This dataset includes the attendance rate for public school students PK-12 by student group and by district during the 2021-2022 school year. Student groups include: Students experiencing homelessness Students with disabilities Students who qualify for free/reduced lunch English learners All high needs students Non-high needs students Students by race/ethnicity (Hispanic/Latino of any race, Black or African American, White, All other races) Attendance rates are provided for each student group by district and for the state. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch. When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

This dataset includes the attendance rate for public school students PK-12 by district during the 2020-2021 school year. Attendance rates are provided for each district for the overall student population and for the high needs student population. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch. When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

This dataset includes the attendance rate for public school students PK-12 by town during the 2022-2023 school year. Attendance rates are provided for each town for the overall student population and for the high needs student population. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch. When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

Daily listing of students enrolled, present, absent or released statistical count by district, borough and school.

Attendance School

## Overview

Attendance School is a dataset for object detection tasks - it contains Face annotations for 3,150 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This file represents the boundaries of all public school attendance areas in the state of Minnesota for school year 2024-25. Attendance areas attempt to represent the detailed breakdowns that determine where students attend elementary, middle, and high schools. These areas are unique to each public school system. The "dissolved" school district boundaries, without attendance area breakdowns, are available in a separate resource. For many districts that operate only a few schools, attendance area breakdowns are not used or defined; in those cases, attendance areas are equivalent to district boundaries.

Each year Minnesota public school systems are asked to report any changes to their elementary, middle and high school attendance area boundaries. This file was developed from this annual information. Users should be aware that reporting compliance is not mandatory, so the information included is only as accurate as provided by those independent, common, and special school districts. Actual decisions on where students attend which schools is entirely decided by the districts, and changes are not always reported to MDE.

Please also see accuracy notes in the school district boundary dataset, many of which also apply to this layer.

Daily Attendance figures are accurate as of 4:00pm, but are not final as schools continue to submit data after we generate this preliminary report.

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

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

This dataset includes indicators that reflect attendance data for students in Massachusetts public schools since 2018. Because the information collected has changed over time, not all indicators are available across all years.

Attendance indicators are calculated for students enrolled in grades PK-12 with at least 20 days in membership. For more information, please view the Profiles Help - About the Data page.

Massachusetts defines chronically absent as missing at least 10 percent of days enrolled (for instance, 18 days absent if enrolled for a typical school year of 180 days), regardless of the reason for the absence. Being chronically absent can have a significant impact on a student's academic progress and their ability to access the variety of academic and non-academic supports that schools provide.

Chronic absenteeism grew during the COVID-19 pandemic and, as of the 2022-23 school year, had not returned to pre-pandemic rates. For more information about chronic absence, visit the Chronic Absence and Student Attendance page.

Economically Disadvantaged was used 2015-2021. Low Income was used prior to 2015, and a different version of Low Income has been used since 2022. Please see the DESE Researcher's Guide for more information.

This dataset contains the same data that is also published on our DESE Profiles site: Student Attendance Report

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.

Attendance boundaries for high schools in the Chicago Public Schools district for school year 2006-2007. Generally, all students in the applicable high school grades who live within one of these boundaries may attend the school. To view or use these shapefiles, compression software, such as 7-Zip, and special GIS software, such as Google Earth or ArcGIS, are required.

This dataset provides an overview of events held under the "All Children Excel" program. Each entry corresponds to a specific event, including its name, date, location, type, and target audience. The events are diverse, ranging from storytime sessions and STEM activities to makerspace events and bilingual programs.

This dataset is useful for data science because it offers rich, multi-faceted data that can be analyzed from various perspectives. Some types of analysis that can be performed include:

• Attendance Trend Analysis: By analyzing the attendance data over different months and fiscal years, data scientists can identify seasonal trends and the overall growth or decline in program participation. This can help in planning future events and resource allocation.
• Event Type Effectiveness: Comparing the attendance figures across different types of events (e.g., storytime vs. STEM activities) can reveal which types of events are more popular and engaging. This can guide libraries in organizing more of the events that attract higher participation.
• Demographic Insights: Analyzing the age groups targeted by different events can help understand which age groups are more engaged with certain types of activities. This information is valuable for designing targeted programs that cater specifically to the interests and needs of various age demographics.

DOE attendance and enrollment statistics broken down by school district

This dataset includes the attendance rate for public school students PK-12 by school during the 2020-2021 school year.

When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

This CESE bulletin presents analysis of students attendance data at New South Wales government schools in semester one. Results are presented for primary and secondary students. Attendance rates are also presented for Aboriginal students, as well as data on absence reasons and attendance levels.

Due to COVID-19 Semester 1 2020 data is a break in series and should not be compared to previous years. The Attendance Bulletin was not published in its regular format, and instead CESE published a factsheet, ‘Effects of COVID-19 on attendance during Semester 1 2020’. This is available here: https://education.nsw.gov.au/about-us/educational-data/cese/publications/statistics/effects-of-covid-19-on-attendance-2020.

Data source:

• Statistical publications. Centre for Education Statistics and Evaluation.

School Attendance Management Software Market Outlook

The global School Attendance Management Software market size was valued at approximately USD 1.3 billion in 2023, and it is projected to reach USD 3.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.5% during the forecast period. The primary growth factors driving this market include the increasing adoption of digital solutions in educational institutions, the need for efficient attendance tracking systems, and the rising demand for real-time monitoring and data analytics in schools.

The demand for school attendance management software is significantly driven by the increasing need for automation in schools. Educational institutions are progressively shifting from traditional methods of attendance tracking, such as paper-based registers, to digitized systems. This transition is largely due to the benefits of improved efficiency, accuracy, and time savings that automated attendance management systems offer. Furthermore, these systems enable real-time attendance tracking, which is crucial for maintaining accurate records and ensuring student safety.

Another critical factor contributing to the market growth is the growing emphasis on reducing absenteeism and improving student performance. Attendance management software provides detailed insights into attendance patterns, which can help educators identify students with frequent absences and address the underlying issues. Moreover, integrating these software solutions with other educational management systems can facilitate better communication between teachers, parents, and administrators, thereby fostering a more collaborative and supportive educational environment.

The increasing availability of internet connectivity and the proliferation of mobile devices have also played a significant role in the market's expansion. With the rise of cloud-based solutions, schools can now access powerful attendance management systems without the need for substantial upfront investments in hardware or software infrastructure. This has made it easier for educational institutions of all sizes to implement and benefit from these advanced systems. Additionally, mobile apps and web-based platforms have made it more convenient for teachers and students to interact with the attendance management system from anywhere, further enhancing its adoption.

The implementation of an Attendance Tracking System is becoming increasingly essential in educational institutions worldwide. This system not only streamlines the process of monitoring student attendance but also provides educators with valuable data that can be used to enhance teaching strategies and improve student engagement. By automating attendance tracking, schools can reduce the administrative burden on teachers, allowing them to focus more on instructional activities. Furthermore, an Attendance Tracking System can be integrated with other school management software, providing a comprehensive view of student performance and facilitating data-driven decision-making. This integration is particularly beneficial in identifying patterns of absenteeism and addressing them proactively, thereby supporting overall student success.

Regionally, North America holds a significant share of the school attendance management software market, driven by the widespread adoption of technology in education and supportive government policies. Asia Pacific is expected to witness the fastest growth during the forecast period, owing to the increasing number of schools and the rising focus on improving the quality of education in countries like China and India. Europe, Latin America, and the Middle East & Africa also present substantial growth opportunities due to the ongoing digital transformation in the education sector and the increasing awareness of the benefits of attendance management systems.

Component Analysis

The school attendance management software market is segmented into two main components: software and services. The software component encompasses the actual attendance management systems used by educational institutions, while the services component includes implementation, training, maintenance, and support services.

Within the software segment, the demand for advanced functionalities such as biometric integration, RFID, and mobile applications is on the rise. Schools are increasingly looking for comprehensive solutions that not only track atten

Attendance boundaries for high schools in the Chicago Public Schools district for school year 2023-2024.

This dataset is in a forma​​t for spatial datasets that is inherently tabular but allows for a map as a derived view. Please click the indicated link below for such a map.

To export the data in either tabular or geographic format, please use the Export button on this dataset.