In 2023/24 the share of female students achieving a first class degree in the United Kingdom was 30 percent, compared with 27 percent of male students.

In an impressive increase from years past, 39 percent of women in the United States had completed four years or more of college in 2022. This figure is up from 3.8 percent of women in 1940. A significant increase can also be seen in males, with 36.2 percent of the U.S. male population having completed four years or more of college in 2022, up from 5.5 percent in 1940.

4- and 2-year colleges

In the United States, college students are able to choose between attending a 2-year postsecondary program and a 4-year postsecondary program. Generally, attending a 2-year program results in an Associate’s Degree, and 4-year programs result in a Bachelor’s Degree.

Many 2-year programs are designed so that attendees can transfer to a college or university offering a 4-year program upon completing their Associate’s. Completion of a 4-year program is the generally accepted standard for entry-level positions when looking for a job.

Earnings after college

Factors such as gender, degree achieved, and the level of postsecondary education can have an impact on employment and earnings later in life. Some Bachelor’s degrees continue to attract more male students than female, particularly in STEM fields, while liberal arts degrees such as education, languages and literatures, and communication tend to see higher female attendance.

All of these factors have an impact on earnings after college, and despite nearly the same rate of attendance within the American population between males and females, men with a Bachelor’s Degree continue to have higher weekly earnings on average than their female counterparts.

This dataset provides information on the number of persons attending evening schools and illiteracy eradication centers in the State of Qatar. The data is classified by level of education (Primary), grade (e.g., First and Second Grade, Third and Fourth Grade, Fifth Grade), nationality (Qatari, Non-Qatari), and gender (Male, Female). It supports the analysis of literacy and education access among different population groups.

Student Performance Dataset 2024 Overview This dataset comprises detailed information about high school students in China, collected from various universities and schools. It is designed to analyze the factors influencing student performance, well-being, and engagement. The data includes a wide range of features such as demographic details, academic performance, health status, parental support, and more. The participating institutions include prominent universities such as Tsinghua University, Peking University, Fudan University, Shanghai Jiao Tong University, and Zhejiang University.

Dataset Description Features Student ID: Unique identifier for each student. Gender: Gender of the student (Male/Female). Age: Age of the student. Grade Level: The grade level of the student (e.g., 9, 10, 11, 12). Attendance Rate: The percentage of days the student attended school. Study Hours: Average number of hours the student spends studying daily. Parental Education Level: The highest level of education attained by the student's parents. Parental Involvement: The level of parental involvement in the student's education (High, Medium, Low). Extracurricular Activities: Whether the student participates in extracurricular activities (Yes/No). Socioeconomic Status: Socioeconomic status of the student's family (High, Medium, Low). Previous Academic Performance: Previous academic performance level (High, Medium, Low). Class Participation: The level of participation in class (High, Medium, Low). Health Status: General health status of the student (Good, Average, Poor). Access to Learning Resources: Whether the student has access to necessary learning resources (Yes/No). Internet Access: Whether the student has access to the internet (Yes/No). Learning Style: Preferred learning style of the student (Visual, Auditory, Kinesthetic). Teacher-Student Relationship: Quality of the relationship between the student and teachers (Positive, Neutral, Negative). Peer Influence: Influence of peers on the student's behavior and performance (Positive, Neutral, Negative). Motivation Level: Student's level of motivation (High, Medium, Low). Hours of Sleep: Average number of hours the student sleeps per night. Diet Quality: Quality of the student's diet (Good, Average, Poor). Transportation Mode: Mode of transportation used by the student to commute to school (Bus, Car, Walk, Bike). School Type: Type of school attended by the student (Public, Private). School Location: Location of the school (Urban, Rural). Homework Completion Rate: The rate at which the student completes homework assignments. Reading Proficiency: Proficiency level in reading. Math Proficiency: Proficiency level in mathematics. Science Proficiency: Proficiency level in science. Language Proficiency: Proficiency level in language. Physical Activity Level: The level of physical activity (High, Medium, Low). Screen Time: Average daily screen time in hours. Bullying Incidents: Number of bullying incidents the student has experienced. Special Education Services: Whether the student receives special education services (Yes/No). Counseling Services: Whether the student receives counseling services (Yes/No). Learning Disabilities: Whether the student has any learning disabilities (Yes/No). Behavioral Issues: Whether the student has any behavioral issues (Yes/No). Attendance of Tutoring Sessions: Whether the student attends tutoring sessions (Yes/No). School Climate: Overall perception of the school's environment (Positive, Neutral, Negative). Parental Employment Status: Employment status of the student's parents (Employed, Unemployed). Household Size: Number of people living in the student's household. Performance Score: Overall performance score of the student (Low, Medium, High).

The total gross enrollment ratio of students from the pre-primary to second grade across the northern state of Himachal Pradesh in India during financial year 2024 was around ** percent. The enrollment ratio for male students was lower at ***** percent compared to female students with around *** percent that year.

The joint collection of UNESCO-OECD-Eurostat (UOE) data on formal education systems providesOna annual data on the participation and completion of educational programs by students as well as personnel data, cost and type of resources dedicated to education. The reference period for non-monetary education data is the curSo school and for monetary data it is the calendar year. The International Statistics of Education and Training Systems - Questionnaire UNESCO-UIS / OECD / Eurostat (UOE) aims to provide the data required by international organizationsIn addition to offering results at the national level. It is a synthesis and analysis operation that appears in the National Statistical Plan 2021-2024 (Nº Prog. 8677) and is carried out by the S.G. of Statistics and Studies of the Ministry of EducationAnd Vocational Training in collaboration with the Ministry of Universities and the National Institute of Statistics. Its purpose is to integrate the statistical information of the activity of the educational-training system in its different educational levels withIn order to meet the demands of international statistics, of the same name, requested by Eurostat, OECD and UNESCO-UIS. Main results Below is a selection of tables with data derived from this statistic, along with a non-Presentation summary: Summary note 2022-2023

According to the Global Gender Gap Report 2020, 88 percent of females worldwide had primary education, compared to 91 percent of males. By comparison, more females than males had attained tertiary education. The Global Gender Index benchmarks national gender gaps on economic, political, education, and health-based criteria. In 2020, the leading country was Iceland with a score of 0.87.

Comparison of gender and change in grades.

The joint collection of UNESCO-OECD-Eurostat (UOE) data on formal education systems providesOna annual data on the participation and completion of educational programs by students as well as personnel data, cost and type of resources dedicated to education. The reference period for non-monetary education data is the curSo school and for monetary data it is the calendar year. The International Statistics of Education and Training Systems - Questionnaire UNESCO-UIS / OECD / Eurostat (UOE) aims to provide the data required by international organizationsIn addition to offering results at the national level. It is a synthesis and analysis operation that appears in the National Statistical Plan 2021-2024 (Nº Prog. 8677) and is carried out by the S.G. of Statistics and Studies of the Ministry of EducationAnd Vocational Training in collaboration with the Ministry of Universities and the National Institute of Statistics. Its purpose is to integrate the statistical information of the activity of the educational-training system in its different educational levels withIn order to meet the demands of international statistics, of the same name, requested by Eurostat, OECD and UNESCO-UIS. Main results Below is a selection of tables with data derived from this statistic, along with a non-Presentation summary: Summary note 2022-2023

The joint collection of UNESCO-OECD-Eurostat (UOE) data on formal education systems providesOna annual data on the participation and completion of educational programs by students as well as personnel data, cost and type of resources dedicated to education. The reference period for non-monetary education data is the curSo school and for monetary data it is the calendar year. The International Statistics of Education and Training Systems - Questionnaire UNESCO-UIS / OECD / Eurostat (UOE) aims to provide the data required by international organizationsIn addition to offering results at the national level. It is a synthesis and analysis operation that appears in the National Statistical Plan 2021-2024 (Nº Prog. 8677) and is carried out by the S.G. of Statistics and Studies of the Ministry of EducationAnd Vocational Training in collaboration with the Ministry of Universities and the National Institute of Statistics. Its purpose is to integrate the statistical information of the activity of the educational-training system in its different educational levels withIn order to meet the demands of international statistics, of the same name, requested by Eurostat, OECD and UNESCO-UIS. Main results Below is a selection of tables with data derived from this statistic, along with a non-Presentation summary: Summary note 2022-2023

Goal 4Ensure inclusive and equitable quality education and promote lifelong learning opportunities for allTarget 4.1: By 2030, ensure that all girls and boys complete free, equitable and quality primary and secondary education leading to relevant and effective learning outcomesIndicator 4.1.1: Proportion of children and young people (a) in grades 2/3; (b) at the end of primary; and (c) at the end of lower secondary achieving at least a minimum proficiency level in (i) reading and (ii) mathematics, by sexSE_TOT_PRFL: Proportion of children and young people achieving a minimum proficiency level in reading and mathematics (%)Indicator 4.1.2: Completion rate (primary education, lower secondary education, upper secondary education)SE_TOT_CPLR: Completion rate, by sex, location, wealth quintile and education level (%)Target 4.2: By 2030, ensure that all girls and boys have access to quality early childhood development, care and pre-primary education so that they are ready for primary educationIndicator 4.2.1: Proportion of children aged 24-59 months who are developmentally on track in health, learning and psychosocial well-being, by sexiSE_DEV_ONTRK: Proportion of children aged 36−59 months who are developmentally on track in at least three of the following domains: literacy-numeracy, physical development, social-emotional development, and learning (% of children aged 36-59 months)Indicator 4.2.2: Participation rate in organized learning (one year before the official primary entry age), by sexSE_PRE_PARTN: Participation rate in organized learning (one year before the official primary entry age), by sex (%)Target 4.3: By 2030, ensure equal access for all women and men to affordable and quality technical, vocational and tertiary education, including universityIndicator 4.3.1: Participation rate of youth and adults in formal and non-formal education and training in the previous 12 months, by sexSE_ADT_EDUCTRN: Participation rate in formal and non-formal education and training, by sex (%)Target 4.4: By 2030, substantially increase the number of youth and adults who have relevant skills, including technical and vocational skills, for employment, decent jobs and entrepreneurshipIndicator 4.4.1: Proportion of youth and adults with information and communications technology (ICT) skills, by type of skillSE_ADT_ACTS: Proportion of youth and adults with information and communications technology (ICT) skills, by sex and type of skill (%)Target 4.5: By 2030, eliminate gender disparities in education and ensure equal access to all levels of education and vocational training for the vulnerable, including persons with disabilities, indigenous peoples and children in vulnerable situationsIndicator 4.5.1: Parity indices (female/male, rural/urban, bottom/top wealth quintile and others such as disability status, indigenous peoples and conflict-affected, as data become available) for all education indicators on this list that can be disaggregatedSE_GPI_PTNPRE: Gender parity index for participation rate in organized learning (one year before the official primary entry age), (ratio)SE_GPI_TCAQ: Gender parity index of trained teachers, by education level (ratio)SE_GPI_PART: Gender parity index for participation rate in formal and non-formal education and training (ratio)SE_GPI_ICTS: Gender parity index for youth/adults with information and communications technology (ICT) skills, by type of skill (ratio)SE_IMP_FPOF: Immigration status parity index for achieving at least a fixed level of proficiency in functional skills, by numeracy/literacy skills (ratio)SE_NAP_ACHI: Native parity index for achievement (ratio)SE_LGP_ACHI: Language test parity index for achievement (ratio)SE_TOT_GPI: Gender parity index for achievement (ratio)SE_TOT_SESPI: Low to high socio-economic parity status index for achievement (ratio)SE_TOT_RUPI: Rural to urban parity index for achievement (ratio)SE_ALP_CPLR: Adjusted location parity index for completion rate, by sex, location, wealth quintile and education levelSE_AWP_CPRA: Adjusted wealth parity index for completion rate, by sex, location, wealth quintile and education levelSE_AGP_CPRA: Adjusted gender parity index for completion rate, by sex, location, wealth quintile and education levelTarget 4.6: By 2030, ensure that all youth and a substantial proportion of adults, both men and women, achieve literacy and numeracyIndicator 4.6.1: Proportion of population in a given age group achieving at least a fixed level of proficiency in functional (a) literacy and (b) numeracy skills, by sexSE_ADT_FUNS: Proportion of population achieving at least a fixed level of proficiency in functional skills, by sex, age and type of skill (%)Target 4.7: By 2030, ensure that all learners acquire the knowledge and skills needed to promote sustainable development, including, among others, through education for sustainable development and sustainable lifestyles, human rights, gender equality, promotion of a culture of peace and non-violence, global citizenship and appreciation of cultural diversity and of culture’s contribution to sustainable developmentIndicator 4.7.1: Extent to which (i) global citizenship education and (ii) education for sustainable development are mainstreamed in (a) national education policies; (b) curricula; (c) teacher education; and (d) student assessmentTarget 4.a: Build and upgrade education facilities that are child, disability and gender sensitive and provide safe, non-violent, inclusive and effective learning environments for allIndicator 4.a.1: Proportion of schools offering basic services, by type of serviceSE_ACS_CMPTR: Schools with access to computers for pedagogical purposes, by education level (%)SE_ACS_H2O: Schools with access to basic drinking water, by education level (%)SE_ACS_ELECT: Schools with access to electricity, by education level (%)SE_ACC_HNDWSH: Schools with basic handwashing facilities, by education level (%)SE_ACS_INTNT: Schools with access to the internet for pedagogical purposes, by education level (%)SE_ACS_SANIT: Schools with access to access to single-sex basic sanitation, by education level (%)SE_INF_DSBL: Proportion of schools with access to adapted infrastructure and materials for students with disabilities, by education level (%)Target 4.b: By 2020, substantially expand globally the number of scholarships available to developing countries, in particular least developed countries, small island developing States and African countries, for enrolment in higher education, including vocational training and information and communications technology, technical, engineering and scientific programmes, in developed countries and other developing countriesIndicator 4.b.1: Volume of official development assistance flows for scholarships by sector and type of studyDC_TOF_SCHIPSL: Total official flows for scholarships, by recipient countries (millions of constant 2018 United States dollars)Target 4.c: By 2030, substantially increase the supply of qualified teachers, including through international cooperation for teacher training in developing countries, especially least developed countries and small island developing StatesIndicator 4.c.1: Proportion of teachers with the minimum required qualifications, by education leveliSE_TRA_GRDL: Proportion of teachers who have received at least the minimum organized teacher training (e.g. pedagogical training) pre-service or in-service required for teaching at the relevant level in a given country, by sex and education level (%)

Descriptive statistics Descriptive statistics for the dependent and independent variables of this study were presented in Table 1. Out of 3314 undergraduate students in the sample, 2583 (77.9%) expressed their willingness to accept a free HIV test. More than two thirds (66.9%) of these subjects were females and the majority of respondents (94.5%) were Han. Of college students in this sample, nearly two fifths (37.4%) lived in the local city less than one year and about one third (31.0%) were freshmen. Nearly one half (48.2%) of our participants were medical students. To our surprise, 15.2% reported their sexual orientation is non-heterosexual and 55.9% spent less than one thousand Yuan on their monthly living expenses. HIV/AIDS-related knowledge was lacking with only 39.1% of participants answering more than 10 out of twelve questions correctly. Furthermore, stigma and discrimination towards people living with HIV/AIDS were serious, since the number of correct responses that nearly half (45.5%) of the respondents responded to the 24 specific situations was no more than eighteen. The majority of college students mentioned at least one free HIV testing site and also recognized the necessity to provide a free HIV test in the local university (78.8% and 88.7%, respectively). Beyond our expectation, more than half (56.2%) of college students were ignorant of the "Four Frees and One Care" policy. Despite the fact that 18.9% of college students reported having had sexual behavior, only 49.5% perceived the risk of HIV infection. Bivariable analysis The results of the bivariable analysis were shown in Table 1. Those who expressed greater willingness to accept a free HIV test tended to be medical students, higher levels of HIV-related knowledge, lower levels of stigma and discrimination, awareness of the "Four Frees and One Care" policy, knowledge of free HIV testing centers, recognition of the necessity to provide a free HIV test in the local university, and higher perception of the risk of HIV infection. No significant differences were reported between willingness and unwillingness in gender, race, grade, length of time, sexual orientation, monthly living expense, and history of sexual behavior. Multivariable logistic regression analysis The stepwise multiple logistic regression model predicting willingness to accept a free HIV test was shown in Table 2. When all seven significant variables were included into the logistic regression model, only four variables (i.e., stigma and discrimination towards people living with HIV/AIDS, knowledge of free HIV testing centers, recognition of the necessity to provide a free HIV test in the local university, perceived risk of HIV infection) remained statistically significantly related to willingness to participate in a free HIV test, while three variables including major, HIV-related knowledge, and awareness of the “Four Frees and One Care” policy lost their statistical significance, as indicated in Table 2. Among all these four significant predictors, the odds ratio(OR) was the highest for recognition of the necessity to provide a free HIV test in the local university. The college students having recognized the necessity were more likely to express their willingness to accept to a free HIV test (OR=2.20, 95CI=1.73--2.80, P<0.001) than those having not recognized the necessity. The odds of willingness were 1.41 times (95CI=1.17--1.68, P<0.001) of respondents who had lower levels of stigma and discrimination towards people living with HIV/AIDS, compared to that of those with high levels of stigma and discrimination. In addition, being more knowledgeable about free HIV testing centers (OR = 1.44, 95%CI=1.17--1.77, P<0.001) and having higher HIV risk perception (OR =1.64, 95%CI=1.37--1.95, P<0.001) were significantly associated with greater willingness to use VCT service.

Background, Methodology: Local Law 102 enacted in 2015 requires the Department of Education of the New York City School District to submit to the Council an annual report concerning physical education for the prior school year. This report provides information about average frequency and average total minutes per week of physical education as defined in Local Law 102 as reported through the 2015-2016 STARS database. It is important to note that schools self-report their scheduling information in STARS. The report also includes information regarding the number and ratio of certified physical education instructors and designated physical education instructional space. This report consists of six tabs: PE Instruction Borough-Level PE Instruction District-Level PE Instruction School-Level Certified PE Teachers PE Space Supplemental Programs PE Instruction Borough-Level This tab includes the average frequency and average total minutes per week of physical education by borough, disaggregated by grade, race and ethnicity, gender, special education status and English language learner status. This report only includes students who were enrolled in the same school across all academic terms in the 2015-16 school year. Data on students with disabilities and English language learners are as of the end of the 2015-16 school year. Data on adaptive PE is based on individualized education programs (IEP) finalized on or before 05/31/2016. PE Instruction District-Level This tab includes the average frequency and average total minutes per week of physical education by district, disaggregated by grade, race and ethnicity, gender, special education status and English language learner status. This report only includes students who were enrolled in the same school across all academic terms in the 2015-16 school year. Data on students with disabilities and English language learners are as of the end of the 2015-16 school year. Data on adaptive PE is based on individualized education programs (IEP) finalized on or before 05/31/2016. PE Instruction School-Level This tab includes the average frequency and average total minutes per week of physical education by school, disaggregated by grade, race and ethnicity, gender, special education status and English language learner status. This report only includes students who were enrolled in the same school across all academic terms in the 2015-16 school year. Data on students with disabilities and English language learners are as of the end of the 2015-16 school year. Data on adaptive PE is based on individualized education programs (IEP) finalized on or before 05/31/2016. Certified PE Teachers This tab provides the number of designated full-time and part-time physical education certified instructors. Does not include elementary, early childhood and K-8 physical education teachers that provide physical education instruction under a common branches license. Also includes ratio of full time instructors teaching in a physical education license to students by school. Data reported is for the 2015-2016 school year as of 10/31/2015. PE Space This tab provides information on all designated indoor, outdoor and off-site spaces used by the school for physical education as reported through the Principal Annual Space Survey and the Outdoor Yard Report. It is important to note that information on each room category is self-reported by principals, and principals determine how each room is classified. Data captures if the PE space is co-located, used by another school or used for another purpose. Includes gyms, athletic fields, auxiliary exercise spaces, dance rooms, field houses, multipurpose spaces, outdoor yards, off-site locations, playrooms, swimming pools and weight rooms as designated PE Space. Supplemental Programs This tab provides information on the department's supplemental physical education

The total gross enrollment ratio of students from the pre-primary to second grade across the state of West Bengal in India during financial year 2024 was around ** percent. The enrollment ratio of students from sixth grade to eighth grade was higher among female students compared to male students that year.

The total gross enrollment ratio of students from the pre-primary to second grade across the north-western state of Rajasthan in India during financial year 2024 was ** percent. The enrollment ratio of students from sixth grade to eighth grade was slightly higher among male students compared to female students that year.

36.1% of white undergraduate students got a first class degree in the 2021 to 2022 academic year, compared with 17.3% of black students.

The full-time apparent retention rate (ARR) measures the proportion of a cohort of full-time students that moves from one grade to the next, based on an expected rate of progression of one grade per year. It does not track individual students through their final years of secondary schooling.

Data Notes:

• The ARR is the ratio of the total number of full-time school students in a designated year (e.g. Year 12 in 2014) divided by the total number of full-time students in a previous year (e.g. Year 7 in 2009). This would be the Year 7 to 12 apparent retention rate in 2014.

• From 2020, students in mainstream support classes (previously excluded from this table) are reported by their underlying grade of enrolment. This has resulted in an increase in apparent retention rates across all years. As a result, data from 2020 onwards is not directly comparable to previous years.

• With the New School Leaving Age legislation coming into effect in January 2010, the Year 12 cohort in 2012 is the first group of students who were required to complete Year 10. This means that the Year 10 enrolment figure was higher than in previous years, resulting in a relative decline in Year 10 to Year 12 apparent retention in 2012, despite the Year 7 to Year 12 apparent retention rate increasing slightly.

• All enrolments are reported in full-time equivalent units, and include full-time and part-time students. Students enrolled in distance education classes are included with their appropriate grade levels.

• ARRs can exceed 100 per cent due to factors including student migration from interstate and overseas and between school sectors.

• Retention Rates are ‘apparent’ as they do not track individual students through their final years of secondary schooling.

Data Source:

• Schools and Students: Statistical Bulletin. Centre for Education Statistics and Evaluation.

The number of postsecondary enrolments, by registration status, institution type, International Standard Classification of Education (ISCED), Classification of Instructional Programs, Primary groupings (CIP_PG), status of student in Canada and gender.

The total gross enrollment ratio of students from the pre-primary to second grade across the northern state of Uttarakhand in India during financial year 2024 was ** percent. The enrollment ratio of students from sixth grade to eighth grade was higher among female students compared to male students that year.

The total gross enrollment ratio of students from the pre-primary to second grade across the western state of Gujarat in India during financial year 2024 was around ** percent. The enrollment ratio for male students was lower at **** percent compared to female students at ** percent that year.