This policy outlines the framework that the MOE uses to assess and manage the risk to the children participating in all of its programs, including any donor-funded programs, and the measures and systems put in place to respond to concerns about their wellbeing.

Despite the critical information security issues faced by academic institutions, little research has been conducted at the policy, practice, or theoretical levels to address these issues, and few policies or cost-effective controls have been developed. The purpose of this research study was three-fold: (1) to create an empirically-based profile of issues and approaches, (2) to develop a practical road map for policy and practice, and (3) to advance the knowledge, policy, and practice of academic institutions, law enforcement, government, and researchers. The study design incorporated three methods of data collection: a quantitative field survey, qualitative one-on-one interviews, and an empirical assessment of the institutions' network activity. Survey data collection involved simple random sampling of 600 academic institutions from the Department of Education's National Center for Education Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) database, recruitment via postcard, telephone, and email, Web-based survey administration, and three follow-ups. Results are contained in Part 1, Quantitative Field Survey Data. Interview data collection involved selecting a sample size of 15 institutions through a combination of simple random and convenience sampling, recruitment via telephone and email, and face-to-face or telephone interviews. Results are contained in Part 2, Qualitative One-on-One Interview Data. Network analysis data collection involved convenience sampling of two academic institutions, recruitment via telephone and email, installing Higher Education Network Analysis (HENA) on participants' systems, and six months of data collection. Results are in Part 3, Subject 1 Network Analysis Data, and Part 4, Subject 2 Network Analysis Data. The Quantitative Field Survey Data (Part 1) contains 19 variables on characteristics of institutions that participated in the survey component of this study, as well as 263 variables derived from responses to the Information Security in Academic Institutions Survey, which was organized into five sections: Environment, Policy, Information Security Controls, Information Security Challenges, and Resources. The Qualitative One-on-One Interview Data (Part 2) contains qualitative responses to a combination of closed-response and open-response formats. The data are divided into the following seven sections: Environment, Institution's Potential Vulnerability, Institution's Potential Threat, Information Value and Sharing, End Users, Countermeasures, and Insights. Data collected through the empirical analysis of network activity (Part 3 and Part 4) include type and protocol of attack, source and destination information, and geographic location.

The indicator measures the number of children who had been the subject of a Child Protection Plan continuously for two years or longer against the number of children ceasing to be the subject of a Child Protection Plan during the year, expressed as percentage

Source: CPR3 statutory return form local authorities to Department for Children Schools and Families (DCSF).

Publisher: DCLG Floor Targets Interactive

Geographies: County/Unitary Authority, Government Office Region (GOR), National

Geographic coverage: England

Time coverage: 2006/07 to 2008/09

Type of data: Administrative data

Snapshot img

Cloud Computing Market In K-12 Education Sector Size 2024-2028

The cloud computing market in K-12 education sector size is forecast to increase by USD 51.22 billion at a CAGR of 33.3% between 2023 and 2028.

In the K-12 education sector, cloud computing has emerged as a transformative technology, revolutionizing the way education is delivered. The integration of cloud computing in e-learning has facilitated easy access to educational content from anywhere, anytime. The trend towards IoT integration with cloud computing is further enhancing this accessibility, enabling the use of devices like tablets and glass for interactive learning experiences. However, this shift towards cloud-based solutions also brings about new challenges, particularly In the realm of security. Data security, network security, application security, endpoint security, and multi-factor authentication are key concerns for educators and administrators. Cloud security solutions are essential to mitigate these risks and ensure the safety of sensitive student information. As the adoption of cloud computing continues to grow In the education sector, addressing these security challenges will be crucial for successful implementation.

What will be the Size of the Market During the Forecast Period?

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The K-12 education sector's adoption of cloud computing continues to gain momentum as schools and districts seek to modernize their IT infrastructure and enhance the learning experience for students. Cloud computing offers numerous benefits, including scalability, cost savings, and access to a vast array of resources. However, this shift to cloud-first organizations also presents new challenges, particularly In the areas of security and user identity management. Traditional security measures, such as firewalls and VPNs, are being supplemented with cloud-based solutions, including network security, user authentication, and endpoint security. In the hybrid world of cloud and on-premises infrastructure, context-aware security policies and user identity management are essential to mitigating risks.
Furthermore, the cloud computing market in K-12 education is characterized by a growing attack surface, with network-based attacks, DDoS, ransomware, malware, and server scanning posing significant threats. Cloud security solutions, such as Zscaler, are increasingly being adopted to address these risks, with a focus on application infrastructure security and a need-to-know model. As the use of cloud computing in K-12 education continues to expand, business policies and user authentication protocols will become increasingly important to ensure data privacy and security. Cloud computing offers a VPN alternative, enabling secure access to resources from anywhere, while maintaining network security and user identity management.

How is this Market segmented and which is the largest segment?

The cloud computing in K-12 education sector industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Service

  Software-as-a-Service (SaaS)
  Infrastructure-as-a-Service (IaaS)
  Platform-as-a-Service (PaaS)


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  South America



  Middle East and Africa

By Service Insights

The software-as-a-service (SaaS) segment is estimated to witness significant growth during the forecast period.

The market is primarily driven by the Software-as-a-Service (SaaS) segment due to its cost-effective and scalable benefits. SaaS enables schools to access cloud-based applications over the Internet, reducing expenses on installation, licensing, and maintenance. This model allows K-12 institutions to expand their data reach to larger audiences, fostering the exchange of innovative ideas among students. Security is a significant concern in cloud adoption for educational institutions. User identity, context, and business policy are crucial elements of a strong security model. Traditional security measures, such as VPNs, are being replaced by Cloud Security solutions that offer user authentication, resources protection, and access control policies.

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The Software-as-a-Service (SaaS) segment was valued at USD 5.74 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 62% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share o

In this example lesson plan, students will learn how to use the How’s My Waterway tool to explore and visualize water quality data and determine the health of their local waterbodies.

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.

The indicator measures the number who had previously been the subject of a child protection plan, or on the child protection register of that council, regardless of how long ago that was against the number of children subject to a child protection plan at any time during the Year, expressed as a percentage

Source: CPR3 statutory return form local authorities to Department for Children Schools and Families (DCSF).

Publisher: DCLG Floor Targets Interactive

Geographies: County/Unitary Authority, Government Office Region (GOR), National

Geographic coverage: England

Time coverage: 2006/07 to 2008/09

Type of data: Administrative data

Notes: This is a count of each occasion in the year, and may count the same child more than once.

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

School meals were introduced in the Brazilian political agenda by a group of scholars known as nutrition scientists' in the 1940s. In 1955, the Campanha de Merenda Escolar, the first official school food program, was stablished, and sixty years after its inception, school food in Brazil stands as a decentralised public policy, providing services to students enrolled in public schools, which involve the Brazilian federal government, twentyseven federative units, and their 5,570 municipalities. Throughout its history, school food has gone through many stages that reflect the social transformations in Brazil: from a campaign to implement school food focused on the problem of malnutrition and the ways to solve it, to the creation of a universal public policy relying on social participation and interface between other modern, democratic, and sustainable policies, establishing a strategy for promoting food and nutrition security, development, and social protection. In this article, the School Food Program is analyzed from the perspective of four basic structures that support it as public policy: the formal structure, consisting of legal milestones that regulated the program; substantive structure, referring to the public and private social actors involved; material structure, regarding the way in which Brazil sponsors the program; and finally, the symbolic structure, consisting of knowledge, values, interests, and rules that legitimatize the policy.

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...

Yunlin County Community Environmental Education Empowerment and Environmental Protection Primary School Promotion Project Application Form.

Success.ai’s Education Marketing Data offers businesses and organizations direct access to verified contact details for educators, administrators, and marketing professionals in the education sector. Sourced from over 170 million verified professional profiles, this dataset includes work emails, direct phone numbers, and LinkedIn profiles, ensuring precise and meaningful connections with decision-makers at schools, universities, training centers, and educational service providers. By using continuously updated and AI-validated data, Success.ai empowers you to engage with the right contacts and drive targeted marketing campaigns, recruitment efforts, and partnership opportunities within the education landscape.

Why Choose Success.ai’s Education Marketing Data?

1. Comprehensive Contact Information

   • Access verified work emails, direct phone numbers, and social profiles of school administrators, university professors, department heads, and education marketers.
   • AI-driven validation ensures 99% accuracy, enabling confident outreach and reducing wasted efforts.

2. Global Reach Across Education Segments

   • Includes contacts from K-12 schools, higher education institutions, vocational training centers, e-learning platforms, and professional certification organizations.
   • Covers regions including North America, Europe, Asia-Pacific, South America, and the Middle East, ensuring a broad spectrum of educational institutions and markets.

3. Continuously Updated Datasets

   • Real-time updates guarantee that your contact data remains current, reflecting changes in roles, institutional structures, and academic priorities.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring your outreach is both ethical and legally compliant.

Data Highlights:

• 170M+ Verified Professional Profiles: Includes education sector leaders, influencers, and key decision-makers.
• 50M Work Emails: AI-validated for seamless communication and reduced bounce rates.
• 30M Company (Institution) Profiles: Gain insights into school types, program offerings, and organizational structures.
• 700M Global Professional Profiles: Enriched datasets to support market analysis, competitive benchmarking, and strategic planning.

Key Features of the Dataset:

1. Education Decision-Maker Profiles

   • Identify and connect with principals, superintendents, deans, admissions directors, marketing managers, and department heads shaping curriculum, enrollment, and academic initiatives.

2. Advanced Filters for Precision Targeting

   • Filter by institution type, geographic region, academic level, specialty programs, or job function to refine your outreach and campaigns.
   • Tailor messaging to align with unique educational needs, cultural contexts, and policy frameworks.

3. AI-Driven Enrichment

   • Profiles are enriched with actionable data, offering insights into institutional priorities, enrollment trends, and academic focal points, enabling more personalized and effective engagement.

Strategic Use Cases:

1. Marketing and Enrollment Campaigns

   • Target admissions and marketing professionals at universities, colleges, and language schools to promote your educational products, tutoring services, or learning management systems.
   • Craft campaigns that resonate with educators’ challenges, such as student retention, curriculum innovation, or digital learning adoption.

2. EdTech and Resource Partnerships

   • Connect with decision-makers evaluating new technologies, software platforms, and resource providers to enhance teaching and learning experiences.
   • Position your EdTech solutions to solve institutional pain points like remote learning effectiveness or data-driven student success strategies.

3. Academic Collaboration and Research

   • Identify contacts in academic research, curriculum development, or accreditation bodies to foster partnerships, co-develop programs, or share research findings.
   • Engage with administrators overseeing funding, grants, and educational policy to influence institutional decision-making.

4. Recruitment and Talent Acquisition

   • Find HR professionals and department heads seeking qualified instructors, administrative staff, or specialized educators.
   • Offer recruitment and professional development services to institutions aiming to attract top-tier academic talent.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access top-quality verified data at competitive prices, ensuring cost-effective growth and strategic advantage in education-focused outreach.

2. Seamless Integration

   • Integrate verified contact data into your CRM or marketing automation tools using APIs or downloadable formats for efficient data management.

3. Data Accuracy with AI Validation

   • Rely on 99% accuracy to inform decisions, refine targeting, and enhance campai...

This article presents a model to evaluate municipal management of the Brazilian National School Meal Program (NSMP) and verifies its applicability in the ten largest cities of the State of Santa Catarina. The model was constructed during workshops with experts and was adapted using the Delphi Method, with participation by 14 collaborators. The model addresses two dimensions of municipal management: the political-organizational dimension, organized in the sub-dimensions resources, inter-sector action, and social control, and the operational-technical dimension, which involves the sub-dimensions of nutritional and feeding efficacy, nutritional monitoring, and educational activities for healthy eating. A total of 22 indicators were defined, based on interviews with nutritionists in charge of the NSMP. As for the model’s applicability, the indicators appeared feasible with regard to addressing the municipality’s responsibilities in the program, and the model should be employed to improve NSMP management at the local level.

: In South Africa, the interlacing socio-economic challenges of crime and high unemployment have long been a cause for concern. These challenges have also prompted extensive attention from scholars. Thus, the relationship between crime and unemployment emerges as one of the important niche areas of inquiry. The study used time series data from 1995 to 2020 to address this nexus. An Autoregressive Distributive Lag was deemed fit for this study as the data properties point to its use.

This sub-decree aims to determine the implementation of the school feeding program using agricultural products in the community within the framework of the national social protection policy called "program". This sub-decree applies to public schools selected to implement the program.

Abstract Objective Identify and discuss strategies for execution the National School Feeding Program by state administrations during the coronavirus disease pandemic 2019. Methods This is a descriptive cross-sectional investigation. An exploratory review of the official publication of state governments and the Federal District to find out the strategies for the execution of the National School Feeding Program, after school closures due to the Covid-19 pandemic. Information on the form of execution and the public served by the action were reviewed in a descriptive manner. Results Out of the 27 federative units, 55% distributed food kits, 26% supplied food cards/vouchers and 19% provided food kits and food cards/vouchers. As to the scope, 37% maintained general service, 30% attended schoolchildren from families registered in the Brazilian cash transfer program (Bolsa Família) and 26% attended schoolchildren from families registered in the Underprivileged Families Registry. Conclusion The National School Feeding Program was weak in terms of assuring the Human Right to Adequate Food and Food and Nutrition Security. The slowness of the federal administration and the gaps in the regulations issued may explain the changes in the reported strategies, which, in their majority, violate the principle of universality.

The data are drawn from the OPE Campus Safety and Security Statistics website database to which crime statistics and fire statistics (as of the 2010 data collection) are submitted annually, via a web-based data collection, by all postsecondary institutions that receive Title IV funding (i.e., those that participate in federal student aid programs). This data collection is required by the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act and the Higher Education Opportunity Act.

The development plan (BPL) contains the legally binding determinations for the urban planning order. In principle, the development plan must be developed from the land use plan. The available data is the development plan “Noise Protection Wall/Sports School and Cultural Area 2nd Amendment” of the municipality of Friolzheim from XPlanung 5.0. Description: Noise protection wall/sports school and culture area 2. Change.

Context

The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

Content

This dataset combines key education statistics from a variety of sources to provide a look at global literacy, spending, and access.

For more information, see the World Bank website.

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:world_bank_health_population

http://data.worldbank.org/data-catalog/ed-stats

https://cloud.google.com/bigquery/public-data/world-bank-education

Citation: The World Bank: Education Statistics

Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @till_indeman from Unplash.

Inspiration

Of total government spending, what percentage is spent on education?