School enrollment, tertiary (% gross) in United States was reported at 79.36 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - School enrollment, tertiary (% gross) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

United States US: Adjusted Net Enrollment Rate: Primary: Male: % of Primary School Age Children data was reported at 93.137 % in 2015. This records an increase from the previous number of 92.551 % for 2014. United States US: Adjusted Net Enrollment Rate: Primary: Male: % of Primary School Age Children data is updated yearly, averaging 94.128 % from Dec 1986 (Median) to 2015, with 25 observations. The data reached an all-time high of 98.628 % in 1991 and a record low of 91.823 % in 2004. United States US: Adjusted Net Enrollment Rate: Primary: Male: % of Primary School Age Children data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Education Statistics. Adjusted net enrollment is the number of pupils of the school-age group for primary education, enrolled either in primary or secondary education, expressed as a percentage of the total population in that age group.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students

School enrollment, primary (% gross) in United States was reported at 96.97 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - School enrollment, primary (% gross) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

SELECTED SOCIAL CHARACTERISTICS IN THE UNITED STATES SCHOOL ENROLLMENT - DP02 Universe - Population 3 Year and over enrolled in school Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 People were classified as enrolled in school if they were attending a public or private school or college at any time during the 3 months prior to the time of interview. The question included instructions to “include only nursery or preschool, kindergarten, elementary school, home school, and schooling which leads to a high school diploma, or a college degree.” Respondents who did not answer the enrollment question were assigned the enrollment status and type of school of a person with the same age, sex, race, and Hispanic or Latino origin whose residence was in the same or nearby area.

The purpose of this data collection was to provide a more accurate measure the racial/ethnic enrollment in postsecondary institutions in the United States. The National Center for Education Statistics (NCES) collects racial/ethnic enrollment data from higher education institutions on a biennial basis. Some institutions do not report this data, and their "unknown" categories have previously been distributed in direct proportion to the "knowns." This resulted in lower than accurate figures for the racial/ethnic categories. With the advent of the Integrated Postsecondary Education Data System (IPEDS), NCES has attempted to eliminate this problem by distributing all race/ethnicity unknown students. This is done in a two-stage process. First, the differences between reported totals and racial/ethnic details are allocated on a gender and institutional basis by distributing the differences in direct proportion to reported distributions. The second stage distribution is designed to eliminate the remaining "race/ethnicity" unknowns. The procedure is to accumulate the reported racial/ethnic total enrollments by state, level, control, and gender, calculate the percentage distributions, and apply these percentages to the reported total enrollments of institutional respondents (in the same state, level, and control) that did not supply race/ethnicity detail. In addition, the original "race unknown" data were also left unaltered in order to provide data for those who wish to review numbers actually distributed. The racial/ethnic status was broken down into non-resident alien, Black non-Hispanic, American Indian or Alaskan Native, Asian or Pacific Islander, Hispanic, and White non-Hispanic. Variables include the educational level of all male/female full-time and part-time students enrolled in courses for credit at the institution. The unit of analysis is the postsecondary institution.

United States US: Pupil-Teacher Ratio: Primary data was reported at 14.457 % in 2015. This records a decrease from the previous number of 14.537 % for 2014. United States US: Pupil-Teacher Ratio: Primary data is updated yearly, averaging 14.454 % from Dec 1984 (Median) to 2015, with 25 observations. The data reached an all-time high of 16.173 % in 1995 and a record low of 13.591 % in 2010. United States US: Pupil-Teacher Ratio: Primary data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Education Statistics. Primary school pupil-teacher ratio is the average number of pupils per teacher in primary school.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

United States Health Insurance: Enrollment: Medicare Supplement data was reported at 5.000 USD mn in 2023. This stayed constant from the previous number of 5.000 USD mn for 2022. United States Health Insurance: Enrollment: Medicare Supplement data is updated yearly, averaging 4.000 USD mn from Dec 2007 (Median) to 2023, with 17 observations. The data reached an all-time high of 5.000 USD mn in 2023 and a record low of 4.000 USD mn in 2018. United States Health Insurance: Enrollment: Medicare Supplement data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG022: Health Insurance: Operations by Lines of Business.

The American Community Survey (ACS) is designed to estimate the characteristic distribution of populations* and estimated counts should only be used to calculate percentages. They do not represent the actual population counts or totals. Beginning in 2019, the Washington Student Achievement Council (WSAC) has measured educational attainment for the Roadmap Progress Report using one-year American Community Survey (ACS) data from the United States Census Bureau. These public microdata represents the most current data, but it is limited to areas with larger populations leading to some multi-county regions**.

*The American Community Survey is not the official source of population counts. It is designed to show the characteristics of the nation's population and should not be used as actual population counts or housing totals for the nation, states or counties. The official population count — including population by age, sex, race and Hispanic origin — comes from the once-a-decade census, supplemented by annual population estimates (which do not typically contain educational attainment variables) from the following groups and surveys:
-- Washington State Office of Financial Management (OFM): https://www.ofm.wa.gov/washington-data-research/population-demographics -- US Census Decennial Census: https://www.census.gov/programs-surveys/decennial-census.html and Population Estimates Program: https://www.census.gov/programs-surveys/popest.html

**In prior years, WSAC used both the five-year and three-year (now discontinued) data. While the 5-year estimates provide a larger sample, they are not recommended for year to year trends and also are released later than the one-year files.

Detailed information about the ACS at https://www.census.gov/programs-surveys/acs/guidance.html

This volume of PISA 2009 results examines how human, financial and material resources, and education policies and practices shape learning outcomes. Following an introduction to PISA and a Reader's Guide explaining how to interpret the data, Chapter 1 presents a summary of features shared by "successful" school systems. Chapter 2 details how resources, policies and practices relate to student performance. Chapter 3 provides detailed descriptions and in-depth analyses of selected organisational features (how students are sorted into grades, schools, and programmes, school autonomy, etc.) of schools and systems and how those aspects affect performance. Chapter 4 describes and analyzes key aspects of the learning environment (behaviours, discipline, parental involvement, school leadership, etc.) and how they affect performance. The final chapter discusses the policy implications of the findings. Annexes provides detailed statistical data and technical background.

Context

More international students are flocking to China than ever before. According to a report, over 540,000 foreigners studied in China in 2018 – marking a 40 percent increase from 2012. China attracts more international students than any other Asian power and ranks third globally, behind the United States and the United Kingdom.

Content

In 2018 there were a total of 492,185 international students from 196 countries/areas pursuing their studies in 1,004 higher education institutions in China’s 31 provinces/autonomous regions/provincial-level municipalities, marking an increase of 3,013 students or 0.62% compared to 2017. International students in Hong Kong, Macau and Taiwan are not included in the datasets. The datasets contain three CSV files (Continent, Country, Province) with different data about international students in China.

Columns Description

@Continent (Number/percent of international students by continent) Continent- The name of continent Number - The number of total international students Deaths- The percentage of total international students

@Country (Number of international students by country of origin) Rank- The rank of the country based on total students in China Country- The name of the country Number- The number of total international students

@Province (The top provinces/cities with the largest number of international students) Province- The name of the city/province Number- The number of total international students

Acknowledgements

This data collected from moe.gov.cn.

Inspiration

Currently, I'm studying at a Chinese university. Every year many international students come to China for their higher study, and the ratio of international students is growing steadily. This data will help us to understand the ratio of international students in China.

Comprehensive dataset of 1 Student unions in Wyoming, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 541 Student dormitories in Massachusetts, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This is a source dataset for a Let's Get Healthy California indicator at https://letsgethealthy.ca.gov/. Data originally from the California Department of Education Fitnessgram website at http://www.cde.ca.gov/ta/tg/pf/.

This page provides data for the 3rd Grade Reading Level Proficiency performance measure.

The dataset includes the student performance results on the English/Language Arts section of the AzMERIT from the Fall 2017 and Spring 2018. Data is representive of students in third grade in public elementary schools in Tempe. This includes schools from both Tempe Elementary and Kyrene districts. Results are by school and provide the total number of students tested, total percentage passing and percentage of students scoring at each of the four levels of proficiency.

The performance measure dashboard is available at 3.07 3rd Grade Reading Level Proficiency.

Additional Information

Source: Arizona Department of Education
Contact: Ann Lynn DiDomenico
Contact E-Mail: Ann_DiDomenico@tempe.gov
Data Source Type: Excel/ CSV
Preparation Method: Filters on original dataset: within "Schools" Tab School District [select Tempe School District and Kyrene School District]; School Name [deselect Kyrene SD not in Tempe city limits]; Content Area [select English Language Arts]; Test Level [select Grade 3]; Subgroup/Ethnicity [select All Students] Remove irrelevant fields; Add Fiscal Year
Publish Frequency: Annually as data becomes available
Publish Method: Manual
Data Dictionary

This data collection presents follow-up data for the NATIONAL EDUCATION LONGITUDINAL STUDY, 1988 (ICPSR 9389). The base-year study collected information from student surveys and tests and from surveys of parents, school administrators, and teachers. It was designed to provide trend data about critical transitions experienced by students as they leave elementary school and progress through high school and postsecondary institutions or the work force. This collection provides the first opportunity for longitudinal measurement of the 1988 baseline samples. It also provides a point of comparison with high school sophomores from ten years before, as studied in HIGH SCHOOL AND BEYOND, 1980: A LONGITUDINAL SURVEY OF STUDENTS IN THE UNITED STATES (ICPSR 7896). Further, the study captures the population of early dropouts (those who leave school prior to the end of the tenth grade), while monitoring the transition of the student population into secondary schooling. The student component (Part 1) collected basic background information about students' school and home environments, participation in classes and extracurricular activities, current jobs, and students' goals, aspirations, and opinions about themselves. The student component also measured tenth-grade achievement and cognitive growth between 1988 and 1990 in the subject areas of mathematics, science, reading, and social studies. The school component (Part 3) supplies general descriptive information about the educational setting and environment in which surveyed students were enrolled. These data were collected from the chief administrator of each base-year school and cover school characteristics, grading and testing structure, school culture and academic climate, program and facilities information, parental interactions and involvement, and teaching staff characteristics. The dropout component (Part 5) provides data on the process of dropping out of school as it occurs from eighth grade on. Variables include school attendance, determinants of leaving school, self-perceptions and attitudes, work history, and relationships with school personnel, peers, and family. The teacher component (Part 7) was administered to teachers of follow-up students in four basic subject areas: mathematics, science, English, and history. The questionnaire elicited teacher evaluations of student characteristics and performance in the classroom, curriculum information about the classes taught, teacher demographic and professional characteristics, information about parent-teacher interactions, time spent on various tasks, and perceptions of school climate and culture.

The Decennial Census provides population estimates and demographic information on residents of the United States.

The Census Summary Files contain detailed tables on responses to the decennial census. Data tables in Summary File 1 provide information on population and housing characteristics, including cross-tabulations of age, sex, households, families, relationship to householder, housing units, detailed race and Hispanic or Latino origin groups, and group quarters for the total population. Summary File 2 contains data tables on population and housing characteristics as reported by housing unit.

Researchers at NYU Langone Health can find guidance for the use and analysis of Census Bureau data on the Population Health Data Hub (listed under "Other Resources"), which is accessible only through the intranet portal with a valid Kerberos ID (KID).

IPEDS collects data on postsecondary education in the United States in seven areas: institutional characteristics, institutional prices, enrollment, student financial aid, degrees and certificates conferred, student persistence and success, and institutional human and fiscal resources. Because enrollment patterns differ greatly among the various types of postsecondary institutions, there is a need for both different measures of enrollment and several indicators of access. In IPEDS, the following enrollment-related data are collected: Fall Enrollment — Fall enrollment is the traditional measure of student access to higher education. Fall enrollment data can be looked at by race/ethnicity; gender; enrollment status (part-time or full-time); and or level of study (undergraduate or graduate). Residence of First-Time Students — Data on the number of first-time freshmen by state of residence, along with data on the number who graduated from high school the previous year, serve to monitor the flow of students across state lines and calculate college-going rates by state. These data are collected in even-numbered years. Age Data — The age distribution of enrolled students offers insight into the relationship between the changing demographics of college-going cohorts and enrollment in different types of postsecondary institutions. They also permit detailed projections of enrollment by institutional type and by age. Because a student’s dependency status is strongly related to age, the data can be used to provide estimates of the number of independent and dependent students attending postsecondary institutions. These data are collected in odd-numbered years. Unduplicated 12-Month Head Count — Enrollment figures based on the unduplicated head count of students enrolled over a 12-month period is particularly valuable for institutions that use non-traditional calendar systems and offer short-term programs. Because this enrollment measure encompasses an entire year, it provides a more complete picture of the number of students these schools serve. Instructional Activity — Data on instructional activity is measured in total credit and/or contact hours delivered by institutions during a 12-month period. Total Entering Class — Data on the number of incoming students (students enrolling for the first time in a postsecondary institution versus students transferring in from another postsecondary institution) at an institution. This measure permits the calculation of the graduation rate cohort as a proportion of the total entering student body.

This layer shows public vs. private school enrollment by sex by grade group. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Any schools that receives public funding are considered public, including continuation schools and some charter & online schools. This layer is symbolized to show the percentage and count of students in kindergarten through 12th grade who are enrolled in a private school. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B14002 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Note: After May 3, 2024, this dataset will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, hospital capacity, or occupancy data to HHS through CDC’s National Healthcare Safety Network (NHSN). The related CDC COVID Data Tracker site was revised or retired on May 10, 2023.

This dataset represents weekly COVID-19 hospitalization data and metrics aggregated to national, state/territory, and regional levels. COVID-19 hospitalization data are reported to CDC’s National Healthcare Safety Network, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN and included in this dataset represent aggregated counts and include metrics capturing information specific to COVID-19 hospital admissions, and inpatient and ICU bed capacity occupancy.

Reporting information:

• As of December 15, 2022, COVID-19 hospital data are required to be reported to NHSN, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Prior to December 15, 2022, hospitals reported data directly to the U.S. Department of Health and Human Services (HHS) or via a state submission for collection in the HHS Unified Hospital Data Surveillance System (UHDSS).
• While CDC reviews these data for errors and corrects those found, some reporting errors might still exist within the data. To minimize errors and inconsistencies in data reported, CDC removes outliers before calculating the metrics. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks.
• Many hospital subtypes, including acute care and critical access hospitals, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are included in the metric calculations provided in this report. Psychiatric, rehabilitation, and religious non-medical hospital types are excluded from calculations.
• Data are aggregated and displayed for hospitals with the same Centers for Medicare and Medicaid Services (CMS) Certification Number (CCN), which are assigned by CMS to counties based on the CMS Provider of Services files.
• Full details on COVID-19 hospital data reporting guidance can be found here: https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf

Metric details:

• Time Period: timeseries data will update weekly on Mondays as soon as they are reviewed and verified, usually before 8 pm ET. Updates will occur the following day when reporting coincides with a federal holiday. Note: Weekly updates might be delayed due to delays in reporting. All data are provisional. Because these provisional counts are subject to change, including updates to data reported previously, adjustments can occur. Data may be updated since original publication due to delays in reporting (to account for data received after a given Thursday publication) or data quality corrections.
• New COVID-19 Hospital Admissions (count): Number of new admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction.
• New COVID-19 Hospital Admissions (7-Day Average): 7-day average of new admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction.
• Cumulative COVID-19 Hospital Admissions: Cumulative total number of admissions of patients with labo