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 https://nces.ed.gov/ccd/files.asp#Fiscal:2,LevelId:5,SchoolYearId:35,Page:1">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: https://www.cdc.gov/mmwr/volumes/70/wr/mm7039e2.htm" target="_blank">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

1. K-12 School Opening Tracker. Burbio 2021; https://cai.burbio.com/school-opening-tracker/. June 2021.
2. COVID-19 IMPACT: School District Operational Status. MCH Strategic Data: 2021; https://www.mchdata.com/covid19/schoolclosings. (30 June 2021, date last accessed).
3. Return to Learn Tracker. American Enterprise Institute 2021; https://www.returntolearntracker.net/. (30 June 2021, date last accessed).
4. Colorado School District Reopening Plans. Colorado Department of Education 2021; https://docs.google.com/spreadsheets/d/e/2PACX-1vQipdjO8QWhilhhJ4bX0FBebnHEzK1G3LEDQbE_S-xRvs2t0oHNm--acHwMRFmL9uKw4cXcOUqy1V66/pubhtml. (30 June 2021, date last accessed).
5. Connecticut Report Cards. Connecticut State Department of Education 2021; http://edsight.ct.gov/SASPortal/main.do. (30 June 2021, date last accessed).
6. COVID Planning Tool. Hawaii Department of Education 2021; https://public.tableau.com/views/COVIDPlanningTool-Embed700px/SimpleDashboard. (30 June 2021, date last accessed).
7. Idaho School District Status. Idaho State Department of Education 2021; https://ioem.maps.arcgis.com/apps/opsdashboard/index.html#/379299e56f234fa89cff3c8c6acf3ac8. (30 June 2021, date last accessed).
8. How Illinois Districts Are Providing Instruction: Virtual, In-Person, and Blended Learning. Illinois State Board of Education 2021; https://isp.maps.arcgis.com/apps/opsdashboard/index.html#/fceeacb37da04de4b237ed941dd7d5c4. (30 June 2021, date last accessed).
9. Digital Learning Model Schools. Louisiana Department of Education 2021; https://analytics.la.gov/t/DOE/views/DigitalLearningModel/SchoolLevelModeofLearning. (30 June 2021, date last accessed).
10. Safe Learning Model Dashboard. Minnesota Department of Education 2021; https://analytics.education.state.mn.us/t/MDEPublic/views/MN_Safe_Learning_Model/Dashboard. (30 June 2021, date last accessed).
11. Coronavirus (COVID-19) information. Missouri Department of Elementary & Secondary Education 2021; https://dese.mo.gov/communications/coronavirus-covid-19-information. (30 June 2021, date last accessed).
12. North Carolina School Dashboard. North Carolina Department of Public Instruction 2021. https://docs.google.com/spreadsheets/d/1We8gDpa4Do5NR83Nf8niGE_YxzLDf-KZh-tVWifStxE/edit#gid=0. (30 June 2021, date last accessed).
13. Ring the Bell: Return to In-Person Learning. New Mexico Public Education Department 2021; https://webnew.ped.state.nm.us/reentry-district-and-school-guidance/. (30 June 2021, date last accessed).
14. Reset and Restart Education. Ohio Department of Education 2021; http://education.ohio.gov/Topics/Reset-and-Restart. (30 June 2021, date last accessed).
15. 2020-21 School Status. Oregon Department of Education 2021; https://www.oregon.gov/ode/students-and-family/healthsafety/Pages/2020-21-School-Status.aspx. (30 June 2021, date last accessed).
16. Current Operational Status. South Carolina Department of Education 2021; https://ed.sc.gov/districts-schools/schools/district-and-school-closures/operational-status/#blk2102. (30 June 2021, date last accessed).
17. Tennessee Department of Education COVID-19 District Information. Tennessee Department of Education 2021; https://districtinformation.tnedu.gov/covid-information/search/-1. (30 June 2021, date last accessed).
18. State Snapshot: Virginia School Operational Status. Virginia Department of Education 2021; https://www.doe.virginia.gov/support/health_medical/office/reopen-status.shtml. (30 June 2021, date last accessed).
19. Current Reopening Status of Vermont's K-12 Public Schools. Vermont Department of Financial Regulation 2021; https://dfr.vermont.gov/about-us/covid-19/school-reopening#public-school. (30 June 2021, date last accessed).
20. School Reopening Data. Washington Office of Superintendent of Public Instruction 2021; https://www.k12.wa.us/about-ospi/press-releases/novel-coronavirus-covid-19-guidance-resources/school-reopening-data. (30 June 2021, date last accessed).
21. Public school districts. National Center for Education Statistics 2021; https://nces.ed.gov/ccd/districtsearch/. (30 June 2021, date last accessed).

NCES relies on information about school location to construct school-based surveys, support program administration, identify associations with other types of geographic entities, and to help investigate the social and spatial context of education. EDGE creates and assigns address geocodes (estimated latitude/latitude values) and other geographic indicators to public schools, public local education agencies, private schools, and postsecondary schools. The geographic data are provided for download as shapefiles and are also directly accessible as GIS web services.Geocodes for public schools are based on data reported in the NCES Common Core of Data (CCD), an annual collection of administrative data about enrollment, staffing, and program participation for schools, local education agencies (LEAs), and state education agencies (SEAs). SEAs report these data to the United States Department of Education in a series of file submissions throughout the year. Additional information about the CCD collection and data resources for public schools are available at https://nces.ed.gov/ccd/ccddata.asp.

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 https://nces.ed.gov/ccd/files.asp#Fiscal:2,LevelId:5,SchoolYearId:35,Page:1">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.

• 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: https://www.cdc.gov/mmwr/volumes/70/wr/mm7039e2.htm" target="_blank">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.

• 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 1, 2022 to December 31, 2022 correspond to the 2022-2023 school year and were processed in a similar manner to data from the 2021-2022 school year.
• 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.

1. K-12 School Opening Tracker. Burbio 2021; https://cai.burbio.com/school-opening-tracker/. June 2021.
2. COVID-19 IMPACT: School District Operational Status. MCH Strategic Data: 2021; https://www.mchdata.com/covid19/schoolclosings. (30 June 2021, date last accessed).
3. Return to Learn Tracker. American Enterprise Institute 2021; https://www.returntolearntracker.net/. (30 June 2021, date last accessed).
4. Colorado School District Reopening Plans. Colorado Department of Education 2021; https://docs.google.com/spreadsheets/d/e/2PACX-1vQipdjO8QWhilhhJ4bX0FBebnHEzK1G3LEDQbE_S-xRvs2t0oHNm--acHwMRFmL9uKw4cXcOUqy1V66/pubhtml. (30 June 2021, date last accessed).
5. Connecticut Report Cards. Connecticut State Department of Education 2021; http://edsight.ct.gov/SASPortal/main.do. (30 June 2021, date last accessed).
6. COVID Planning Tool. Hawaii Department of Education 2021; https://public.tableau.com/views/COVIDPlanningTool-Embed700px/SimpleDashboard. (30 June 2021, date last accessed).
7. Idaho School District Status. Idaho State Department of Education 2021; https://ioem.maps.arcgis.com/apps/opsdashboard/index.html#/379299e56f234fa89cff3c8c6acf3ac8. (30 June 2021, date last accessed).
8. How Illinois Districts Are Providing Instruction: Virtual, In-Person, and Blended Learning. Illinois State Board of Education 2021; https://isp.maps.arcgis.com/apps/opsdashboard/index.html#/fceeacb37da04de4b237ed941dd7d5c4. (30 June 2021, date last accessed).
9. Digital Learning Model Schools. Louisiana Department of Education 2021; https://analytics.la.gov/t/DOE/views/DigitalLearningModel/SchoolLevelModeofLearning. (30 June 2021, date last accessed).
10. Safe Learning Model Dashboard. Minnesota Department of Education 2021; https://analytics.education.state.mn.us/t/MDEPublic/views/MN_Safe_Learning_Model/Dashboard. (30 June 2021, date last accessed).
11. Coronavirus (COVID-19) information. Missouri Department of Elementary & Secondary Education 2021; https://dese.mo.gov/communications/coronavirus-covid-19-information. (30 June 2021, date last accessed).
12. North Carolina School Dashboard. North Carolina Department of Public Instruction 2021. https://docs.google.com/spreadsheets/d/1We8gDpa4Do5NR83Nf8niGE_YxzLDf-KZh-tVWifStxE/edit#gid=0. (30 June 2021, date last accessed).
13. Ring the Bell: Return to In-Person Learning. New Mexico Public Education Department 2021; https://webnew.ped.state.nm.us/reentry-district-and-school-guidance/. (30 June 2021, date last accessed).
14. Reset and Restart Education. Ohio Department of Education 2021; http://education.ohio.gov/Topics/Reset-and-Restart. (30 June 2021, date last accessed).
15. 2020-21 School Status. Oregon Department of Education 2021; https://www.oregon.gov/ode/students-and-family/healthsafety/Pages/2020-21-School-Status.aspx. (30 June 2021, date last accessed).
16. Current Operational Status. South Carolina Department of Education 2021; https://ed.sc.gov/districts-schools/schools/district-and-school-closures/operational-status/#blk2102. (30 June 2021, date last accessed).
17. Tennessee Department of Education COVID-19 District Information. Tennessee Department of Education 2021; https://districtinformation.tnedu.gov/covid-information/search/-1. (30 June 2021, date last accessed).
18. State Snapshot: Virginia School Operational Status. Virginia Department of Education 2021; https://www.doe.virginia.gov/support/health_medical/office/reopen-status.shtml. (30 June 2021, date last accessed).
19. Current Reopening Status of Vermont's K-12 Public Schools. Vermont Department of Financial Regulation 2021; https://dfr.vermont.gov/about-us/covid-19/school-reopening#public-school. (30 June 2021, date last accessed).
20. School Reopening Data. Washington Office of Superintendent of Public Instruction 2021; https://www.k12.wa.us/about-ospi/press-releases/novel-coronavirus-covid-19-guidance-resources/school-reopening-data. (30 June 2021, date last accessed).
21. Public school districts. National Center for Education Statistics 2021; https://nces.ed.gov/ccd/districtsearch/. (30 June 2021, date last accessed).

The Fast Response Survey System (FRSS) was established in 1975 by the National Center for Education Statistics (NCES), United States Department of Education. FRSS is designed to collect issue-oriented data within a relatively short time frame. FRSS collects data from state education agencies, local education agencies, public and private elementary and secondary schools, public school teachers, and public libraries. To ensure minimal burden on respondents, the surveys are generally limited to three pages of questions, with a response burden of about 30 minutes per respondent. Sample sizes are relatively small (usually about 1,000 to 1,500 respondents per survey) so that data collection can be completed quickly. Reported data are weighted to produce national estimates of the sampled education sector. The sample size permits limited breakouts by classification variables. However, as the number of categories within the classification variables increases, the sample size within categories decreases, which results in larger sampling errors for the breakouts by classification variables. The Teachers' Use of Educational Technology in U.S. Public Schools, 2009 survey provides national estimates on the availability and use of educational technology among teachers in public elementary and secondary schools during 2009. This is one of a set of three surveys (at the district, school, and teacher levels) that collected data on a range of educational technology resources. A stratified multistage sample design was used to select teachers for this study. Data collection was conducted September 2008 through July 2009, and 3,159 eligible teachers completed the survey by web, mail, fax, or telephone. The survey asked respondents to report information on the use of computers and Internet access in the classroom; availability and use of computing devices, software, and school or district networks (including remote access) by teachers; students' use of educational technology; teachers' preparation to use educational technology for instruction; and technology-related professional development activities. Respondents reported quantities for the following: computers located in the classroom every day, computers that can be brought into the classroom, and computers with Internet access. Data on the availability and frequency of using computers and other technology devices during instructional time were also collected. Respondents reported on students' use of educational technology resources during classes and teachers' use of modes of technology to communicate with parents and students. Additional survey topics included teacher training and preparation to effectively use educational technology for instruction, and teachers' opinions related to statements about their participation in professional development for educational technology. Respondents were also asked for administrative information such as school instructional level, school enrollment size, main teaching assignment, and years of experience.

Updated yearly using enrollment data, employment data, information from websites, phone calls, and any other resources as available. At time of update fields were added to include employment data, enrollment data, building code, school code, TAZ08, and school website. Please verify information before use as it will be updated on an ongoing basis. Please contact COMPASS with any questions or any knowledge of updates, alterations or modifications that need to be made. FIELDS:UpdateBy: Name or initials of last person to update the recordUpdateOn: Date the record was last updated onSchoolName: Name of the school at the pointSchoolDist: School district the point physically is withinType: Describes the nature of the building and grade/age range of students enrolledValues:PRE K: Preschool &/or Nursery school & Day CareELEMENTARY: Traditional Kindergarten through 6thgradeK-8: Kindergarten through 8th gradeK-12: Kindergarten through 12th grade MIDDLE: 6thgrade through 8thgradeJUNIOR HS: 7thgrade through 9th gradeSENIOR HS: 9th through 12thgradePOST SR: College, University, Technical or Professional SchoolsOTHER: Irregular range of grades or ages ADMIN: Administrative Building/ServicesRETAIL-EDU: Retailor or seller of educational materials or suppliesSiteAddres: Physical address of the school or buildingSiteCity: City the school or building is located inSiteState: State the school or building is located inSiteZip: Zip code the school or building is located inSiteCounty: County the school or building is located inBuilding_Code: Building Code assigned to the school according to the 2012 Enrollment data sheet, where the number is not available or this does not apply the value used is ‘N/A’School_Code:School Code assigned to the school according to the 2012 Enrollment data sheet, where the number is not available or this does not apply the value used is ‘N/A’School_JoinID: Concatonated field of Building Code + School Code as a 7 digit code assigned by the 2012 Enrollment data sheet. If the School Code is only a three digit code an additional ‘0’ is added before the code to achieve the full seven digits necessary for the field. Where the number is not available or this does not apply the value used is ‘N/A’Notes: Any pertinent information that was not suited for another fieldEmploy13:Number of employees according to the 2013 employment final point fileTAZ08: TAZ08 in which the point liesType_II:Describes the nature of the school – public vs private runValues:PUBLIC: Owned, operated, funded, governed and sanctioned by the Idaho Department of EducationPRIVATE: Owned, operated & funded by private donors, foundation, trust or other source. May or may not meet State or Federal curriculum requirements/standardsOPT_ENROLL: Y/N field indicating if there is an open enrollment boundary for the schoolType_III:Any further information or description about the school. Values:AG PRODUCTION & RESEARCH: U of I extension campuses with specific research focus and use intentionALTERNATIVE: Any alternative learning environment, field may contain a ‘-_’ for a further description about what the alternative style is; teen parents, night school, at risk, ect…CHARTER: Any public school classified as a charter by the State Board of EducationCOLLEGE, UNIVERSITY, TRADE SCHOOL: Any post-secondary education institution, includes graduate programs, law schools and vocational training programs.COMMUNITY EDUCATION – ENVIRONMENTAL: Nontraditional classroom facilities which offer courses for the community (child and adult) to promote higher learning and understanding of the environment, care of the environment and environmental issues.CULTURAL: Any school which offers cultural enrichment or a multi-cultural learning environment. Field may also contain ‘-_’ to describe what the specific culture the school educates in.DURRING INCARCERATION: Schools are run through the Juvenile Detention Centers. These schools are acknowledged by the State Department of Education, and are recognized by the State. Available to students during the time of their incarceration. FAITH BASED: Any school run by or affiliated with a religious organization or faith based system of beliefs, and incorporates values and beliefs into the curriculum.FAITH BASED BOARDING: Any school run by or affiliated with a religious organization or faith based system of beliefs, and incorporates values and beliefs into the curriculum. These school also offer a live in facility option to students.HEADSTART: Formal pre-kindergarten education programsINTERNATIONAL BACCALAUREATE: School which offers programs for International Baccalaureate credit for studentsLANGUAGE AND CULTURE: Private (non-charter) language and culture focused schools. Field may also contain ‘-_’ to describe what the specific culture the school educates in.MAGNET: Any school with a particular subject area focus intended to draw students with natural aptitudes or specific interests, these schools have open enrollment boundaries with an application process, as long as the student resides within the school district to which the school is a part of. MONTESSORI: Private schools with a focus on experiential learning rather than traditional learning methods. MUSIC: Schools with an additional focus on musical aptitude and methodsONLINE OR HOME SCHOOL: Virtual or online classroom optionsSPECIAL NEEDS: Schools with facilities and resources for students with special needs or additional assistance and attention. Access: Indicates whether the point is the actual building location itself or an access point. Building locations are coded as "Loc" and access points are coded as "PV" for pedestrian/vehicle access.Main_Acc: Identifies if an access point is the main entrance/exit location for each school.Source: Where the numbers for the employment data and/or student enrollment were gathered from.Enrollment: # of students enrolled according to the 2012 enrollment data, or based on best information we were otherwise able to obtain (if not on the 2012 enrollment data).Website:Most recent URL if able to locate, if unable to locate indicated in field with “UTL”Status: Used to describe if the school is currently active, closed, or planned (used to query out inactive schools for performance monitoring purposes)UniqueID: Made by combining District number and building number in from DDDBBBB. _Updated Fall 2013 From School District WebsitesUpdated 9/11/11 From School District WebsitesJuly 2010 . Canyon County has since requested a new data structure to match their address points. The new schools file has the new structure. The point location of this file is identical to the new schools point file May 2010 - Edited the Ada County schools to align with school sites on NAIP imagery and confirmed schools against respective school district websites Jan - March 2010 - Worked with Jay Young over a several month period and several renditions to reconcile the Canyon County side of this file. December 2009 - Merged with Jay Young's Canyon point file in order to build a new data structure that meets Emergency Service data standards. Went through point by point to ensure alignement with buildings on NAIP imagery and attribute values.

Policymakers and researchers have for many years advocated disparate approaches to ensuring teachers deliver high-quality instruction, including requiring that teachers complete specific training requirements, possess a minimum level of content knowledge, and use curriculum materials and professional development resources available from schools and districts. In this paper, we investigate the extent to which these factors, which we conceptualize as resources for teaching, predict instructional quality in upper elementary mathematics classrooms. Results show that teachers’ mathematical knowledge and their district context explained a moderate share of the variation in mathematics-specific teaching dimensions; other factors, such as teacher experience, preparation, non-instructional work hours, and measures of the school environment, explained very little variation in any dimension.

The purpose of this research was to develop an accurate description of the current involvement of law enforcement in schools. The researchers administered a school survey (Part 1) as well as a law enforcement survey (Part 2 and Part 3). The school survey was designed specifically for this research, but did incorporate items from previous surveys, particularly the School Survey on Crime and Safety and the National Assessment of School Resource Officer Programs Survey of School Principals. The school surveys were then sent out to a total of 3,156 school principals between January 2002 and May 2002. The researchers followed Dillman's mail survey design and received a total of 1,387 completed surveys. Surveys sent to the schools requested that each school identify their primary and secondary law enforcement providers. Surveys were then sent to those identified primary law enforcement agencies (Part 2) and secondary law enforcement agencies (Part 3) in August 2002. Part 2 and Part 3 each contain 3,156 cases which matches the original sample size of schools. For Part 2 and Part 3, a total of 1,508 law enforcement surveys were sent to both primary and secondary law enforcement agencies. The researchers received 1,060 completed surveys from the primary law enforcement agencies (Part 2) and 86 completed surveys from the secondary law enforcement agencies (Part 3). Part 1, School Survey Data, included a total of 309 variables pertaining to school characteristics, type of law enforcement relied on by the schools, school resource officers, frequency of public law enforcement activities, teaching activities of law enforcement officers, frequency of private security activities, safety plans and meetings with law enforcement, and crime/disorder in schools. Part 2, Primarily Relied Upon Law Enforcement Agency Survey Data, and Part 3, Secondarily Relied Upon Law Enforcement Agency Survey Data, each contain 161 variables relating to school resource officers, frequency of public law enforcement activities, teaching activities of law enforcement agencies, safety plans and meetings with schools, and crime/disorder in schools reported to police according to primary/secondary law enforcement.

Abstract

The Service Delivery Indicators (SDI) are a set of health and education indicators that examine the effort and ability of staff and the availability of key inputs and resources that contribute to a functioning school or health facility. The indicators are standardized allowing comparison between and within countries over time.

The Education SDIs include teacher effort, teacher knowledge and ability, and the availability of key inputs (for example, textbooks, basic teaching equipment, and infrastructure such as blackboards and toilets). The indicators provide a snapshot of the learning environment and the key resources necessary for students to learn.

Nigeria Service Delivery Indicators Education Survey was implemented in 2013 by the World Bank and the Research Triangle Institute International. The survey implementation was preceded by consultations with stakeholders in Nigeria to adapt instruments to the country context while maintaining comparability across countries. In addition, the implementation was done with close collaboration with the Universal Basic Education Commission, and in close coordination with the relevant state authorities (i.e. State Ministries of Education, and the State Universal Education Boards where they existed). Data was collected from primary schools in four states (Anambra, Bauchi, Ekiti, and Niger) using personal interviews and provider assessments. A total of 760 randomly selected public and private schools (190 per state) were surveyed, with 2,435 and 5,754 teachers assessed for knowledge and effort respectively. The sample was selected to make the survey representative at the State level, allowing for disaggregation by provider type (private/public) and location (rural/urban).

Geographic coverage

Four states: Anambra, Bauchi, Ekiti, and Niger.

Analysis unit

Schools, teachers, students.

Universe

All primary schools.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling strategy was designed aiming to produce state representative estimates and estimating a proportion with an absolute error of three percentage points for a variable proportion of 0.5 (i.e., has highest variance) with 95 percent degree of confidence per state (equal number used for state).

The strata were constructed according to ownership, urban/rural, and socioeconomic poverty status. The allocation was made in proportion to size for each sub-stratum within public and private. Within strata, simple random sampling was used. Finally, replacement schools were preselected, with a predetermined replacement order within strata.

A total of 190 schools were sampled from each of the four states (Anambra, Bauchi, Ekiti, and Niger).

The target population is all public primary-level school children. Since parts of the school questionnaire were administered to teachers and pupils at the grade four level, all public schools with at least one grade four class formed the sampling frame. The sample frame was created using the list of public schools from UBEC (Universal Basic Education Commission) and private schools from states.

Sampling deviation

None.

Mode of data collection

Face-to-face [f2f]

Research instrument

The SDI Education Survey Questionnaire consists of six modules:

Module 1: School Information - Administered to the head of the school to collect information on school type, facilities, school governance, pupil numbers, and school hours. It includes direct observations of school infrastructure by enumerators.

Module 2a: Teacher Absence and Information - Administered to the headteacher and individual teachers to obtain a list of all schoolteachers, to measure teacher absence and to collect information on teacher characteristics (this module was not included in this dataset).

Module 2b: Teacher Absence and Information - Unannounced visit to the school to assess absence rate.

Module 3: School Finances - Administered to the headteacher to collect information on school finances (this data is unharmonized).

Module 4: Classroom Observation - An observation module to assess teaching activities and classroom conditions.

Module 5: Pupil Assessment - A test of pupils to have a measure of pupil learning outcomes in mathematics and language in grade four.

Module 6: Teacher Assessment - A test of teachers covering mathematics and language subject knowledge and teaching skills.

Cleaning operations

Data entry was done using CSPro; quality control was performed in Stata.

The Fast Response Survey System (FRSS) was established in 1975 by the National Center for Education Statistics (NCES), United States Department of Education. FRSS is designed to collect issue-oriented data within a relatively short time frame. FRSS collects data from state education agencies, local education agencies, public and private elementary and secondary schools, public school teachers, and public libraries. To ensure minimal burden on respondents, the surveys are generally limited to three pages of questions, with a response burden of about 30 minutes per respondent. Sample sizes are relatively small (usually about 1,000 to 1,500 respondents per survey) so that data collection can be completed quickly. Data are weighted to produce national estimates of the sampled education sector. The sample size is large enough to permit limited breakouts by classification variables. However, as the number of categories within the classification variables increases, the sample size within categories decreases, which results in larger sampling errors for the breakouts by classification variables. The Elementary School Arts Education Survey, Fall 2009 data provide national estimates on student access to arts education and resources available for such instruction in public elementary schools during fall 2009. This is one of a set of seven surveys that collected data on arts education during the 2009-10 school year. In addition to this survey, the set includes a survey of secondary school principals, three elementary teacher-level surveys, and two secondary teacher-level surveys. A stratified sample design was used to select principals for this survey. Data collection was conducted September 2009 through June 2010, and 988 eligible principals completed the survey by web, mail, fax, or telephone. The elementary school survey collected data on the availability and characteristics of music, visual arts, dance, and drama/theatre instruction; the type of space used for arts instruction; the availability of curriculum guides for arts teachers to follow; the availability of curriculum-based arts education activities outside of regular school hours; and whether those teaching the subject are arts specialists. Principals also reported on school or district provision of teacher professional development in the arts; arts education programs, activities, and events; and school-community partnerships. Principals were also asked to provide administrative information such as school instructional level, school enrollment size, community type, and percent of students eligible for free or reduced-price lunch.

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]

These boundaries represent the boundaries of the nine educational services districts in Washington State. ESDs were formed when individual County Superintendent of School offices were consolidated and reorganized to reduce duplication, equalize educational opportunities, and provide a more effective reporting and accountability system to the state legislature. ESDs link local public and private schools with one another and with state and national resources. ESD Cooperatives and programs enhance educational opportunities because they realize significant savings, allowing districts to send more dollars directly to the classroom and provide special services that might otherwise be unavailable to their regions. ESDs serve as regional liaisons between the State Superintendent of Public Instruction (OSPI), State Board of Education, and the Legislature.

K-12 Online Education Market Outlook

The global K-12 online education market size was valued at approximately $92 billion in 2023 and is projected to reach around $270 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. This significant growth is driven by several factors, including technological advancements, increased internet penetration, and the growing acceptance of online learning among educators and parents. The COVID-19 pandemic has accelerated the adoption of online education, highlighting its potential to deliver quality education remotely. The increasing demand for personalized learning experiences and the flexibility offered by digital platforms are also key contributors to market expansion.

One of the major growth factors in the K-12 online education market is the rapid technological advancements in digital learning tools. The integration of Artificial Intelligence (AI), Augmented Reality (AR), and Virtual Reality (VR) into educational content has transformed the way students learn, making education more interactive and engaging. These technologies enable personalized learning experiences, adapting content to suit individual student needs and learning paces, thus improving student outcomes. Furthermore, advancements in cloud computing have facilitated easier access to educational resources, enabling educators to store and share large volumes of data efficiently. The evolution of technology continues to shape the future of K-12 education, creating new opportunities for growth in this market.

Another pivotal growth factor is the increasing internet penetration and access to digital devices worldwide. The proliferation of affordable smartphones and tablets, coupled with the expansion of high-speed internet connectivity, has made online education more accessible to students across various geographies. This digital transformation has not only democratized education but also bridged the gap between urban and rural areas, providing students from remote locations with access to quality educational content. Governments and private sectors are investing heavily in infrastructure to support digital education, further driving the market expansion. As more households gain access to reliable internet services, the adoption of K-12 online education is expected to continue its upward trajectory.

The growing acceptance and recognition of online learning as a legitimate educational pathway is another significant factor contributing to the market's growth. Educational institutions, including public and private schools, are increasingly incorporating online modules into their curricula, offering a blended learning approach that combines traditional classroom teaching with online resources. This shift is driven by the realization of the benefits associated with online learning, such as the flexibility it offers in terms of time and location, as well as its ability to cater to diverse learning styles. Additionally, parents are recognizing the value of online education in supplementing their children's traditional learning, which has led to increased enrollment in online courses and programs. As online education becomes more mainstream, it continues to fuel the growth of the K-12 online education market.

K-12 Blended E-Learning is emerging as a transformative approach within the educational landscape, combining the strengths of traditional classroom experiences with the flexibility of online education. This hybrid model allows students to benefit from face-to-face interactions with teachers and peers while also engaging with digital content and resources at their own pace. The integration of blended learning strategies is particularly beneficial in addressing diverse learning needs, as it offers a more personalized and adaptable educational experience. Schools are increasingly adopting K-12 Blended E-Learning to enhance student engagement and outcomes, leveraging technology to create a more interactive and collaborative learning environment. As the demand for flexible learning solutions continues to grow, blended e-learning is poised to play a significant role in the evolution of K-12 education.

Regionally, North America holds a significant share of the K-12 online education market, driven by the presence of a well-established educational infrastructure and high levels of internet penetration. The United States, in particular, is a leading contributor to market growth, with numerous technological innovations and substantial investments in the education sector. The Asia Pacific r

Note: This version supersedes version 1: https://doi.org/10.15482/USDA.ADC/1522654. In Fall of 2019 the USDA Food and Nutrition Service (FNS) conducted the third Farm to School Census. The 2019 Census was sent via email to 18,832 school food authorities (SFAs) including all public, private, and charter SFAs, as well as residential care institutions, participating in the National School Lunch Program. The questionnaire collected data on local food purchasing, edible school gardens, other farm to school activities and policies, and evidence of economic and nutritional impacts of participating in farm to school activities. A total of 12,634 SFAs completed usable responses to the 2019 Census. Version 2 adds the weight variable, “nrweight”, which is the Non-response weight. Processing methods and equipment used The 2019 Census was administered solely via the web. The study team cleaned the raw data to ensure the data were as correct, complete, and consistent as possible. This process involved examining the data for logical errors, contacting SFAs and consulting official records to update some implausible values, and setting the remaining implausible values to missing. The study team linked the 2019 Census data to information from the National Center of Education Statistics (NCES) Common Core of Data (CCD). Records from the CCD were used to construct a measure of urbanicity, which classifies the area in which schools are located. Study date(s) and duration Data collection occurred from September 9 to December 31, 2019. Questions asked about activities prior to, during and after SY 2018-19. The 2019 Census asked SFAs whether they currently participated in, had ever participated in or planned to participate in any of 30 farm to school activities. An SFA that participated in any of the defined activities in the 2018-19 school year received further questions. Study spatial scale (size of replicates and spatial scale of study area) Respondents to the survey included SFAs from all 50 States as well as American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and Washington, DC. Level of true replication Unknown Sampling precision (within-replicate sampling or pseudoreplication) No sampling was involved in the collection of this data. Level of subsampling (number and repeat or within-replicate sampling) No sampling was involved in the collection of this data. Study design (before–after, control–impacts, time series, before–after-control–impacts) None – Non-experimental Description of any data manipulation, modeling, or statistical analysis undertaken Each entry in the dataset contains SFA-level responses to the Census questionnaire for SFAs that responded. This file includes information from only SFAs that clicked “Submit” on the questionnaire. (The dataset used to create the 2019 Farm to School Census Report includes additional SFAs that answered enough questions for their response to be considered usable.) In addition, the file contains constructed variables used for analytic purposes. The file does not include weights created to produce national estimates for the 2019 Farm to School Census Report. The dataset identified SFAs, but to protect individual privacy the file does not include any information for the individual who completed the questionnaire. Description of any gaps in the data or other limiting factors See the full 2019 Farm to School Census Report [https://www.fns.usda.gov/cfs/farm-school-census-and-comprehensive-review] for a detailed explanation of the study’s limitations. Outcome measurement methods and equipment used None Resources in this dataset:Resource Title: 2019 Farm to School Codebook with Weights. File Name: Codebook_Update_02SEP21.xlsxResource Description: 2019 Farm to School Codebook with WeightsResource Title: 2019 Farm to School Data with Weights CSV. File Name: census2019_public_use_with_weight.csvResource Description: 2019 Farm to School Data with Weights CSVResource Title: 2019 Farm to School Data with Weights SAS R Stata and SPSS Datasets. File Name: Farm_to_School_Data_AgDataCommons_SAS_SPSS_R_STATA_with_weight.zipResource Description: 2019 Farm to School Data with Weights SAS R Stata and SPSS Datasets

The Fast Response Survey System (FRSS) was established in 1975 by the National Center for Education Statistics (NCES), United States Department of Education. FRSS is designed to collect issue-oriented data within a relatively short time frame. FRSS collects data from state education agencies, local education agencies, public and private elementary and secondary schools, public school teachers, and public libraries. To ensure minimal burden on respondents, the surveys are generally limited to three pages of questions, with a response burden of about 30 minutes per respondent. Sample sizes are relatively small (usually about 1,000 to 1,500 respondents per survey) so that data collection can be completed quickly. Data are weighted to produce national estimates of the sampled education sector. The sample size is large enough to permit limited breakouts by classification variables. However, as the number of categories within the classification variables increases, the sample size within categories decreases, which results in larger sampling errors for the breakouts by classification variables. The Secondary School Arts Education Survey, Fall 2009 data provide national estimates on student access to arts education and the resources available for such instruction in public secondary schools during fall 2009. This is one of a set of seven surveys that collected data on arts education during the 2009-10 school year. In addition to this survey, the set includes a survey of elementary school principals, three elementary teacher-level surveys, and two secondary teacher-level surveys. A stratified sample design was used to select principals for this survey. Data collection was conducted September 2009 through June 2010, and 1,014 eligible principals completed the survey by web, mail, fax, or telephone. The secondary school survey collected data on the availability of music, visual arts, dance, and drama/theatre instruction; enrollment in these courses, the type of space used for arts instruction, the availability of curriculum guides for arts teachers to follow, and the number of arts teachers who are specialists in the subject. Principals reported on graduation requirements for coursework in the arts; school or district provision of teacher professional development in the arts; and arts education programs, activities, and events. Principals also reported on community partnerships and support from outside sources for arts education. Furthermore, principals were also asked to provide administrative information such as school instructional level, school enrollment size, community type, and percent of students eligible for free or reduced-price lunch.

IPEDS collects data on postsecondary education in the United States in seven areas: institutional characteristics, institutional prices, enrollment, student financial aid, degrees and certificates conferred, student persistence and success, and institutional human and fiscal resources. IPEDS collects institutional data on human resources and finances. Finance data includes institutional revenues by source, expenditures by category, and assets and liabilities. This information provides context for understanding the cost of providing postsecondary education. It is used to calculate the contribution of postsecondary education to the gross national product. IPEDS collects finance data conforming to the accounting standards that govern public and private institutions. Generally, private institutions use standards established by the Financial Accounting Standards Board (FASB) and public institutions use standards established by the Governmental Accounting Standards Board (GASB).

Description of the experiment setting: location, influential climatic conditions, controlled conditions (e.g. temperature, light cycle)In Fall of 2023 the USDA Food and Nutrition Service (FNS) conducted the fourth Farm to School Census. The 2023 Census was sent via email to 18,833 school food authorities (SFAs) including all public, private, and charter SFAs, as well as residential care institutions, participating in the National School Lunch Program. The questionnaire collected data on local food purchasing, edible school gardens, other farm to school activities and policies, and outcomes and challenges of participating in farm to school activities. A total of 12,559 SFAs submitted a response to the 2023 Census.Processing methods and equipment usedThe 2023 Census was administered solely via the web. The study team cleaned the raw data to ensure the data were as correct, complete, and consistent as possible. This process involved examining the data for logical errors and removing implausible values. The study team linked the 2023 Census data to information from the National Center of Education Statistics (NCES) Common Core of Data (CCD). Records from the CCD were used to construct a measure of urbanicity, which classifies the area in which schools are located.Study date(s) and durationData collection occurred from October 2, 2023 to January 7, 2024. Questions asked about activities prior to, during and after SY 2022-23. The 2023 Census asked SFAs whether they currently participated in, had ever participated in or planned to participate in any of 32 farm to school activities. Based on those answers, SFAs received a defined set of further questions.Study spatial scale (size of replicates and spatial scale of study area)Respondents to the survey included SFAs from all 50 States as well as American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, the U.S. Virgin Islands, and Washington, DC.Level of true replicationUnknownSampling precision (within-replicate sampling or pseudoreplication)No sampling was involved in the collection of this data.Level of subsampling (number and repeat or within-replicate sampling)No sampling was involved in the collection of this data.Study design (before–after, control–impacts, time series, before–after-control–impacts)None – Non-experimentalDescription of any data manipulation, modeling, or statistical analysis undertakenEach entry in the dataset contains SFA-level responses to the Census questionnaire for SFAs that responded. This file includes information from only SFAs that clicked “Submit” on the questionnaire. (The dataset used to create the 2023 Farm to School Census Report includes additional SFAs that answered enough questions for their response to be considered usable.)In addition, the file contains constructed variables used for analytic purposes. The file does not include weights created to produce national estimates for the 2023 Farm to School Census Report.The dataset identified SFAs, but to protect individual privacy the file does not include any information for the individual who completed the questionnaire. All responses to open-ended questions (i.e., containing user-supplied text) were also removed to protect privacy.Description of any gaps in the data or other limiting factorsSee the full 2023 Farm to School Census Report [https://www.fns.usda.gov/research/f2s/2023-census] for a detailed explanation of the study’s limitations.Outcome measurement methods and equipment usedNone

Snapshot img

Private Tutoring Market in US Size 2025-2029

US private tutoring market size is forecast to increase by USD 28.85 billion at a CAGR of 11.1% between 2024 and 2029.

US Private Tutoring Market is experiencing significant growth due to the increasing emphasis on Science, Technology, Engineering, and Mathematics (STEM) education. This trend is driven by the recognition of the importance of these subjects in today's workforce and the desire to prepare students for future careers. Additionally, the availability of open-source educational materials has led to an increase in microlearning, where students can access targeted instruction on specific topics at their own pace. However, this market also faces challenges, including the need for personalized instruction to address individual learning styles and the high cost of private tutoring services, which can be a barrier for many families.
Companies seeking to capitalize on market opportunities should focus on providing affordable, personalized tutoring services and leveraging technology to deliver effective microlearning solutions. Navigating the challenges will require a deep understanding of student needs and the ability to offer flexible, customized solutions.

What will be the Size of the US private tutoring market during the forecast period?

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US private tutoring market continues to evolve, adapting to the ever-changing educational landscape and the diverse needs of students. Academic achievement remains a top priority for parents and students alike, driving the demand for personalized learning experiences that cater to individual strengths and weaknesses. Tutor credentials and curriculum development are crucial factors in ensuring effective academic support. Blended learning, which combines traditional classroom instruction with online resources, has gained popularity in recent years. This approach allows for more flexible scheduling and customized instruction, addressing the unique needs of students. Special needs tutoring and ESL tutoring are essential services for students facing academic challenges due to learning disabilities or language barriers.
Parental involvement is another critical component of successful tutoring, with research showing that parental engagement significantly impacts student achievement. Technology integration plays a significant role in the private tutoring market. The digital divide between students with and without access to technology has become increasingly apparent, highlighting the importance of making educational resources accessible to all. Mobile learning and online learning platforms have become essential tools for tutors, enabling them to reach students in remote areas and provide flexible scheduling options. The use of learning management systems, interactive whiteboards, and educational resources allows for more effective lesson planning and tutor matching.
Personalized learning pathways and adaptive learning technologies enable tutors to tailor instruction to individual students' needs, leading to improved student engagement and academic success. Test anxiety is a common challenge for many students, particularly during standardized testing seasons. Tutoring services offering test preparation for exams such as the SAT, ACT, LSAT, GRE, MCAT, and GMAT have become increasingly popular. Tutor background checks are also essential to ensure student safety and confidence in the tutoring process. College admissions are a significant concern for many families, and tutoring services that specialize in college application preparation are in high demand.

How is this US Private Tutoring Industry segmented?

The private tutoring in US 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.

Type

  Curriculum-based learning
  Test preparation


Learning Method

  Online
  Blended
  Classroom-based


End-User

  Preschool and Primary Students
  Middle School Students


Provider Type

  Independent Tutors
  Tutoring Companies
  Online Platforms


Geography

  North America

    US

By Type Insights

The curriculum-based learning segment is estimated to witness significant growth during the forecast period.

US private tutoring market continues to evolve, with a focus on academic achievement and individualized learning. Tutors' credentials and qualifications are increasingly important, as parents seek experts to help their children excel in various subjects, including STEM, arts, and foreign languages. Curriculum development is a key trend, with tutoring services offering personalized learning pathways and adaptive learning technologies to cater to diverse student needs. Student engagement is prioritized through blended learning, which combines traditio

ABSTRACT In this article, considering the redefinition of the role of the State and the re-dimensioning of the relations between the public and the private spheres from the 1990s onwards, we aim to approach the new regulation form of the collaboration regime between federated entities and the proposal of Education Development Arrangements (EDAs). For this reason, the Bill No. 5.182/19 (BRASIL, 2019) is presented in order to explain the new dispute strategies of the public fund for education. Along the analysis, we describe the qualification of entities in the third sector, public investment in basic education in Brazil, the transfer mechanisms of public resources to private initiative with or without profit purposes, and the particularity of the EDAs proposal. The methodology was based on quantitative research, documentary and legislative analysis, as well as qualitative research. The study revealed that the institutionalization and encouragement of EDAs correspond to a process of construction of educational policies for basic education, linked to private interests with or without profit motives.

IPEDS collects data on postsecondary education in the United States in seven areas: institutional characteristics, institutional prices, enrollment, student financial aid, degrees and certificates conferred, student persistence and success, and institutional human and fiscal resources. IPEDS collects institutional data on human resources and finances. Finance data includes institutional revenues by source, expenditures by category, and assets and liabilities. This information provides context for understanding the cost of providing postsecondary education. It is used to calculate the contribution of postsecondary education to the gross national product. IPEDS collects finance data conforming to the accounting standards that govern public and private institutions. Generally, private institutions use standards established by the Financial Accounting Standards Board (FASB) and public institutions use standards established by the Governmental Accounting Standards Board (GASB).