India Number of Schools: Secondary School data was reported at 252,176.000 Unit in 2015. This records an increase from the previous number of 244,653.000 Unit for 2014. India Number of Schools: Secondary School data is updated yearly, averaging 114,629.000 Unit from Sep 1950 (Median) to 2015, with 34 observations. The data reached an all-time high of 252,176.000 Unit in 2015 and a record low of 7,416.000 Unit in 1950. India Number of Schools: Secondary School data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDC001: Number of Schools: Secondary School.

The Dataset covers number of Teachers available at the various stages of schooling in the Indian education system, which generally follow a progression from primary to secondary and finally to higher secondary education. Data is captured at country level, State level and at the district level geographies.

Tables -

• Teachers Count
• Teachers Appointment Count
• School Headmaster Stats
• Pupil Teacher Ratio

Dataset Description: Indian School Education Statistics (2021-2022)

This dataset offers a comprehensive overview of Indian School Education Statistics, covering the years 2021-2022. It provides a valuable resource for individuals embarking on their Data Science journey by consolidating various datasets from the Indian Government into a single, easily accessible source. The dataset is available in seven separate .csv files, each with its distinct focus, enabling users to explore diverse aspects of the education landscape in India.

The Seven Key Datasets Included:

1. Percentage of Schools with Drinking Water Facility (2021-22)
2. Gross Enrolment Ratio (2021-22)
3. Drop-out Rate (2021-22)
4. Percentage of Schools with Computers (2021-22)
5. Percentage of Schools with Electricity (2021-22)
6. Schools with Boys' Toilets (2021-22)
7. Schools with Girls' Toilets (2021-22)

This dataset is a treasure trove of information, offering a window into the dynamic landscape of education in India and its evolution over time. By delving into this dataset, you can unlock answers to various pressing questions and tackle pivotal issues, including:

• Identifying states with the highest dropout ratios.
• Assessing the availability of crucial amenities like drinking water and sanitation facilities for boys and girls.
• Exploring the Gross Enrolment Ratio across different educational levels.
• Monitoring shifts in enrollment trends at various stages of education.

Sourced from the Open Government Data (OGD) Platform India, this dataset not only serves as a valuable resource for beginners in their Data Science journey but also presents an array of opportunities for in-depth analysis and research within the realm of Indian education.

Data have been generated as part of an ESRC-funded research project that investigates the potential of community-school partnerships in raising government school children’s learning in rural India. This longitudinal study covers three rounds of in-person surveys and one round of phone survey, which took place during the Covid-19 pandemic. The sample was selected from children attending government schools in primary grades 2, 3, and 4 who had not achieved foundational literacy at the time of the survey. In addition to measuring children’s foundational literacy and numeracy (in all in-person survey rounds), information was collected on each selected child’s household as well as on the schools attended by these children. The baseline survey was conducted between October 2018 and January 2019, and the survey was done for the following units at the baseline: Child, Household, School, Head Teacher, Teacher, and Classroom observations. Similarly, the midline data (i.e., the second survey round) was collected between November 2019 and March 2020. Due to the pandemic, we tracked the sampled children’s households and teachers through a phone survey during the period of school closures (from October – November 2021) to capture the status of education-related activities. The final survey (from December 2021 – January 2022) only collected information on the sample children’s enrolment status and their learning levels after the pandemic.

School enrollment, secondary, female (% gross) in India was reported at 78.75 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - School enrollment, secondary, female (% gross) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

India Number of Schools: Primary School data was reported at 840,546.000 Unit in 2015. This records a decrease from the previous number of 847,118.000 Unit for 2014. India Number of Schools: Primary School data is updated yearly, averaging 632,737.500 Unit from Sep 1950 (Median) to 2015, with 34 observations. The data reached an all-time high of 858,916.000 Unit in 2013 and a record low of 209,671.000 Unit in 1950. India Number of Schools: Primary School data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDB001: Number of Schools: Primary School.

The Residential School Locations Dataset [IRS_Locations.csv] contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Indian Residential School Settlement Agreement are included in this dataset, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The dataset was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this dataset,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School.When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites.

This dataset contains the details about the cumulative number of schools in India and also by the type of school management such as Government, Private, Government Aided and others.

The data shows the year-wise number of institutions and schools for higher education and school education for different levels of education.

Note: 1. Data for Higher Education is till 2021-22 only. 2. No. of Colleges (2000-01 to 2009-10) includes stand alone Institutions like Polytechnics. 3. Total of all schools includes schools from Class I to Class XII for General Education Only (Pre-Primary and other Technical/Vocational Schools not included).
4. Stand Alone Institutions includes: Polytechnics, PGDM, Nursing, Teacher Training and Institutes under Ministries. 5. Data for universities and colleges only till 2020-21. 6. In a few states such as Odisha higher secondary is part of higher education which may not have been covered under U-DISE.

School enrollment, primary, private (% of total primary) in India was reported at 45.12 % in 2020, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - School enrollment, primary, private (% of total primary) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

This dataset contains the details of key school performance indicators like the drop-out rate, retention rate, repetition rate, and the promotion rate by levels of education for all schools.

Abstract

UNHCR India implemented a telephone survey to measure the satisfaction with educational services provided by UNHCR and NGO partners. Most reported financial issues as reasons for their children not attending UNHCR partner led schools and lack of devices for not being able to make use of the online program. The survey also covers a few questions on the impact of COVID-19 on school attendance, and the education quality of public schools. The household survey spans a sample of more than 1,500 households and 2,200 children.

Geographic coverage

National coverage

Analysis unit

Households

Universe

Refugees in India

Kind of data

Sample survey data [ssd]

Sampling procedure

Not Applicable

Mode of data collection

Computer Assisted Telephone Interview [cati]

The Residential Schools Locations Dataset in shapefile format contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Indian Residential School Settlement Agreement are included in this data set, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The data set was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this data set,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School. When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites. The geographic coordinate system for this dataset is WGS 1984. The data in shapefile format [IRS_locations.zip] can be viewed and mapped in a Geographic Information System software. Detailed metadata in xml format is available as part of the data in shapefile format. In addition, the field name descriptions (IRS_locfields.csv) and the detailed locations descriptions (IRS_locdescription.csv) should be used alongside the data in shapefile format.

Number of Schools: Secondary School: Senior: Uttarakhand data was reported at 2,218.000 Unit in 2015. This records an increase from the previous number of 2,155.000 Unit for 2014. Number of Schools: Secondary School: Senior: Uttarakhand data is updated yearly, averaging 1,348.000 Unit from Sep 2001 (Median) to 2015, with 15 observations. The data reached an all-time high of 2,218.000 Unit in 2015 and a record low of 907.000 Unit in 2001. Number of Schools: Secondary School: Senior: Uttarakhand data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDC003: Number of Schools: Secondary School: Senior.

School enrollment, primary, female (% gross) in India was reported at 111 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - School enrollment, primary, female (% gross) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Number of Schools: Primary School: Uttar Pradesh data was reported at 155,756.000 Unit in 2015. This records an increase from the previous number of 154,982.000 Unit for 2014. Number of Schools: Primary School: Uttar Pradesh data is updated yearly, averaging 132,403.000 Unit from Sep 2001 (Median) to 2015, with 15 observations. The data reached an all-time high of 155,756.000 Unit in 2015 and a record low of 88,927.000 Unit in 2001. Number of Schools: Primary School: Uttar Pradesh data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDB001: Number of Schools: Primary School.

Abstract

This impact evaluation was conducted by IDinsight for STIR Education in Delhi and Uttar Pradesh in India, and was funded by a World Bank Strategic Impact Evaluation Fund grant. The study seeks to evaluate the impact of STIR's purely motivational, pedagogically neutral, teacher-focused model on student learning levels. STIR works with teachers in low-cost and government schools in order to improve student learning by empowering teachers to act as change-makers and to innovate to overcome challenges in the classroom. IDinsight conducted two three-armed randomized control trials. The study looks at outcomes from 180 Affordable Private Schools (APS) in Delhi and 270 government schools in the Raebareli and Varanasi districts of Uttar Pradesh. The study began in early 2015, and lasted two academic years. In addition to measuring STIR's impact in two different contexts, the study simultaneously tests two iterations of STIR's model in these two contexts.

Geographic coverage

One district in Delhi - East Delhi, and two districts in Uttar Pradesh - Raebareli and Varanasi

Analysis unit

For student learning, the basic unit of analysis is students. For classroom practices, the basic unit of analysis is teachers. For teacher motivation, the basic unit of analysis is teachers.

Universe

• 180 Affordable Private Schools in Delhi, 540 teachers amongst these schools and 5,400 students
• 270 Government Schools in Uttar Pradesh, 810 teachers amongst these schools and 8,100 students

Kind of data

Sample survey data [ssd]

Sampling procedure

Baseline Respondent Identification and Sampling Strategy:

Delhi:

Teacher Motivation: STIR initially did a search process of several hundred Affordable Private Schools (APS) in east Delhi. From these schools, STIR passed school names onto IDinsight where the teachers might be interested in working with IDinsight. IDinsight attempted to sample all schools for the Teacher Motivation survey. In total, IDinsight interviewed 1,259 teachers for the Teacher Motivation survey.

Classroom Observation: From these 1,259 teachers, STIR did an additional round of screening to determine which teachers were the most interested and returned a list of 810 teachers to IDinsight. This list formed the basis of the classroom observation. However, due to attrition and refusals at the school level we were unable to meet our target of teachers and ended up surveying only 342 teachers.

Student Testing: For sampling students in the classroom, IDinsight sampled 10 students per classroom in classes (of all teachers covered for the classroom observation) with more than 10 students using the attendance register for the day the enumerator came to the class. In classes with fewer than 10 students, all children were sampled.

Uttar Pradesh:

Teacher Motivation: In Uttar Pradesh, IDinsight obtained a list of all clusters in Raebareli and Varanasi districts that STIR was working in. From this list, IDinsight selected all clusters with more than 16 schools. This was done to ensure that there would be enough schools in the cluster to assign some to the control group while also maintaining enough treatment schools for STIR to form a network. For the Teacher Motivation survey, IDinsight surveyed all teachers in the school, yielding 1,145 teachers.

Classroom Observation: For the classroom observation, IDinsight sampled roughly 2/3 of the teachers who completed the Teacher Motivation questionnaire, to get a final list of roughly 810 teachers. Teachers were added to this list due to teachers dropping out and the final number was 838 teachers.

Student Testing: For sampling students in the classroom, IDinsight sampled 10 students per classroom in classes with more than 10 students using the attendance register for the day the enumerator came to the class. In classes with fewer than 10 students, all children were sampled.

Midline Respondent Identification and Sampling Strategy:

For midline, which took place at the beginning of the second academic year, we followed up with teachers and students surveyed at baseline. Teachers were added only in the case where the number of teachers still teaching in the school from our baseline lists fell below a certain number. In Delhi, teachers were added if less than two teachers from our list in a given school were available and in Uttar Pradesh, new teachers were added only if all teachers from our baseline lists in a given school dropped out.

The sampling strategy had two clear advantages: 1) It helped us target teachers and students that have been exposed to STIR for as long as possible since the timeline for the overall evaluation is relatively short. 2) The evaluations are already quite complex and this helped have a clear interpretation and narrative surrounding the results.

Delhi:

Teacher Motivation: From the list of 1,259 teachers surveyed at teacher motivation baseline, 453 teachers dropped out of schools during the academic year and hence were not available for surveying during midline. A further 65 teachers refused to participate and 84 teachers were not available during the data collection period. Given this, the total number of teachers surveyed at teacher motivation midline was 657. These teachers formed the sample for analyses.

Classroom Observation: For classroom observations, we attempted to collect data for all 811 teachers on the Delhi original list. For those schools where the number of teachers available from our 811 list fell below two, 148 new teachers were added based on a random selection from those teachers employed at that school as of 1 July 2015. A total of 459 teachers were surveyed as part of the classroom observation midline.

Student Testing: For testing of student learning levels, all students surveyed at baseline formed the potential sample at midline. Among the 3,367 students from baseline, 1,956 students were tracked and surveyed at midline. 1,127 students had dropped out from the schools. 40 students were absent throughout the course of the data collection, and were not found in schools during any of the five revisits. The remaining 244 students were in schools where we could not survey.

Uttar Pradesh:

Teacher Motivation: From the 1,145 teachers surveyed at baseline, 288 teachers dropped out of schools during the course of the academic year and were hence not available for data collection. An additional 61 refused to participate in the data collection and 41 were not available through the course of the data collection. The final number of teachers surveyed at midline were 755. This was the sample for analysis.

Classroom Observation: From the list of 838 teachers surveyed at baseline, we successfully observed the classrooms of 734 of these teachers at midline. Another 13 teachers were added in schools where all teachers from our 838 had dropped out. 12 of these 13 were in Raebareli and 1 was in Varanasi. In total, 747 teachers were surveyed. 82 teachers dropped out of the schools in our sample. 13 teachers refused to participate in the data collection and 14 teachers were absent throughout the survey period and were not available on either of our visits.

Student Testing: Of the 7,386 students tested at baseline, a total of 4,560 students were also tested at midline. 615 students were absent all days of visits to the schools. 149 students were in the four schools that refused data collection. 2,062 dropped out of the schools in our sample.

Endline Respondent Identification and Sampling Strategy:

For endline, which took place after the end of the second academic year, we followed up with teachers and students surveyed at midline. In Delhi, one teacher was added per school to the classroom observation sample where possible. Additional teachers were added to the teacher motivation sample by offering the survey to all the teachers in our sample schools. The sampling strategy had two clear advantages:

1) It helped us target teachers and students that have been exposed to STIR for as long as possible since the timeline for the overall evaluation is relatively short. 2) The evaluations are already quite complex and this helped have a clear interpretation and narrative surrounding the results.

Delhi:

Teacher Motivation: From the list of 657 teachers surveyed at teacher motivation midline, 101 teachers dropped out of schools during the academic year and hence were not available for surveying during endline. A further 25 teachers refused to participate and 50 teachers were not available during the data collection period. Given this, the total number of teachers surveyed at teacher motivation midline was 481. These teachers formed the sample for analyses.

Classroom Observation: For classroom observations, we attempted to collect data for all 459 teachers on the Delhi midline list as well as 102 teachers we surveyed at baseline and couldn't at midline but were hopeful of covering in the last survey. A new teacher was added to each school's sample where possible. A total of 376 teachers were surveyed as part of the classroom observation endline.

Student Testing: For testing of student learning levels, all students surveyed at midline formed the potential sample at endline. Among the 1,956 students from baseline, 1,843 students were tracked and surveyed at midline. 49 students had dropped out from the schools. 45 students were absent throughout the course of the data collection, and were not found in schools during any of the five revisits.

Uttar Pradesh:

Teacher Motivation: From the 967 teachers surveyed at midline, 105 teachers were transfered and 17 retired during the course of the academic year and were hence not available for data collection. An additional 36 refused to participate in the data collection and 26 were not available through

Number of Schools: Primary School: West Bengal data was reported at 76,703.000 Unit in 2015. This records a decrease from the previous number of 77,029.000 Unit for 2014. Number of Schools: Primary School: West Bengal data is updated yearly, averaging 49,986.000 Unit from Sep 2001 (Median) to 2015, with 15 observations. The data reached an all-time high of 77,104.000 Unit in 2012 and a record low of 49,851.000 Unit in 2002. Number of Schools: Primary School: West Bengal data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDB001: Number of Schools: Primary School.

This dataset has state-wise student enrollment data for Primary,Secondary and Higher secondary schools. This data is for the year of 2015-2016.

Abstract

For this study, enumerators made unannounced visits to primary schools in India and recorded whether they found teachers in the facility. In rural India, enumerators also collected data from private schools and non-formal education centers located in the same village as public schools. Three unannounced visits were made to each of about 3,000 public schools from October 2002 to April 2003. Since the average school in the sample had around four teachers, investigators gathered nearly 35,000 observations on teacher attendance.

The survey also gathered data on reasons of teacher absence, characteristics of teachers, schools and communities.

In India, the survey was designed to be representative in each of 20 states, which together account for 98 percent of India's population.

A Quantitative Service Delivery Survey that assessed employee attendance in primary health care facilities in India was carried out at the same time with this research. Moreover, similar studies were conducted in education and health sectors in Bangladesh, Uganda, Ethiopia, Kenya, Indonesia, Peru and Ecuador.

Geographic coverage

National

Kind of data

Sample survey data [ssd]

Sampling procedure

The description of the sampling procedure below is taken from "Initial Project Description: Survey of Education and Health Providers" (p.9-10). This document is available in external resources.

"For schools, we plan to use a population-based random sampling. We will choose randomly ten villages or towns (urban blocks) within each district, after stratifying by rural/urban location. Enumerators will then proceed to the village or town and find out from villagers where the closest government and private schools are. They will then visit up to a total of three schools and carry out the facility survey in each one. (Where there are more than three schools, enumerators will choose schools on a randomized basis, in a way that ensures that both government and private schools are included in the sample).

To reduce travel and transportation costs, it may sometimes be necessary to cluster villages/towns or facilities. Under the facility-based selection approach, for example, five areas may be randomly chosen within each district, and two schools in that area will be selected, rather than choosing a random sample of ten areas. During data analysis, we will adjust standard errors to account for clustered sampling.

At the facility level, we will also obtain a roster of teachers in the school. If the facility is large (for example, if there are more than 25 teachers in a school), we will interview a random sample of the teachers to keep the size of the survey manageable.

This survey is focused on basic education. Given time and personnel constraints, it will therefore focus only on primary schools, not secondary schools. In each Indian state, we will survey 10 districts, with at least two visits each to a representative sample of at least 10 health facilities and 10 or more primary schools within each; if the average village has 1.5 schools, the sample will actually be 15 schools per district. This means detailed and representative provider- and facility-level results from perhaps 150 schools and some 100 health centers for each jurisdiction. In addition, there will be a third visit to some smaller sub-sample of the schools and to all of the health centers, as a check and to provide additional data on long-term absence. With these repeated visits, we expect to carry out some 300 school visits and 300 health center visits in each jurisdiction, which should provide several thousand observations of presence/absence for individual providers and all of the necessary facility-level correlates."

Mode of data collection

Face-to-face [f2f]