In 2021, the Gender Parity Index (GPI) for primary education enrollment in Bhutan and Hong Kong was 1.04. This indicator is calculated by dividing the female gross enrollment ratio in primary education by the male gross enrollment ratio in primary education. A GPI of less than 1 suggests that girls are more disadvantaged than boys in learning opportunities, while a GPI of greater than 1 suggests that boys are more disadvantaged than girls in learning opportunities.

In 2023, around 87 percent of primary schools in Indonesia were public schools. The proportion of private schools in Indonesia rises as the educational level gets higher. In comparison, 50.92 percent of Indonesian high schools were private schools in that year.

Russia: Primary school completion rate: The latest value from 2019 is 100.02 percent, an increase from 99.6 percent in 2018. In comparison, the world average is 91.99 percent, based on data from 134 countries. Historically, the average for Russia from 1994 to 2019 is 95.35 percent. The minimum value, 90.6 percent, was reached in 2011 while the maximum of 100.02 percent was recorded in 2019.

The Statistic shows an international comparison of the enrollment rate for the primary school sector in 2017. The enrollment rate in primary schools for China was 100 percent in 2017.

Focus of this study is the data of the educational system, collected in different European countries. Mainly there is information about the pupils’ number in primary schools, secondary schools and in universities collected. There is no information about the vocational education and adult education, because the differences between these systems are too strong.

In order to get comparable data, the pupils’ registration quota (relative school-/high-school attendance) were collected (this means the proportion of pupils or students of the corresponding population’s age cohort).

For each country the information are presented in three different tables:

• Information about the primary school-system,
• Information about the secondary school-system,
• Information about the higher education (universities).

Furthermore, time series about the population of the countries as well as about the population’s alphabetisation quota in Prussia/the German Empire, France, England and Wales and Russia are available.

Topics:

Time Series available via HISTAT

In the ZA-Online-Database HISTAT are tables available containing the following information for each country:

Primary-Schools: Number of pupils in all schools, abs. Number of pupils in public schools, abs. Number of pupils in private schools in % of all pupils in primary schools. Number of pupils in % of the population’s 5-14 age group in primary schools. Number of pupils in % of the population’s 5-14 age group in public primary schools. Number of teachers in all primary schools. Number of teachers in public primary schools. Pupils per teacher in all primary schools, in public schools and in private schools. Primary Teacher Schools: Number of students and number of female students.

Secondary Schools: Number of pupils in post-primary schools, abs. Number of pupils in lower secondary schools, abs. Number of pupils in % of the population’s 10-14 age group in lower secondary schools. Number of pupils in all schools of general higher secondary education, abs. Number of pupils in public schools of general higher secondary education, abs. Number of pupils in private schools of general higher secondary education in % of all schools. Number of pupils in % of the population’s 10-19 age group in all schools of general higher secondary education. Number of pupils in % of the population’s 10-19 age group in public schools of general higher secondary education. Number of female pupils in all schools of general higher secondary education. Percentage of female pupils in all schools of general higher secondary education. Number of pupils in Technical / Commercial higher secondary schools. Number of pupils in all higher secondary schools, abs. Number of pupils in all higher secondary schools in % of the population’s 10-19 age group.

Universities, higher education: Number of students in technological institutes of higher education, abs. Number of students in commercial institutes of higher education, abs. Number of students in other institutes of higher education, abs. Number of students in universities, abs. Number of students in universities in % of the population’s 20-24 age group. Number of female students in % of all students. Students by faculty in percent of all students: theology, law medicine, philosophy, mathematics/science, economics/social sciences, technology. Total number of students in higher education, abs. Total number of students in higher education in % of the population’s 20-24 age group.

Additional: Estimated population (including the USA and Russia). Concerning Prussia/German Empire, France, England and Wales, Russia: Alphabetisation-quota. Development of the primary education per 100 inhabitants / development of the secondary education per 1000 inhabitants / Development of the higher education per 10 000 inhabitants.

The SPSS data file containing abridged data information collected from the TCoA-III Long questionnaire administered in 2007 for secondary schools. The full questionnaire of 50 items was administered but only the abridged data was used for reporting. The AMOS data file is the confirmatory factor analysis input for the secondary school TCoA-IIIA data set. Results were published in:Brown, G. T. L. (2011). Teachers' conceptions of assessment: Comparing primary and secondary teachers in New Zealand. Assessment Matters, 3, 45-70.

Chad: Primary school enrollment, percent of all eligible children: The latest value from 2023 is 91.77 percent, an increase from 90.36 percent in 2022. In comparison, the world average is 98.82 percent, based on data from 97 countries. Historically, the average for Chad from 1971 to 2023 is 63.56 percent. The minimum value, 31.27 percent, was reached in 1972 while the maximum of 102.35 percent was recorded in 2014.

This dataset contains quantitative student data acquired during the administration of two validated Computational Thinking (CT) assessments for upper primary school (grades 3 and 4): the Beginners' CT test (BCTt) [1] and the comptent CT test (cCTt) [2]

To compare the psychometric properties of both instruments a comparative analysis was conducted with data acquired in schools in Portugal from the same school districts. More specifically, we analyse the results of:

• the BCTt test administered in March 2020 to 374 students in grades 3-4,

• the cCTt test administered in April 2021 to 201 different students in grades 3-4.

These students had no prior experience in Computational Thinking, as this was not part of the national curriculum at the times of administration.

The detailed psychometric comparison is published in Frontiers in Psychology - Educational Psychology [3] and provides indications regarding the use of both instruments for grades 3-4.

A README is included and provides additional information regarding :

• the requirements for re-use.

• the specific content of the 2 csv files

The BCTt is available upon request to maria.zapata@urjc.es and the cCTt items are available in [2] with an editable version being available upon request to laila.elhamamsy@epfl.ch.

In case of other inquiries, please contact: laila.elhamamsy@epfl.ch, maria.zapata@urjc.es or pedro.marcelino@treetree2.org

References

[1] M. Zapata-Cáceres, E. Martín-Barroso and M. Román-González, "Computational Thinking Test for Beginners: Design and Content Validation," 2020 IEEE Global Engineering Education Conference (EDUCON), 2020, pp. 1905-1914, doi: 10.1109/EDUCON45650.2020.9125368.

[2] El-Hamamsy, L., Zapata-Cáceres, M., Barroso, E. M., Mondada, F., Zufferey, J. D., & Bruno, B. (2022). The Competent Computational Thinking Test: Development and Validation of an Unplugged Computational Thinking Test for Upper Primary School. Journal of Educational Computing Research, 60(7), 1818–1866. https://doi.org/10.1177/07356331221081753

[3] Laila El-Hamamsy* , María Zapata-Cáceres, Pedro Marcelino, Jessica Dehler Zufferey, Barbara Bruno, Estefanía Martín-Barroso and Marcos Román-González (2022). Comparing the psychometric properties of two primary school Computational Thinking (CT) assessments for grades 3 and 4: the Beginners' CT test (BCTt) and the competent CT test (cCTt). Front. Psychol. doi:10.3389/fpsyg.2022.1082659

Togo: Primary school completion rate: The latest value from 2023 is 90.7 percent, an increase from 89.14 percent in 2022. In comparison, the world average is 88.32 percent, based on data from 83 countries. Historically, the average for Togo from 1971 to 2023 is 58.54 percent. The minimum value, 23.7 percent, was reached in 1971 while the maximum of 93.82 percent was recorded in 2016.

There were 15,531 primary schools in Germany in 2023. Numbers fluctuated during the specified time period, though generally they decreased. For comparison, there were 16,290 primary schools in Germany in 2010.

Monaco: Primary school completion rate: The latest value from 2016 is 150.18 percent, an increase from 116.73 percent in 1992. In comparison, the world average is 91.95 percent, based on data from 136 countries. Historically, the average for Monaco from 1991 to 2016 is 127.01 percent. The minimum value, 114.12 percent, was reached in 1991 while the maximum of 150.18 percent was recorded in 2016.

The transition project at the University of Leeds examined the change in academic language encountered by school students when they transition from primary to secondary school. The project involved focus group interviews with school students spanning 12 months. There were four interview phases: when students were midway through their final year of primary school; just before the end of primary school; early in their first year of secondary school, and midway through the first year of secondary school. Five interviews with six students in each were conducted in the first three phases, and two in the final phase (which was interrupted by school closures due to Covid-19). Students were asked for their feelings about the transition and their views on the academic and language demands of their work at each stage.The project moves forward research in educational registers and academic language through a set of detailed and ambitious linguistic and discourse analytic studies. It will generate a substantial body of knowledge about the academic language that students need in order to access the early secondary school curriculum in England. Comparative studies will show how this differs from the academic language of primary school, and from language use in public domains outside school. It is well documented that many students find the transition from primary to secondary school difficult. In the first years of secondary school, there is often a drop in attainment and motivation in the performance of children at all ability levels, partly for social reasons, such as joining a much larger school, changes in friendship groups, etc. The new academic and linguistic demands that students face also play a very important part. Language is central in enabling or preventing access to the curriculum. We are concerned with native speakers of English rather than the specific issues faced by second language speakers; the teachers we have spoken to confirm reports from the literature that many native English-speaking students lack the language needed to access the secondary school curriculum. Most schools have remedial language provision, and there are various text and web-based language support resources. However, these do not draw on rigorous accounts of school language; as yet there has been no large-scale systematic study that can provide such accounts. This project will use innovative computational techniques from the discipline of corpus linguistics to address this gap. We have already begun working with teachers to identify what should go into collections of written and transcribed spoken texts ('corpora') that can represent the academic language encountered by students at different points in their schooling and in different subjects. The project will continue this work with teachers and students through interviews and workshops. We will collect texts with and from our partner schools, which are situated across a range of socio-economically diverse communities in the Yorkshire region and in Birmingham. These will be used to build two corpora representing the academic language experience of, firstly, late primary school, and, secondly, early secondary school. Each will consist of approximately 1,000,000 words and will be stored securely in machine-readable form. A third source of data will be components of existing corpora, selected as approximately representative of the 'public' language experience of students outside school, for example, fiction, magazines, websites and social media. The two purpose built corpora will be analysed and compared using corpus software, some of which will be developed specifically for this project. They will also be compared with the third dataset. The analysis will identify differences at the level of word, including word meaning and collocational patterns (which can signal specialised meaning and register) and grammar. Discourse patterns are less easy to analyse using corpus tools, so we will conduct manual discourse analyses on selected texts. Ongoing interviews and workshops will inform our analyses, and will help us to develop understandings of how the transition is experienced by students and seen by teachers. The project will produce a systematic description of the academic language of secondary school, and accounts of how this differs from the language of primary school and public language outside school. It will produce versions of this for different readerships, including academic readers with a specialist interest in educational registers, materials writers, and classroom teachers and teaching assistants. Teachers and school students will be involved from the early stages, as contributors to the project as well as building their own research capacity and knowledge base. Focus group interviews were held with five groups of six students, from five different primary schools. The same students in the same groups were interviewed at two points in Year 6, and either one or two points in year 7. School students were selected by their primary school class teacher, who was asked to select a range of ability levels. They were taken out of class and interviewed as a group by the project Research Fellow. Pseudonyms are used.

The SPSS data file containing abridged data information collected from the TCoA-III Long questionnaire administered in 2007 for secondary schools. The full questionnaire of 50 items was administered but only the abridged data was used for reporting. The AMOS data file is the confirmatory factor analysis input for the secondary school TCoA-IIIA data set. Results were published in:Brown, G. T. L. (2011). Teachers' conceptions of assessment: Comparing primary and secondary teachers in New Zealand. Assessment Matters, 3, 45-70.

Abstract

To support national goals of educational access and equity, Senegal has launched PAQEEB 2013-2017 (Projet d’Amelioration de la Qualité et de l’Equité dans l’Education de Base), which is a comprehensive government strategic plan to improve school governance, as well as increase equity and access to formal education. This is a collaborative effort of the Ministry of Education (MoE) of Senegal, the World Bank (WB), and other International agencies to improve the quality and equity of basic education (World Bank, 2013). A sub-component of this wide initiative is the objective to reach children who do not typically access formal education and are enrolled in religious education in Koranic schools known as Daaras. With Muslims comprising around 95% of the Senegalese population, a vast majority of Senegalese males would have attended Daaras at one time or another, and it is estimated that between 800,000 and one million children and youth attend Daaras (D’Aoust, 2013; as cited in Goensch, 2016). In Senegal, a “Traditional Daara” is dedicated only to memorization of the Koran and advanced studies (Islamic law, etc.) and do not offer any additional instruction in science, math, French or other core courses under the official curriculum. “Modern Daaras”, on the other hand, train students not only in religious education like memorization of Koran but also in Math and French as per the official curriculum.

This subcomponent of the PAQEEB project aims to upgrade and improve Traditional Daaras to have language and math curricula like the Modern Daaras. This is an innovative intervention that provides pedagogical support through disbursement of “grants for results.” In return for this funding, project schools commit to perform the following activities: (1) to implement the specific “Modern Daaras” math and French curriculum; and (2) to ensure that students achieve learning results as reported through indicators measuring their levels of proficiency in reading and mathematics. The project stakeholders selected 100 Daaras in 20 counties based on the lowest gross enrollment ratios out of the 46 counties in Senegal (effectively, counties with gross enrollment ratios between 29 percent and 69 percent) to pilot the Daara modernization efforts (Bureau des Statistiques Scolaires et Universitaires, 2007).

This survey was used to evaluate this sub-component of the larger PAQEEB project that provides “grants for results” to selected Daaras. The survey consists of three distinct instruments that collected relevant data from teachers, caretakers of students, and students.

Geographic coverage

Rural and peri-urban areas only.

Analysis unit

Individuals, households, and schools.

Universe

Primary school and Daara teachers, students, and caretakers of students.

Kind of data

Sample survey data [ssd]

Sampling procedure

Identifying Eligible Treatment and Control Schools The first step in the sampling implementation process was to identify a list of eligible treatment and control schools for the sample. To this effect, The World Bank and the Inspections des Daara committee provided IMPAQ with lists of Daaras that participated in the selection process for the Daara sub-component of the PAQEEB program within each of the 20 included districts. These lists included details on the ranking assigned to each candidate Daara and which Daaras were selected into the program (treatment Daaras) based on those rankings. Using this data on school rankings and characteristics, as well as information gathered during an initial visit to candidate Daaras, IMPAQ began the sample selection process by disqualifying schools from the sample that have previously been deemed ineligible for program allocation based on PAQEEB guidelines.

Selecting from Eligible Treatment and Comparison Schools In order to decrease spillover effects between individuals in treatment school communities and those in comparison school communities, IMPAQ used GPS data to apply a set of minimum distance criteria to all eligible comparison schools and remove any that were too close to treatment schools. More specifically, comparison Daaras were removed from the sample if they were less than 2 kilometers from a treatment school. This decision was based on Theunynck (2009), who shows that distance to school is inversely related to the probability of being enrolled in school in Senegal. Additionally, Theunynck explains that evidence from multiple countries in Africa shows that enrollment and retention decline significantly when students must walk more that 1 to 2 kilometers to get to school. This trend is particularly strong among younger children. Thus, at a distance of two kilometers, we should see minimal interference between treatment and comparison Daaras.

Additionally, in order to be able to distinguish the communities around comparison schools, comparison Daaras were removed from the sample if they were less than ½ kilometer away from other comparison school. In these cases, one school out of the two was randomly chosen to remain as eligible for selection. The radius around comparison schools is smaller because there is no concern of spillover effects between these Daaras. Rather, this radius ensured the research team that they were not measuring the outcomes of two comparison Daaras within the same community. The concern that children from comparison communities may enroll in other nearby comparison Daaras is not considered a major source of bias in the ITT estimate, as the comparison Daaras are generally considered to be of similar quality, making it less likely for a child in a comparison community to commute to a Daara in a different comparison community.

Remaining eligible Daaras were selected for inclusion in the sample based on their ranking in the PAQEEB program selection process. Specifically, Daaras included in the PAQEEB program that were ranked closest to (just above) the program selection threshold were identified as treatment Daaras. Daaras not included in the PAQEEB program that were ranked closest to (just below) the program selection threshold were identified as comparison Daaras. In this way, IMPAQ ensured that treatment and comparison Daaras were as similar as possible concerning the key criteria used for program selection. In the event that multiple comparison schools received equivalent rankings, a random number generator was used to select among them for inclusion into the sample. If an appropriate comparison school could not be identified within a given IEF, all schools from that IEF were dropped from the sample. In most IEFs, IMPAQ selected 3 treatment Daaras and 3 comparison Daaras into the sample.

Selecting Eligible Secondary Comparison Schools In addition to the comparison Daaras, IMPAQ included a second comparison group consisting of formal government schools. These schools were selected based on proximity to treatment Daaras, while still meeting the minimum distance criteria outlined above for comparison Daaras (i.e. 2-kilometer distance).

Household and Child Selection IMPAQ performed a house-listing census of all households with children under the age 16 within a 1-kilometer perimeter (school catchment areas) of each Daara and formal school selected into the sample. For details on this house listing please see section 6.3.3 below. Once all households within the established perimeter of a selected school that had at least one child aged 7-10 were identified, IMPAQ randomly selected 15 households with at least one girl aged 7-10 and 15 households with at least one boy aged 7-10 for inclusion in the study. Only one child of each gender was selected from a given household in order to minimize the influence of larger households on the study outcome. Lastly, if a selected household had more than one child aged 7-10 of a single gender, IMPAQ randomly selected which of those children would be included in the sample, in order to prevent any bias in the selection of children within households.

Mode of data collection

Face-to-face [f2f]

Research instrument

All instruments were originally developed in French, but have been translated to English as well.

Instruments The baseline survey consisted of three unique instruments: A caretaker survey, a child survey and academic assessment, and a teacher survey.

Caretakers’ instrument (Enquête sur les personnes qui s’occupent des enfants) The caretaker survey was designed to learn about the decisions and opinions within each household in the sample. A caretaker was defined as “the person who takes care of the child and makes decisions about what he/she eats and how he/she spends his/her time.” The survey instrument was divided into a schooling section and a household information section. Within the schooling section, caretakers were asked about schools and Daaras in their community, last year’s schooling choices, this year’s schooling choices, their opinions about education, and the child’s school participation/attendance. The household information section briefly captured some basic household characteristics, such as household size, number of children, education levels, and household assets.

Children’s instrument (Enquête sur les enfants) The children’s survey begins with a few questions for the child’s caretaker, which are used to confirm the child’s name, age, and the school he or she attends. The rest of the survey is addressed to the child. First, the enumerator spent 3 to 5 minutes speaking with the child and setting him/her at ease. Next, the child answers questions about the school/Daara they attend. There are different sets of questions depending on whether he/she attends

The USA: Primary school enrollment, percent of all eligible children: The latest value from 2022 is 96.97 percent, a decline from 98.31 percent in 2021. In comparison, the world average is 100.44 percent, based on data from 149 countries. Historically, the average for the USA from 1971 to 2022 is 99.98 percent. The minimum value, 91.86 percent, was reached in 1972 while the maximum of 105.48 percent was recorded in 1990.

Thematic maps on school dropouts of general education schools in Rhineland-Palatinate. As at 31.12.2013: Change in the proportion of general education schools with a secondary school leaving certificate compared to 2006 in Rhineland-Palatinate at district level. As of 2013

This Intelligence Unit update (Update 04-2013) uses Department for Education (DfE) data sources to examine the following:

1. Cross border mobility of primary school age children by residence;
2. Cross border mobility of primary school age children by school location;
3. Comparison of DfE estimates with ONS 2011 Census-based data; and
4. Estimates of the number of pupils per borough attending independent school.

Update 04-2013 and the supporting data are both downloadable (updated 26th February 2013).

The data is also available as an interactive visualisation.

The equivalent report for secondary school age children was published in November 2012 as Update 25-2012. Both the report and the accompanying data can be downloaded from the Datastore.

Mexico: Primary school completion rate: The latest value from 2022 is 102.24 percent, a decline from 102.82 percent in 2021. In comparison, the world average is 92.43 percent, based on data from 124 countries. Historically, the average for Mexico from 1975 to 2022 is 95.43 percent. The minimum value, 67.83 percent, was reached in 1975 while the maximum of 111.54 percent was recorded in 2014.