This guide provides ways to keep children active while protecting their health when air pollution reaches unhealthy levels.

Interested parties can now request extracts of data from the NPD using an improved application process accessed through the following website; GOV.UK The first version of the NPD, including information from the first pupil level School Census matched to attainment information, was produced in 2002. The NPD is one of the richest education datasets in the world holding a wide range of information about pupils and students and has provided invaluable evidence on educational performance to inform independent research, as well as analysis carried out or commissioned by the department. There are a range of data sources in the NPD providing information about children’s education at different phases. The data includes detailed information about pupils’ test and exam results, prior attainment and progression at each key stage for all state schools in England. The department also holds attainment data for pupils and students in non-maintained special schools, sixth form and further education colleges and (where available) independent schools. The NPD also includes information about the characteristics of pupils in the state sector and non-maintained special schools such as their gender, ethnicity, first language, eligibility for free school meals, awarding of bursary funding for 16-19 year olds, information about special educational needs and detailed information about any absences and exclusions. Extracts of the data from NPD can be shared (under strict terms and conditions) with named bodies and third parties who, for the purpose of promoting the education or well-being of children in England, are:- • Conducting research or analysis • Producing statistics; or • Providing information, advice or guidance. The department wants to encourage more third parties to use the data for these purposes and produce secondary analysis of the data. All applications go through a robust approval process and those granted access are subject to strict terms and conditions on the security, handling and use of the data, including compliance with the Data Protection Act. Anyone requesting access to the most sensitive data will also be required to submit a business case. More information on the application process including the User Guide, Application Form, Security Questionnaire and a full list of data items available can be found from the NPD web page at:- https://www.gov.uk/national-pupil-database-apply-for-a-data-extract

The U.S. Environmental Protection Agency (EPA) created the Indoor Air Quality Tools for Schools (IAQ Tools for Schools) Program to help schools assess and improve indoor air quality (IAQ). IAQ is becoming an increasingly important issue in our nation’s schools. Approximately 20 percent of the U.S. population—nearly 56 million people—spend their days inside elementary and secondary schools. Good IAQ assists schools with their core mission—educating children.

This guidance provides common-sense measures for preventing, diagnosing and resolving most indoor air problems with minimal cost and involvement. This guide helps schools understand how IAQ problems develop, the importance of good IAQ and its impact on students, staff and building occupants. One section provides suggestions for dealing with an IAQ crisis and for communicating IAQ information to students, staff, parents and the community. The appendices of this guide offer detailed information on IAQ-related topics including:

Mold; Radon; Secondhand smoke; Asthma; Portable classrooms; Basic measurement equipment; Hiring professional assistance; Codes and regulations; and Integrated pest management.

Cyber Explorers is the government’s cyber and digital skills programme for 11 to 14 year olds. It offers a fun, free and interactive learning platform to help young people improve their digital skills and find out about careers in the cyber and digital.

The programme complements the school curriculum and can form part of in-school or after-school activities. It supports the government’s work to improve UK cyber defences, develop our cyber ecosystem and improve cyber security skills at all levels.

These statistics show the number of pupils, teachers and schools taking part in the programme, as well as details of region and location.

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This study was designed to collect college student victimization data to satisfy four primary objectives: (1) to determine the prevalence and nature of campus crime, (2) to help the campus community more fully assess crime, perceived risk, fear of victimization, and security problems, (3) to aid in the development and evaluation of _location-specific and campus-wide security policies and crime prevention measures, and (4) to make a contribution to the theoretical study of campus crime and security. Data for Part 1, Student-Level Data, and Part 2, Incident-Level Data, were collected from a random sample of college students in the United States using a structured telephone interview modeled after the redesigned National Crime Victimization Survey administered by the Bureau of Justice Statistics. Using stratified random sampling, over 3,000 college students from 12 schools were interviewed. Researchers collected detailed information about the incident and the victimization, and demographic characteristics of victims and nonvictims, as well as data on self-protection, fear of crime, perceptions of crime on campus, and campus security measures. For Part 3, School Data, the researchers surveyed campus officials at the sampled schools and gathered official data to supplement institution-level crime prevention information obtained from the students. Mail-back surveys were sent to directors of campus security or campus police at the 12 sampled schools, addressing various aspects of campus security, crime prevention programs, and crime prevention services available on the campuses. Additionally, mail-back surveys were sent to directors of campus planning, facilities management, or related offices at the same 12 schools to obtain information on the extent and type of planning and design actions taken by the campus for crime prevention. Part 3 also contains data on the characteristics of the 12 schools obtained from PETERSON'S GUIDE TO FOUR-YEAR COLLEGES (1994). Part 4, Census Data, is comprised of 1990 Census data describing the census tracts in which the 12 schools were located and all tracts adjacent to the schools. Demographic variables in Part 1 include year of birth, sex, race, marital status, current enrollment status, employment status, residency status, and parents' education. Victimization variables include whether the student had ever been a victim of theft, burglary, robbery, motor vehicle theft, assault, sexual assault, vandalism, or harassment. Students who had been victimized were also asked the number of times victimization incidents occurred, how often the police were called, and if they knew the perpetrator. All students were asked about measures of self-protection, fear of crime, perceptions of crime on campus, and campus security measures. For Part 2, questions were asked about the _location of each incident, whether the offender had a weapon, a description of the offense and the victim's response, injuries incurred, characteristics of the offender, and whether the incident was reported to the police. For Part 3, respondents were asked about how general campus security needs were met, the nature and extent of crime prevention programs and services available at the school (including when the program or service was first implemented), and recent crime prevention activities. Campus planners were asked if specific types of campus security features (e.g., emergency telephone, territorial markers, perimeter barriers, key-card access, surveillance cameras, crime safety audits, design review for safety features, trimming shrubs and underbrush to reduce hiding places, etc.) were present during the 1993-1994 academic year and if yes, how many or how often. Additionally, data were collected on total full-time enrollment, type of institution, percent of undergraduate female students enrolled, percent of African-American students enrolled, acreage, total fraternities, total sororities, crime rate of city/county where the school was located, and the school's Carnegie classification. For Part 4, Census data were compiled on percent unemployed, percent having a high school degree or higher, percent of all persons below the poverty level, and percent of the population that was Black.

OBJECTIVE To identify teaching managers' perceptions regarding the relationship of school feeding and the promotion of healthy eating habits among students. METHODS A descriptive study with a qualitative approach was developed in the city of Guarulhos (Southeast Brazil). Key informants from municipal public schools were interviewed. Public schools were selected (n=13) and classified as to the level of social exclusion, size and economic activity of the region where the school was located. Pedagogic coordinators and school principals were individually interviewed with semi-structured questions. RESULTS From school principals and pedagogical coordinators' perceptions, three categories were identified: Food in the school context; School feeding program's role and the Concept of food and nutrition security, which indicate that they considered meals as part of school routine in order to attain physiological needs of energy and nutrients. Their answers also indicated that they did not consider school meals as a pedagogical action related to their specific responsibilities. CONCLUSIONS The relationship between the school feeding and the formation of eating habits is not a topic usually discussed between the different professionals involved with health and education. The implementation of health promoting policies will only be possible after a debate about how schools and their pedagogical team adopt the program guidelines and how the professionals decode these strategies in daily activities.

The global classroom wearables devices market is poised for significant growth, driven by increasing adoption of technology in education and a growing focus on personalized learning. While precise market size data for 2025 is unavailable, considering a conservative Compound Annual Growth Rate (CAGR) of 15% from a hypothetical 2019 market size of $2 billion and factoring in market maturation, we can estimate the 2025 market value to be approximately $5 billion. Key drivers include the need for enhanced student engagement, improved monitoring of student health and well-being, and the ability to gather real-time data on learning effectiveness. Trends such as the integration of artificial intelligence (AI) for personalized feedback and the development of robust data analytics platforms for educators are accelerating market expansion. However, restraints include concerns regarding data privacy and security, the high initial cost of implementation for schools, and the need for comprehensive teacher training on effectively utilizing the technology. The market is segmented by application (e.g., student tracking, health monitoring, classroom interaction tools) and type (e.g., smartwatches, fitness trackers, other wearables). Major players like Apple, Google, and Samsung are increasingly investing in developing education-focused wearables, while specialized companies catering to the education sector are also emerging. The North American market is expected to dominate initially due to higher technology adoption rates, followed by Europe and Asia Pacific. The forecast period (2025-2033) anticipates continued growth, albeit at a potentially moderating CAGR (estimated at 12%), as the market reaches a higher level of saturation. Future growth will be fueled by advancements in wearable technology, including improved battery life, more sophisticated sensors, and the development of more user-friendly and intuitive interfaces designed specifically for educational settings. The focus will shift towards integrating wearables seamlessly into existing learning management systems and creating more comprehensive solutions that address multiple aspects of the learning process. Addressing data privacy and security concerns through the implementation of robust ethical guidelines and regulatory frameworks will also be crucial for sustained market growth.

Attitudes towards the EU. Protection of online personal data.

Topics: 1. Attitudes towards the EU: life satisfaction; frequency of discussions about political matters on national, European, and local level; assessment of the current situation of the national economy; expected development of the national economy in the next twelve months; most important problems in the own country, personally, and in the EU; general direction things are going in the own country and in the EU; trust in selected institutions: national government, national parliament, European Union; EU image; attitude towards the following issues: European economic and monetary union with one currency, common European defence and security policy, free trade and investment agreement between the EU and the USA, common European migration policy, common European energy policy; optimism about the future of the EU.

1. Protection of online personal data: internet use at home, at work, at school; frequency of the following online activities: use social networks, buy goods or services, use instant messaging or chat websites, use peer-to-peer software or sites to exchange movies etc., make or receive phone or video calls, banking, play games; approval of the following statements: national government asks increasingly for personal information of citizens, feeling of obligation to provide personal information online, provision of personal information as a precondition for obtaining certain products or services, respondent does not bother much with the provision of personal information, provision of personal information as an increasing part of modern life, willingness to provide personal information in return for free online services; main reasons for providing personal information online; feeling of control over personal information provided online; extent of concern about not having complete control; awareness of recent revelations about government agencies collecting personal data for the purpose of national security; impact of these revelations on personal trust regarding the use of online personal data; most serious risks of providing personal information online; attempts made to change the privacy settings of the personal profile on an online social network; assessment of the changes as easy; reasons for not changing; preferred persons or authorities to ensure the safety of online personal data; concern about the recording of everyday life activities: on the internet, in public spaces, in private spaces, via mobile phone, via payment cards, via store or loyalty cards; awareness of the conditions of collection and the further use of personal data provided online; attention payed to privacy statements on the internet; reasons for not paying attention; feeling of discomfort regarding tailored advertisements or content based on personal online activity; attitude towards the requirement of an explicit personal approval before collecting or processing personal information; trust in selected institutions with regard to protecting personal information: national public authorities, European institutions, banks and financial institutions, health and medical institutions, shops and stores, online businesses, phone companies and internet service providers; concern about personal data being used for different purposes without personal consent; importance of the possibility to transfer personal data in the case of change of online service provider; desire to be informed if personal data are stolen; preferred authorities to inform users in case personal information are stolen; importance of equal rights and protections over personal data regardless of the country in which the authority or company is located; preferred level on which the enforcement of the rules on personal data protection should be dealt with: European, national, regional or local; knowledge of a public authority in the own country responsible for protecting citizens’ rights regarding personal data; preferred body to address a complaint to regarding problems concerning the protection of personal data; data most concerned about when lost or stolen: data stored on mobile phone or tablet, data stored online in the cloud, data stored on PC.

Demography: nationality; left-right self-placement; marital status; family situation; age at end of education; sex; age; occupation; professional position; type of community; household composition and household size; possession of durable goods (entertainment electronics, Internet connection, possession of a car, a flat/a house have finished paying for or still paying for); financial difficulties during the last year; Internet use (at home, at work, at school); self-reported belonging to the working class, the middle class or the upper class of society; own voice counts in the own country and in the EU.

Additionally coded was: country; date of interview; time of the beginning of the interview; duration of the interview; number of persons present during the...

This data is a graphical representation of the listing of licensed active child care centers in NJ. It was created for the State of New Jersey's initiative regarding child care centers near contaminated sites. The Child Care Centers GIS layer contains all active, licensed child care facilities within the State of New Jersey based on a spreadsheet provided to the NJDEP Site Remediation Waste Management Program (SRWMP) by the New Jersey Department of Children and Families (NJDCF) Office of Licensing. This monthly report also includes facilities operating in public schools (FOIPS) although these facilities are not required in most cases to submit environmental data to the NJDEP for NJDCF licensing. Proposed child care centers are not listed until a NJDCF License number is issued. ADVISORY: This data was created only to be used as guidance to find active child care centers. The data should not be used as the determining factor in conducting receptor evaluations and the actions taken to protect them. The child care data will be updated on a monthly basis as monthly updates of active child care facilities operation in New Jersey are provided to the NJDEP SRWMP by the NJDCF Office of Licensing. Users are hereby notified that data on NJDEP mapping applications for this data set may be more current than any downloadable shapefile, if provided.

This data set provides a list of accredited Career and Technical Institutions located in New Jersey. This list can be used by the public to verify if a career or technical school of interest accepts Federal Funding to assist with Career and Technical educational services. This data was pulled from the US. department of Education database of Accredited Postsecondary Institutions and programs and filtered to represent institutions located in NJ. Youth involved with the NJ Foster Care Scholars program and youth transitioning into higher education can access this data within the Financial Aid and Education Resource Guide, implemented by Embrella through Division of Children and Family Chaffee ETV funded, NJ Foster Care Scholars program and distributed to the community.

The local authority interactive tool (LAIT) is an app that presents information in interactive tables and charts, along with local authorities’ rank positions in England and against statistical neighbours.

It includes local authority, regional and national data on:

• children looked after and adoption
• child protection
• the children’s social care workforce
• special educational needs and disability
• pupil attainment and attendance
• children’s health
• youth justice
• post-16
• finance

The ‘Children’s services statistical neighbour benchmarking tool’ allows you to select a local authority and display its ‘closest statistical neighbours’ (local authorities with similar characteristics). The tool has been reviewed and rebuilt to include updated socio-economic variables from the 2021 census. More information is available in the associated update note and technical report.

The goal of this study is to understand parents’ views on and perceptions of children’s cybersecurity. We have conducted semi-structured interviews with 25 parents. This dataset contains the interview guide and transcripts of the interviews. Considering that the term “cybersecurity” may be confusing or unclear to some parents, we used the term “online security” in our questions, as both represent similar concerns and meanings in our context.

We asked the following questions to the participants.

1. Considering the current situation, all the children are using the Internet and digital platforms more than before for educational activities and entertainment. How concerned are you about their online security (when they are using the Internet and digital platforms)?
2. Do you know about a real story in your environment concerning the secure use of the Internet and digital tools for children? Can you tell me something about it?
3. We are trying to understand what types of security parents have concerns about. What kind of online security concerns do you have for your children?
4. How much do your children already know about online security, in your opinion? i) Do your child/children pursue any education in ICT and/or programming? ii) Did your child/children attend any coding activity inside or outside schools?
5. In your opinion, what kind of security knowledge should children have when they use the Internet?
6. When it comes to educating children about online security, in your opinion, who should be responsible? Parents or the schools?
7. What do you do at home to ensure online security for your children?
8. Can you describe an episode/example of your child learning about online security?
9. What do you think is most challenging in keeping/ensuring online security for your children?
10. Is there anything we have not covered in this interview that you consider important to mention regarding children’s security?

NOTE: The original interviews were conducted in Norwegian. The transcripts in this dataset are the translated version of the original transcripts.

The intent of this guide is not to recommend any schools or programs, but to provide a listing of available programs that may help advance an individual’s career in the field of cyber security.

Abstract: The COVID-19 pandemic poses one of this century’s greatest public health challenges, with impacts on the health and living conditions of populations worldwide. The literature has reported that the pandemic affects the hegemonic food system in various ways. In Brazil, the pandemic amplifies existing social, racial, and gender inequalities, further jeopardizing the Human Right to Adequate Food (HRAF) and the attainment of food and nutritional security, especially among more vulnerable groups. In this context, the article aims to analyze the first measures by the Brazilian Federal Government to mitigate the pandemic’s effects and that may have repercussions on food and nutritional security, considering the recent institutional changes in policies and programs. A narrative literature review was performed, and the information sources were the bulletins of the Center for Coordination of Operations by the Crisis Committee for Supervising and Monitoring the Impacts of COVID-19 and homepages of various government ministries, from March to May 2020. The actions were systematized according to the guidelines of the National Policy for Food and Nutritional Security. The analysis identified the creation of institutional crisis management arrangements. The proposed actions feature those involving access to income, emergency aid, and food, such as authorization for food distribution outside schools with federal funds from the National School Feeding Program. However, the setbacks and dismantlement in food and nutritional security may undermine the Federal Government’s capacity to respond to COVID-19.

ABSTRACT Objective To describe and analyze the implementation aspects of the purchase of food from family farms, according to the management of the School Feeding Program and characteristics of municipalities in the state of São Paulo. Methods A cross-sectional descriptive-analytical study, including 25 municipalities from São Paulo that purchased from family farms for school feeding, where 105 schools were drawn, in 2013, to verify: knowledge of the Law No.11,947/2009, disclosure of the purchasing process, guidance on the management of food, kitchen improvements, variety of food purchased and the use ≤30% of the Program’s resources with family farming. Absolute and relative frequencies were analyzed, and Chi-square test and Fisher’s exact test were performed (p

Locations represent Hawaii's public schools. List of schools was furnished by the Hawaii Department of Education (DOE). Locations were developed by the US EPA Region 9 using address geocoding methods. Hawai'i's public schools are grouped into complexes consisting of a high school and the middle and elementary schools that feed into it. For administrative and support purposes, 2-4 complexes may be grouped together to form what is called a complex area. Public Charter Schools provide a public (vs private) alternative to regular public schools. The direction of each of these publicly funded schools is determined by its "board". Community Schools (aka "Adult Ed") provide basic classes for adult literacy, high school degree programs, citizenship training, and English for Second Language Learners classes as well as non-academic "interest" courses.

Generative-AI (GAI) models like ChatGPT are becoming widely discussed and utilized tools in medical education. For example, it can be used to assist with studying for exams, shown capable of passing the USMLE board exams. However, there have been concerns expressed regarding its fair and ethical use. We designed an electronic survey for students across North American medical colleges to gauge their views on and current use of ChatGPT and similar technologies in May, 2023. Overall, 415 students from at least 28 medical schools completed the questionnaire and 96% of respondents had heard of ChatGPT and 52% had used it for medical school coursework. The most common use in pre-clerkship and clerkship phase was asking for explanations of medical concepts and assisting with diagnosis/treatment plans, respectively. The most common use in academic research was for proof reading and grammar edits. Respondents recognized the potential limitations of ChatGPT, including inaccurate responses, patient privacy, and plagiarism. Students recognized the importance of regulations to ensure proper use of this novel technology. Understanding the views of students is essential to crafting workable instructional courses, guidelines, and regulations that ensure the safe, productive use of generative-AI in medical school.

This layer shows total population counts by sex, age, and race groups data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, School District Unified, School District Elementary, School District Secondary boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   To see the full list of attributes available in this service, go to the "Data" tab above, and then choose "Fields" at the top right. Each attribute contains definitions, additional details, and the formula for calculated fields in the field description.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this feature layer.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, School District Unified, School District Elementary, School District SecondaryNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This layer is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.