During a survey conducted in Spring 2023 in the United States, the most popular factor for choosing online education was the affordability of the program, with 77 percent of respondents reporting this was one of their top three reasons. The second most popular factor was the reputation of the school or program.

This statistic shows the opinion of postsecondary students in the United States on whether they would have attended community college after high school if it had been free in 2017. In 2017, 46 percent of respondents said that if community college had been free it would not have changed their decisions about where to attend school after high school.

We leverage variation in the adoption of coeducation by U.S. women's colleges to study how exposure to a mixed-gender collegiate environment affects women's human capital investments. Our event-study analyses of newly collected historical data find a 3.0-3.5 percentage-point (30-33%) decline in the share of women majoring in STEM. While coeducation caused a large influx of male peers and modest increase in male faculty, we find no evidence that it altered the composition of the female student body or other gender-neutral inputs. Extrapolation of our main estimate suggests that coeducational environments explain 36% of the current gender gap in STEM.

In a survey conducted in August 2020 among high school seniors in the United States, 21 percent of respondents said that coronavirus pandemic has made them somewhat less likely to want to enroll in college. 54 percent of respondents reported that the pandemic has had no difference on their desire to enroll in college.

This dataset tracks annual american indian student percentage from 2005 to 2008 for Challenges, Choices & Images Charter School vs. Colorado and School District No. 1 In The County Of Denver And State Of C

This statistic shows the distribution of factors that had an influence on student choice of college in the United States in 2015, by Pell Grant status. In 2015, about 34 percent of the non-Pell Grant students stated not being able afford their first choice college as an influential factor on their college decision in the United States.

This dataset tracks annual american indian student percentage from 2007 to 2011 for Choices Alternative Ed High School vs. Michigan and Fowlerville Community Schools School District

In 2022, 75 percent of college-bound students agreed that emails from colleges influenced their college decision-making process in the United States. Letters, text messages, video chats, and phone calls rounded out the top five influential communications from colleges in that year.

This dataset tracks annual american indian student percentage from 2001 to 2023 for San Juan Choices Charter vs. California and San Juan Unified School District

From 1980 to 2000, the rise in the US college/high school graduate wage gap coincided with increased geographic sorting as college graduates concentrated in high wage, high rent cities. This paper estimates a structural spatial equilibrium model to determine causes and welfare consequences of this increased skill sorting. While local labor demand changes fundamentally caused the increased skill sorting, it was further fueled by endogenous increases in amenities within higher skill cities. Changes in cities' wages, rents, and endogenous amenities increased inequality between high school and college graduates by more than suggested by the increase in the college wage gap alone. (JEL D31, I26, J24, J31, J61, R23)

This graph shows recent university graduates responses to a survey question about which major they would have chosen instead of the one they did in order improve their chances for success. The survey was coducted in the United States in 2012. 17 percent of graduates surveyed said they would have chosen a business major, like finance or accounting if they had it to do again.

This dataset tracks annual american indian student percentage from 1995 to 2022 for Atascadero Choices In Education Academy (Ace) vs. California and Atascadero Unified School District

Selection Criteria for Fall 2021 High School Admissions.

Success.ai’s Education Industry Data with B2B Contact Data for Education Professionals Worldwide enables businesses to connect with educators, administrators, and decision-makers in educational institutions across the globe. With access to over 170 million verified professional profiles, this dataset includes crucial contact details for key education professionals, including school principals, department heads, and education directors.

Whether you’re targeting K-12 educators, university faculty, or educational administrators, Success.ai ensures your outreach is effective and efficient, providing the accurate data needed to build meaningful connections.

Why Choose Success.ai’s Education Professionals Data?

1. Comprehensive Contact Information
2. Access verified work emails, direct phone numbers, and LinkedIn profiles for educators, administrators, and education leaders worldwide.

3. AI-driven validation guarantees 99% accuracy, ensuring the highest level of reliability for your outreach.

4. Global Reach Across Educational Roles

5. Includes profiles of K-12 teachers, university professors, education directors, and school administrators.

6. Covers regions such as North America, Europe, Asia-Pacific, South America, and the Middle East.

7. Continuously Updated Datasets

8. Real-time updates ensure that you’re working with the most current contact information, keeping your outreach relevant and timely.

9. Ethical and Compliant

10. Success.ai’s data is fully GDPR, CCPA, and privacy regulation-compliant, ensuring ethical data usage in all your outreach efforts.

Data Highlights:

• 170M+ Verified Professional Profiles: Includes educators and administrators across various levels of education.
• 50M Work Emails: Verified and AI-validated emails for seamless communication.
• 30M Company Profiles: Rich insights into educational institutions, supporting detailed targeting.
• 700M Global Professional Profiles: Enriched datasets for comprehensive outreach across the education sector.

Key Features of the Dataset:

1. Education Decision-Maker Profiles
2. Identify and connect with decision-makers at educational institutions, including principals, department heads, and education directors.

3. Reach K-12 educators, higher education faculty, and administrative professionals with relevant needs.

4. Advanced Filters for Precision Targeting

5. Filter by educational level, subject area, location, and specific roles to tailor your outreach campaigns for precise results.

6. AI-Driven Enrichment

7. Profiles are enriched with actionable data to provide valuable insights, ensuring your outreach efforts are impactful and effective.

Strategic Use Cases:

1. Educational Product and Service Marketing
2. Promote educational tools, software, or services to decision-makers in schools, colleges, and universities.

3. Build relationships with educators to present curriculum solutions, digital learning platforms, and teaching resources.

4. Recruitment and Talent Acquisition

5. Target educational institutions and administrators with recruitment solutions or staffing services for teaching and support staff.

6. Engage with HR professionals in the education sector to promote job openings and talent acquisition services.

7. Professional Development Programs

8. Reach educators and administrators to offer professional development courses, certifications, or training programs.

9. Provide online learning solutions to enhance the skills of educators worldwide.

10. Research and Educational Partnerships

11. Connect with education leaders for research collaborations, institutional partnerships, and academic initiatives.

12. Foster relationships with decision-makers to support joint ventures in the education sector.

Why Choose Success.ai?

1. Best Price Guarantee

2. Success.ai offers high-quality, verified data at the best possible prices, making it a cost-effective solution for your outreach needs.

3. Seamless Integration

4. Integrate this verified contact data into your CRM using APIs or download it in your preferred format for streamlined use.

5. Data Accuracy with AI Validation

6. With AI-driven validation, Success.ai ensures 99% accuracy for all data, providing you with reliable and up-to-date information.

7. Customizable and Scalable Solutions

8. Tailor data to specific education sectors or roles, making it easy to target the right contacts for your campaigns.

APIs for Enhanced Functionality:

1. Data Enrichment API

2. Enhance existing records in your database with verified contact data for education professionals.

3. Lead Generation API

4. Automate lead generation campaigns for educational services and products, ensuring your marketing efforts are more efficient.

Leverage Success.ai’s B2B Contact Data for Education Professionals Worldwide to connect with educators, administrators, and decision-makers in the education sector. With veri...

Historical Dataset of San Juan Choices Charter is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2003-2023),Total Classroom Teachers Trends Over Years (2002-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2002-2023),American Indian Student Percentage Comparison Over Years (2001-2023),Asian Student Percentage Comparison Over Years (2003-2023),Hispanic Student Percentage Comparison Over Years (2003-2023),Black Student Percentage Comparison Over Years (2003-2023),White Student Percentage Comparison Over Years (2003-2023),Native Hawaiian or Pacific Islander Student Percentage Comparison Over Years (2011-2014),Two or More Races Student Percentage Comparison Over Years (2012-2023),Diversity Score Comparison Over Years (2003-2023),Free Lunch Eligibility Comparison Over Years (2002-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2002-2023),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2011-2022),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2011-2022),Graduation Rate Comparison Over Years (2012-2022)

Key demographics of the first-year college students’ who were included, and excluded, from the roommate analysis.

Developing robust professional networks can help shape the trajectories of early career scientists. Yet, historical inequities in STEM fields make access to these networks highly variable across academic programs, and senior academics often have little time for mentoring. Here, we illustrate the success of a Virtual Lab Meeting Program (LaMP). In this program, we matched students (“Mentees”) with a more experienced researcher (“Mentors”) from a research group. The Mentees then attended the Mentors’ lab meetings during the academic year with two lab meetings specifically dedicated to the Mentee’s professional development. Survey results indicate that Mentees expanded their knowledge of the hidden curriculum as well as their professional network, while only requiring a few extra hours of their Mentor’s time over eight months. In addition, host labs benefitted from Mentees sharing new perspectives and knowledge in lab meetings. The diversity of the Mentees was significantly higher than the Mentors, suggesting that the program increased the participation of traditionally underrepresented groups. Finally, we found that providing a stipend was very important to many mentees. We conclude that Virtual Lab Meeting Programs can be an inclusive and cost-effective way to foster trainee development and increase diversity within STEM fields with little additional time commitment. Methods Running the Virtual Lab Meeting Training Program The first three months of the program require dedicated time for recruiting and matching Mentees and Mentors (for a summary of program timelines, see Figure 2). One month prior to the start of the academic year, we began to advertise the program by sharing a link to our webpage with potential mentors and mentees (https://rcn-ecs.github.io/VLMTP/; Figure 2). Then, we recruited mentors through a list of personal invitations, listservs, and members of the Evolution in Changing Seas RCN. We completed the process of recruiting 30-40 mentors approximately two weeks after the academic year started (Figure 2). Mentors were required to agree with a document that outlined expectations and best practices for including their Mentee in lab meetings (see Supplemental Materials). After mentors were recruited, we began the process of recruiting student Mentees. We generated an email contact list to contact as many participants as possible across a diverse group of scientific societies and institutions. The contact list consisted of scientific societies or diversity lists (e.g. Society for Advancement of Chicanos/Hispanics & Native Americans in Science, Diversify Ecology and Evolutionary Biology, Black Women in Ecology Evolution and Marine Science, American Geophysical Union BRIDGE, Association for the Sciences of Limnology and Oceanography Multicultural program, Ecology Society of America SEEDs program, Asian Americans & Pacific Islanders in Geosciences), and a list of 608 professors teaching courses in biology, ecology, or evolution at Historically Black Colleges and Universities, Hispanic Serving Institutions, and Tribal Colleges and Universities. In addition, we advertised to the RCN-ECS listserv and asked colleagues to distribute the information among peers. To apply, Mentees submitted a 300-word statement that described their current research interests and/or experiences related to the themes under the Evolution in Changing Seas RCN, future career interests, how interactions with a host lab would help to advance their careers and/or support their professional development, and a description of how their participation in this program would help to increase diversity (broadly defined) within the network. They also (i) answered questions about their time zone, (ii) listed their top three choices for mentors, (iii) selected two keywords that described their research interests from a list, (iv) submitted a CV or resume, and (v) optionally answered demographic questions. Approximately six weeks into the academic year, we closed applications for mentees and started pairing them with mentors. Matching was made by two members of the RCN diversity committee based on the Mentee’s academic interests, who they listed as their top three choices for a Mentor, and time zone alignment, taking into account how many Mentees could be assigned to a single Mentor (i.e. usually 1-2 Mentees per lab group). Due to high request rates for well-known Mentors, sometimes we were unable to match a Mentee with one of their top three choices. In the few cases where Mentees did not get their top choices, pairings were made based on affinity between Mentors’ and Mentees’ research interests. By the second month of the academic year, we had completed the process of pairing mentees with Mentors. Pairs were introduced to each other by email and reminded of the program guidelines and expectations (Supp Doc: Example Email). Over the course of the academic year, Mentees attended lab meetings on an independent basis. At the end of the academic year, we distributed stipends to students for their participation in the program. To obtain a stipend, students had to provide a letter from their Mentor that stated the student had completed the program requirements. Mentee and Mentor Surveys At the end of the academic year in 2022, we distributed surveys to Mentees and another survey to Mentors who had participated in the program (for complete surveys, see Supplemental Documents). Both surveys included optional questions on demographic information, year(s) of participation, activities that were part of lab meetings, potential for future collaborations, a Likert scale on how they ranked the program from 1 to 10, and open-ended feedback (Table 1, left column). We also had an open-ended question where participants were encouraged to leave constructive feedback. The Mentee survey included unique Likert scale questions on whether the program helped them extend their professional network, advance their expertise in subject matter, and how important the stipend was to completing the program. We also asked Mentees what kind of interactions most helped to advance their professional development, what knowledge they gained during the program, and whether they planned to continue interactions with the host lab (Table 1, middle column). The Mentor survey included questions on the number of Mentees hosted, professional development activities discussed in lab meetings, Mentee contributions to lab meetings, how much time mentors invested in the program, whether Mentees attended lab meetings beyond the program requirements, how many people attended their lab meetings, whether Mentees had 1:1 interactions with other lab members, and Likert questions on whether they agreed with statements regarding continued interactions and benefits of having the Mentee join lab meetings (Table 1, right column). IRB Review Our surveys were reviewed by the Institutional Review Board at Northeastern University (IRB #: 22-03-33) and were considered exempt (DHHS Review Category: EXEMPT, CATEGORY #2 Revised Common Rule 45CFR46.104(d)(2)(iii)).

School segregation is determined by residential sorting, but also by policy choices such as the drawing of attendance boundaries and school siting. This paper develops a new approach to understanding the importance of each of these factors, by combining detailed census data with attendance boundary maps for nearly 1,600 school districts. I find that residential segregation explains more than 100 percent of school segregation. On average, attendance boundaries create 5 percent more integration than a distance-minimizing baseline. School siting plays almost no role. Some local governments act to mitigate school segregation, although their impact is small compared to residential choice.

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...