"Enrollment counts are based on the October 31 Audited Register for the 2017-18 to 2019-20 school years. To account for the delay in the start of the school year, enrollment counts are based on the November 13 Audited Register for 2020-21 and the November 12 Audited Register for 2021-22. * Please note that October 31 (and November 12-13) enrollment is not audited for charter schools or Pre-K Early Education Centers (NYCEECs). Charter schools are required to submit enrollment as of BEDS Day, the first Wednesday in October, to the New York State Department of Education." Enrollment counts in the Demographic Snapshot will likely exceed operational enrollment counts due to the fact that long-term absence (LTA) students are excluded for funding purposes. Data on students with disabilities, English Language Learners, students' povery status, and students' Economic Need Value are as of the June 30 for each school year except in 2021-22. Data on SWDs, ELLs, Poverty, and ENI in the 2021-22 school year are as of March 7, 2022. 3-K and Pre-K enrollment totals include students in both full-day and half-day programs. Four-year-old students enrolled in Family Childcare Centers are categorized as 3K students for the purposes of this report. All schools listed are as of the 2021-22 school year. Schools closed before 2021-22 are not included in the school level tab but are included in the data for citywide, borough, and district. Programs and Pre-K NYC Early Education Centers (NYCEECs) are not included on the school-level tab. Due to missing demographic information in rare cases at the time of the enrollment snapshot, demographic categories do not always add up to citywide totals. Students with disabilities are defined as any child receiving an Individualized Education Program (IEP) as of the end of the school year (or March 7 for 2021-22). NYC DOE "Poverty" counts are based on the number of students with families who have qualified for free or reduced price lunch, or are eligible for Human Resources Administration (HRA) benefits. In previous years, the poverty indicator also included students enrolled in a Universal Meal School (USM), where all students automatically qualified, with the exception of middle schools, D75 schools and Pre-K centers. In 2017-18, all students in NYC schools became eligible for free lunch. In order to better reflect free and reduced price lunch status, the poverty indicator does not include student USM status, and retroactively applies this rule to previous years. "The school’s Economic Need Index is the average of its students’ Economic Need Values. The Economic Need Index (ENI) estimates the percentage of students facing economic hardship. The 2014-15 school year is the first year we provide ENI estimates. The metric is calculated as follows: * The student’s Economic Need Value is 1.0 if: o The student is eligible for public assistance from the NYC Human Resources Administration (HRA); o The student lived in temporary housing in the past four years; or o The student is in high school, has a home language other than English, and entered the NYC DOE for the first time within the last four years. * Otherwise, the student’s Economic Need Value is based on the percentage of families (with school-age children) in the student’s census tract whose income is below the poverty level, as estimated by the American Community Survey 5-Year estimate (2020 ACS estimates were used in calculations for 2021-22 ENI). The student’s Economic Need Value equals this percentage divided by 100. Due to differences in the timing of when student demographic, address and census data were pulled, ENI values may vary, slightly, from the ENI values reported in the School Quality Reports. In previous years, student census tract data was based on students’ addresses at the time of ENI calculation. Beginning in 2018-19, census tract data is based on students’ addresses as of the Audited Register date of the g

Historical Dataset starting with School Year 2016-2017 through the most Current School Year enrollments for all publicly funded schools in Pennsylvania as reported by school districts, area vocational-technical schools, charter schools, intermediate units, and state operated educational facilities. Local education agencies were asked to report those students who were enrolled and attending as of October 1, of the later year.

County and Statewide Totals Notes:

Statewide and county totals include counts of students attending education classes on a full-time basis outside their parents' district of residence. This data was obtained from the Bureau of Special Education.

Intermediate Unit and CTC Part-day enrollments are excluded from county and state totals.

Statewide and county totals are unique counts of students being educated by public Local Education Agencies. LEA and School level reports may not sum to the County and Statewide totals.

Source: Pennsylvania Information Management System (PIMS)

Notes regarding County Totals:

Enrollment for School Districts, Charter Schools, State Juvenile Correctional Institutions and Comprehensive CTCs are included. Enrollments for Occupational CTCs and IUs are not included.

Counts of students attending education classes on a full-time basis outside their parents' district of residence are included. This data was obtained from the Bureau of Special Education.

Morning and afternoon detail for Half day grades is not available in PENN Data. Therefore, PKH equals the sum of PKA and PKP enrollment, K4H equals the sum of K4A and K4P enrollment, and K5H equals the sum of K5A and K5P enrollment.

County totals are unique counts of students being educated by public Local Education Agencies. LEA and School level reports may not sum to the County total.

The 2020-2021 School Learning Modalities dataset provides weekly estimates of school learning modality (including in-person, remote, or hybrid learning) for U.S. K-12 public and independent charter school districts for the 2020-2021 school year, from August 2020 – June 2021. These data were modeled using multiple sources of input data (see below) to infer the most likely learning modality of a school district for a given week. These data should be considered district-level estimates and may not always reflect true learning modality, particularly for districts in which data are unavailable. If a district reports multiple modality types within the same week, the modality offered for the majority of those days is reflected in the weekly estimate. All school district metadata are sourced from the National Center for Educational Statistics (NCES) for 2020-2021. School learning modality types are defined as follows: In-Person: All schools within the district offer face-to-face instruction 5 days per week to all students at all available grade levels. Remote: Schools within the district do not offer face-to-face instruction; all learning is conducted online/remotely to all students at all available grade levels. Hybrid: Schools within the district offer a combination of in-person and remote learning; face-to-face instruction is offered less than 5 days per week, or only to a subset of students. Data Information School learning modality data provided here are model estimates using combined input data and are not guaranteed to be 100% accurate. This learning modality dataset was generated by combining data from four different sources: Burbio [1], MCH Strategic Data [2], the AEI/Return to Learn Tracker [3], and state dashboards [4-20]. These data were combined using a Hidden Markov model which infers the sequence of learning modalities (In-Person, Hybrid, or Remote) for each district that is most likely to produce the modalities reported by these sources. This model was trained using data from the 2020-2021 school year. Metadata describing the location, number of schools and number of students in each district comes from NCES [21]. You can read more about the model in the CDC MMWR: COVID-19–Related School Closures and Learning Modality Changes — United States, August 1–September 17, 2021. The metrics listed for each school learning modality reflect totals by district and the number of enrolled students per district for which data are available. School districts represented here exclude private schools and include the following NCES subtypes: Public school district that is NOT a component of a supervisory union Public school district that is a component of a supervisory union Independent charter district “BI” in the state column refers to school districts funded by the Bureau of Indian Education. Technical Notes Data from September 1, 2020 to June 25, 2021 correspond to the 2020-2021 school year. During this timeframe, all four sources of data were available. Inferred modalities with a probability below 0.75 were deemed inconclusive and were omitted. Data for the month of July may show “In Person” status although most school districts are effectively closed during this time for summer break. Users may wish to exclude July data from use for this reason where applicable. Sources K-12 School Opening Tracker. Burbio 2021; https

Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students

Overall attendance data include students in Districts 1-32 and 75 (Special Education). Students in District 79 (Alternative Schools & Programs), charter schools, home schooling, and home and hospital instruction are excluded. Pre-K data do not include NYC Early Education Centers or District Pre-K Centers; therefore, Pre-K data are limited to those who attend K-12 schools that offer Pre-K. Transfer schools are included in citywide, borough, and district counts but removed from school-level files. Attendance is attributed to the school the student attended at the time. If a student attends multiple schools in a school year, the student will contribute data towards multiple schools. Starting in 2020-21, the NYC DOE transitioned to NYSED's definition of chronic absenteeism. Students are considered chronically absent if they have an attendance of 90 percent or less (i.e. students who are absent 10 percent or more of the total days). In order to be included in chronic absenteeism calculations, students must be enrolled for at least 10 days (regardless of whether present or absent) and must have been present for at least 1 day. The NYSED chronic absenteeism definition is applied to all prior years in the report. School-level chronic absenteeism data reflect chronic absenteeism at a particular school. In order to eliminate double-counting students in chronic absenteeism counts, calculations at the district, borough, and citywide levels include all attendance data that contribute to the given geographic category. For example, if a student was chronically absent at one school but not at another, the student would only be counted once in the citywide calculation. For this reason, chronic absenteeism counts will not align across files. All demographic data are based on a student's most recent record in a given year. Students With Disabilities (SWD) data do not include Pre-K students since Pre-K students are screened for IEPs only at the parents' request. English language learner (ELL) data do not include Pre-K students since the New York State Education Department only begins administering assessments to be identified as an ELL in Kindergarten. Only grades PK-12 are shown, but calculations for "All Grades" also include students missing a grade level, so PK-12 may not add up to "All Grades". Data include students missing a gender, but are not shown due to small cell counts. Data for Asian students include Native Hawaiian or Other Pacific Islanders . Multi-racial and Native American students, as well as students missing ethnicity/race data are included in the "Other" ethnicity category. In order to comply with the Family Educational Rights and Privacy Act (FERPA) regulations on public reporting of education outcomes, rows with five or fewer students are suppressed, and have been replaced with an "s". Using total days of attendance as a proxy , rows with 900 or fewer total days are suppressed. In addition, other rows have been replaced with an "s" when they could reveal, through addition or subtraction, the underlying numbers that have been redacted. Chronic absenteeism values are suppressed, regardless of total days, if the number of students who contribute at least 20 days is five or fewer. Due to the COVID-19 pandemic and resulting shift to remote learning in March 2020, 2019-20 attendance data was only available for September 2019 through March 13, 2020. Interactions data from the spring of 2020 are reported on a separate tab. Interactions were reported by schools during remote learning, from April 6 2020 through June 26 2020 (a total of 57 instructional days, excluding special professional development days of June 4 and June 9). Schools were required to indicate any student from their roster that did not have an interaction on a given day. Schools were able to define interactions in a way that made sense for their students and families. Definitions of an interaction included: • Student submission of an assignment or completion of an

Citywide Class Size Report, including District, Program, Grade or Service Category. SOURCES: 10/31/06 Official Register (K-9) and 12/15/06 Register/Schedule (9-12) Grade 9 not in high schools Indicates how special class is delivered For schools with students in any grades between Kindergarten and 9th grade (where 9th grade is the termination grade for the school), class size is reported by four program areas: general education, special education self-contained class, collaborative team teaching and gifted and talented self-contained class. Within each program area class size is reported by grade or service category, which indicates how a special education self-contained class is delivered. Class size is calculated by dividing the number of students in a program and grade by the number of official classes in that program and grade. The following data is excluded from all the reports: District 75 schools, bridge classes which span more than one grade, classes with fewer than five students (for other than special education self-contained classes) and classes with one student (for special education self-contained classes). On the summary reports programs and grades with three or fewer classes are excluded from the citywide, borough and region reports and programs and grades with one class are excluded from the district report. For schools with students in any grades between 9th and 12th grade (where 9th grade is not the termination grade for the school), class size is reported by two program areas: general education and special education. For general education students class size is reported by grade for each core subject area: English, Math, Science and Social Studies. For special education students with a self-contained program recommendation, class size is reported by service category (self-contained or mainstream) for each core subject area. Since high school classes may contain students in multiple grades and programs, class size is calculated by taking a weighted average of all the classes in a core subject area with students in a particular grade or program. For example, there are 75 ninth graders enrolled at a high school. 25 ninth graders attend a Math class with 28 students, a second group of 25 ninth graders attend a Math class with 25 students, and a third group of 25 ninth graders attend a Math class with 30 students. Average class size for ninth grade Math equals: (25x28 + 25x25 + 25x30)/75 = 27.7. The Pupil Teacher Ratio is also provided on the school level report. Pupil Teacher Ratio is another means to evaluate the instructional resources provided at a school. Pupil Teacher Ratio for All Students is calculated by dividing the number of students at a school by the number of full-time equivalent teachers, including both teachers in classes taught by two teachers, “cluster” teachers providing instruction in specialized topics like art or science, and teachers providing special education instruction. Pupil Teacher Ratio Excluding Special Education is calculated by dividing the number of non-special education students at a school by the number of full-time equivalent non-special education teachers.

The 2021-2022 School Learning Modalities dataset provides weekly estimates of school learning modality (including in-person, remote, or hybrid learning) for U.S. K-12 public and independent charter school districts for the 2021-2022 school year and the Fall 2022 semester, from August 2021 – December 2022. These data were modeled using multiple sources of input data (see below) to infer the most likely learning modality of a school district for a given week. These data should be considered district-level estimates and may not always reflect true learning modality, particularly for districts in which data are unavailable. If a district reports multiple modality types within the same week, the modality offered for the majority of those days is reflected in the weekly estimate. All school district metadata are sourced from the National Center for Educational Statistics (NCES) for 2020-2021. School learning modality types are defined as follows: In-Person: All schools within the district offer face-to-face instruction 5 days per week to all students at all available grade levels. Remote: Schools within the district do not offer face-to-face instruction; all learning is conducted online/remotely to all students at all available grade levels. Hybrid: Schools within the district offer a combination of in-person and remote learning; face-to-face instruction is offered less than 5 days per week, or only to a subset of students. Data Information School learning modality data provided here are model estimates using combined input data and are not guaranteed to be 100% accurate. This learning modality dataset was generated by combining data from four different sources: Burbio [1], MCH Strategic Data [2], the AEI/Return to Learn Tracker [3], and state dashboards [4-20]. These data were combined using a Hidden Markov model which infers the sequence of learning modalities (In-Person, Hybrid, or Remote) for each district that is most likely to produce the modalities reported by these sources. This model was trained using data from the 2020-2021 school year. Metadata describing the _location, number of schools and number of students in each district comes from NCES [21]. You can read more about the model in the CDC MMWR: COVID-19–Related School Closures and Learning Modality Changes — United States, August 1–September 17, 2021. The metrics listed for each school learning modality reflect totals by district and the number of enrolled students per district for which data are available. School districts represented here exclude private schools and include the following NCES subtypes: Public school district that is NOT a component of a supervisory union Public school district that is a component of a supervisory union Independent charter district “BI” in the state column refers to school districts funded by the Bureau of Indian Education. Technical Notes Data from August 1, 2021 to June 24, 2022 correspond to the 2021-2022 school year. During this time frame, data from the AEI/Return to Learn Tracker and most state dashboards were not available. Inferred modalities with a probability below 0.6 were deemed inconclusive and were omitted. During the Fall 2022 semester, modalities for districts with a school closure reported by Burbio were updated to either “Remote”, if the closure spanned the entire week, or “Hybrid”, if the closure spanned 1-4 days of the week. Data from August

Enrollment counts are based on the October 31 Audited Register for 2019 for Pre-K data which includes students in 3-K, K-8 and 9-12 grades. 2019-20 is the first year this report includes side-by-side comparisons of the racial and ethnic demographics of schools and special programs with the racial and ethnic demographics of all students in their respective attendance zones and districts. As such, the 2019-20 report does not include information on whether schools and special programs are becoming more or less similar to their zones and districts. English Language Arts and Math state assessment results for students in grades 3 through 8 are not available for inclusion in this report, as the spring 2020 exams did not take place.

LearnPlatform is a technology platform in the kindergarten-12th grade (K-12) market providing a broadly interoperable platform to the breadth of educational technology (edtech) solutions in the United States K-12 field. A key component of edtech effectiveness is integrated reporting on tool usage and, where applicable, evidence of efficacy. With COVID closures, LearnPlatform is a resource to measure whether students are accessing digital resources within distance learning constraints. This platform provides a source of data to understand if students are accessing digital resources, and where resources have disparate usage and impact. This study includes educational technology usage across over 8,000 tools used in the education field in 2020.

From July 2016 to May 2020, there were 27 intentional data breaches that revealed academic records and personally identifiable identification (PII) with the responsible actors being students. The most common reason for students to commit data breaches was in order to change grades.

This file includes Report Card enrollment data from 2024-25 school year. Data is disaggregated by school, district, and the state level and includes counts of students by the following groups: grade level, gender, race/ethnicity, and student programs and special characteristics. Please review the notes below for more information.

Enrollment counts are based on the October 31st Audited Register for 2015. Data on students with disabilities, English language learners and students poverty status are as of February 2nd 2016. Due to missing demographic information in rare cases, demographic categories do not always add up to citywide totals. In order to view all data there is an excel file attached which you would select to open.

Once-eliminated vaccine-preventable childhood diseases, such as measles, are resurging across the United States. Understanding the spatio-temporal trends in vaccine exemptions is crucial to targeting public health intervention to increase vaccine uptake and anticipating vulnerable populations as cases surge. However, prior available data on childhood disease vaccination is either on too rough a spatial scale for this spatially-heterogeneous issue, or is only available for small geographic regions, making general conclusions infeasible. Here, we have collated school vaccine exemption data across the United States and provide it at the county-level for all years available. We demonstrate the fine-scale spatial heterogeneity in vaccine exemption levels, and show that many counties may fall below the herd immunity threshold. We also show that vaccine exemptions increase over time in most states, and non-medical exemptions are highly prevalent where allowed. Our dataset also highlights the need for greater data sharing and standardized reporting across the United States.

Methods We collected data from all US states where school vaccine exemption information was freely available from the Department of Health website in any format. We were able to locate that data in 24 states. Within these states, the number of years available varied relatively widely, between 19 years in California and a single year in 6 states. The most represented year in our dataset was 2017 (corresponding to school year 2017-2018). Because the dataset was compiled in June-July 2019, we note that it is possible that additional data for recent years may not be available, or that data may have become available in additional states not included in our dataset.

The data format varied widely between states, and exemptions were reported either as a number of exemptions or as a percentage of the enrolled students. We have elected to use number of students rather than percentages, and have transformed data as needed. For most states included in our dataset, the data are provided at the county level. In several states (Arizona, Colorado, Illinois, Maine, Michigan, South Dakota, Tennessee, Vermont, Oregon, and Washington), the data was provided at the school level, which we aggregated to the county. Additional data processing was necessary in some cases. In Virginia, data was provided by school name, but county or city information was not included. We used a list of public and private schools to match school names with their respective county using fuzzy matching (with the fuzzywuzzy Python package) with an 80\% matching requirement. Our algorithm was unable to find a suitable match for between 3.8\% and 6.8\% of schools (depending on year), and these schools were not included in the final counts at the county level. Similarly, in Idaho, data at the school level included city information but county was not provided. We first matched city and county names, before aggregating the exemption data at the county level. Finally in New York state, exemptions were provided as percentages at the school level but enrollment information was not included. We obtained enrollment for public and private schools separately from the New York State Education Department, and used the school unique code to calculate exemption number from enrollment and exemption percentages. We then aggregated these numbers at the county level.

States reported data for exemptions based on varying definitions, so we selected data records based on data availability to make the data comparable cross states. We aimed to achieve parsimonious definitions of total medical exemptions, total non-medical exemptions, and total exemptions, which includes both types of exemptions. We define medical exemptions as reported total medical exemptions. In Florida, permanent medical exemptions were reported separately from temporary medical exemptions, so permanent medical exemptions was chosen to represent total medical exemptions. To define total non-medical exemptions, we considered the state law regarding non-medical exemptions and the data availability. If the state reported total aggregated non-medical exemptions, that was selected as total non-medical exemptions. If the state reported only religious exemptions and only allows religious exemptions, that was selected as total non-medical exemptions. If the state reported only religious exemptions, but also allows philosophical exemptions, that was considered missing data. If the state allows philosophical exemptions and only reports philosophical exemptions, that was selected as total non-medical exemptions, as the state may not differentiate religious from philosophical. If the state allows philosophical exemptions and reports both religious and philosophical exemptions separately, these values were summed for total non-medical exemptions. To define total exemptions, if the state reported a total exemptions value, this value was used. If the state did not report a total exemptions value, but reported values for total medical exemptions and total non-medical exemptions, as defined above, these were summed for total exemptions. If the state was missing either medical or non-medical exemptions, but reported the total number of students with completed vaccinations, the total exemptions was the difference between the number of students enrolled and the number of students completed.

We also considered disease-specific exemptions reports. If a state reported the number of exemptions for a vaccine specific to a given infection, that value was used. If the state did not report exemptions, but did provide the total number complete for that disease, the difference between the enrolled students and the completed students was used. For pertussis-specific vaccination, we used DTaP exemptions where available, and TDaP exemptions where DTaP was not available. For measles-specific vaccination, if separate reports were available for measles, mumps, and rubella, the value for measles was used. If measles was not available, then the mumps or rubella exemptions were used, if available.

The data in the figures is only data reported for kindergartens in states where kindergarten-specific data was available, or K-12 data in states where kindergarten-specific data was not reported. States reported age groups heterogeneously, and data by other age groups is available in the data file.

Attendance data include 3K-12 students in Districts 1-32 and 75 (Special Education). Students in District 79 (Alternative Programs), charter schools, home schooling, and home and hospital instruction are excluded. Infants in D79 (LYFE program) and students with a grade level "AD" (adult) are also excluded. Pre-K data do not include NYC Early Education Centers; therefore, Pre-K data are limited to those who attend K-12 schools that offer Pre-K and District Pre-K Centers. This spreadsheet reports attendance rates (overall and remote) for September 13 through September 30, 2021. Data comprises attendance records for those dates reported by schools in ATS as of October 28, 2021. Overall Attendance Rate is defined as the percent of days where students have present attendance status, out of total days with reported attendance data, across 13 days of attendance data September 13-30. Remote Instruction Attendance Rate is defined as the percent of days where students have present remote attendance status out of total days with a remote attendance record reported, across 13 days of attendance data. Full attendance definitions for SY2021-22 can be found here: https://infohub.nyced.org/school-year/school-year-2021-22/attendance Students participating in the Shared Instruction (SHIN) model may have their attendance recorded at separate sites, but attendance records are attributed to students’ home school of record as of the date of attendance. Student demographic data is based on student records in ATS pulled on October 28, 2021. Because a small number of students are missing demographics data in ATS, demographic disaggregations may not roll up to higher-level aggregations. Data for Asian students include Native Hawaiian or Other Pacific Islanders. Students in temporary housing (STH) include all students who lack a fixed, regular, and adequate nighttime residence as defined by Section 725 of the McKinney-Vento Act. It includes students who are identified as "doubled up" (sharing the housing of others due to economic hardship), or living in some other unstable, temporary housing. It does not include students who are identified as residing in shelters. In order to comply with the Family Educational Rights and Privacy Act (FERPA) regulations on public reporting of education outcomes, subgroups with fewer than 5 students are suppressed and have been replaced with an "s". Instances where there are no reported attendance data have been marked with an "NA". Remote Instruction Attendance Rate includes students receiving Medically Necessary Instruction (MNI), students receiving remote instruction while quarantining, and system-wide remote learning days.

Attendance data reported by borough includes students in district 1-32, 75 (Special Education), district 79 (Alternative Schools & Programs), charter schools, home schooling. Home and hospital instruction are excluded. Pre-K data does not include NYC Early Education Centers or District Pre-K Centers therefore data is limited to those who attend K-12 schools that offer Pre-K. Transfer schools are included in citywide, borough, district counts but removed from school level file. Attendance is registered to school student is attending at the time. If a student attend multiple schools in a school year the data will be reflected in multiple schools. Chronically absence is defined if a student has an attendance rate of less than 90 percent ( students who are absent 10 percent or more of the total days).

According to a survey conducted during the 2023-24 school year, **************** was the top learning management system used by K-12 students and teachers in the United States. Learning management systems are used to provide schools with a centralized platform to facilitate course management, content authoring and delivery, reporting grades and data, and communication between students, families, and educators. In that same year, the top study tool in K-12 schools was *******, while the top site or learning resource was *******.

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K-12 Online Tutoring Market Size 2025-2029

The k-12 online tutoring market size is forecast to increase by USD 136.8 billion, at a CAGR of 13.6% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing importance of Science, Technology, Engineering, and Mathematics (STEM) education. The emergence of learning via mobile devices further fuels this market's expansion, as students and parents seek flexible, accessible educational solutions. However, the market faces challenges, including the threat from open tutoring resources and private tutors. These competitors offer free or low-cost alternatives, putting pressure on market players to differentiate their offerings through personalized instruction, advanced technology, and additional resources. To capitalize on opportunities and navigate challenges effectively, companies must focus on delivering high-quality, interactive, and engaging online tutoring experiences that cater to the unique needs of individual students.

What will be the Size of the K-12 Online Tutoring Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, with dynamic applications across various sectors. Standardized testing, social studies, college admissions counseling, and subscription models are seamlessly integrated into personalized learning programs. Accessibility features, such as closed captioning and text-to-speech, ensure inclusivity. Educational content creation and assessment tools cater to STEM education and adaptive learning. Progress tracking and small group instruction enable teachers to monitor student progress and provide personalized feedback. Teacher dashboards offer insights into student performance and allow for data-driven instruction. Freemium models provide access to basic services, while subscription models offer premium features. Special education and recorded lessons cater to diverse learning needs, while virtual classroom technology and mobile learning facilitate flexibility and convenience. Teacher training and student engagement tools ensure effective implementation of online tutoring platforms. Curriculum development and test preparation services cater to specific academic requirements. Blended learning and interactive learning tools enhance student engagement and understanding. Security and privacy measures protect student data. Compliance regulations ensure adherence to industry standards. Math, science, writing, and reading tutoring cater to various subjects. Homework help and one-on-one tutoring offer personalized assistance. Parent communication tools keep families informed. Live online tutoring and group tutoring provide opportunities for real-time interaction and collaboration. Asynchronous learning resources offer flexibility for students with varying schedules. Administrative tools streamline platform management. Interactive learning tools and gamification in education keep students engaged and motivated. Middle school students benefit from these services, as they prepare for high school and beyond. Overall, the market is a continuously unfolding landscape of innovation and growth.

How is this K-12 Online Tutoring Industry segmented?

The k-12 online tutoring industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TypeStructured tutoringOn-demand tutoringCoursesAssessmentsSubjectsApplicationHigh schoolsPrimary schoolsJunior high schoolsKindergartenPre-kindergartenGeographyNorth AmericaUSEuropeFranceGermanySpainUKAPACAustraliaChinaIndiaJapanSouth KoreaRest of World (ROW)

By Type Insights

The structured tutoring segment is estimated to witness significant growth during the forecast period.The markets offer various solutions to enhance educational experiences, with accessibility features ensuring access to personalized learning programs for students. Companies provide educational content creation and assessment tools, catering to STEM education, progress tracking, and small group instruction. Teacher dashboards enable real-time monitoring, while freemium models offer flexibility for various budgets. Math tutoring, SAT prep, student support services, and homework help are popular offerings. High schools and middle schools utilize live online tutoring for AP courses and test preparation. Elementary schools focus on adaptive learning and writing tutoring. Compliance regulations and standardized testing requirements are met through security and privacy measures. Virtual classroom technology, mobile learning, and teacher training foster student engagement. Curriculum development and test preparation cater to variou

Integrated computing uses computing tools and concepts to support learning in other disciplines while giving all students opportunities to experience computer science. Integrated computing is often motivated as a way to introduce computing to students in a low-stakes environment, reducing barriers to learning computer science, often especially for underrepresented groups. This dataset examined integrated computing activities implemented in US schools to examine which programming and CT concepts they teach and whether those concepts differed across contexts. We gathered data on 262 integrated computing activities from in-service K-12 teachers and 20 contextual factors related to the classroom (i.e., primary discipline, grade level, programming paradigm, programming language, minimum amount of time the lesson takes, source of the lesson plan), the teacher (i.e., years teaching, current role (classroom teacher, tech specialist, STEM specialist, etc.), grade levels taught, disciplines taught, degrees and certifications, institutional support received for integrated computing, gender, race, self-efficacy), and the school (e.g., socioeconomic status of students, racial composition, number of CS courses offered, number of CS teachers, years CS courses have been taught, number of students, school location (urban, suburban, rural)). Methods Procedure Data about integrated computing lessons in non-CS classrooms were collected from in-service K-12 teachers in the United States via an online survey, and 262 surveys were completed. Participants were recruited first through teacher networks and districts to include diverse populations and then through LinkedIn. Teachers received a $100 gift card upon completion of the survey, which took approximately 30 minutes. Due to the incentive, submissions were screened during data collection to ensure eligibility (i.e., having a valid school district email) and quality (described below).

Instrument The survey asked about the programming and CT concepts taught in the activities and 20 factors related to classroom, teacher, and school context. The programming concepts included were based on a framework developed by Margulieux et al., 2023. A full list of concepts and contextual factors can be found below. Due to the large sample size, the survey was designed to be primarily quantitative but included a few qualitative questions (e.g., "Please describe in 1-2 sentences the computing learning objective of this activity") and requested teachers to submit their lesson plans. The research team used these qualitative elements to verify data quality, such as by ensuring the lesson included computing and comparing elements of the lesson plans to the quantitative data provided by the teachers. Overall, we found, and excluded, very few instances of low-quality data.

Survey Questions and Descriptive Statistics Qualitative Questions: Title of lesson plan One sentence describing the activity topic (e.g., In this activity, students apply their computational thinking skills to explore the life cycle of a butterfly.) One sentence describing the disciplinary learning objective (e.g., The primary learning goal is to model the life cycle of a butterfly.) One sentence describing the computing learning objective (e.g., Students will conditionals to match body features to life stages.) 1-3 sentences describing the instructional paradigm (e.g., Students will discuss butterflies and life cycles with their partners. Then they will modify the program and use conditionals to create the model.)

Quantitative Question Topic: Response Options (descriptive statistics in parentheses)

Programming and CT Concepts Programming paradigm: Select one: No Programming (80), Unplugged (87), Block-based (69), Text-based (26) Programming language: Open-ended Programming concepts: Select all that apply: Operator-arithmetic, Operator-Boolean, Operator-relational, Conditional-if-else, Conditional-if-then, Loop-for loop, Loop-while loop, Loop-loop index variable, Function-define/call, Function-parameter, Variable, Data types (string, integer, etc.), List, Multimedia component (sprite, sound, button, etc.), Multimedia properties (color, location, etc.), Multimedia movement (forward, back, turn), Output-string, Output-variable, User input, Event (M = 3.2, SD = 2.7) CT concepts: Select all that apply: Algorithms–sequences (158), Algorithms–parallelism (10), Pattern recognition (142), Abstraction (84), Decomposition (89), Debugging (40), Automation (40) (M = 2.1, SD = 1.1)

Classroom Context Integrated discipline: Select one: Art (5), Language arts (37), Foreign language (2), Math (67), Music (3), Science (61), Social Studies (13) Grades taught in lesson: Select all that apply: Kindergarten through 12th grade (activities that spanned K-5 = 107, 6-8 = 53, 9-12 = 93, K-12 = 9) Minimum amount of time the lesson takes: Select one: < 1 hour (90), 1-3 hours (126), 3-8 hours (32), 8+ hours (14) Source of the lesson plan: Select all that apply: Colleague (16), Online search (18), Professional development (20), Professional organization (23), Created based on an external source by myself or with colleagues (28), Modified from an external source (33), Created by myself or with colleagues (124)

Teacher Information Number of years teaching: Open-ended, M = 14.11, SD = 7.6 Current role: Select one: Teacher (220), STEM/Tech specialist (24), Librarian (9), Computer lab director (1), Other (8) Grade levels taught: Select all that apply: K-2, 3-5, 6-8, 9-10, 11-12 (grade levels that spanned K-5 = 79, 6-8 = 45, 9-12 = 93, K-12 = 45) Disciplines taught: Select all that apply: Art (13), Language arts (71), Foreign language (5), Math (134), Music (4), Science (100), Social Studies (54), Computer science (80), Technology (78), Other (8) Degrees, Certs, endorsements, etc. attained: Select all that apply: Teaching certificate in primary discipline(s) (164), Teaching certificate in CS (17), Bachelor’s degree in primary discipline education (129), Bachelor’s degree in CS or CS education (4), Master’s degree in primary discipline education (163), Master’s degree in CS or CS education (0), Endorsement in computer science education (47), EdD or PhD in education (17), Other (86) Support for integrated CS/CT development and implementation: Select all that apply: Professional development through my school/district/LEA/RESA (157), Professional development through external organizations (117), Peer/colleague/department collaboration in my school/district/LEA/RESA (130), Peer/colleague collaboration in external organizations (73), Funding for software licensing, hardware, or curricula (69) Self-efficacy: Views of CT and self-efficacy scale from Yadav, Caeli, Ocak, and Macann, 2022 (M = 4.23 out of 5, SD = 0.60) Gender: Select one: Man (60), Woman (198), Non-binary/third gender (2), Prefer not to say (2) Race: Select one: African American or Black (31), American Indian or Indigenous (1), Asian (13), Caucasian or White (193), Latino/a/x or Hispanic (10), Middle Eastern (0), Pacific Islander (0), Other (14)

School Context Number of students: Open-ended (M = 1179, SD = 741) Number of CS teachers: Open-ended (M = 1.6, SD = 1.4) Number of CS courses: Open-ended (M = 2.1, SD = 2.0) Number of years CS courses taught: Open-ended (M = 3.0, SD = 2.1) Racial composition: Give % of each race: American Indian or Native American (M = 1.8%), Asian (M = 4.5%), Black or African American (M = 23.3%), Hispanic or Latino (M = 17.2%), White or Caucasian (M = 47.5%), Other (M = 2.4%) % of students eligible for free or reduced lunch: Open-ended (M = 56%, SD = 34%) Type of area: Select one: Rural (90), Suburban (122), Urban (50)

Dataset Description

  Dataset Summary

The Fine-grained Error ANalysis for English Learners (FEANEL) dataset aims to advance research in fine-grained error analysis. The benchmark includes a large-scale dataset of 1,000 essays written by K-12 students, with 500 essays from elementary school students and 500 from secondary school students, covering a wide range of age groups and proficiency levels. Each error analysis has been meticulously annotated with an error type… See the full description on the dataset page: https://huggingface.co/datasets/Feanel/FEANEL.

This dataset tracks annual distribution of students across grade levels in American Leadership Academy Ironwood K12