Database is provided by ASL Marketing and covers the United States of America. With ASL Marketing Reaching GenZ has never been easier. Current high school student data customized by: Class year Date of Birth Gender GPA Geo Household Income Ethnicity Hobbies College-bound Interests College Intent Email

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...

The 2021-2022 School Learning Modalities dataset provides weekly estimates of school learning modality (including in-person, remote, or hybrid learning) for U.S. K-12 public and independent charter school districts for the 2021-2022 school year and the Fall 2022 semester, from August 2021 – December 2022.

These data were modeled using multiple sources of input data (see below) to infer the most likely learning modality of a school district for a given week. These data should be considered district-level estimates and may not always reflect true learning modality, particularly for districts in which data are unavailable. If a district reports multiple modality types within the same week, the modality offered for the majority of those days is reflected in the weekly estimate. All school district metadata are sourced from the https://nces.ed.gov/ccd/files.asp#Fiscal:2,LevelId:5,SchoolYearId:35,Page:1">National Center for Educational Statistics (NCES) for 2020-2021.

School learning modality types are defined as follows:

• In-Person: All schools within the district offer face-to-face instruction 5 days per week to all students at all available grade levels.
• Remote: Schools within the district do not offer face-to-face instruction; all learning is conducted online/remotely to all students at all available grade levels.
• Hybrid: Schools within the district offer a combination of in-person and remote learning; face-to-face instruction is offered less than 5 days per week, or only to a subset of students.

• School learning modality data provided here are model estimates using combined input data and are not guaranteed to be 100% accurate. This learning modality dataset was generated by combining data from four different sources: Burbio [1], MCH Strategic Data [2], the AEI/Return to Learn Tracker [3], and state dashboards [4-20]. These data were combined using a Hidden Markov model which infers the sequence of learning modalities (In-Person, Hybrid, or Remote) for each district that is most likely to produce the modalities reported by these sources. This model was trained using data from the 2020-2021 school year. Metadata describing the location, number of schools and number of students in each district comes from NCES [21].
• You can read more about the model in the CDC MMWR: https://www.cdc.gov/mmwr/volumes/70/wr/mm7039e2.htm" target="_blank">COVID-19–Related School Closures and Learning Modality Changes — United States, August 1–September 17, 2021.
• The metrics listed for each school learning modality reflect totals by district and the number of enrolled students per district for which data are available. School districts represented here exclude private schools and include the following NCES subtypes:
  • Public school district that is NOT a component of a supervisory union
  • Public school district that is a component of a supervisory union
  • Independent charter district
• “BI” in the state column refers to school districts funded by the Bureau of Indian Education.

• Data from August 1, 2021 to June 24, 2022 correspond to the 2021-2022 school year. During this time frame, data from the AEI/Return to Learn Tracker and most state dashboards were not available. Inferred modalities with a probability below 0.6 were deemed inconclusive and were omitted. During the Fall 2022 semester, modalities for districts with a school closure reported by Burbio were updated to either “Remote”, if the closure spanned the entire week, or “Hybrid”, if the closure spanned 1-4 days of the week.
• Data from August

AI in Student Assessment Market Outlook

As per our latest research, the AI in Student Assessment market size reached USD 2.13 billion in 2024 globally, fueled by the rapid adoption of artificial intelligence in education systems. The market is expected to grow at a robust CAGR of 18.7% during the forecast period, reaching an estimated USD 10.96 billion by 2033. This surge is primarily driven by the increasing demand for scalable, efficient, and personalized assessment solutions that cater to diverse learning needs, as well as the ongoing digital transformation initiatives across educational institutions worldwide.

A significant growth factor for the AI in Student Assessment market is the mounting pressure on educational institutions to deliver personalized and equitable learning experiences. Traditional assessment methods often fall short in addressing the unique learning trajectories and needs of individual students. AI-powered assessment tools are capable of analyzing vast datasets in real time, providing educators with actionable insights into student performance, learning gaps, and behavioral patterns. This enables the creation of tailored learning plans, early intervention strategies, and adaptive testing environments, ultimately improving student outcomes and engagement. The integration of AI not only enhances the accuracy of assessments but also reduces educator workload, allowing them to focus on high-value tasks such as mentoring and curriculum development.

Another critical driver is the increasing emphasis on formative and continuous assessment in both K-12 and higher education sectors. The shift from high-stakes, summative assessments to ongoing, formative evaluations requires tools that can efficiently process frequent, diverse, and complex data inputs. AI technologies, such as natural language processing and machine learning, are uniquely positioned to automate the grading of open-ended responses, provide instant feedback, and support adaptive learning environments. These capabilities are particularly valuable in remote and hybrid learning contexts, where traditional assessment methods may be impractical or inefficient. Furthermore, the ability of AI to minimize human bias and ensure consistency in grading is gaining traction among educational policymakers and accreditation bodies.

The proliferation of cloud computing and advancements in data analytics infrastructure are also propelling the growth of the AI in Student Assessment market. With cloud-based deployment, educational institutions can access scalable, cost-effective assessment platforms without the need for significant upfront investments in hardware or IT personnel. This democratizes access to advanced assessment technologies, particularly for resource-constrained schools and organizations. Additionally, the integration of AI with learning management systems (LMS) and enterprise resource planning (ERP) platforms enhances interoperability and streamlines administrative workflows. As a result, the adoption of AI-driven assessment solutions is expanding beyond traditional academic environments to include vocational training centers and corporate learning programs, further broadening the market’s potential.

Regionally, North America continues to dominate the AI in Student Assessment market, accounting for the largest share in 2024 due to early technology adoption, strong government support, and significant investments in EdTech innovation. Europe follows closely, driven by progressive education policies and widespread digitalization initiatives. The Asia Pacific region is emerging as a high-growth market, buoyed by increasing internet penetration, a large student population, and government-led digital education programs. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as local governments and private sector players invest in modernizing their education systems. The global landscape is characterized by dynamic regional trends, with each market presenting unique opportunities and challenges for AI-driven assessment solutions.

Component Analysis

The AI in Student Assessment market by component is broadly segmented into Software and Services. The software segment encompasses platforms and applications that leverage AI algorithms to automate grading, provide adaptive assessments, generate feedback, and support reporting. In 2024, the software segment accounted for the largest market share, driven by the prol

Reports in reference to local law 15; health instructor data by community school, city council district and school DBN.

Local Law 15 (2016) requires that the NYCDOE provide citywide Health Education Instructor data, disaggregated by community school district, city council district, and each individual school Data reported in this report is from the 2016-17 school year. This report provides the number of licensed full- time and part-time instructors, the number of instructors assigned to teach at least one health education class, the number and percentage of instructors who received professional development training and the total number and percentage of instructors attending multiple sessions of professional development. Data is reported from the 2016-17 school year.

Success.ai’s Education Industry Data provides access to comprehensive profiles of global professionals in the education sector. Sourced from over 700 million verified LinkedIn profiles, this dataset includes actionable insights and verified contact details for teachers, school administrators, university leaders, and other decision-makers. Whether your goal is to collaborate with educational institutions, market innovative solutions, or recruit top talent, Success.ai ensures your efforts are supported by accurate, enriched, and continuously updated data.

Why Choose Success.ai’s Education Industry Data? 1. Comprehensive Professional Profiles Access verified LinkedIn profiles of teachers, school principals, university administrators, curriculum developers, and education consultants. AI-validated profiles ensure 99% accuracy, reducing bounce rates and enabling effective communication. 2. Global Coverage Across Education Sectors Includes professionals from public schools, private institutions, higher education, and educational NGOs. Covers markets across North America, Europe, APAC, South America, and Africa for a truly global reach. 3. Continuously Updated Dataset Real-time updates reflect changes in roles, organizations, and industry trends, ensuring your outreach remains relevant and effective. 4. Tailored for Educational Insights Enriched profiles include work histories, academic expertise, subject specializations, and leadership roles for a deeper understanding of the education sector.

Data Highlights: 700M+ Verified LinkedIn Profiles: Access a global network of education professionals. 100M+ Work Emails: Direct communication with teachers, administrators, and decision-makers. Enriched Professional Histories: Gain insights into career trajectories, institutional affiliations, and areas of expertise. Industry-Specific Segmentation: Target professionals in K-12 education, higher education, vocational training, and educational technology.

Key Features of the Dataset: 1. Education Sector Profiles Identify and connect with teachers, professors, academic deans, school counselors, and education technologists. Engage with individuals shaping curricula, institutional policies, and student success initiatives. 2. Detailed Institutional Insights Leverage data on school sizes, student demographics, geographic locations, and areas of focus. Tailor outreach to align with institutional goals and challenges. 3. Advanced Filters for Precision Targeting Refine searches by region, subject specialty, institution type, or leadership role. Customize campaigns to address specific needs, such as professional development or technology adoption. 4. AI-Driven Enrichment Enhanced datasets include actionable details for personalized messaging and targeted engagement. Highlight educational milestones, professional certifications, and key achievements.

Strategic Use Cases: 1. Product Marketing and Outreach Promote educational technology, learning platforms, or training resources to teachers and administrators. Engage with decision-makers driving procurement and curriculum development. 2. Collaboration and Partnerships Identify institutions for collaborations on research, workshops, or pilot programs. Build relationships with educators and administrators passionate about innovative teaching methods. 3. Talent Acquisition and Recruitment Target HR professionals and academic leaders seeking faculty, administrative staff, or educational consultants. Support hiring efforts for institutions looking to attract top talent in the education sector. 4. Market Research and Strategy Analyze trends in education systems, curriculum development, and technology integration to inform business decisions. Use insights to adapt products and services to evolving educational needs.

Why Choose Success.ai? 1. Best Price Guarantee Access industry-leading Education Industry Data at unmatched pricing for cost-effective campaigns and strategies. 2. Seamless Integration Easily integrate verified data into CRMs, recruitment platforms, or marketing systems using downloadable formats or APIs. 3. AI-Validated Accuracy Depend on 99% accurate data to reduce wasted outreach and maximize engagement rates. 4. Customizable Solutions Tailor datasets to specific educational fields, geographic regions, or institutional types to meet your objectives.

Strategic APIs for Enhanced Campaigns: 1. Data Enrichment API Enrich existing records with verified education professional profiles to enhance engagement and targeting. 2. Lead Generation API Automate lead generation for a consistent pipeline of qualified professionals in the education sector. Success.ai’s Education Industry Data enables you to connect with educators, administrators, and decision-makers transforming global...

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

Time-Series Data (Teeny-Tiny Castle)

This dataset is part of a tutorial tied to the Teeny-Tiny Castle, an open-source repository containing educational tools for AI Ethics and Safety research.

  How to Use

from datasets import load_dataset

dataset = load_dataset("AiresPucrs/time-series-data", split = 'train')

New York City high school progress reporting on student progress, performance, school environment, college and career readiness and closing the achievement gap for fiscal year 2011 - 2012.

Directory of Department of Education High Schools in 2021.

One of OPT’s main functions is to plan efficient and fiscally responsible school bus routes. OPT staff use a variety of systems to generate and share bus route information with bus vendors and the public. Specific bus route paths cannot be publicly disclosed because they could reveal personally identifiable information about individual students. In this dataset, OPT has provided all the route information that does not risk disclosing personally identifiable information. School-age service for students in grades K through 12 are contracted with bus vendors on a per route basis. OPT also manages bus service for Pre-K students who require curb-to-curb service as per a student’s Individualized Education Plan (IEP). This Pre-K bus service is contracted on a per student basis, instead of per route. As a consequence of this difference, OPT does not design bus routes for Pre-K service, so those routes are not included in this dataset. There are a variety of different vehicles used on routes that serve students requiring curb-to-curb service because an Individualized Education Plan (IEP) indicates specific transportation needs. The standard bus is the only vehicle used for general education routes with students eligible for bus service but who do not have an IEP. Users may occasionally see a route without a garage assignment. Because this dataset is derived from a snapshot of a transactional system, there may be routes that are in the process of being assigned to a garage. In those cases, the garage information will appear as NULL until the assignment is complete.

AI21 Jamba-Specific Enkrypt Alignment Dataset

  Overview

The AI21 Jamba-Specific Enkrypt Alignment Dataset is a targeted dataset created by Enkrypt AI to improve the alignment of the AI21 Jamba-1.5-mini model. This dataset was developed using insights gained from Enkrypt AI’s custom red-teaming efforts on the Jamba-1.5-mini model.

  Data Collection Process

Enkrypt AI leveraged its proprietary SAGE-RT (Synthetic Alignment data Generation for Safety Evaluation and… See the full description on the dataset page: https://huggingface.co/datasets/enkryptai/Jamba-Alignment-Data.

This report provides data regarding students enrolled in New York City schools during the 2015-2016 school year, according to the guidelines set by Local Law 2011/042. At the citywide, borough and district levels, the DOE is required to report discharge, transfer and graduation counts by grade level (middle school only), cohort (high school only) and disability status. At the school level, the DOE is required to report discharge and transfer counts by grade level (middle school only), cohort (high school only), disability status broken down by, age as of 12/31 of the previous calendar year age, race/ethnicity, and gender.

Directory of The New York City Public High Schools 2013-2014

New York City Department of Education 2012 - 2013 High School Progress Report.

Data is used to identify the zoning for the High School grades.

Attendance data includes students in district 1-32, 75 (Special Education), district 79 (Alternative Schools & Programs), charter schools, home schooling. Home and hospital instruction are excluded. Pre-K data does not include NYC Early Education Centers or District Pre-K Centers therefore data is limited to those who attend K-12 schools that offer Pre-K. Transfer schools are included in citywide, borough, district counts but removed from school level file. Attendance is registered to school student is attending at the time. If a student attend multiple schools in a school year the data will be reflected in multiple schools. Chronically absence is defined if a student has an attendance rate of less than 90 percent ( students who are absent 10 percent or more of the total days).

The number of submitted and completed FAFSAs among first-time filing applicants no older than 19 at the cutoff date who will have received their high school diploma by the start of the school year to which they are applying for aid.

Lake County, Illinois Demographic Data. Explanation of field attributes: Total Population – The entire population of Lake County. White – Individuals who are of Caucasian race. This is a percent.African American – Individuals who are of African American race. This is a percent.Asian – Individuals who are of Asian race. This is a percent. Hispanic – Individuals who are of Hispanic ethnicity. This is a percent. Does not Speak English- Individuals who speak a language other than English in their household. This is a percent. Under 5 years of age – Individuals who are under 5 years of age. This is a percent. Under 18 years of age – Individuals who are under 18 years of age. This is a percent. 18-64 years of age – Individuals who are between 18 and 64 years of age. This is a percent. 65 years of age and older – Individuals who are 65 years old or older. This is a percent. Male – Individuals who are male in gender. This is a percent. Female – Individuals who are female in gender. This is a percent. High School Degree – Individuals who have obtained a high school degree. This is a percent. Associate Degree – Individuals who have obtained an associate degree. This is a percent. Bachelor’s Degree or Higher – Individuals who have obtained a bachelor’s degree or higher. This is a percent. Utilizes Food Stamps – Households receiving food stamps/ part of SNAP (Supplemental Nutrition Assistance Program). This is a percent. Median Household Income - A median household income refers to the income level earned by a given household where half of the homes in the area earn more and half earn less. This is a dollar amount. No High School – Individuals who have not obtained a high school degree. This is a percent. Poverty – Poverty refers to families and people whose income in the past 12 months is below the poverty level. This is a percent.