on the security, handling and use of the data, including compliance with the Data Protection Act. Anyone requesting access to the most sensitive data will also be required to submit a business case. More information on the application process including the User Guide, Application Form, Security Questionnaire and a full list of data items available can be found from the NPD web page at:- https://www.gov.uk/national-pupil-database-apply-for-a-data-extract

School locations in Kenya. It comprises Primary and Secondary Schools. The dataset was provided by Kenya Ministry of Education.

This survey by the United Nations Educational, Scientific and Cultural Organization (UNESCO), the United Nations Children's Fund (UNICEF) and the World Bank seeks to collect information on national education responses to school closures related to the COVID-19 pandemic. The questionnaire is designed for Ministry of Education officials at central or decentralized level in charge of school education. The questionnaire does not cover higher education or technical and vocational education and training. Analysis of results will allow for policy learning across the diversity of country settings in order to better inform local/national responses and prepare for the reopening of schools. The survey will be run on a regular basis to ensure that the latest impact and responses are captured. In light of the current education crisis, the COVID-19 education response coordinated by UNESCO with our partners is deemed urgent. A first wave of data collection started in May and lasted until mid-June 2020. A second wave of data collection will start at the beginning of July. A link to the online survey questionnaire, as well as other formats, will be available shortly.

Dataset Overview 📝

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 🔍 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 🤔

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 ⚠️ - 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.

Success.ai’s Education Industry Data with B2B Contact Data for Education Professionals Worldwide enables businesses to connect with educators, administrators, and decision-makers in educational institutions across the globe. With access to over 170 million verified professional profiles, this dataset includes crucial contact details for key education professionals, including school principals, department heads, and education directors.

Whether you’re targeting K-12 educators, university faculty, or educational administrators, Success.ai ensures your outreach is effective and efficient, providing the accurate data needed to build meaningful connections.

Why Choose Success.ai’s Education Professionals Data?

1. Comprehensive Contact Information
2. Access verified work emails, direct phone numbers, and LinkedIn profiles for educators, administrators, and education leaders worldwide.

3. AI-driven validation guarantees 99% accuracy, ensuring the highest level of reliability for your outreach.

4. Global Reach Across Educational Roles

5. Includes profiles of K-12 teachers, university professors, education directors, and school administrators.

6. Covers regions such as North America, Europe, Asia-Pacific, South America, and the Middle East.

7. Continuously Updated Datasets

8. Real-time updates ensure that you’re working with the most current contact information, keeping your outreach relevant and timely.

9. Ethical and Compliant

10. Success.ai’s data is fully GDPR, CCPA, and privacy regulation-compliant, ensuring ethical data usage in all your outreach efforts.

Data Highlights:

• 170M+ Verified Professional Profiles: Includes educators and administrators across various levels of education.
• 50M Work Emails: Verified and AI-validated emails for seamless communication.
• 30M Company Profiles: Rich insights into educational institutions, supporting detailed targeting.
• 700M Global Professional Profiles: Enriched datasets for comprehensive outreach across the education sector.

Key Features of the Dataset:

1. Education Decision-Maker Profiles
2. Identify and connect with decision-makers at educational institutions, including principals, department heads, and education directors.

3. Reach K-12 educators, higher education faculty, and administrative professionals with relevant needs.

4. Advanced Filters for Precision Targeting

5. Filter by educational level, subject area, location, and specific roles to tailor your outreach campaigns for precise results.

6. AI-Driven Enrichment

7. Profiles are enriched with actionable data to provide valuable insights, ensuring your outreach efforts are impactful and effective.

Strategic Use Cases:

1. Educational Product and Service Marketing
2. Promote educational tools, software, or services to decision-makers in schools, colleges, and universities.

3. Build relationships with educators to present curriculum solutions, digital learning platforms, and teaching resources.

4. Recruitment and Talent Acquisition

5. Target educational institutions and administrators with recruitment solutions or staffing services for teaching and support staff.

6. Engage with HR professionals in the education sector to promote job openings and talent acquisition services.

7. Professional Development Programs

8. Reach educators and administrators to offer professional development courses, certifications, or training programs.

9. Provide online learning solutions to enhance the skills of educators worldwide.

10. Research and Educational Partnerships

11. Connect with education leaders for research collaborations, institutional partnerships, and academic initiatives.

12. Foster relationships with decision-makers to support joint ventures in the education sector.

Why Choose Success.ai?

1. Best Price Guarantee

2. Success.ai offers high-quality, verified data at the best possible prices, making it a cost-effective solution for your outreach needs.

3. Seamless Integration

4. Integrate this verified contact data into your CRM using APIs or download it in your preferred format for streamlined use.

5. Data Accuracy with AI Validation

6. With AI-driven validation, Success.ai ensures 99% accuracy for all data, providing you with reliable and up-to-date information.

7. Customizable and Scalable Solutions

8. Tailor data to specific education sectors or roles, making it easy to target the right contacts for your campaigns.

APIs for Enhanced Functionality:

1. Data Enrichment API

2. Enhance existing records in your database with verified contact data for education professionals.

3. Lead Generation API

4. Automate lead generation campaigns for educational services and products, ensuring your marketing efforts are more efficient.

Leverage Success.ai’s B2B Contact Data for Education Professionals Worldwide to connect with educators, administrators, and decision-makers in the education sector. With veri...

This research aimed to examine primary school teachers’ understanding of the concept of global competence and the way they perceive they are implementing global competence in their classrooms in Punjab, Pakistan. A qualitative study has been designed to achieve this aim. The proposed research collected data using a point in time survey with purposeful sampling from 121 teachers of primary schools of South Punjab, Central Punjab, and North Punjab. Analysis of the survey questionnaire responses conducted through a thematic analysis to establish the teachers’ understandings and methods of teaching of global concepts. The findings of this research may be used to inform policy design and decisions about how to plan and implement professional development for teachers of Punjab, Pakistan to meet national strategic goals and to contribute in the achievement of Sustainable Development Goals 2030.

This table gives an overview of government expenditure on regular education in the Netherlands since 1900. All figures presented have been calculated according to the standardised definitions of the OECD.

Government expenditure on education consists of expenditure by central and local government on education institutions and education. Government finance schools, colleges and universities. It pays for research and development conducted by universities. Furthermore it provides student grants and loans, allowances for school costs, provisions for students with a disability and child care allowances to households as well as subsidies to companies and non-profit organisations.

Total government expenditure is broken down into expenditure on education institutions and education on the one hand and government expenditure on student grants and loans and allowances for school costs to households on the other. If applicable these subjects are broken down into pre-primary and primary education, special needs primary education, secondary education, senior secondary vocational and adult education, higher professional education and university education. Data are available from 1900. Figures for the Second World War period are based on estimations due to a lack of source material.

The table also includes the indicator government expenditure on education as a percentage of gross domestic product (GDP). This indicator is used to compare government expenditure on education internationally. The indicator is compounded on the basis of definitions of the OECD (Organisation for Economic Cooperation and Development). The indicator is also presented in the StatLine table education; Education expenditure and CBS/OECD indicators. Figures for the First World War and Second World War period are not available for this indicator due to a lack of reliable data on GDP for these periods.

The statistic on education spending is compiled on a cash basis. This means that the education expenditure and revenues are allocated to the year in which they are paid out or received. However, the activity or transaction associated with the payment or receipt can take place in a different year.

Statistics Netherlands published the revised National Accounts in June 2018. Among other things, GDP has been adjusted upwards as a result of the revision. The revision has not been extended to the years before 1995. In the indicator “Total government expenditure as % of GDP”, a break occurs between 1994 and 1995 as a result of the revision.

Data available from: 1900

Status of the figures: The figures from 1995 to 2020 are final. The 2021 figures are revised provisional, the 2022 figures are provisional.

Changes on 7 December 2023: The revised provisional figures of 2021 and the provisional figures of 2022 have been added.

When will new figures be published? The final figures for 2021 will be published in the first quarter of 2024. The final figures for 2022 and the provisional figures for 2023 will be published in December 2024.

The dataset contains the responses of 603 Czech teachers about the importance and use of school world atlases in their geography classes. The dataset is an export from JotForm. All the responses are in the Czech language + machine translation using Google Translator.

Alternatively, the data can be interactively inspected using Fluorish.

Most of the teachers who completed the questionnaire worked at primary schools (487 teachers). A total of 116 respondents taught in secondary schools and grammar schools. The total number of all Czech geography teachers is not available. Based on the number of schools in the Czech Republic, the authors estimated that the questionnaire was filled in by approximately 15% of geography teachers in the Czech Republic. The questionnaire survey contained a total of 30 questions focused on four main areas. Three areas are associated with the proposed research questions: which atlases are used in teaching and their importance; the types of task students solve with them; other teaching aids the teachers use.

Abstract

The SABER Service Delivery survey tool was developed in 2016 in the Global Engagement and Knowledge Unit of the Education Global Practice (GP) at the World Bank, as an initiative to uncover bottlenecks that inhibit student learning in low and middle income countries and to better understand the quality of education service delivery in a country as well as gaps in policy implementation. The SABER SD survey collects strategic information on school inputs and processes that influence learning outcomes. The data collected aims to uncover the extent to which policies translate into implementation and practice. As a global initiative, SABER SD provides data for the new global lead indicator on learning, which makes it easier to monitor the Sustainable Development Goal of achieving universal primary education.

SABER SD was created using knowledge and expertise from two major initiatives at the World Bank: SABER (Systems Approach for Better Education Results) and the SDI (Service Delivery Indicators) tools. The SABER program conducts research and knowledge from leading expertise in various themes of education. Using diagnostic tools and detailed policy information, the SABER program collects and analyzes comparative data and knowledge on education systems around the world and highlights the policies and institutions that matter most to promote learning for all children and youth. The SDI program is a large-scale survey of education and health facilities across Africa. The new SABER SD tool builds on and contributes to the growing SABER evidence base by capturing policy implementation measures identified as important in the frameworks of the core SABER domains of School Autonomy and Accountability, Student Assessment, Teachers, Finance, Education Management Information Systems, and Education Resilience.

The SABER SD instrument collects data at the school level and asks questions related to the roles of all levels of government (including local and regional). The tool provides comprehensive data on teacher effort and ability; principal leadership; school governance, management, and finances; community participation; and student performance in math and language and includes a classroom observation module.

Geographic coverage

The SABER SD survey in Lao PDR was nationally representative. Schools from all 18 provinces in Lao PDR were included in the sample.

Analysis unit

The unit of analysis varies for each of the modules. They are as follows: Module 1, the unit of analysis is the school. Module 2, the unit of analysis is the teacher. Module 3, the unit of analysis is the school/principal. Module 4, the unit of analysis is the classroom/school/teacher. Module 5, the unit of analysis is the student. Module 6, the unit of analysis is the teacher.

For modules where the unit of analysis is not the school (i.e., teachers and/or students), it is possible to create an average for the school based on groupings by the unique identifier – the school code.

Universe

The target was to have a nationally representative sample. All primary schools in Lao PDR were included in the original sample. The final pool of primary schools from which the sample was drawn included those with a grade 4 population of students.

Kind of data

Sample survey data [ssd]

Sampling procedure

The SABER Service Delivery survey was implemented in primary schools across Lao PDR, with detailed information being collected from Grade 4. According to official records from the Ministry of Education and Sports (MOES) education management information system for the school year 2015-2016, 8,864 primary schools exist across the country. The sample was created using probability proportional to size (PPS) according to the size of students enrolled in Grade 4. The target population of the survey was Grade 4 students, so all schools with at least one student enrolled in Grade 4 were considered in the sample.

Schools were stratified for sampling along four dimensions to ensure representation. For each of these, stratification was done on a discrete variable. The four sampling strata used for this survey with a target sample size of 200 schools across Lao PDR are the following: Urban/Rural, Public/Private, Single grade/Multi-grade, Priority/Non-Priority.

Multiple sampling scenarios were created according to the number of schools within a stratum. The final sample option was selected based on the standard errors of the sample as a whole and the errors within a subgroup. Please see the sampling appendix in the final report for more information (Appendix A).

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

After a first round of cleaning and editing carried out by IRL, the raw data was sent to the World Bank team by the survey firm. The World Bank team ran data checks on the raw data files, with comments and questions sent back to the survey firm on inconsistencies and/or missing data. The survey firm then responded to the questions, if any data are missing, the field team collects the data again or corrects the incorrect information. This happened in a few cases where the principals of the schools were contacted again to confirm and verify certain answers from the school.

Once the data was finalized, the weights were attached back to the dataset. The weighting procedure was done by the Development Economics Vice Presidency (DEC) team at the World Bank. Finally, with weights attached, the final datasets for each module (1 through 6) were produced.

For this data, many modules were also merged together to run analysis across different school components. There is one final data file which has merged modules 1, 2, 3, 5, and 6.

Response rate

100% response rate from all 200 schools in sample. No reserve schools were activated.

India Number of Schools: Secondary School data was reported at 252,176.000 Unit in 2015. This records an increase from the previous number of 244,653.000 Unit for 2014. India Number of Schools: Secondary School data is updated yearly, averaging 114,629.000 Unit from Sep 1950 (Median) to 2015, with 34 observations. The data reached an all-time high of 252,176.000 Unit in 2015 and a record low of 7,416.000 Unit in 1950. India Number of Schools: Secondary School data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDC001: Number of Schools: Secondary School.

license: apache-2.0 tags: - africa - sustainable-development-goals - world-health-organization - development

  Schools with access to computers for pedagogical purposes (%)





  Dataset Description

This dataset provides country-level data for the indicator "4.a.1 Schools with access to computers for pedagogical purposes (%)" across African nations, sourced from the World Health Organization's (WHO) data portal on Sustainable Development Goals (SDGs). The data is… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/schools-with-access-to-computers-for-pedagogical-purposes-for-african-countries.

This dataset represents Exclusive Economic Zones (EEZ) of the world. Up to now, there was no global public domain cover available.
Therefore, the Flanders Marine Institute decided to develop its own database. The database includes two global GIS-layers: one contains polylines that represent the maritime boundaries of the world countries, the other one is a polygon layer representing the Exclusive Economic Zone of countries. The database also contains digital information about treaties. Please note that the EEZ shapefile also includes the internal waters of each country.

The “NSDI Zambia Operational School Points and Names, Version 01 (Beta)” provides school and school affiliated point locations, names, types, and source of the data. The dataset was developed by compiling and standardizing existing government data sources of school point locations and names data from the 2010 census cartography from Zambia Statistics Agency (ZamStats), the Ministry of General Education (MoGE) and the Ministry of Water Development Sanitation and Environmental Protection (MWDSEP).The dataset is available for download in shapefile format and can be viewed in Geographic Information Systems(GIS) such as ArcMap, QGIS, or Google Earth (Pro). The data is available for all of Zambia. It has the following attributes: Name, Original Name (from original dataset), Type, Sub Type, Source, Province (Code), District (Code), Ward (Code), Constituency (Code), Global IDFor more information about the settlement data and methodology, see the data release notes here.