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 contains details of allocated preferences in past primary admissions rounds - that is when applications are received for York children starting primary or infant school in Reception for the first time. It includes only those preferences allocated on National Offer Day by admissions criteria group (catchment, religion, sibling etc). For further information and advice on school admissions you may wish to consult the Guide for Parents or School Admissions at CYC's webpage

Number of places and applications; number and proportion of offers by preference rank; number and proportion of offers inside and outside home local authority

Purpose: A retrospective review of speech-language pathology graduate school applications was conducted to identify ways in which the application process may act as barriers to admission for three populations of underrepresented students: students who are Black, Indigenous, and people of color; first-generation college students; and students with low socioeconomic status.Method: Graduate school applications were analyzed to identify application components that act as barriers to admission. The data set included application data from one program that uses composite cutoff scores and demographics in admissions decisions. Quantitative methods were used to probe for evidence of three types of barriers: barriers to application, group differences, and differential predictive validity. Applicants from underrepresented populations were compared to applicants from overrepresented populations.Results: Applicants from underrepresented populations were more likely to submit late or incomplete applications. Group differences were found for grade point averages (GPAs) and Graduate Records Examination (GRE) percentiles, but not for letters of recommendation or personal statements. All application components made significant contributions to decisions about initial application results. Differential predictive validity was found in the analysis of initial application results. For letters of recommendation, GPAs, and personal statements, group-specific regression lines with the same slopes were found for applicants from underrepresented populations and applicants from overrepresented populations. For GRE percentiles, group-specific regression lines had different slopes.Conclusions: This barrier assessment found quantitative evidence of several barriers to admission for applicants from underrepresented populations. These barriers help perpetuate the lack of diversity in the profession. Actionable steps to mitigate barriers are proposed.Supplemental Material S1. Instructions, scoring tables, and scoring rubrics for graduate school application components used by the Department of Communication Sciences and Disorders in the 2017–2018, 2018–2019, and 2019–2020 application cycles.Kovacs, T. (2022). Assessing barriers to graduate school admission for applicants from underrepresented populations in a master’s level speech-language pathology program. American Journal of Speech-Language Pathology. Advance online publication. https://doi.org/10.1044/2021_AJSLP-21-00124

Australia School Enrollment: Primary: % Net data was reported at 96.381 % in 2017. This records a decrease from the previous number of 96.650 % for 2016. Australia School Enrollment: Primary: % Net data is updated yearly, averaging 96.710 % from Dec 1971 (Median) to 2017, with 46 observations. The data reached an all-time high of 99.984 % in 1978 and a record low of 93.857 % in 2001. Australia School Enrollment: Primary: % Net data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Social: Education Statistics. Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music.;UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of February 2020.;Weighted average;

1. Primary and post-primary schools are extracted from the Department of Education’s Unified Data Model (UDM) database2. Additional capacity in the school system is measured as the number of new schools, the number of new buildings added to existing schools plus the number of additional permanent places provided each year3. Data is reported on an academic year basis which starts in September of a year and finishes in June of the following year (First week of June for post-primary schools and last week of June for primary schools)4. The most recent data available for additional capacity is 2020/21 and is what is included 5. Additional Capacity is excluded from the most recent indicator report

As of October 2020, the main reason why school-aged respondents in Nigeria could not attend school was school closures caused by the coronavirus pandemic. The most affected age groups were those 10 to 14 years as well as pupils in primary education. Other factors preventing school-aged individuals from attending schools included lack of money and the waiting time for admissions. Governments of many countries worldwide closed schools and encouraged remote learning in an attempt to slow the spread of COVID-19.

In 2022, close to 10.4 million students were enrolled in primary schools in Kenya. The number slightly increased from approximately 10.3 million in the previous year. Primary education in the country begins at the ages of five to seven years old, and the school year commences in January and ends in November.

This project on multiliteracies involved groups of deaf learners in India, Uganda, and Ghana, both in primary schools and with young adult learners. The Peer-to-Peer Deaf Multiliteracies project examined how some of the dynamics that contribute to learners’ marginalisation can be changed by involving deaf individuals in the design of new teaching approaches, and by using children and young people's lived experiences and existing multilingual-multimodal skills as the starting point for theme-based learning. The aim was for participants to develop not only English literacy, but "multiliteracies", i.e. skills in sign languages, ICT, written English, creative expression through drawing and acting, and other forms of multimodal communication. The data collection includes reports from classroom settings compiled by tutors and by research assistants, pre-and post-tests on language and literacy abilities with learners, samples from an online learning platform, and multimedia portfolios collected from learners. A total of 124 young deaf adults and 79 deaf primary school children took part in the research

The exclusion of deaf children and young adults from access to school systems in the developing world results in individuals and communities being denied quality education; this not only leads to unemployment, underemployment, low income, and a high risk of poverty, but also represents a needless waste of human talent and potential. To target this problem, this project extends work conducted under a pilot project addressing issues of literacy education with young deaf people in the Global South. Creating, implementing and evaluating our innovative intervention based on the peer teaching of English literacy through sign language-based tutoring, everyday real life texts such as job application forms, and the use of a bespoke online resource, enabled us to generate a sustainable, cost-effective and learner-directed way to foster literacy learning amongst deaf individuals. To reach further target groups and conduct more in-depth research, the present project extends our work to new groups of learners in India, Uganda, Ghana, Rwanda and Nepal, both in primary schools (ca 60 children in India, Ghana, and Uganda) and with young adult learners (ca 100 learners in interventions, plus ca 60 young adults in scoping workshops in Nepal and Rwanda). In the targeted countries, marginalisation begins in schools, since many have no resources for teaching through sign language, even though this is the only fully accessible language to a deaf child. This project intends to examine how we can change some of the dynamics that contribute to this, by involving deaf individuals in the design of new teaching approaches, and by using children and young people's everyday experiences and existing literacy practices as the basis for their learning. Participants in such a programme not only develop English literacy, but "multiliteracies", i.e. skills in sign languages, technology, written English, gesture, mouthing, and other forms of multimodal communication. Developing a multilingual toolkit is an essential element of multiliteracies. Being 'literate' in the modern world involves a complex set of practices and competencies and engagement with various modes (e.g. face-to-face, digital, remote), increasing one's abilities to act independently. Our emphases on active learning, contextualised assessments and building portfolios to document progress increases the benefit to deaf learners in terms of their on-going educational and employment capacity. Apart from the actual teaching and interventions, the research also investigates factors in existing systems of educational provisions for deaf learners and how these may systematically undermine and isolate deaf communities and their sign languages. Our analyses identify the local dynamics of cultural contexts that our programmes and future initiatives need to address and evaluate in order to be sustainable. One challenge we encountered in the pilot was the lack of trained deaf peer tutors. There is a need for investment in local capacity building and for the creation of opportunities and pathways for deaf people to obtain formal qualifications. Therefore, we develop training in literacy teaching and in research methods for all deaf project staff. We also develop and adapt appropriate assessment tools and metrics to confirm what learning has taken place and how, with both children and young adults. This includes adapting the Common European Framework of Reference for Languages (CEFR) for young deaf adult learners and the 'Language Ladder' for deaf children so that we use locally-valid test criteria. To document progress in more detail and in relation to authentic, real life literacy demands we need to create our own metrics, which we do by using portfolio based assessments that are learner-centred and closely linked to the local curricula.

In 2020, some ** percent of children in Nigeria completed the last grade of elementary school, with **** percent of males and **** percent of females. In the following educational levels, the disparity between male students and female students became larger, reaching a gap of **** and **** percentage points in the completion rates of middle school and high school, respectively.

Historical Dataset of G. Weaver Hipps Elementary School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2011-2023),Total Classroom Teachers Trends Over Years (2011-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2011-2023),Asian Student Percentage Comparison Over Years (2010-2023),Hispanic Student Percentage Comparison Over Years (2011-2023),Black Student Percentage Comparison Over Years (2011-2023),White Student Percentage Comparison Over Years (2011-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (2011-2023),Free Lunch Eligibility Comparison Over Years (2011-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2010-2020),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2011-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2011-2023)

The average score of Polish students in terms of reading comprehension was *** points in 2022. In maths, Polish 15-year-olds scored *** points, ** points higher than the OECD average. In the natural sciences, Polish students achieved an average score of *** points, which places them in sixth place among the European Union countries. Examinations in primary school Primary school students take an exam at the end of the eighth grade. The eighth-grade exam is a mandatory exam, which means that every student must take it to graduate from school. There is no specified minimum score that a student should obtain, so the eighth-grade exam cannot be failed. The eighth-grade examination is carried out in written form. Students take the exam in three compulsory subjects, i.e., Polish language, mathematics, and a foreign language of their choice. A student may choose only the language that is taught at school as part of compulsory education classes. In 2023, primary school students in Poland had the best results in exams in the ****** language. High school graduation exam (Matura) The Matura exam is taken at the end of general secondary and technical secondary school and its result is a prerequisite for further education. In 2023, over *** thousand graduates of secondary schools passed the Matura exams. The most popular foreign language was English, passed by ** percent of students. English and mathematics were the most popular subjects at an extended level. The exam pass rate amounted to ** percent, which was ** percentage points higher than in the previous school year.

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.

Out of the OECD countries, Luxembourg was the country that spent the most on educational institutions per full-time student in 2020. On average, 23,000 U.S dollars were spent on primary education, nearly 27,000 U.S dollars on secondary education, and around 53,000 U.S dollars on tertiary education. The United States followed behind, with Norway in third. Meanwhile, the lowest spending was in Mexico.

Public school enrolments in regular programs for youth in elementary and secondary schools, by grade and sex.

Background:
The Millennium Cohort Study (MCS) is a large-scale, multi-purpose longitudinal dataset providing information about babies born at the beginning of the 21st century, their progress through life, and the families who are bringing them up, for the four countries of the United Kingdom. The original objectives of the first MCS survey, as laid down in the proposal to the Economic and Social Research Council (ESRC) in March 2000, were:

• to chart the initial conditions of social, economic and health advantages and disadvantages facing children born at the start of the 21st century, capturing information that the research community of the future will require
• to provide a basis for comparing patterns of development with the preceding cohorts (the National Child Development Study, held at the UK Data Archive under GN 33004, and the 1970 Birth Cohort Study, held under GN 33229)
• to collect information on previously neglected topics, such as fathers' involvement in children's care and development
• to focus on parents as the most immediate elements of the children's 'background', charting their experience as mothers and fathers of newborn babies in the year 2000, recording how they (and any other children in the family) adapted to the newcomer, and what their aspirations for her/his future may be
• to emphasise intergenerational links including those back to the parents' own childhood
• to investigate the wider social ecology of the family, including social networks, civic engagement and community facilities and services, splicing in geo-coded data when available

Additional objectives subsequently included for MCS were:

• to provide control cases for the national evaluation of Sure Start (a government programme intended to alleviate child poverty and social exclusion)
• to provide samples of adequate size to analyse and compare the smaller countries of the United Kingdom, and include disadvantaged areas of England

Further information about the MCS can be found on the Centre for Longitudinal Studies web pages.

The content of MCS studies, including questions, topics and variables can be explored via the CLOSER Discovery website.

The first sweep (MCS1) interviewed both mothers and (where resident) fathers (or father-figures) of infants included in the sample when the babies were nine months old, and the second sweep (MCS2) was carried out with the same respondents when the children were three years of age. The third sweep (MCS3) was conducted in 2006, when the children were aged five years old, the fourth sweep (MCS4) in 2008, when they were seven years old, the fifth sweep (MCS5) in 2012-2013, when they were eleven years old, the sixth sweep (MCS6) in 2015, when they were fourteen years old, and the seventh sweep (MCS7) in 2018, when they were seventeen years old.

Safeguarded versions of MCS studies:
The Safeguarded versions of MCS1, MCS2, MCS3, MCS4, MCS5, MCS6 and MCS7 are held under UK Data Archive SNs 4683, 5350, 5795, 6411, 7464, 8156 and 8682 respectively. The longitudinal family file is held under SN 8172.

Polygenic Indices
Polygenic indices are available under Special Licence SN 9437. Derived summary scores have been created that combine the estimated effects of many different genes on a specific trait or characteristic, such as a person's risk of Alzheimer's disease, asthma, substance abuse, or mental health disorders, for example. These polygenic scores can be combined with existing survey data to offer a more nuanced understanding of how cohort members' outcomes may be shaped.

Sub-sample studies:
Some studies based on sub-samples of MCS have also been conducted, including a study of MCS respondent mothers who had received assisted fertility treatment, conducted in 2003 (see EUL SN 5559). Also, birth registration and maternity hospital episodes for the MCS respondents are held as a separate dataset (see EUL SN 5614).

Release of Sweeps 1 to 4 to Long Format (Summer 2020)
To support longitudinal research and make it easier to compare data from different time points, all data from across all sweeps is now in a consistent format. The update affects the data from sweeps 1 to 4 (from 9 months to 7 years), which are updated from the old/wide to a new/long format to match the format of data of sweeps 5 and 6 (age 11 and 14 sweeps). The old/wide formatted datasets contained one row per family with multiple variables for different respondents. The new/long formatted datasets contain one row per respondent (per parent or per cohort member) for each MCS family. Additional updates have been made to all sweeps to harmonise variable labels and enhance anonymisation.

How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from MCS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Secure Access datasets:
Secure Access versions of the MCS have more restrictive access conditions than versions available under the standard Safeguarded Licence or Special Licence (see 'Access data' tab above).

Secure Access versions of the MCS include:

• detailed sensitive variables not available under EUL. These have been grouped thematically and are held under SN 8753 (socio-economic, accommodation and occupational data), SN 8754 (self-reported health, behaviour and fertility), SN 8755 (demographics, language and religion) and SN 8756 (exact participation dates). These files replace previously available studies held under SNs 8456 and 8622-8627
  
• detailed geographical identifier files which are grouped by sweep held under SN 7758 (MCS1), SN 7759 (MCS2), SN 7760 (MCS3), SN 7761 (MCS4), SN 7762 (MCS5 2001 Census Boundaries), SN 7763 (MCS5 2011 Census Boundaries), SN 8231 (MCS6 2001 Census Boundaries), SN 8232 (MCS6 2011 Census Boundaries), SN 8757 (MCS7), SN 8758 (MCS7 2001 Census Boundaries) and SN 8759 (MCS7 2011 Census Boundaries). These files replace previously available files grouped by geography SN 7049 (Ward level), SN 7050 (Lower Super Output Area level), and SN 7051 (Output Area level)
• linked education administrative datasets for Key Stages 1, 2, 4 and 5 held under SN 8481 (England). This replaces previously available datasets for Key Stage 1 (SN 6862) and Key Stage 2 (SN 7712)
  
• linked education administrative datasets for Key Stage 1 held under SN 7414 (Scotland)
• linked education administrative dataset for Key Stages 1, 2, 3 and 4 under SN 9085 (Wales)
  
• linked NHS Patient Episode Database for Wales (PEDW) for MCS1 – MCS5 held under SN 8302
• linked Scottish Medical Records data held under SNs 8709, 8710, 8711, 8712, 8713 and 8714;
• Banded Distances to English Grammar Schools for MCS5 held under SN 8394
• linked Health Administrative Datasets (Hospital Episode Statistics) for England for years 2000-2019 held under SN 9030
• linked Hospital of Birth data held under SN 5724.

The linked education administrative datasets held under SNs 8481,7414 and 9085 may be ordered alongside the MCS detailed geographical identifier files only if sufficient justification is provided in the application.

Researchers applying for access to the Secure Access MCS datasets should indicate on their ESRC Accredited Researcher application form the EUL dataset(s) that they also wish to access (selected from the MCS Series Access web page).