Financial overview and grant giving statistics of Education Equity Fund Nfp

The World Bank EdStats All Indicator Query holds over 4,000 internationally comparable indicators that describe education access, progression, completion, literacy, teachers, population, and expenditures. The indicators cover the education cycle from pre-primary to vocational and tertiary education. The query also holds learning outcome data from international and regional learning assessments (e.g. PISA, TIMSS, PIRLS), equity data from household surveys, and projection/attainment data to 2050. For further information, please visit the EdStats website.

For further details, please refer to https://datatopics.worldbank.org/education/wRsc/about

Financial overview and grant giving statistics of Coalition for Education Equity Inc.

Financial overview and grant giving statistics of Rjb Education Equity Foundation

The total equity of American Public Education with headquarters in the United States amounted to 303.88 million U.S. dollars in 2024. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2020 this is a total decrease by approximately 3.04 million U.S. dollars. The trend from 2020 to 2024 shows, however, that this decrease did not happen continuously.

Data set on the impact of information technology on educational equity

These files contain the data files and programs used to generate the estimates found in “States as Sites of Educational (In)Equality: State Contexts and the Socioeconomic Achievement Gradient” published in AERA Open, 2019. The abstract for the paper is found below: Socioeconomic achievement gaps have long been a central focus of educational research. However, not much is known about how (and why) between-district gaps vary among states, even though states are a primary organizational level in the decentralized education system in the United States. Using data from the Stanford Education Data Archive (SEDA), this study describes state-level socioeconomic achievement gradients and the growth of these gradients from Grades 3 to 8. We also examine state-level correlates of the gradients and their growth, including school system funding equity, preschool enrollment patterns, the distribution of teachers, income inequality, and segregation. We find that socioeconomic gradients and their growth rates vary considerably among states, and that between-district income segregation is positively associated with the socioeconomic achievement gradient.

OECD Education statistics database includes the UNESCO/OECD/EUROSTAT (UOE) database on education covering the outputs of educational institutions, the policy levers that shape educational outputs, the human and financial resources invested in education, structural characteristics of education systems, and the economic and social outcomes of education, learning and training throughout life, including on employment and unemployment. Also included in the database are the PISA 2015 dataset, Teaching and Learning International Survey (TALIS) data, the annual Education at a Glance data and data relating to Gender equality in education.

This fascinating compilation of the recent data on gender differences in education presents a wealth of data, analysed from a multitude of angles in a clear and lively way. In particular it looks at underperformance among boys, lack of self confidence among girls and family, school and societal influences before addressing policies to help boys and girls reach their full potential.

This table contains data on the percent of population age 25 and up with a four-year college degree or higher for California, its regions, counties, county subdivisions, cities, towns, and census tracts. Greater educational attainment has been associated with health-promoting behaviors including consumption of fruits and vegetables and other aspects of healthy eating, engaging in regular physical activity, and refraining from excessive consumption of alcohol and from smoking. Completion of formal education (e.g., high school) is a key pathway to employment and access to healthier and higher paying jobs that can provide food, housing, transportation, health insurance, and other basic necessities for a healthy life. Education is linked with social and psychological factors, including sense of control, social standing and social support. These factors can improve health through reducing stress, influencing health-related behaviors and providing practical and emotional support. More information on the data table and a data dictionary can be found in the Data and Resources section. The educational attainment table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity. The goal of HCI is to enhance public health by providing data, a standardized set of statistical measures, and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health. The creation of healthy social, economic, and physical environments that promote healthy behaviors and healthy outcomes requires coordination and collaboration across multiple sectors, including transportation, housing, education, agriculture and others. Statistical metrics, or indicators, are needed to help local, regional, and state public health and partner agencies assess community environments and plan for healthy communities that optimize public health. More information on HCI can be found here: https://www.cdph.ca.gov/Programs/OHE/CDPH%20Document%20Library/Accessible%202%20CDPH_Healthy_Community_Indicators1pager5-16-12.pdf The format of the educational attainment table is based on the standardized data format for all HCI indicators. As a result, this data table contains certain variables used in the HCI project (e.g., indicator ID, and indicator definition). Some of these variables may contain the same value for all observations.

The Education Big Data market is projected to reach USD XXX million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). The increasing adoption of digital technologies in the education sector, growing need for personalized learning experiences, and government initiatives to improve educational outcomes are the major factors driving the market growth. The Education Big Data market is segmented by application into Public Educational Institutions and Private Educational Institutions. The Public Educational Institutions segment is expected to account for a larger market share due to increasing government investments in digital infrastructure and initiatives to improve educational equity. The market is also segmented by type into Applicability Education Support, Discovery of Educational Laws, and Precise Management Support. The Applicability Education Support segment is expected to dominate the market as it helps improve teaching methods and optimize learning outcomes. Education Big Data plays a pivotal role in shaping the future of education, transforming the learning landscape with its immense potential to enhance student outcomes, improve educational practices, and optimize resource allocation.

The total equity of Laureate Education with headquarters in the United States amounted to 959.55 million U.S. dollars in 2024. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2020 this is a total decrease by approximately 1.3 billion U.S. dollars. The trend from 2020 to 2024 shows, however, that this decrease did not happen continuously.

Financial overview and grant giving statistics of Center for Educational Equity

This event features the documentary Teach Us All available on Netflix. You can view the trailer on YouTube. The film also has a Twitter feed. Our conversation will center around the bias that seems inherent in the education systems across much of the United States, the historical roots of the system, and the definitions and differences between equality and equity in terms of education.

Historical Dataset of Bridges A School For Exploration And Equity is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2021-2023),Total Classroom Teachers Trends Over Years (2021-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2021-2023),American Indian Student Percentage Comparison Over Years (2021-2022),Asian Student Percentage Comparison Over Years (2021-2023),Hispanic Student Percentage Comparison Over Years (2021-2023),Black Student Percentage Comparison Over Years (2021-2023),White Student Percentage Comparison Over Years (2021-2023),Two or More Races Student Percentage Comparison Over Years (2021-2023),Diversity Score Comparison Over Years (2021-2023),Free Lunch Eligibility Comparison Over Years (2021-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2021-2023),Reading and Language Arts Proficiency Comparison Over Years (2021-2022),Math Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2021-2022)

Historical Dataset of Equity Project Charter School School District is provided by PublicSchoolReview and contain statistics on metrics:Comparison of Diversity Score Trends,Total Expenditure Trends,Average Expenditure Per Student Trends,Reading and Language Arts Proficiency Trends,Math Proficiency Trends,Overall School District Rank Trends,Asian Student Percentage Comparison Over Years (2012-2023),Hispanic Student Percentage Comparison Over Years (2013-2023),Black Student Percentage Comparison Over Years (2013-2023),White Student Percentage Comparison Over Years (2013-2023),Two or More Races Student Percentage Comparison Over Years (2011-2023),Comparison of Students By Grade Trends

Volume II of PISA's 2009 results looks at how successful education systems moderate the impact of social background and immigrant status on student and school performance. The volume opens with an introduction to PISA and a Reader's Guide providing information that will help readers understand the data. Chapter 1 focuses on the magnitude of differences in student performance across countries and the extent to which these differences relate to socio-economic background. Chapter 2 examines the extent to which students and schools with different socio-economic backgrounds have access to similar educational resources, and the impact of background and school on learning outcomes. Chapter 3 examines the relationship between student performance and different aspects of socio-economic background. Chapter 4 compares the performance of students with an immigrant background with the performance of other students. Chapter 5 analyses the impact of socio-economic background of schools on reading performance. The final chapter examines policy implications of the findings. Annexes provide detailed statistical data and technical information.

This feature layer provides the educational attainment levels in the City of Tempe by census tract. The feature layer was created by clipping the ACS Educational Attainment Variables - Boundaries 2014-18, downloaded from Esri's Living Atlas, to the City of Tempe boundary layer.https://tempegov.maps.arcgis.com/home/item.html?id=84e3022a376e41feb4dd8addf25835a3

Financial overview and grant giving statistics of Center for Racial Equity in Education

BackgroundStrengthening health education is an important measure to improve the health of mobile populations and a key objective of China’s basic public health services. Existing studies demonstrate that health education affects the health of mobile populations, but insufficient attention is paid to the importance of factors that influence health education. Moreover, few studies examine how these factors contribute to health education equity among mobile populations in China. Therefore, this study aims to reveal the importance of factors affecting health education based on a comprehensive understanding of mobile populations’ overall health education status. Furthermore, the contribution of these important factors to health education equity is analyzed to inform differentiated intervention strategies, thereby providing a reference for enhancing mobile populations’ health level and achieving equal access to basic public health services.MethodsThis study utilized data from the 2018 China Migrants Dynamic Survey (CMDS), with a final sample of 103,910 participants after data cleaning. Chi-square tests were first conducted to examine differences in health education across various characteristics of the mobile population. The relative importance of influencing factors was then assessed using a random forest model, followed by key factor identification through LASSO regression. Subsequently, binary logistic regression was performed to quantify the effects of these key factors. Finally, concentration indices were calculated to identify these factors’ contributions to health education equity.ResultsThe self-assessed health status of China’s mobile population was good, with 81.89% reporting receipt of health education, while 18.11% had not received any health education. Seven key factors were identified as most influential in determining health education access among the mobile population: income, education, age, health record, scope of mobility, reason for mobility and gender. The health education concentration index of the mobile population was 0.0121, indicating a significant inequality in the utilization of health education services. Each important factor had a significant negative amplifying effect on health education equity among the mobile population.ConclusionHealth education among the mobile population requires further enhancement. Special attention should be directed toward vulnerable groups, including low-income individuals, the older adult, those with lower educational levels, and those with wider migration ranges. Moreover, the impact of key factors on health education equity among the mobile population should be carefully considered to improve health education equity.