This study is to understand how perceptions and the organization of school safety and security are associated with the level and type of law enforcement engagement in rural schools. A triangulation mixed methods design was used to collect and examine individual, school, and community level quantitative and qualitative data. The social-ecological theory of violence prevention guides the research by predicting that an interplay of factors at multiple levels influences the type and level of law enforcement engagement in rural schools. Specifically, it was predicted that the more organized and coordinated a school is in the area of safety and security, the more likely it is to be formally engaged with law enforcement. Formal engagement is defined as use of some version of the school resource officer (SRO) model or defined roles and responsibilities for law enforcement in schools that are articulated in documents such as a memorandum of agreement or understanding.

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 report provides data regarding students enrolled in New York City schools during the 2012-2013 school year, according to the guidelines set by Local Law 2011/042. Consistent with other school-year reporting, these results include students enrolled and events that occurred between October 26, 2012 and July 1, 2013. Prior to October 26th, 15,552 students transferred between New York City schools, 4,758 students were discharged outside of NYC schools, and 3,592 students dropped out or were discharged under other codes. School level results represent all events for all students. School level results are not presented for District 79 programs or YABCs. All results exclude District 84. Citywide, Borough, and District results represent the last discharge or transfer for each student. 32 students in grades six through eight and 147 students in grades nine through twelve enrolled in school at correctional facilities or detention programs during the 2012-13 school year. Pursuant to the legislation and in accordance with the Family Educational Rights and Privacy Act (FERPA), if a category contains between 0 and 9 students, the number has been replaced with a symbol. In addition, certain numbers have been replaced with a symbol when they could reveal, through addition or subtraction of other numbers that have not been redacted, the underlying count of a number that has been redacted. Codes for dropouts and other accountable discharges include 02, 12, 21, 29, 35, and 39. In addition, codes 08X, 10X, and 11X are considered dropouts in order to align with state guidance. These codes reflect the subset of all discharges that indicate that a student has discontinued schooling without having obtained a diploma.

This is a current list of approved security guard schools. The Security Guard Act of 1992 requires registration and training of security guards in New York State. The Division of Criminal Justice Services (DCJS) approves the private security training schools and provides administrative oversight for mandated security training.

Abstract copyright UK Data Service and data collection copyright owner.The National Pupil Database (NPD) is one of the richest education datasets in the world. It is a longitudinal database which links pupil characteristics to information about attainment for those who attend schools and colleges in England. There are a range of data sources in the NPD providing detailed information about children's education at different stages (pre-school, primary and secondary education and further education). Pupil level information was first collected in January 2002 as part of the Pupil Level Annual Schools Census (PLASC). The School Census replaced the PLASC in 2006 for secondary schools and in 2007 for nursery, primary and special schools. The School Census is carried out three times a year in the spring, summer and autumn terms (January, May and October respectively) and provides the Department for Education with both pupil and school-level data. The NPD is available through the UK Data Archive in three tiers. Tiers two and three are the most sensitive and must be accessed via the Archive's safe room, whereas tier four can be accessed remotely through the Archive's Secure Lab. Tier two contains individual pupil level data which is identifiable and sensitive. Individual pupil level extracts include sensitive information about pupils and their characteristics, including items described as 'sensitive personal data' within the UK Data Protection Act 1998 which have been recoded to become less sensitive. Examples of sensitive data items include ethnic group major, ethnic group minor, language group major, language group minor, Special Educational Needs and eligibility for Free School Meals. Tier three represents aggregated school level data which is identifiable and sensitive. Included are aggregated extracts of school level data from the Department of Education's School Level Database which include items described as 'sensitive personal data' within the Data Protection Act 1998 and could include small numbers and single counts. For example, there is 1 white boy eligible for Free School Meals in school x who did not achieve level 4 in English and maths at Key Stage 2. Tier four represents less sensitive data than tiers two and three. Included are individual pupil level extracts that do not contain information about pupils and their characteristics which are considered to be identifying or described as sensitive personal data within the Data Protection Act 1998. For example, the extracts may include information about pupil attainment, prior attainment, progression and pupil absences but do not include any identifying data items like names and addresses and any information about pupil characteristics other than gender. Extracts from the NPD are also available directly from the Department of Education through GOV.UK's National pupil database: apply for a data extract web page. The fourth edition (September 2017) includes a data file and documentation for the year 2016.

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.

Total enrollment count for students whose learning preference is remote or blended or missing and who have attended school in person at least once since September 16, 2020. Students attending charter schools, students receiving home or hospital instruction, pre-K students (3-K) attending New York City Early Education Centers (NYCEECs), and students attending some District 79 programs are not included. In order to comply with regulations of the Family Educational Rights and Privacy Act (FERPA) on public reporting of education outcomes, data for groups with 5 or fewer students enrolled are suppressed with an “s”. In addition, corresponding groups with the next lowest number of students enrolled are suppressed when they could reveal, through addition or subtraction, the underlying numbers that have been redacted.

1) Enrollment as of last day of reporting period 2) Only schools and programs in Districts 1-32 and District 75 3) NYCEECs and District PreK Centers are excluded 4) District 75 Home and Hospital Instruction programs and students are excluded 5) For consent and consent withdrawal, only Covid-19 testing eligible students are included (Grades 1-12) 6) For unvaccinated population, only students aged 5 or above as of the day before the beginning of the reporting period are included "7) Under the Family Educational Rights and Privacy Act (FERPA), educational agencies and institutions reporting or releasing data derived from education records are responsible for protecting personally identifiable information (PII) in their reports from disclosure. a) If a cell is ≤ 5 the value is suppressed (""S""), and the next highest value in that row is also suppressed (""S""). b) If a cell is within 5 of the total number of students for the subgroup, the value is suppressed (""T""), and the next highest value in that row is also suppressed (""T""). This is necessary, because it is a FERPA violation to disclose that no students in a subgroup were vaccinated. This report includes counts of unvaccinated students, therefore data suppression is necessary on the maximum values also." 8) An empty cell indicates that there are no students for that grade or subgroup

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

The McKinney-Vento Homeless Assistance Act is federal legislation that ensures educational rights and protection for youth and children experiencing homelessness. According to federal legislation, any child who lacks a fixed, regular, and adequate nighttime residence is considered homeless. If a student is found eligible for services under the McKinney-Vento Act, Norfolk Public Schools can provide transportation, free meals, clothing and school supplies, and referrals to local resources.

This dataset includes, by school year, the total number of students enrolled in Norfolk Public Schools that are identified as homeless. The data is pulled from Virginia’s Program for the Education of Homeless Children and Youth: Project HOPE-Virginia. This dataset will be updated annually.

Attitude to data protection.

Topics: Occupational contact with personal data; self-assessment of willingness to provide information about personal matters regarding authorities; detailed determination of type and frequency of contacts with authorities; perceived disturbances by the requests for personal data by authorities; personal determination of wrong decisions by authorities due to incorrect storage of personal data; attitude to a data protection law and assessment of a government demand for storage of personal data; detailed determinations of those authorities to whom one would provide information without hesitation; assessment of the danger of abuse of data; attitude to a personal identification and a computer network of authorities; attitude to innovations and computers; attitude to protection of the private sphere; classification of activities in the areas private sphere and public; receipt of social services; type of borrowing and taxes paid; completed insurance policies; last medical treatment and number of visits to the doctor in the last year; last hospital stay; membership in clubs or citizen initiatives; self-assessment of status in various roles, such as e.g. patient, borrower, citizen, insurance policy holder or in occupation; satisfaction with democracy and the political system in the FRG; attitude to reforms and more social justice; relationship with neighborhood; assessment of the size of personal circle of friends.

Scales: attitudes to democracy and the social system.

Demography: age; sex; marital status; school education; vocational training; occupation; employment; household income; size of household; composition of household; head of household; self-assessment of social class.

As of March 2021, YouTube Kids and Facebook Messenger Kids were the mobile apps for children found to collect the largest amount of data from global iOS users. The apps collected a total of 15 data points from each of the examined data types,. Language learning app Lingokids and educational app ABCmouse followed with 10 data points. The type of data that the examined children's apps collected mostoften were contact information and diagnostics.

Children mobile privacy From online education to gaming and social media, children and young users are increasingly active in online environments via mobile devices. In 2021, playing online games and watching YouTube videos figured among the most popular mobile activities for kids worldwide, while less than five in 10 reported using their phones to complete assignments for school. As vulnerable users, children are entitled to institutional protection and lower interference from tech companies. However, mobile apps designed for children still collect data from their young users. As of the beginning of 2022, money management and gaming apps were the app categories found to track the largest number of data segments from children, with 10.1 and 9.3 data points tracked, respectively.

Child proof social media? While the impact of social media on younger users’ development is yet to be fully understood, parents and educators were quick to realize that social media expands the range of dangers children can encounter while being online. In 2021, children in the United States and in the United Kingdom spent an average of 98 minutes per day on TikTok, as well as 83 minutes daily on Snapchat. In the U.S., both Snapchat and TikTok agreed to respect the age limit restrictions set by the Children's Online Privacy Protection Act (COPPA), and while Snapchat discontinued its children-specific Snapkidz app in 2016, TikTok relies on its TikTok Younger Users platform for users younger than 13. Despite the majority of social media services requiring users to be at least 13 years old, a survey conducted in 2021 in the United Kingdom has found that 60 percent of all surveyed kids aged between eight and 11 had their own social media profile.

The McKinney-Vento Homeless Assistance Act is federal legislation that ensures educational rights and protection for youth and children experiencing homelessness. According to federal legislation, any child who lacks a fixed, regular, and adequate nighttime residence is considered homeless. If a student is found eligible for services under the McKinney-Vento Act, Norfolk Public Schools can provide transportation, free meals, clothing and school supplies, and referrals to local resources.

This dataset includes, by school year, the total number of students enrolled in Norfolk Public Schools that are identified as homeless. The data is pulled from Virginia’s Program for the Education of Homeless Children and Youth: Project HOPE-Virginia. This dataset will be updated annually.

Abstract copyright UK Data Service and data collection copyright owner.The National Pupil Database (NPD) is one of the richest education datasets in the world. It is a longitudinal database which links pupil characteristics to information about attainment for those who attend schools and colleges in England. There are a range of data sources in the NPD providing detailed information about children's education at different stages (pre-school, primary and secondary education and further education). Pupil level information was first collected in January 2002 as part of the Pupil Level Annual Schools Census (PLASC). The School Census replaced the PLASC in 2006 for secondary schools and in 2007 for nursery, primary and special schools. The School Census is carried out three times a year in the spring, summer and autumn terms (January, May and October respectively) and provides the Department for Education with both pupil and school-level data. The NPD is available through the UK Data Archive in three tiers. Tiers two and three are the most sensitive and must be accessed via the Archive's safe room, whereas tier four can be accessed remotely through the Archive's Secure Lab. Tier two contains individual pupil level data which is identifiable and sensitive. Individual pupil level extracts include sensitive information about pupils and their characteristics, including items described as 'sensitive personal data' within the UK Data Protection Act 1998 which have been recoded to become less sensitive. Examples of sensitive data items include ethnic group major, ethnic group minor, language group major, language group minor, Special Educational Needs and eligibility for Free School Meals. Tier three represents aggregated school level data which is identifiable and sensitive. Included are aggregated extracts of school level data from the Department of Education's School Level Database which include items described as 'sensitive personal data' within the Data Protection Act 1998 and could include small numbers and single counts. For example, there is 1 white boy eligible for Free School Meals in school x who did not achieve level 4 in English and maths at Key Stage 2. Tier four represents less sensitive data than tiers two and three. Included are individual pupil level extracts that do not contain information about pupils and their characteristics which are considered to be identifying or described as sensitive personal data within the Data Protection Act 1998. For example, the extracts may include information about pupil attainment, prior attainment, progression and pupil absences but do not include any identifying data items like names and addresses and any information about pupil characteristics other than gender. Extracts from the NPD are also available directly from the Department of Education through GOV.UK's National pupil database: apply for a data extract web page. The fourth edition (September 2017) includes a data file and documentation for the year 2016.

The New York State calculation method consists of all students who first entered 9th grade in a given school year (e.g., the Cohort of 2006 entered 9th grade in the 2006-2007 school. In order to comply with the Family Educational Rights and Privacy Act (FERPA) regulations on public reporting of education outcomes, rows with fewer than 5 students are suppressed, and replaced with an "s" and for "Transfer School" tab rows with cohorts of 10 or fewer students are suppressed. As of January 1, 2014, the GED test is no longer offered in New York State. The GED has been replaced by the TASC (Test Assessing Secondary Completion) exam which will continue to lead students to a High School Equivalency (HSE) Diploma.

Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students in pre-K and 15,480 in 3K for the school year 2020-2021. • Charter: Charter School • FCC: Family Child Care Center, Network Level (FCC enrollment data is reported at the Network level) • Missing – DBN: Missing Site ID, enrollment reported at DBN level • NYCEEC: NYC Early Education Centers (NYCEECs) are independent, community-based organizations that partner with the NYC Department of Education to provide free full-day high-quality pre-K • PKC: Pre-K Center • PS: Public School • SE: Special Education" In order to comply with regulations of the Family Educational Rights and Privacy Act (FERPA) on public reporting of education data, groups with 5 or students are suppressed with an “s”. In addition, groups with the next lowest number of students are suppressed when they could reveal, through addition or subtraction, the underlying numbers that have been redacted.

PLEASE NOTE: The complete data file can be downloaded from the "ATTACHMENT" section

Participation in Career Technical Education (CTE) programs has been proposed as a valuable strategy for supporting transition to independence among students with disabilities. We exploit a discontinuity created by admissions thresholds from a statewide system of CTE high schools. Our findings suggest attending CTE high schools has large positive effects on completing high school on time, employment, and earnings, including for individuals 22 years or older. Attending CTE schools also results in more time spent with non-disabled peers and higher 10th grade test scores. These results appear concentrated among male students, but the sample of female students is too small to support strong conclusions about outcomes. Notably, these estimates are for a system of CTE high schools operating at scale and serving students across a wide spectrum of disabilities, and the estimated effects appear broad based over disability type, time spent with non-disabled peers in 8th grade and previous academic..., The dataset combines three primary data sources. Student admission records come from the Connecticut Technical Education and Career System (CTECS), a statewide school district consisting of 16 high schools. Information on student achievement, demographics, high school graduation and college attendance come from administrative records maintained by the Connecticut State Department of Education (CSDE). Information on quarterly earnings and quarters with earnings come from the Connecticut State Department of Labor, through Connecticut’s P20Win process., , # Can Technical Education in High School Smooth Postsecondary Transitions for Students with Disabilities?

https://doi.org/10.5061/dryad.qfttdz0r8

This readme file describes the various attached .do files and .csv files, and how to replicate the analysis in our paper. All .do files were run using Stata 17. Of course, all the analyses can also be run in the open-source software R, after converting the code in the do files. All the variables in the datasets included with the replication files are labeled to provide a description of the data. If you have any questions about these data or .do files, please contact eric.brunner@uconn.edu.

Restricted Use Data

Our research relies on data protected by the Family Educational Rights and Privacy Act (FERPA), and as a result this data cannot be posted on-line or made freely available. Rather, an individual who wishes to replicate or extend this work would need to con...

The global Teaching Information System (TIS) market is experiencing robust growth, driven by increasing adoption of technology in education and the need for efficient student management and performance tracking. The market, currently valued at approximately $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated $45 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising demand for personalized learning experiences is leading educational institutions to implement TIS solutions that offer customized learning pathways and adaptive assessments. Secondly, the increasing emphasis on data-driven decision-making in education is driving the adoption of TIS for better performance analysis and resource allocation. Cloud-based solutions are gaining significant traction due to their scalability, accessibility, and cost-effectiveness compared to local-based systems. The education management application segment dominates the market, followed by public service applications, reflecting the widespread need for streamlined administration and enhanced communication within these sectors. However, factors such as high initial investment costs, lack of technical expertise, and data security concerns act as restraints to market growth. The competitive landscape is characterized by a mix of established players and emerging startups. Companies like iFlytek, Talkweb, and Lanxum are leading the market with comprehensive TIS offerings. The market is witnessing increased innovation in areas such as artificial intelligence (AI)-powered personalized learning, gamification of learning experiences, and integration with other educational technologies. Geographical expansion is also a key trend, with regions like North America and Asia-Pacific exhibiting substantial growth potential due to rising investments in education technology and increasing digital literacy rates. While the North American market currently holds a significant share, the Asia-Pacific region is expected to experience the fastest growth over the forecast period, driven by increasing internet penetration and government initiatives promoting digital education. The continued development and adoption of innovative TIS solutions will further propel market growth in the coming years.

Australian Secondary Students' Alcohol and Drug Survey - Sun Protection

The data is presented by the ACT Government for the purpose of disseminating information for the benefit of the public. The ACT Government has taken great care to ensure the information in this report is as correct and accurate as possible. Whilst the information is considered to be true and correct at the date of publication, changes in circumstances after the time of publication may impact on the accuracy of the information. Differences in statistical methods and calculations, data updates and guidelines may result in the information contained in this report varying from previously published information.

Analysis of ‘2005-2019 Graduation Rates Citywide - ALL’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/56e0d45a-f64f-4294-97c3-53bd37513c58 on 26 January 2022.

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

The New York State calculation method consists of all students who first entered 9th grade in a given school year (e.g., the Cohort of 2006 entered 9th grade in the 2006-2007 school. In order to comply with the Family Educational Rights and Privacy Act (FERPA) regulations on public reporting of education outcomes, rows with fewer than 5 students are suppressed, and replaced with an "s" and for "Transfer School" tab rows with cohorts of 10 or fewer students are suppressed. As of January 1, 2014, the GED test is no longer offered in New York State. The GED has been replaced by the TASC (Test Assessing Secondary Completion) exam which will continue to lead students to a High School Equivalency (HSE) Diploma.

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