In 2022, about 37.7 percent of the U.S. population who were aged 25 and above had graduated from college or another higher education institution, a slight decline from 37.9 the previous year. However, this is a significant increase from 1960, when only 7.7 percent of the U.S. population had graduated from college. Demographics Educational attainment varies by gender, location, race, and age throughout the United States. Asian-American and Pacific Islanders had the highest level of education, on average, while Massachusetts and the District of Colombia are areas home to the highest rates of residents with a bachelor’s degree or higher. However, education levels are correlated with wealth. While public education is free up until the 12th grade, the cost of university is out of reach for many Americans, making social mobility increasingly difficult. Earnings White Americans with a professional degree earned the most money on average, compared to other educational levels and races. However, regardless of educational attainment, males typically earned far more on average compared to females. Despite the decreasing wage gap over the years in the country, it remains an issue to this day. Not only is there a large wage gap between males and females, but there is also a large income gap linked to race as well.

In which post secondary sectors (private/public, 2 Year / CTC / 4 Year, in-state/out of state) do high school graduates enroll in the first year after graduation?

Educational Youth Indicators

School Enrollment, Attendance, Achievement, and Engagement

By City of Baltimore [source]

About this dataset

This dataset from the Baltimore Neighborhood Indicators Alliance-Jacob France Institute (BNIA-JFI) gathers information about education and youth across Baltimore. Through tracking 27 indicators grouped into seven categories - student enrollment and demographics, dropout rate and high school completion, student attendance, suspensions and expulsions, elementary and middle school student achievement, high school performance, youth labor force participation, and youth civic engagement - BNIA-JFI paints a comprehensive picture of education trends within the city limits. Data sourced from the Baltimore City Public School System (BCPSS), American Community Survey (ACS), as well as Maryland Department of Education allows for cross program comparison to better map connections between educational outcomes affected by neighborhood context. The 2009-2010 school year was used based on readily available data with an approximated 3.4% of address unable to be matched or geocoded and therefore not included in these calculations. Leveraging this data provides perspective to help guide decisions made at local government level that could impact thousands of lives in years ahead

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How to use the dataset

This dataset contains valuable information about the educational performance and youth engagement in Baltimore City. It provides data on 27 indicators, grouped into seven categories: student enrollment and demographics; dropout rate and high school completion; student attendance, suspensions and expulsions; elementary and middle school student achievement; high school performance; youth labor force participation; and youth civic engagement. This dataset can be used to answer important questions about education in Baltimore, such as examining the relationship between community conditions and educational outcomes.

Before using this dataset, it’s important to understand the source of data for each indicator (e.g., Baltimore City Public School System, American Community Survey) so you can understand potential limitations inherent in each data set. Additionally, keep in mind that this dataset does not include students whose home address cannot be geocoded or matched between datasets due to inconsistency of information or other issues - this means that comparisons between some of these indicators may not be as accurate as is achievable with other datasets available from sources such as the Maryland Department of Education or the Baltimore City Public Schools System.

Once you are familiar with where the data comes from you can use it to answer these questions by exploring different trends within Baltimore city over time:

• How have student enrollment numbers changed over time?
• What has been the overall trend in dropout rates across elementary schools?
• Are there any differences in student attendance based on school type?
• What correlations exist between neighborhood community characteristics (such as crime rates or poverty levels), and academic achievement scores?
• How have rates of labor force participation among adolescents shifted year-over-year?

And more! By looking at trends by geography within this diverse city we can gain valuable insight into what factors may play a role influencing educational outcomes for children growing up in different areas around Baltimore City - an essential step for developing methodologies for successful policy interventions targeting our most vulnerable populations!

Research Ideas

• Analyzing the correlation between student achievement and socio-economic status of the neighborhoods in which students live.
• Creating targeted policies that are tailored to address specific educational issues showcased in each Baltimore neighborhood demographic.
• Using data visualizations to demonstrate to residents and community leaders how their area is performing compared to other communities in terms of education, dropout rates, suspension rates, and more

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. [See Other Information](https://creativecommons.org/public...

What percent of high school graduates enrolled in post secondary institutions in the first year after graduation?

Historical Dataset of Positive Outcomes Charter School District is provided by PublicSchoolReview and contain statistics on metrics:Comparison of Diversity Score Trends,Total Revenues Trends,Total Expenditure Trends,Average Revenue Per Student Trends,Average Expenditure Per Student Trends,Reading and Language Arts Proficiency Trends,Math Proficiency Trends,Science Proficiency Trends,Graduation Rate Trends,Overall School District Rank Trends,Asian Student Percentage Comparison Over Years (1999-2023),Hispanic Student Percentage Comparison Over Years (2001-2023),Black Student Percentage Comparison Over Years (1998-2023),White Student Percentage Comparison Over Years (1998-2023),Two or More Races Student Percentage Comparison Over Years (2011-2023),Comparison of Students By Grade Trends

According to a 2023 survey, over half of teachers in the United States believe that artificial intelligence (AI) has improved educational outcomes of the teaching and learning process. In contrast, only 18 percent felt that AI had hindered educational outcomes in that year.

College Completion Dataset

Graduation Rates, Race, Efficiency Measures and More

By Jonathan Ortiz [source]

About this dataset

This College Completion dataset provides an invaluable insight into the success and progress of college students in the United States. It contains graduation rates, race and other data to offer a comprehensive view of college completion in America. The data is sourced from two primary sources – the National Center for Education Statistics (NCES)’ Integrated Postsecondary Education System (IPEDS) and Voluntary System of Accountability’s Student Success and Progress rate.

At four-year institutions, the graduation figures come from IPEDS for first-time, full-time degree seeking students at the undergraduate level, who entered college six years earlier at four-year institutions or three years earlier at two-year institutions. Furthermore, colleges report how many students completed their program within 100 percent and 150 percent of normal time which corresponds with graduation within four years or six year respectively. Students reported as being of two or more races are included in totals but not shown separately

When analyzing race and ethnicity data NCES have classified student demographics since 2009 into seven categories; White non-Hispanic; Black non Hispanic; American Indian/ Alaskan native ; Asian/ Pacific Islander ; Unknown race or ethnicity ; Non resident with two new categorize Native Hawaiian or Other Pacific Islander combined with Asian plus students belonging to several races. Also worth noting is that different classifications for graduate data stemming from 2008 could be due to variations in time frame examined & groupings used by particular colleges – those who can’t be identified from National Student Clearinghouse records won’t be subjected to penalty by these locations .

When it comes down to efficiency measures parameters like “Awards per 100 Full Time Undergraduate Students which includes all undergraduate completions reported by a particular institution including associate degrees & certificates less than 4 year programme will assist us here while we also take into consideration measures like expenditure categories , Pell grant percentage , endowment values , average student aid amounts & full time faculty members contributing outstandingly towards instructional research / public service initiatives .

When trying to quantify outcomes back up Median Estimated SAT score metric helps us when it is derived either on 25th percentile basis / 75th percentile basis with all these factors further qualified by identifying required criteria meeting 90% threshold when incoming students are considered for relevance . Last but not least , Average Student Aid equalizes amount granted by institution dividing same over total sum received against what was allotted that particular year .

All this analysis gives an opportunity get a holistic overview about performance , potential deficits &

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How to use the dataset

This dataset contains data on student success, graduation rates, race and gender demographics, an efficiency measure to compare colleges across states and more. It is a great source of information to help you better understand college completion and student success in the United States.

In this guide we’ll explain how to use the data so that you can find out the best colleges for students with certain characteristics or focus on your target completion rate. We’ll also provide some useful tips for getting the most out of this dataset when seeking guidance on which institutions offer the highest graduation rates or have a good reputation for success in terms of completing programs within normal timeframes.

Before getting into specifics about interpreting this dataset, it is important that you understand that each row represents information about a particular institution – such as its state affiliation, level (two-year vs four-year), control (public vs private), name and website. Each column contains various demographic information such as rate of awarding degrees compared to other institutions in its sector; race/ethnicity Makeup; full-time faculty percentage; median SAT score among first-time students; awards/grants comparison versus national average/state average - all applicable depending on institution location — and more!

When using this dataset, our suggestion is that you begin by forming a hypothesis or research question concerning student completion at a given school based upon observable characteristics like financ...

The University of Washington - Beyond High School (UW-BHS) project surveyed students in Washington State to examine factors impacting educational attainment and the transition to adulthood among high school seniors. The project began in 1999 in an effort to assess the impact of I-200 (the referendum that ended Affirmative Action) on minority enrollment in higher education in Washington. The research objectives of the project were: (1) to describe and explain differences in the transition from high school to college by race and ethnicity, socioeconomic origins, and other characteristics, (2) to evaluate the impact of the Washington State Achievers Program, and (3) to explore the implications of multiple race and ethnic identities. Following a successful pilot survey in the spring of 2000, the project eventually included baseline and one-year follow-up surveys (conducted in 2002, 2003, 2004, and 2005) of almost 10,000 high school seniors in five cohorts across several Washington school districts. The high school senior surveys included questions that explored students' educational aspirations and future career plans, as well as questions on family background, home life, perceptions of school and home environments, self-esteem, and participation in school related and non-school related activities. To supplement the 2000, 2002, and 2003 student surveys, parents of high school seniors were also queried to determine their expectations and aspirations for their child's education, as well as their own educational backgrounds and fields of employment. Parents were also asked to report any financial measures undertaken to prepare for their child's continued education, and whether the household received any form of financial assistance. In 2010, a ten-year follow-up with the 2000 senior cohort was conducted to assess educational, career, and familial outcomes. The ten year follow-up surveys collected information on educational attainment, early employment experiences, family and partnership, civic engagement, and health status. The baseline, parent, and follow-up surveys also collected detailed demographic information, including age, sex, ethnicity, language, religion, education level, employment, income, marital status, and parental status.

Labour market outcomes of postsecondary graduates, employment status, annual earnings, work placements, by province of residence at interview, level and field of study and gender.

The American Institutes for Research conducted a multisite randomized study that tested an online learning model for credit recovery at 24 high schools in Los Angeles, California in 2018 and 2019. The study focused on first-year high school students who failed Algebra 1 or English 9 (their ninth-grade English course) and retook the course during the summer before their second year of high school. Within each participating school, we used a lottery to determine whether each student was placed in either the school’s typical teacher-directed class (business-as-usual control condition) or a class that used an online learning model (treatment condition). For the online learning model, an online provider supplied the main course content, and the school provided a subject-appropriate, credentialed in-class teacher who could supplement the digital content with additional instruction.The study compared outcomes of students assigned to the treatment condition to outcomes of students assigned to the control condition. Analyses focused both on proximal outcomes (ex: student course experiences, content knowledge, and credit recovery rates) and distal outcomes (ex: on-time graduation and cumulative credits earned by the end of the 4th year of high school). We estimated average treatment effects for the intent-to-treat sample using regression models that control for student characteristics and randomization blocks. We conducted separate analyses for students who failed Algebra 1 and students who failed at least one semester of their English 9 course.This ICPSR data deposit includes our final analytical dataset and three supplemental files. Data come from three sources: (1) extant district data on student information and academic outcomes, (2) end-of-course surveys of students’ and teachers’ experiences, and (3) end-of-course test of students’ content knowledge. Data fields include:Sample information: term, school (anonymized), teacher (anonymized), course, randomization block, student cohort, treatment statusDemographics: sex, race/ethnicity, National School Lunch Program status, inclusion in the Gifted/Talented program, Special Education status, and English language learner statusPre-treatment information (treatment group only): 9th grade GPA, 9th grade attendance rate, number of 9th grade courses failed, 8th grade test scoresOnline course engagement information: percentage of online course completed, average score on online activities, minutes spent in online platformStudent survey data: responses a survey administered at the end of the course for treatment and control students. Questions cover degree of student engagement with the course, perceptions of teacher support and course difficulty, and clarity of course expectations.End-of-course test data: answers and scores on an end-of-course assessment administered to treatment and control students to evaluate content knowledge (Algebra 1 or English 9). The test did not count towards the final course grade and included 17-20 multiple choice questions.Academic outcomes: grade in credit recovery course, credits attempted/earned in each year of high school, GPA in each year of high school, credits/GPA in math and ELA in each year of high school, indicator for on-time high school graduation, 10th grade PSAT scoresTeacher survey and logs: teacher-reported logs on the use of different instructional activities and responses to surveys about course pacing, content, goals, and degree of student support

Graduation Outcomes - 4 Year Outcome - Cohorts of 2001 through 2006 (Classes of 2005 through 2010)

•The New York State calculation method was first adopted for the Cohort of 2001 (Class of 2005). The cohort consists of all students who first entered 9th grade in a given school year (e.g., the Cohort of 2006 entered 9th grade in the 2006-2007 school year). Graduates are defined as those students earning either a Local or Regents diploma and exclude those earning either a special education (IEP) diploma or GED. For the most recent cohort, graduation rates as of both June and August (including summer graduates) are reported.

•Records with cohorts of 20 students or less are suppressed. August outcomes are only reported for the most recent cohort.

•August outcomes include all June and August graduates.

•In school-level reporting, students who were in a school for less than 5 months are not included in the school’s cohort, but are included in citywide totals.

•School level results are not presented for District 79 schools, but their outcomes are included in citywide totals.

The New York State calculation method was first adopted for the Cohort of 2001 (Class 2005). The cohort consists of al students who first entered 9th grade in a given school year (e.g., the cohort of 2006 entered 9th grade in 2006-2007 school year). Graduates are defined as those students earning either a local or regents diploma and exclude those earning either a special education (IEP) diploma for GED. In order to comply with FERPA regulations on public reporting of education outcomes, rows with a cohort of 20 or fewer students are suppressed. Due to small number of students identified as Native American or Multi-Racial these ethnicities are not reported on the Ethnicity tab, however these students are included in the counts on all other tabs.

This dataset shows broad performance metrics for education for Maryland as a whole, from School Years 2005/2006 to 2013/2014. Data are provided by the Maryland State Department of Education (MSDE) and were originally submitted to the Governor's Office.

Associations between preterm birth and (a) postsecondary education enrollment (age 18–22 years) and (b) postsecondary education attainment (age 22–27 years) for individuals born in 1991–1996 in Canada.

Analysis of ‘2005-2010 Graduation Outcomes - School Level’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/9bce6d7c-2f2a-450d-a539-a11c412beabb on 28 January 2022.

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

Graduation Outcomes - Cohorts of 2001 through 2006 (Classes of 2005 through 2010) ¶¶•The New York State calculation method was first adopted for the Cohort of 2001 (Class of 2005). The cohort consists of all students who first entered 9th grade in a given school year (e.g., the Cohort of 2006 entered 9th grade in the 2006-2007 school year). Graduates are defined as those students earning either a Local or Regents diploma and exclude those earning either a special education (IEP) diploma or GED. For the most recent cohort, graduation rates as of both June and August (including summer graduates) are reported. ¶¶•Records with cohorts of 20 students or less are suppressed. August outcomes are only reported for the most recent cohort. ¶¶•August outcomes include all June and August graduates. In school-level reporting, students who were in a school for less than 5 months are not included in the school’s cohort, but are included in citywide totals. ¶¶•School level results are not presented for District 79 schools, but their outcomes are included in citywide totals. ¶¶•Schools are listed by their current DBN's.

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

Public schools have managed to maintain revenue growth despite significant shifts in funding, enrollment and parental preferences. Class sizes are shrinking every year as birth rates drop and the high school retention rate stagnates, straining revenue as smaller schools see lessened funding from governments. Public schools have contended with heightened competition from alternative education options, especially homeschooling and private institutions, as parents seek more personalized educational experiences. States have increasingly adopted school choice systems, allowing parents to use public funds or tax credits to pay for private schooling. The Trump administration has taken steps to promote these programs even more and has proposed establishing a federal voucher system. Despite heightened competition and a rigorous competitive atmosphere, strong per-pupil funding amid strong state and local budgets has buoyed public schools. Public schools' revenue has been climbing at a CAGR of 1.4% to an estimated $1.0 billion over the five years through 2025, including a rise of 0.9% in 2025 alone. Governments fully fund public schools. Support from state and local governments is especially vital, as they provide nearly nine-tenths of public schools' revenue. Despite a slight dip in 2022, strong tax income pushed up government funding for primary and secondary schools by 6.2% in 2023. These resources are enabling public schools to invest in tutoring and counseling to improve their educational outcomes and better compete with alternative primary and secondary schools. Public schools also used funds to help transition to online and augmented education and have avoided taking on further losses as shrinking class sizes leave them without pressure to continue purchasing new laptops or tablets. Still, public schools are not profitable and largely operate at a loss every year. Public schools are set to face a continued drop in enrollment as well as intensifying competition. To sustain revenue and support, schools will focus on retaining students and improving academic outcomes despite potential federal funding changes. The expansion of school choice programs will compel public schools to enhance their quality and offer additional services like after-school programs to sustain enrollment and win parental support as families gain more access to private schools. Still, charter schools will leverage their unique value propositions to remain competitive and buoy enrollment in the public school system. Public schools' revenue is set to stagnate, swelling at a CAGR of just 0.2% to an estimated $1.0 billion through the end of 2030.

In which post secondary institutions do graduates of WA public high schools enroll in the first year after HS graduation?

See notice below about this dataset

This dataset provides the number of graduates who enrolled in each type of postsecondary education per district.

Wage records are obtained from the Massachusetts Department of Unemployment Assistance (DUA) using a secure, anonymized matching process with limitations. For details on the process and suppression rules, please visit the Employment and Earnings of High School Graduates dashboard.

This dataset is one of three containing the same data that is also published in the Employment and Earnings of High School Graduates dashboard: Average Earnings by Student Group Average Earnings by Industry College and Career Outcomes

List of Outcomes

• Total Postsecondary Enrollment
• In-State Public 2-Year
• In-State Public 4-Year
• In-State Private
• Out-of-State
• Total Employed
• Total Missing

The data link between high school graduates and future earnings makes it possible to follow students beyond high school and college into the workforce, enabling long-term evaluation of educational programs using workforce outcomes.

While DESE has published these data in the past, as of June 2025 we are temporarily pausing updates due to an issue conducting the link that was brought to our attention in 2023 by a team of researchers. The issue impacts the earnings information for students who never attended a postsecondary institution or who only attended private or out-of-state colleges or universities, beginning with the 2017 high school graduation cohort, with growing impact in each successive high school graduation cohort.

The issue does not impact the earnings information for students who attended a Massachusetts public institution of higher education, and earnings data for those students will continue to be updated.

Once a solution is found, the past cohorts of data with low match rates will be updated. DESE and partner agencies are exploring linking strategies to maximize the utility of the information.

More detailed information can be found in the attached memo provided by the research team from the Annenberg Institute. We thank them for calling this issue to our attention.