All schools (public & private) in the state are listed. Contact details such as postal, street address and telephone numbers are also available. For further information or changes contact (08) 9264 4562. Show full description

A school zone or catchment area is a defined area from which the school or preschool accepts its core intake of students. The school or preschool gives priority enrolment to children who live inside that zone or catchment area.

The number of staff employed and full-time equivalents (FTE) at SA Government Schools in teaching and non-teaching roles, collected as part of the annual Term 3 data collection. Teaching staff spend the majority of their time in contact with students either by direct class contact or on an individual basis and have teaching duties. Teacher leadership positions are also included (Principals, Deputy Principals and Leader Band B). Non-teaching staff includes primary child wellbeing officers (student counsellors), school services officers, Aboriginal education workers, grounds persons, building operations, general maintenance, etc. FTE is based on a 10 day working fortnight. For example, a person employed on an 8 day basis per working fortnight has a FTE of 0.8 FTE (8/10). Applicable to school sector staff only. Includes sites such as special needs units that are attached to schools. Excludes employees on four or more continuous week’s leave, employees on work compensation, temporary relieving teachers (claim paid) and preschool sector staff.

The file set is a freely downloadable aggregation of information about Australian schools. The individual files represent a series of tables which, when considered together, form a relational database. The records cover the years 2008-2014 and include information on approximately 9500 primary and secondary school main-campuses and around 500 subcampuses. The records all relate to school-level data; no data about individuals is included. All the information has previously been published and is publicly available but it has not previously been released as a documented, useful aggregation. The information includes:
(a) the names of schools
(b) staffing levels, including full-time and part-time teaching and non-teaching staff
(c) student enrolments, including the number of boys and girls
(d) school financial information, including Commonwealth government, state government, and private funding
(e) test data, potentially for school years 3, 5, 7 and 9, relating to an Australian national testing programme know by the trademark 'NAPLAN'

Documentation of this Edition 2016.1 is incomplete but the organization of the data should be readily understandable to most people. If you are a researcher, the simplest way to study the data is to make use of the SQLite3 database called 'school-data-2016-1.db'. If you are unsure how to use an SQLite database, ask a guru.

The database was constructed directly from the other included files by running the following command at a command-line prompt:
sqlite3 school-data-2016-1.db < school-data-2016-1.sql
Note that a few, non-consequential, errors will be reported if you run this command yourself. The reason for the errors is that the SQLite database is created by importing a series of '.csv' files. Each of the .csv files contains a header line with the names of the variable relevant to each column. The information is useful for many statistical packages but it is not what SQLite expects, so it complains about the header. Despite the complaint, the database will be created correctly.

Briefly, the data are organized as follows.
(a) The .csv files ('comma separated values') do not actually use a comma as the field delimiter. Instead, the vertical bar character '|' (ASCII Octal 174 Decimal 124 Hex 7C) is used. If you read the .csv files using Microsoft Excel, Open Office, or Libre Office, you will need to set the field-separator to be '|'. Check your software documentation to understand how to do this.
(b) Each school-related record is indexed by an identifer called 'ageid'. The ageid uniquely identifies each school and consequently serves as the appropriate variable for JOIN-ing records in different data files. For example, the first school-related record after the header line in file 'students-headed-bar.csv' shows the ageid of the school as 40000. The relevant school name can be found by looking in the file 'ageidtoname-headed-bar.csv' to discover that the the ageid of 40000 corresponds to a school called 'Corpus Christi Catholic School'.
(3) In addition to the variable 'ageid' each record is also identified by one or two 'year' variables. The most important purpose of a year identifier will be to indicate the year that is relevant to the record. For example, if one turn again to file 'students-headed-bar.csv', one sees that the first seven school-related records after the header line all relate to the school Corpus Christi Catholic School with ageid of 40000. The variable that identifies the important differences between these seven records is the variable 'studentyear'. 'studentyear' shows the year to which the student data refer. One can see, for example, that in 2008, there were a total of 410 students enrolled, of whom 185 were girls and 225 were boys (look at the variable names in the header line).
(4) The variables relating to years are given different names in each of the different files ('studentsyear' in the file 'students-headed-bar.csv', 'financesummaryyear' in the file 'financesummary-headed-bar.csv'). Despite the different names, the year variables provide the second-level means for joining information acrosss files. For example, if you wanted to relate the enrolments at a school in each year to its financial state, you might wish to JOIN records using 'ageid' in the two files and, secondarily, matching 'studentsyear' with 'financialsummaryyear'.
(5) The manipulation of the data is most readily done using the SQL language with the SQLite database but it can also be done in a variety of statistical packages.
(6) It is our intention for Edition 2016-2 to create large 'flat' files suitable for use by non-researchers who want to view the data with spreadsheet software. The disadvantage of such 'flat' files is that they contain vast amounts of redundant information and might not display the data in the form that the user most wants it.
(7) Geocoding of the schools is not available in this edition.
(8) Some files, such as 'sector-headed-bar.csv' are not used in the creation of the database but are provided as a convenience for researchers who might wish to recode some of the data to remove redundancy.
(9) A detailed example of a suitable SQLite query can be found in the file 'school-data-sqlite-example.sql'. The same query, used in the context of analyses done with the excellent, freely available R statistical package (http://www.r-project.org) can be seen in the file 'school-data-with-sqlite.R'.

Data Notes

• Class size audits are conducted by CESE (Centre for Education Statistics and Evaluation) in March each year. Audits were not conducted in 1998, 1999, 2000 and 2001.

• Data for 2020 should be treated with caution. The collection took place in March when schools were impacted by COVID-19, so fewer data checks were carried out.

• Students attending schools for specific purposes (SSPs), students in support classes in regular schools and distance education students are excluded from average class size calculations.

• The average class size for each grade is calculated by taking the number of students in all classes that a student from that grade is in (including composite/multi age classes) divided by the total number of classes that includes a student from that grade. This can result in a lower Kindergarten to Year 6 average class size than any individual year level.

• From 2017, school size is based on primary enrolment rather than school classification.

• Schools change size, so data in Table 2 is not necessarily comparable to previous iterations in earlier fact sheets.

Data Source

Education Statistics and Measurement, Centre for Education Statistics and Evaluation.

Data quality statement

The Class Size Audit Data Quality Statement addresses the quality of the Class Size Audit dataset using the dimensions outlined in the NSW Department of Education's data quality management framework: institutional environment, relevance, timeliness, accuracy, coherence, interpretability and accessibility. It provides an overview of the dataset's quality and highlights any known data quality issues.

This dataset presents data on education and employment available from the ABS Data by Region statistics. This release of Data by Region presents various data for 2011-2019 and Census of Population and Housing data for 2011 and 2016 and is based on the Statistical Area 4 (SA4) 2016 boundaries. The dataset includes information in the following specified areas of education and employment: Early Childhood - Enrolment and Attendance in Preschool Programs, Non-School Qualifications, Higher Education Loan Program (HELP) Repayments, Highest Year of School Completed, Occupation of Employed Persons, Youth Engagement in Work or Study, Jobs in Australia and Labour Force.

Data by Region contains a standard set of data for each region type, depending on the availability of statistics for particular geographies. Data are sourced from a wide variety of collections, both ABS and non-ABS. When analysing these statistics, care needs to be taken as time periods, definitions, methodologies, scope and coverage can differ across collections. Where available, data have been presented as a time series - to enable users to assess changes over time. However, when looked at on a period to period basis, some series may sometimes appear volatile. When analysing the data, users are encouraged to consider the longer term behaviour of the series, where this extra information is available.

For more information please visit the Explanatory Notes.

AURIN has made the following changes to the original data:

• Spatially enabled the original data with the ABS Australian Statistical Geography Standard (ASGS) SA4 2016 dataset.

• Some data values in Data by Region have been randomly adjusted or suppressed to avoid the release of confidential details.

• Where data was not available, not available for publication, nil or rounded to zero in the original data, it has been set to null.

• Columns and rows that did not contain any values in the original data have been removed.

A dataset of schools apparent retention rates or ARR, all school sector in Victoria, from census year 2012 to 2023.\r This dataset is prepared and based on data collected from schools as part of the February School Census conducted on the last school day of February each year. It presents information for all government and non-government schools and student enrolments in Victoria, in particular secondary school years. The majority of the statistical data in this publication is drawn from school administration systems. The dataset includes analysis by school sector and sex, Koorie status, as well as on government schools by region.\r Apparent retention rates (ARR) are calculated based on aggregate enrolment data and provide an indicative measurement of student engagement in secondary education. The Department of Education and Training (DET) computes and publishes ARR data at a state-wide and DET region level only.\r \r The term "apparent" retention rate reflects that retention rates are influenced by factors not taken into account by this measure such as: Student repeating year levels, Interstate and overseas migration, Transfer of students between education sectors or schools, Student who have left school previously, returning to continue their school education.\r The ARR for year 7 to 12 (ARR 7-12) refers to the Year 12 enrolment expressed as a proportion of the Year 7 enrolment five years earlier. The ARR for year 10 to 12 (ARR 10-12) refers to the Year 12 enrolment expressed as a proportion of the Year 10 enrolment two years earlier.\r \r Please note that the ABS calculates apparent retention using the number of full-time school students only whereas at the DET we use the number of full-time equivalent school enrolments. Data reported in the ABS Schools, Australia collection is based on enrolment data collected in August by all jurisdictions.\r \r The Department has found that computing ARR at geographical areas smaller than DET regions (e.g. LGA, Postcode) can produce erratic and misleading results that are difficult to interpret or make use of. In small populations, relatively small changes in student numbers can create large movements in apparent retention rates. These populations might include smaller jurisdictions, Aboriginal and Torres Strait Islander students, and subcategories of the non-government affiliation. There are a number of reasons why apparent rates may generate results that differ from actual rates. \r Apparent retention rates provide an indicative measure of the number of full-time school students who have stayed in school, as at a designated year and grade of education. It is expressed as a percentage of the respective cohort group that those students would be expected to have come from, assuming an expected rate of progression of one grade per year.\r \r Provided ARR is a result of calculation of the whole census and is NOT to be re-calculated by average or sum.

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 2020) divided by the total number of full-time students in a previous year (e.g. Year 7 in 2015). This would be the Year 7 to 12 apparent retention rate in 2020.

• From 2020, students in mainstream support classes (previously excluded from this data) are reported by their underlying grade of enrolment. As a result, data from 2020 onwards is not directly comparable to previous years. A separate column in the CSV files has been provided to show the 2020 retention rates with both previous and revised counting rules. 2021 retention rates use the revised counting rules.

• Support students at Schools for specific purposes (SSPs) do not have a designated grade and therefore are not counted in the calculation of ARRs. Support students in mainstream schools have been included in the calculations from 2020 (see above).

• Reporting on ARRs based on small numbers of students can lead to unreliable ARR estimates. The tables combine ABS SA4 areas to ensure the underlying number of students is sufficiently large to generate reliable estimates.

• Only full-time students are counted in the calculation of full-time apparent retention. Part-time students are excluded.

• Students enrolled in distance education classes are included with their appropriate grade levels. Sydney-Inner includes enrolments from Sydney Distance Education High School.

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

• Norfolk Island Central School is not included in the Apparent Retention Rate factsheet, but from 2018 to 2021 was included under NSW Government data in the ABS Schools Australia publication. This can lead to a slight difference in reported figures between Schools Australia and this dataset.

• Aboriginal and/or Torres Strait Islander students are identified based on responses to the school enrolment form. This information may change throughout an individual’s schooling.

• Reporting by non-binary gender is not possible due to system limitations.

Data Source:

• Apparent Retention Rate Factsheet. Education Statistics and Measurement, Centre for Education Statistics and Evaluation.

This dataset presents the On Track Survey for Victorian schools. The data spans the year of 2012 and is aggregated by Local Government Areas (LGA) from the 2011 Australian Statistical Geography …Show full descriptionThis dataset presents the On Track Survey for Victorian schools. The data spans the year of 2012 and is aggregated by Local Government Areas (LGA) from the 2011 Australian Statistical Geography Standard (ASGS). The On Track Survey is conducted in April-May and involves a short telephone survey of school leavers who attended school in the previous year and who agreed to participate in the survey. Results for individual schools are usually published in June of the survey year. The On Track survey seeks to: Offer a consistent and comprehensive approach to monitoring the transitions of school leavers. Report the information to schools, TAFE institutions and other education providers, organisations concerned with assisting young people, policy makers, parents and students. Provide detailed analyses of the transitions experienced by different groups of leavers. Enable education providers to use the findings to monitor and improve their programs. Provide a referral service for school leavers who appear to be experiencing difficulties in the transitions process. Copyright attribution: Government of Victoria - Department of Education and Training, (2013): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 3.0 Australia (CC BY 3.0 AU)

This dataset is about book subjects. It has 2 rows and is filtered where the books is Waiting for Gonski : how Australia failed its schools. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This layer shows the location of regulatory signs & Main Roads Western Australia is the responsible authority. The term regulatory sign describes a range of signs that are used to indicate or reinforce traffic laws, regulations or requirements which apply either at all times or at specified times or places upon a street or highway, the disregard of which may constitute a violation. One type of regulatory signs are stop signs or give way signs, intended to instruct road users on what they must or should do (or not do) under a given set of circumstances. Signs can be on the State Road Network or other public access roads and is provided for information only.

A list of Queensland state and non-state schools, including address, phone number, web address, and latitude and longitude.

Information can also be readily accessed from the department's Schools Directory at https://schoolsdirectory.eq.edu.au/

This database is comprised of 951 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 509 males (54%) and 442 females (46%). Their ages ranged from 12 to 16 years (M = 13.69, SD = 0.72). Seven participants did not report their age. The majority were born in Australia (N = 849, 89%). The next most common countries of birth were China (N = 24, 2.5%), the UK (N = 23, 2.4%), and the USA (N = 9, 0.9%). Data were drawn from students at five Australian independent secondary schools.

The data contains item responses for the Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. The Social media question asked about frequency of use with the question “How often do you use social media?”. The response options ranged from constantly to once a week or less. Items measuring Fear of Missing Out were included and incorporated the following five questions based on the APS Stress and Wellbeing in Australia Survey (APS, 2015). These were “When I have a good time it is important for me to share the details online; I am afraid that I will miss out on something if I don’t stay connected to my online social networks; I feel worried and uncomfortable when I can’t access my social media accounts; I find it difficult to relax or sleep after spending time on social networking sites; I feel my brain burnout with the constant connectivity of social media. Internal consistency for this measure was α = .81. Self compassion was measured using the 12-item short-form of the Self-Compassion Scale (SCS-SF; Raes et al., 2011).

The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels.

References:

Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. _{https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4}

Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. _{https://doi.org/10.1002/cpp.702}

Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. _{https://doi.org/10.1016/S0005-7967(98)00034-5}

Average class sizes by class type in Term 3 from 2013 based on the roll class information, collected as part of the annual enrolment data collection in Term 3. Class type represents the school year levels taught in the class (eg year 1, year 1/2 etc). Junior primary classes have Reception through to year 2 students. Some small schools have junior primary classes that include year 3 students. These students have been included in the data. Special schools and classes are excluded.

Attendance rate for semester 1 in SA Government schools by school from 2014. Important notes: • Attendance rate = (number of days attending school / number of days enrolled) x 100. • Attendance rates are only calculated for full time students who were enrolled or left during Semester 1. • Both whole day and part day absences are counted. • Attendance data is not collected from schools 1717 Watarru Anangu School (non operational), 849 Open Access College, 810 Thebarton Senior College , 583 Marden Senior College, 1012 Northern Adelaide Senior College and 195 Youth Education Centre. • To protect the privacy of students, where a school has 5 or less Full Time Equivalent students enrolled, the attendance rate is suppressed for that school. • Attendance rates in 2020 are lower than anticipated due to Covid-19 lockdowns.

Full-time equivalent enrolments of Aboriginal and/or Torres Strait Islander students by type of school, collected as part of the annual enrolment data collection in Term 3, from 2012. Aboriginal and/or Torres Strait Islander students – students who have identified to be of Aboriginal and/or Torres Strait Islander origin. The term ‘origin’ is considered to relate to people’s Aboriginal and/or Torres Strait Islander descent and for some, but not all, their cultural identify.

AU: Secondary Education: Pupils data was reported at 2,554,864.000 Person in 2022. This records an increase from the previous number of 2,532,631.000 Person for 2021. AU: Secondary Education: Pupils data is updated yearly, averaging 2,448,315.500 Person from Dec 1970 (Median) to 2022, with 14 observations. The data reached an all-time high of 2,709,319.000 Person in 2015 and a record low of 1,136,960.000 Person in 1970. AU: Secondary Education: Pupils 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. Secondary education pupils is the total number of pupils enrolled at secondary level in public and private schools.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;Sum;

NSW public schools have defined local enrolment areas, meaning that every child is entitled to enrol in a particular school based on his or her residential address. Every public school reserves …Show full descriptionNSW public schools have defined local enrolment areas, meaning that every child is entitled to enrol in a particular school based on his or her residential address. Every public school reserves enough places within their school for students in their local enrolment area. Disclaimer: Due to the evolving nature of school information and local enrolment areas, no responsibility can be taken by the NSW Department of Education, or any of its associated departments, if information is relied upon. For example, but not limited to, real estate purchases or rentals where the school intake zone data is used as a reference source. School catchments areas can change for multiple reasons. These include schools opening and closing, and changes in population demographics, for example. For more information visit https://data.cese.nsw.gov.au/data/dataset/ffd4298f-c4b2-48de-a939-e8ba8cc1b70a. Copyright attribution: Government of New South Wales - Department of Education, (2017): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 3.0 Australia (CC BY 3.0 AU)

Export Data Access API

National education initiatives and a number of online education services rely on a current and accurate list of schools in Australia. In order to operate, schools must be registered with the respective school registration authority in each state or territory. ACARA has obtained the list from all 14 school registration authorities in Australia in order to create the Australian Schools List. This list provides an update of all schools and campuses in Australia. It also includes school location, school type and school sector attributes.

The ASL was last updated on 28 May 2020 to reflect Term 2 - 2020

https://asl.acara.edu.au/

Metadata

Geographical information about Queensland state school locations and catchment boundaries.