This dataset is a statistical report that provides daily school-wide attendance each day for all schools at 4:00pm classes.

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.

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 assessment, in whichever manner the school is collecting • Student participation in an online forum, chat log, or discussion thread • Student/family phone call, email or response to teacher email • Phone, email, and/or other digital communication with a family member which confirms student interaction/engagement • Other evidence of participation as determined by the principal. Interactions data are attributed to students' school of record on a given day. A student participating in a Shared Instruction (SHIN) model may have recorded interactions at multiple schools on a given day, but only one record is counted for the interaction rate, attributed to students' school of record for that day. Due to the shift to hybrid learning, attendance data for the 2020-21 school year include both in-person and remote instruction. Total days, days absent, and days present fields include both in-person and remote attendance. More information on attendance policies can be found here: https://www.schools.nyc.gov/school-life/rules-for-students/attendance

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

Market Analysis for School Attendance Management Software The global school attendance management software market was valued at $398.5 million in 2025 and is projected to grow at a CAGR of 13.2% during the forecast period (2025-2033). The increasing awareness of student attendance and its impact on educational outcomes, government regulations mandating attendance tracking, and technological advancements in attendance management systems are key factors driving this growth. The market is segmented based on application (schools and educational institutions) and deployment type (cloud-based and on-premises). Cloud-based solutions are gaining popularity due to their cost-effectiveness and scalability. Competitive Landscape and Regional Analysis Major players in the school attendance management software market include Mitrefinch, Teach 'n Go, Synergetic, iClassPro, Skyward, and SCL. The market is geographically segmented into North America, South America, Europe, Middle East & Africa, and Asia Pacific. North America and Europe are the dominant regions in terms of market share, but Asia Pacific is expected to experience significant growth during the forecast period due to rising school enrollment and increased adoption of technology in education. Vendors in the market are focusing on product innovation, strategic partnerships, and expansion into new markets to gain a competitive edge.

The market for School Attendance Management Software (SAMS) is projected to grow from $362.2 million in 2025 to $845.4 million by 2033, exhibiting a CAGR of 12.6% during the forecast period. The increasing adoption of cloud-based SAMS, government initiatives to improve attendance rates, and the growing need for real-time data insights are driving the market growth. Additionally, the integration of AI and machine learning capabilities is enhancing the accuracy and efficiency of attendance tracking. Key trends shaping the SAMS market include the adoption of mobile apps for convenient data entry, the integration of biometrics for secure and reliable attendance tracking, and the growing emphasis on data privacy and security. However, concerns over the high cost of implementation and maintenance, as well as the lack of technical expertise in some schools, may restrain market growth. North America is expected to dominate the SAMS market, followed by Europe and Asia-Pacific. Leading companies in the market include Mitrefinch, Teach 'n Go, Synergetic, iClassPro, Skyward, SCL, Class Technologies, ACTIVE Educate, SchoolPass, and AccuClass.

This data set shows the average attendance rate for students in NSW government schools by Statistical Area 4 (SA4).

Data notes

• 2021 data is not comparable to previous years due to the continued effects of the COVID-19 pandemic, changes to calculation rules to align with ACARA’s national standards (version 3) and changes to the way attendance data is transferred into the department’s centralised data warehouse. Please refer to 2021 Semester 1 student attendance factsheet for more information.

• 2020 data is not provided because students were encouraged to learn from home for several weeks in Semester 1. Please refer to the factsheet on The effects of COVID-19 on attendance during Semester 1 2020 for more information.

• In 2018 NSW government schools implemented the national standards for student attendance data reporting. This resulted in a fall in attendance rates for most schools due to the inclusion of part day absences and accounting for student mobility in the calculation. Data from 2018 onwards is not comparable with earlier years.

• Schools for Specific Purposes (SSPs) are only included from 2021. Prior to this SSP attendance data was not collected centrally.

• The attendance rate is defined as the number of actual full-time equivalent student days attended by full-time students in Years 1–10 as a percentage of the total number of possible student-days attended in Semester 1. Figures are aligned with the National Report on Schooling and the My School website.

• SA4 refers to the ABS Australian Statistical Geography Standard (ASGS) Edition 3 Statistical Area 4 (SA4) – 2021.

• ‘Other Territories’ has been assigned to Norfolk Island Central School, which operated under the responsibility of NSW Department of Education between 2018-2021.

Data source

Semester 1 Return of Absences Collection

Data quality statement

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

The global school attendance software market is projected to reach a value of USD XXX million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). The growth of this market is primarily driven by the increasing adoption of digital tools in educational institutions and the growing need for efficient and accurate attendance tracking systems. The advantages of school attendance software, such as automated attendance tracking, improved communication, and data analytics, are further fueling market expansion. The school attendance software market is segmented by application into colleges and universities, primary and secondary schools, and others. The primary and secondary schools segment is expected to dominate the market during the forecast period due to the widespread adoption of technology in these institutions. Cloud-based attendance software is gaining popularity as it offers flexibility, scalability, and reduced IT infrastructure costs. Key players in the market include ACTIVE Educate, SchoolPass, AccuClass, MySchool, Top Hat, SEAtS Software, K12 Attendance, TeacherKit, MyAttendanceTracker, and Jolly Technologies. The market is also influenced by regional factors, with North America and Asia Pacific expected to be the major contributors to growth.

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

This dataset shows the attendance rates for all NSW government schools in Semester One by alphabetical order.

Data Notes:

• 2021 data is not comparable to previous years due to the continued effects of the COVID-19 pandemic, changes to calculation rules to align with ACARA’s national standards (version 3) and changes to the way attendance data is transferred into the department’s centralised data warehouse. Please refer to 2021 Semester 1 student attendance factsheet for more information.

• 2020 data is not provided because students were encouraged to learn from home for several weeks in Semester 1. Please refer to the factsheet on The effects of COVID-19 on attendance during Semester 1 2020 for more information.

• In 2018 NSW government schools implemented the national standards for student attendance data reporting. This resulted in a fall in attendance rates for most schools due to the inclusion of part day absences and accounting for student mobility in the calculation. Data from 2018 onwards is not comparable with earlier years.

• Schools for Specific Purposes (SSPs) are only included from 2021. Prior to this SSP attendance data was not collected centrally.

• The attendance rate is defined as the number of actual full-time equivalent student days attended by full-time students in Years 1–10 as a percentage of the total number of possible student-days attended in Semester 1. Figures are aligned with the National Report on Schooling and the My School website.

• Data is suppressed "sp" for schools where student numbers are below the reporting threshold.

• Data is not available "na" for senior secondary schools or other schools where no students were enrolled in Years 1-10.

• Blank cells indicate no students were enrolled at the school that census year or the school was out of scope for attendance reporting.

Data Source:

• Education Statistics & Measurement, Centre for Education Statistics and Evaluation

School Attendance Software Market Outlook

The global school attendance software market size is projected to witness a substantial growth trajectory over the forecast period from 2024 to 2032. In 2023, the market was valued at approximately USD 1.2 billion and is expected to reach an impressive USD 3.5 billion by 2032, exhibiting a robust CAGR of 12.3% during this period. This growth can be attributed to the increasing adoption of digital solutions in educational institutions, driven by the need for efficient administrative processes and the desire to enhance student engagement and learning outcomes. The integration of advanced technologies such as artificial intelligence and machine learning in attendance systems is further fueling this market's expansion.

One of the primary growth factors in the school attendance software market is the burgeoning demand for efficient student management systems in educational institutions. With the ever-increasing student population, schools are constantly seeking ways to streamline their operations, manage resources efficiently, and enhance student learning experiences. School attendance software offers a comprehensive solution by automating the attendance tracking process, thereby reducing manual errors and saving administrative time. Additionally, this software often integrates with other student management tools to provide educators and administrators with detailed insights into student performance and attendance patterns, facilitating data-driven decision-making.

Another significant driver for the market's growth is the rising penetration of mobile devices and the internet in educational sectors across the globe. With the proliferation of smartphones and tablets, both students and educators have unprecedented access to digital tools and resources. School attendance software that is compatible with mobile devices allows for seamless communication and information sharing between teachers, students, and parents. This accessibility not only enhances the accuracy of attendance records but also ensures that all stakeholders are promptly informed about student absences or tardiness, fostering a collaborative educational environment.

The impact of the COVID-19 pandemic has also played a crucial role in accelerating the adoption of school attendance software. The shift to remote and hybrid learning models necessitated a reliable way to monitor student attendance and engagement outside traditional classroom settings. As educational institutions continue to navigate the challenges of the post-pandemic landscape, the demand for robust digital attendance solutions is expected to remain strong. Moreover, the integration of features such as real-time analytics and reporting capabilities in attendance software enables schools to maintain continuity in education delivery and adapt to fluctuating learning environments.

The increasing adoption of an Attendance Management System in educational institutions is a testament to the evolving landscape of digital education. These systems provide a structured approach to recording and analyzing student attendance, ensuring that data is captured accurately and efficiently. By leveraging such systems, schools can automate routine tasks, freeing up valuable time for educators to focus on teaching and student interaction. Furthermore, the integration of attendance management systems with other educational technologies facilitates a seamless flow of information, enhancing the overall learning experience for students. As educational institutions strive for operational excellence, the role of attendance management systems becomes increasingly pivotal in achieving these goals.

From a regional perspective, North America currently dominates the school attendance software market, owing to the presence of technologically advanced educational institutions and significant investments in digital infrastructure. However, the Asia Pacific region is anticipated to experience the highest growth during the forecast period. Governments in countries like China and India are increasingly focusing on modernizing their educational systems, which includes the implementation of digital learning tools. This regional focus on educational reform and digital transformation is expected to drive the adoption of school attendance software, contributing to the market's overall expansion.

Component Analysis

The school attendance software market can be segmented by component into software and servi

Statistical report on attendance by borough, grade. Alternate views of same data by grade level and enrollment (register). All students including YABC, adults, LYFE babies and charters, home instruction, home/hospital, CBO UPK.

Analysis of ‘2021 City Council - September Attendance Report’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/e66faae0-0165-4dbb-be0f-e2435c0f6438 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

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.

--- Original source retains full ownership of the source dataset ---

Monthly grade level counts of roster students present, absent and released by School DBN. This dataset updates its statistics numerous times during fiscal year, current data reflects statistics for fiscal year 2017-2020.

In June 2015, 18 New York City public schools closed for poor performance. This report provides data regarding students enrolled in these schools during the 2014-2015 school year, according to the guidelines set by Local Law 2011/043

Market Overview The global School Online Attendance System market is projected to reach a value of USD XXX million by 2033, exhibiting a robust CAGR of XX%. The market's expansion is driven by factors such as the rising adoption of digital technologies in educational institutions, the need for efficient student attendance tracking, and the government's initiatives to incorporate technology into classrooms. The cloud-based segment is expected to dominate the market due to its affordability, scalability, and accessibility advantages. Competitive Landscape and Growth Strategies Key players in the School Online Attendance System market include ACTIVE Educate, SchoolPass, AccuClass, MySchool, and Top Hat. These companies are focusing on developing innovative features, strategic partnerships, and expanding their geographic reach to gain a competitive edge. In addition to expanding into emerging markets, they are exploring acquisitions and mergers to strengthen their market share. The market is also witnessing the emergence of new startups offering cost-effective and user-friendly attendance tracking solutions.

The attendance rate is defined as the number of actual full-time equivalent student days attended by full-time school students in Years 1–10 as a percentage of the total number of possible …Show full descriptionThe attendance rate is defined as the number of actual full-time equivalent student days attended by full-time school students in Years 1–10 as a percentage of the total number of possible student-days attended. Data Notes: Attendance data for NSW government schools only. The attendance rate is calculated as (1 minus absences divided by enrolled days) multiplied by 100. This data includes the student attendance rate for semester 1, semester 2 and the full year. * Students were learning from home for extended periods during Semester 2 2021 due to COVID-19. As a result, attendance rates for Semester 2 and full year are not reliable and have not been published. * 2020 data is not provided because students were encouraged to learn from home for several weeks in Semester 1. * For more detail on how attendance data for 2020 and 2021 were affected by COVID-19, please refer to CESE factsheets: ‘Effects of COVID-19 on attendance during Semester 1 2020’ and ‘2021 Semester 1 student attendance'. All students in Years 1 to 10 in NSW government schools are regarded as full-time. Kindergarten, Year 11, Year 12 students have been excluded in the attendance rates. Ungraded (support) student attendance rates are included as a separate row and excluded from Primary and Secondary totals. Ungraded students in NSW government schools are classified as either primary or secondary according to their level of education. Distance education and Schools for Special Purposes’ attendance data is not currently collected. Bushfires affected many schools' attendance in Term 4 2019 and should be taken into account when comparing Semester 2 data to other years. Prior to 2018 absences equalled ‘all full day absences for the period in question’. From 2020, students in mainstream support classes are reported by their underlying grade of enrolment. Students in schools for specific purposes (SSPs) are included as 'ungraded'. In 2021 attendance figures were calculated differently to align with the third edition of ACARA’s National Standards for Student Attendance Data and Reporting. As a result, data is not directly comparable to previous years. The Department implemented an automated attendance feed (AAF) system in Semester 1 2021. The AAF has significantly improved data quality in 2021, which has affected data comparability with previous years. ** Note** In 2018, NSW government schools implemented the national standards for student attendance data reporting. This resulted in a fall in attendance rates for most schools due to the inclusion of partial absences and accounting for student mobility in the calculation. Data for 2018 is not directly comparable with earlier years. Source: Education Statistics and Measurement. Centre for Education Statistics and Evaluation.

In June 2011, fifteen New York City public schools closed due to poor performance. This report provides data regarding students enrolled in these schools during the 2010-2011 school year, according to the guidelines set by local law 2011/043.

The global market for School Attendance Management Software is experiencing robust growth, driven by the increasing need for efficient and accurate attendance tracking in educational institutions. The market, valued at $832.9 million in 2025, is projected to expand significantly over the forecast period (2025-2033). This growth is fueled by several key factors. Firstly, the rising adoption of technology in education is a primary driver, with schools and educational institutions increasingly seeking software solutions to streamline administrative tasks and improve operational efficiency. Secondly, the demand for improved data analysis and reporting capabilities is significant. School attendance management software provides valuable insights into student attendance patterns, enabling educators to identify at-risk students and implement timely interventions. The shift towards cloud-based solutions further contributes to market expansion, offering scalability, accessibility, and cost-effectiveness compared to on-premise systems. Market segmentation reveals strong demand across both school and educational institution categories, with cloud-based solutions gaining significant traction due to their flexibility and ease of integration. Competition is intense, with numerous established players and emerging startups vying for market share. Geographic analysis indicates strong growth potential in North America and Asia Pacific, driven by high technology adoption rates and increasing government initiatives promoting digitalization in education. The competitive landscape necessitates continuous innovation in features, such as integration with other educational platforms, advanced analytics, and mobile accessibility, to maintain a competitive edge. The market's sustained growth trajectory is anticipated to continue throughout the forecast period, influenced by ongoing technological advancements and the increasing focus on student well-being and academic performance. Further expansion will be propelled by the growing adoption of data-driven decision-making in education, facilitating proactive interventions to address attendance issues and enhance student engagement. The increasing prevalence of mobile learning and blended learning models necessitates software solutions capable of seamlessly integrating across various platforms and devices. The continuing development of user-friendly interfaces and robust security features will remain crucial in attracting more schools and institutions to adopt these solutions. The market will also witness ongoing consolidation through mergers and acquisitions, leading to a more streamlined competitive landscape in the coming years. Overall, the future outlook for School Attendance Management Software remains exceptionally positive, driven by persistent technological advancements and the unwavering commitment of educational institutions to optimize their operational efficiency and enhance student outcomes.