Data includes: board and school information, grade 3 and 6 EQAO student achievements for reading, writing and mathematics, and grade 9 mathematics EQAO and OSSLT. Data excludes private schools, Education and Community Partnership Programs (ECPP), summer, night and continuing education schools.

How Are We Protecting Privacy?

Results for OnSIS and Statistics Canada variables are suppressed based on school population size to better protect student privacy. In order to achieve this additional level of protection, the Ministry has used a methodology that randomly rounds a percentage either up or down depending on school enrolment. In order to protect privacy, the ministry does not publicly report on data when there are fewer than 10 individuals represented.

* Percentages depicted as 0 may not always be 0 values as in certain situations the values have been randomly rounded down or there are no reported results at a school for the respective indicator. * Percentages depicted as 100 are not always 100, in certain situations the values have been randomly rounded up.

The information in the School Information Finder is the most current available to the Ministry of Education at this time, as reported by schools, school boards, EQAO and Statistics Canada. The information is updated as frequently as possible.

This information is also available on the Ministry of Education's School Information Finder website by individual school.

Descriptions for some of the data types can be found in our glossary.

School/school board and school authority contact information are updated and maintained by school boards and may not be the most current version. For the most recent information please visit: https://data.ontario.ca/dataset/ontario-public-school-contact-information.

The Dataset contains data relating to the student population of state upper secondary schools (day and evening courses) from the 2012/2013 school year to the 2016/2017 school year. The data were processed by the Metropolitan City of Milan and refer to the entire territory.

This layer shows public vs. private school enrollment by sex by grade group. This is shown by tract, county, and state boundaries. 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 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.

This dataset tracks annual distribution of students across grade levels in Detroit Public Schools Community School District and average distribution per school district in Michigan

Abstract

Data related to government primary schools (e.g., enrollment, absenteeism, demographics, class sizes, test scores, student health), especially from UP and MP.

Documentation

Contact the Ministry of Human Resource Development, Department of School Education for collaboration opportunities.

Methodology

Unified District Information on School Education (UDISE) initiated in 2012-13 integrating DISE for elementary education and SEMIS for secondary education is one of the largest Management Information Systems on School Education covering more than 1.5 million schools, 8.5 million teachers and 250 million children.

This paper studies the extent to which young children develop their cognitive ability in high and low quality schools. We use a representative panel data set containing cognitive test scores of 4-6 year olds in Dutch schools. School quality is measured by the school’s average achievement test score at age 12. Our results indicate that children in high-quality schools develop their skills substantially faster than those in low-quality schools. The results remain robust to the inclusion of initial ability, parental background, and neighborhood controls. Moreover, using proximity to higher-achieving schools as an instrument for school choice corroborates the results. The robustness of the results points toward a causal interpretation, although it is not possible to erase all doubt about unobserved confounding factors.

Abstract

The Education Quality Improvement Programme in Tanzania (EQUIP-T) is a large, four-year Department for International Development (DFID) funded programme. It targets some of the most educationally disadvantaged regions in Tanzania to increase the quality of primary education and improve pupil learning outcomes, in particular for girls. EQUIP-T covers seven regions in Tanzania and has five components: 1) enhanced professional capacity and performance of teachers; 2) enhanced school leadership and management skills; 3) strengthened systems that support the district and regional management of education; 4) strengthened community participation and demand for accountability; and 5) strengthened learning and dissemination of results. Together, changes in these five outputs are intended to reduce constraints on pupil learning and thereby contribute to better-quality education (outcome) and ultimately improved pupil learning (impact).

The independent impact evaluation (IE) of EQUIP-T conducted by Oxford Policy Management Ltd (OPM) is a four-year study funded by DFID. It covers five of the seven programme regions (the two regions that will join EQUIP-T in a later phase are not included) and the first four EQUIP-T components (see above). The IE uses a mixed methods approach where qualitative and quantitative methods are integrated. The baseline approach consists of three main parts to allow the IE to: 1) capture the situation prior to the start of EQUIP-T so that changes can be measured during the follow-up data collection rounds; impact attributable to the programme assessed and mechanisms for programme impact explored; 2) develop an expanded programme theory of change to help inform possible programme adjustments; and 3) provide an assessment of the education situation in some of the most educationally disadvantaged regions in Tanzania to the Government and other education stakeholders.

This approach includes:

• Quantitative survey of 100 government primary schools in 17 programme treatment districts and 100 schools in eight control districts in 2014, 2016 and 2018 covering:
• Standard three pupils
• Teachers who teach standards 1-3 Kiswahili and/or mathematics;
• Teachers who teach standards 4-7 mathematics;
• Head teachers; and

• Standard two lesson observations in Kiswahili and mathematics.

• Qualitative fieldwork in nine research sites that overlap with a sub-set of the quantitative survey schools, in 2014, 2016 and 2018, consisting of key informant interviews (KIIs) and focus group discussions (FGDs) with head teachers, teachers, pupils, parents, school committee (SC) members, region, district and ward education officials and EQUIP-T programme staff; and

• A mapping of causal mechanisms, and assessment of the strength of assumptions underpinning the programme theory of change using qualitative and quantitative IE baseline data as well as national and international evidence.

The data and documentation contained in the World Bank Microdata Catalog are those from the EQUIP-T IE quantitative baseline survey conducted in 2014. For information on the qualitative research findings see OPM. 2015b. EQUIP-Tanzania Impact Evaluation. Final Baseline Technical Report, Volume II: Methods and Technical Annexes.

Geographic coverage

The survey is representative of the 17 EQUIP-T programme treatment districts. The survey is NOT representative of the eight control districts. For more details see the section on Representativeness and OPM. 2015. EQUIP-Tanzania Impact Evaluation: Final Baseline Technical Report, Volume I: Results and Discussion and OPM. 2015. EQUIP-Tanzania Impact Evaluation. Final Baseline Technical Report, Volume II: Methods and Technical Annexes.

The 17 treatment districts are:

-Dodoma Region: Bahi DC, Chamwino DC, Kongwa DC, Mpwapwa DC -Kigoma Region: Kakonko DC, Kibondo DC -Shinyanga Region: Kishapu DC, Shinyanga DC -Simiyu Region: Bariadi DC, Bariadi TC, Itilima DC, Maswa DC, Meatu DC -Tabora Region: Igunga DC, Nzega DC, Sikonge DC, Uyui DC

The 8 control districts are:

-Arusha Region: Ngorongoro DC -Mwanza Region: Misungwi DC -Pwani Region: Rufiji DC
-Rukwa Region: Nkasi DC -Ruvuma Region: Tunduru DC -Singida Region: Ikungi DC, Singida DC -Tanga Region: Kilindi DC

Analysis unit

• School
• Teacher
• Pupil
• Lesson (not sampled)

Kind of data

Sample survey data [ssd]

Sampling procedure

Because the EQUIP-T regions and districts were purposively selected (see OPM. 2015. EQUIP-Tanzania Impact Evaluation: Final Baseline Technical Report, Volume I: Results and Discussion.), the IE sampling strategy used propensity score matching (PSM) to: (i) match eligible control districts to the pre-selected and eligible EQUIP-T districts (see below), and (ii) match schools from the control districts to a sample of randomly sampled treatment schools in the treatment districts. The same schools will be surveyed for each round of the IE (panel of schools) and standard 3 pupils will be interviewed at each round of the survey (no pupil panel).

Identifying districts eligible for matching

Eligible control and treatment districts were those not participating in any other education programme or project that may confound the measurement of EQUIP-T impact. To generate the list of eligible control and treatment districts, all districts that are contaminated because of other education programmes or projects or may be affected by programme spill-over were excluded as follows:

-All districts located in Lindi and Mara regions as these are part of the EQUIP-T programme, but the impact evaluation does not cover these two regions; -Districts that will receive partial EQUIP-T programme treatment or will be subject to potential EQUIP-T programme spill-overs; -Districts that are receiving other education programmes/projects that aim to influence the same outcomes as the EQUIP-T programme and would confound measurement of EQUIP-T impact; -Districts that were part of pre-test 1 (two districts); and -Districts that were part of pre-test 2 (one district).

Sampling frame

To be able to select an appropriate sample of pupils and teachers within schools and districts, the sampling frame consisted of information at three levels:

-District level; -School level; and -Within school level.

The sampling frame data at the district and school levels was compiled from the following sources: the 2002 and 2012 Tanzania Population Censuses, Education Management Information System (EMIS) data from the Ministry of Education and Vocational Training (MoEVT) and the Prime Minister's Office for Regional and Local Government (PMO-RALG), and the UWEZO 2011 student learning assessment survey. For within school level sampling, the frames were constructed upon arrival at the selected schools and was used to sample pupils and teachers on the day of the school visit.

Sampling stages

Stage 1: Selection of control districts

Because the treatment districts were known, the first step was to find sufficiently similar control districts that could serve as the counterfactual. PSM was used to match eligible control districts to the pre-selected, eligible treatment districts using the following matching variables: Population density, proportion of male headed households, household size, number of children per household, proportion of households that speak an ethnic language at home, and district level averages for household assets, infrastructure, education spending, parental education, school remoteness, pupil learning levels and pupil drop out.

Stage 2: Selection of treatment schools

In the second stage, schools in the treatment districts were selected using stratified systematic random sampling. The schools were selected using a probability proportional to size approach, where the measure of school size was the standard two enrolment of pupils. This means that schools with more pupils had a higher probability of being selected into the sample. To obtain a representative sample of programme treatment schools, the sample was implicitly stratified along four dimensions:

-Districts; -PSLE scores for Kiswahili; -PSLE scores for mathematics; and -Total number of teachers per school.

Stage 3: Selection of control schools

As in stage one, a non-random PSM approach was used to match eligible control schools to the sample of treatment schools. The matching variables were similar to the ones used as stratification criteria: Standard two enrolment, PSLE scores for Kiswahili and mathematics, and the total number of teachers per school.

The midline and endline surveys will be conducted for the same schools as the baseline survey (a panel of schools). However, the IE will not have a panel of pupils as a pupil only attends standard three once (unless repeating). Thus, the IE will have a repeated cross-section of pupils in a panel of schools.

Stage 4: Selection of pupils and teachers within

This table contains figures on the number of pupils in year 8 who received a school recommendation at the end of primary education (excluding special primary education) by household income, gender and country of origin. Statistics Netherlands is switching to a new classification of the population by origin. From now on, where someone was born is more decisive than where someone's parents were born. The word migration background is no longer used. The main division western/non-western is replaced by a division based on continents and common immigration countries. This classification is gradually being introduced in tables and publications with population by origin. In this table, figures from school year 2014/'15 to 2020/'21 are according to the 'old' classification and figures from school year 2021/'22 according to the 'new' classification. See chapter 4 for further explanation. In year 8, the primary school gives advice on secondary education (VO) that suits a pupil. Since school year 2014/'15, the teacher gives a first school advice before March 1, after which a final school advice follows after the final test in April/May. The final school advice may deviate from the first school advice if the final test was made better than might be expected on the basis of the first school advice. The advice can then be considered and adjusted upwards. The table shows the initial school recommendation and the final school recommendation by household income, gender and country of origin. The population in the table consists of pupils in year 8 who received school advice and were enrolled in primary education on 1 October of the school year. Data available from school year 2014/'15. Status of the figures: The figures in this table are final. Changes as of May 26, 2023: Figures for school year 2021/'22 have been added. When will new numbers come out? The figures for the 2022/'23 school year will be available in the second quarter of 2024.

In the academic year 2022, around 49 percent of students at public junior high schools in Japan either passed the A1 level test for English based on the Common European Framework of Reference for Languages (CEFR) or were confirmed by teachers to have attained the equivalent level. The ratio increased by nearly 20 percent compared to the academic year 2012. The CEFR is used to assess the language proficiency of the learners. It comprises six levels from A1 for beginners up to C2 for those who have mastered a language.

Mean test scores by demographic and test-related characteristics of school-aged children with and without a test.

Historical Dataset of Detroit Public Schools Community School District is provided by PublicSchoolReview and contain statistics on metrics:Comparison of Diversity Score Trends,Total Revenues Trends,Total Expenditure Trends,Average Revenue Per Student Trends,Average Expenditure Per Student Trends,Reading and Language Arts Proficiency Trends,Math Proficiency Trends,Science Proficiency Trends,Graduation Rate Trends,Overall School District Rank Trends,Asian Student Percentage Comparison Over Years (2017-2023),Hispanic Student Percentage Comparison Over Years (2017-2023),Black Student Percentage Comparison Over Years (2017-2023),White Student Percentage Comparison Over Years (2017-2023),Comparison of Students By Grade Trends

This dataset tracks annual average revenue per student from 2018 to 2021 for Detroit Public Schools Community School District

Abstract

DataQuest provides access to a wide variety of reports, including school performance, test results, school staffing, graduation and dropout, and more in California.

Documentation

The California Department of Education (CDE) collects student-level data through the California Longitudinal Pupil Achievement Data System (CALPADS) for state and federal reporting purposes. These data, in addition to assessment data, are available at the aggregate level to the public through the CDE's data reporting portal, DataQuestCDE Downloadable Data Files Web page. PHS has ingested the public and private school listings as well as *the most recent *

• Academic Performance Indicator (API)
• California English Language Development Test (CELDT)
• California High School Exit Exam (CAHSEE)
• Physical Fitness Test (PFT)
• Standardized Testing and Reporting (STAR)
• California Assessment of Student Performance and Progress (CAASPP)

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Visit the DataQuest website for archived performance data.

Unit of analysis

The data include information on the school, district, county, and state levels. Whether a row of data concerns school, district, county, or state data is identified by a record type variable.

Links

• CDE's DataQuest
• Understanding API's
• California Assessment of Student Performance and Progress (CAASPP)

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Abstract

This impact evaluation was designed to evaluate Whole School Development (WSD) program, a comprehensive school management and capacity building program in The Gambia. WSD provided a grant and management training to principals, teachers, and community representatives in a set of schools. In order to be able to separate the impact of the capacity building component from the grant, the second intervention group received the grant but did not receive the training. These two interventions were compared to a control group that received neither the grant nor the training. Each of 273 Gambian primary schools were randomized to one of the three groups. A grant of US$500 was given to all the schools in the WSD and the grant-only groups after a school development plan was presented. The schools were required to spend the funds on activities pertaining broadly to learning and teaching.

This study is part of the broader World Bank's Africa Program for Education Impact Evaluation.

The Gambia Bureau of Statistics, under the supervision of the research team, collected the data for this study. The baseline data was collected in 2008 at the onset of the study, the first round of follow-up data was collected in 2009, the second round of follow-up data was collected in 2010, and the endline data was collected in 2011.

The endline survey is documented here. All other rounds of this impact evaluation are published in the Microdata Library.

Analysis unit

• schools,
• teachers,
• students

Universe

The survey covered all primary public schools and government aided and supported schools.

Kind of data

Sample survey data [ssd]

Sampling procedure

The study was designed to cover all lower basic public and government-aided schools in regions 2, 3, 4, and 6 (276 schools). The two regions that were excluded from the study were Region 1, which is essentially only the capital city and was excluded on the basis that it was too urban and distinct from the rest of the country, and Region 5, because it was used extensively to pilot the WSD prior to the large randomized experiment. Of the 276 schools, 3 schools were excluded because they were new schools and had only grade 1 and 2, or were close during the time of the survey.

Of the 273 remaining schools, 90 schools were randomly assigned to the WSD treatment, 94 schools to the grant-only treatment, and 89 schools served as the control group. The schools were clustered in groups of 2 or 3 schools on the basis of geographic proximity to limit contamination while allowing useful exchange and cooperation between nearby schools. Because this represents the universe of schools meeting the inclusion criteria, rather than a sample, clustering of groups of schools is unnecessary in the subsequent analysis. The randomization was further stratified by school size and accessibility. Each group proved to be similar at baseline. As all schools remained in the study between baseline and endline, there is zero attrition.

The following procedures were observed at the school level:

Head teacher questionnaire - Responded by the head teacher of the school - The deputy head teacher can respond only if the head teacher is not present - A senior teacher is allowed to respond in case either deputy or head teacher are not present.

Selection of classes for the classroom visit - The enumerator gets the list of all the classes and selects two classrooms other than the ones participating in the written test - 528 classes were visited, 175 are WSD; 180 are grant only; and 173 are control classes.

Selection of students for the written test One grade 3 class and one grade 5 class were selected randomly in each school. In each of the classes, 20 students were selected randomly. The gender parity was observed throughout. In total 8,959 students were tested and about a third were selected in each treatment group.

Selection of students for the pupils' questionnaire - 10 students (5 from grade 3 and 5 from grade 5) are randomly selected among the 40 who took the written test to respond to the questionnaire. - 2,696 students were interviewed of which, 879 are WSD; 920 are grant only; and 897 are from the control schools.

Mode of data collection

Face-to-face [f2f]

Research instrument

1) Head Teacher Questionnaire

The head teacher questionnaire is designed to collect broad characteristics of the schools as a whole. The main sections of this questionnaire include the examination of the school facilities (main buildings, sanitary, water provision etc), enrollment and staffs, school management (leadership, involvement of the local community, records keeping etc.). The main respondent to this questionnaire is the head teacher. However, in the event of his absence, the deputy head teacher or a senior teacher answers the questions.

In 2010, researchers added many open-ended questions to the head teacher interviews to collect some information about their views regarding school management. We addressed similar questions to parents or caregivers in a few households whose children were in the relevant schools. The research team was also heavily involved on the ground for the entire first year of this program; the associated conversations with the government, the schools, and the communities add important information that is useful for a better understanding of the findings.

2) Classroom Visits Questionnaire

The classroom observation is intended to collect valuable information about the classroom activities and teaching practices. In each of the two classrooms randomly selected per school, the enumerator seats in the back of the class for 15 to 20 minutes and takes note of the teaching activities such as the students participation, teacher control over the class, etc. At the end of the observation, the teacher is asked a few questions about the school and his or her teaching such as lesson plans and lesson notes.

3) Written Numeracy and Literacy Test

The written numeracy and literacy test is made by experts in the field of testing to assess the overall performance of the students in classes 3 and 5. The test has 4 sections: - The math section with 32 basic arithmetic questions (addition, subtraction, multiplication, division) -A word match section with 13 questions where students are given a word (20 questions in total) and they are to identify that word among a list of 4 words - A vocabulary section where student are given a sentence with an underlined word and they are to identify the synonym of the underlined word among a list of 4 word - A missing word section (11 questions) where a word is removed from a sentence and the students are to find the correct word that fits the blank among a list of 4 words.

4) Pupils' Questionnaire and Oral Literacy Test

The pupils' questionnaire is designed to collect some background information about the students and to give then an oral literacy test. This questionnaire collects information about the students' socio-demographic information, performance and progress, and welfare. In addition, the student are given an oral literacy test that has the following components: - Letter name knowledge: The student are given a panel of 100 letters and are asked to read as many as they could in 60 seconds. - Reading: The students are to read a small passage of 60 words and then they are asked a few questions about the content of the passage. - Listening and comprehension: Here the enumerator reads a small passage aloud and then asks a few questions about the passage to the students.

This dataset tracks annual hispanic student percentage from 2017 to 2023 for Detroit Public Schools Community School District vs. Michigan

Dataset used in the publication "Effects of Acute Endurance, Strength, and Coordination Exercise Interventions on Attention in Adolescents: A Randomized Controlled Study"

The American Community Survey Education Tabulation (ACS-ED) is a custom tabulation of the ACS produced for the National Center of Education Statistics (NCES) by the U.S. Census Bureau. The ACS-ED provides a rich collection of social, economic, demographic, and housing characteristics for school systems, school-age children, and the parents of school-age children. In addition to focusing on school-age children, the ACS-ED provides enrollment iterations for children enrolled in public school. The data profiles include percentages (along with associated margins of error) that allow for comparison of school district-level conditions across the U.S. For more information about the NCES ACS-ED collection, visit the NCES Education Demographic and Geographic Estimates (EDGE) program at: https://nces.ed.gov/programs/edge/Demographic/ACSAnnotation values are negative value representations of estimates and have values when non-integer information needs to be represented. See the table below for a list of common Estimate/Margin of Error (E/M) values and their corresponding Annotation (EA/MA) values.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.-9An '-9' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small.-8An '-8' means that the estimate is not applicable or not available.-6A '-6' entry in the estimate column indicates that 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.-5A '-5' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate.-3A '-3' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate.-2A '-2' entry in 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.

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

This dataset tracks annual math proficiency from 2017 to 2022 for Detroit Public Schools Community School District vs. Michigan

The American Community Survey Education Tabulation (ACS-ED) is a custom tabulation of the ACS produced for the National Center of Education Statistics (NCES) by the U.S. Census Bureau. The ACS-ED provides a rich collection of social, economic, demographic, and housing characteristics for school systems, school-age children, and the parents of school-age children. In addition to focusing on school-age children, the ACS-ED provides enrollment iterations for children enrolled in public school. The data profiles include percentages (along with associated margins of error) that allow for comparison of school district-level conditions across the U.S. For more information about the NCES ACS-ED collection, visit the NCES Education Demographic and Geographic Estimates (EDGE) program at: https://nces.ed.gov/programs/edge/Demographic/ACSAnnotation values are negative value representations of estimates and have values when non-integer information needs to be represented. See the table below for a list of common Estimate/Margin of Error (E/M) values and their corresponding Annotation (EA/MA) values.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data. -9 An '-9' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small. -8 An '-8' means that the estimate is not applicable or not available. -6 A '-6' entry in the estimate column indicates that 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. -5 A '-5' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. -3 A '-3' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate. -2 A '-2' entry in 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.