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 Colleges and Universities feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Integrated Post Secondary Education System (IPEDS, http://nces.ed.gov/ipeds/), National Center for Education Statistics (NCES, https://nces.ed.gov/), US Department of Education for the 2018-2019 school year. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories. This feature class contains all MEDS/MEDS+ as approved by the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program (HSIP) Team. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the "Place Keyword" section of the metadata. This feature class does not have a relationship class but is related to Supplemental Colleges. Colleges and Universities that are not included in the NCES IPEDS data are added to the Supplemental Colleges feature class when found. This release includes the addition of 175 new records, the removal of 468 no longer reported by NCES, and modifications to the spatial location and/or attribution of 6682 records.

College-bound seniors are those students that complete the SAT Questionnaire when they register for the SAT and identify that they will graduate from high school in a specific year. For example, the 2010 college-bound seniors are those students that self-reported they would graduate in 2010.

Students are not required to complete the SAT Questionnaire in order to register for the SAT. Students who do not indicate which year they will graduate from high school will not be included in any college-bound senior report.

Students are linked to schools by identifying which school they attend when registering for a College Board exam. A student is only included in a school’s report if he/she self-reports being enrolled at that school.

For data science, this dataset offers a rich source for exploratory data analysis, predictive modeling, and statistical testing. Researchers can explore correlations between SAT scores and other factors like school resources, student-teacher ratios, or geographic locations.

• Exploratory Data Analysis (EDA): Data scientists can use descriptive statistics and visualization techniques to understand the distribution of scores, check for outliers, and identify patterns or anomalies in the data.
• Predictive Modeling: Building models to predict SAT scores based on various predictors, such as school demographics or previous academic performance. This could include regression analysis or more complex machine learning algorithms.
• Time Series Analysis: If data across multiple years were available, analyzing trends over time would be possible, helping in understanding improvements or declines in performance. Comparative Analysis: Comparing scores across different schools or districts to evaluate disparities in educational achievement.
• Statistical Testing: Conducting hypothesis tests to see if the differences in performances across groups (e.g., by geographic region or school type) are statistically significant.

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

Data from the Ministry of Colleges and Universities' College Enrolment Statistical Reporting system.

Provides aggregated key enrolment data for college students, such as:

• Fall term headcount enrolment by campus, credential pursued and level of study
• Fall term headcount enrolment by program and Classification of Instructional Program
• Fall term headcount enrolment by student status in Canada and country of citizenship by institution
• Fall term headcount enrolment by student demographics (e.g., gender, age, first language)

To protect privacy, numbers are suppressed in categories with less than 10 students.

Related

• College enrolments - 1996 to 2011
• University enrolment
• Enrolment by grade in secondary schools
• School enrolment by gender
• Second language course enrolment
• Course enrolment in secondary schools
• Enrolment by grade in elementary schools

There were approximately 18.58 million college students in the U.S. in 2022, with around 13.49 million enrolled in public colleges and a further 5.09 million students enrolled in private colleges. The figures are projected to remain relatively constant over the next few years.

What is the most expensive college in the U.S.? The overall number of higher education institutions in the U.S. totals around 4,000, and California is the state with the most. One important factor that students – and their parents – must consider before choosing a college is cost. With annual expenses totaling almost 78,000 U.S. dollars, Harvey Mudd College in California was the most expensive college for the 2021-2022 academic year. There are three major costs of college: tuition, room, and board. The difference in on-campus and off-campus accommodation costs is often negligible, but they can change greatly depending on the college town.

The differences between public and private colleges Public colleges, also called state colleges, are mostly funded by state governments. Private colleges, on the other hand, are not funded by the government but by private donors and endowments. Typically, private institutions are  much more expensive. Public colleges tend to offer different tuition fees for students based on whether they live in-state or out-of-state, while private colleges have the same tuition cost for every student.

The National Survey of College Graduates is a repeated cross-sectional biennial survey that provides data on the nation's college graduates, with a focus on those in the science and engineering workforce. This survey is a unique source for examining the relationship of degree field and occupation in addition to other characteristics of college-educated individuals, including work activities, salary, and demographic information.

This dataset provides information for Academic Years 2017-2021 which included: By College and VCCS System:

1) Annual Headcount and FTEs 2) Gender (categories are: Female & Male; Unknown may be inferred) 3) Ethnicity (categories are: American Indian & Alaskan Native, Asian, Black & African-American, Native Hawaiian & Pacific Islander, Hispanic, Two or More Races, Unknown/Not Specified, and White) 4) Age (categories are: 17 and Under, 18-19, 20-21, 22-24, 25-29, 30-34, 35-39, 40-49, 50-64, & 65 and Over) 5) 18-Month Outcomes for Dual-Enrolled High School Grads by Year (categories are: Total Grads, Continued in any Higher Ed program, Employed with no Higher Ed, and Unknown) 6) 18-Month Outcomes for VCCS Graduates by Year (categories are: Total Grads, Continued at VCCS, Transferred to a 4yr college, Employed with no Higher Ed, and Unknown)

For Fiscal Years 2018-2021, by Service Area and VCCS System:

1) Fast Forward Credentialers Employed by Fiscal Year (categories are: Total Distinct Students, Employed within 6 Months, Employed within 12 Months, and Employed within 18 Months)

Notes:

1) Headcounts are Unduplicated student counts. 2) One FTE represents 30 credit hours of classes taken by a student over an academic year and is calculated on an annual basis by taking the total credit hours taught divided by 30. 3) 2017 Fiscal Year Fast Forward data was not included as it was considered incomplete- the Fast Forward program began in 2017 and did not encompass all areas for the entire year.
4) In Workforce (Fast Forward data) the service region for the Richmond Metro Area is called CCWA (Community College Workforce Alliance) and combines data for Brightpoint and J Sargeant Reynolds. 4a) Therefore, there are no Reynolds data entries for Fast Forward variables. All CCWA data is listed under Brightpoint for this portion of the data set.
5) 18-Month Outcomes for Fast Forward Credentialers are cumulative (6 months to 12 months to 18 months)

This dataset contains data on the number of students participating in designated Early College programs since school year 2020-21. Early College is a program that designates partnerships between high schools and colleges to support high school students to complete college courses. The list of designated partnerships is available here.

Students are counted in this dataset if they are marked as an Early College student by the district. The district also submits each student's affiliation with a single institution of higher education (IHE), though some Early College students take credits at more than one IHE. The period column allows you to filter for Fall or Spring, or to see the full-year deduplicated count of participants.

The dataset is updated after each semester, when the SIMS collection for that semester is certified.

The data here are the same as the participation data in the Early College Dashboard.

Data note: For the Fall 2021 collection, only 2 digits of the college code were stored, so college names could not be loaded. The incomplete 2-digit codes are shown in this dataset, but the college name field is blank for that collection.

Dual Enrollment Programs and Courses for High School Students, 2002-03 (PEQIS 14), is a study that is part of the Postsecondary Education Quick Information System (PEQIS) program; program data is available since 1997 at . PEQIS 14 (https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2009045) is a cross-sectional survey that collected information on the topic of dual enrollment of high school students at postsecondary institutions. 1,600 Title IV degree-granting postsecondary institutions in the 50 United States and the District of Columbia were sampled. The study was conducted using online or paper surveys. The overall response rates were 92 percent weighted and 91 percent unweighted. Key statistics produced from PEQIS 14 were information on the prevalence of college course-taking by high school students at their institutions during the 2002-03 12-month academic year, both within and outside of dual enrollment programs. Among institutions with dual enrollment programs, additional information was obtained on the characteristics of programs, including course location and type of instructors, program and course curriculum, academic eligibility requirements, and funding.

Analysis of ‘U.S. News and World Report’s College Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/flyingwombat/us-news-and-world-reports-college-data on 28 January 2022.

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

Context

Statistics for a large number of US Colleges from the 1995 issue of US News and World Report.

Content

A data frame with 777 observations on the following 18 variables.

Private A factor with levels No and Yes indicating private or public university

Apps Number of applications received

Accept Number of applications accepted

Enroll Number of new students enrolled

Top10perc Pct. new students from top 10% of H.S. class

Top25perc Pct. new students from top 25% of H.S. class

F.Undergrad Number of fulltime undergraduates

P.Undergrad Number of parttime undergraduates

Outstate Out-of-state tuition

Room.Board Room and board costs

Books Estimated book costs

Personal Estimated personal spending

PhD Pct. of faculty with Ph.D.’s

Terminal Pct. of faculty with terminal degree

S.F.Ratio Student/faculty ratio

perc.alumni Pct. alumni who donate

Expend Instructional expenditure per student

Grad.Rate Graduation rate

Source

This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.

The dataset was used in the ASA Statistical Graphics Section’s 1995 Data Analysis Exposition.

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

New York City school level College Board SAT results for the graduating seniors of 2010. Records contain 2010 College-bound seniors mean SAT scores. Records with 5 or fewer students are suppressed (marked ‘s’). College-bound seniors are those students that complete the SAT Questionnaire when they register for the SAT and identify that they will graduate from high school in a specific year. For example, the 2010 college-bound seniors are those students that self-reported they would graduate in 2010. Students are not required to complete the SAT Questionnaire in order to register for the SAT. Students who do not indicate which year they will graduate from high school will not be included in any college-bound senior report. Students are linked to schools by identifying which school they attend when registering for a College Board exam. A student is only included in a school’s report if he/she self-reports being enrolled at that school. Data collected and processed by the College Board.

This dataset contains data on credits registered and earned by students in designated Early College programs since school year 2021-22. Early College is a program that designates partnerships between high schools and colleges to support high school students to complete college courses. The list of designated partnerships is available here.

Students are counted in this dataset if they are marked as an Early College student by the district. Credits are counted in this dataset if they are submitted to DHE. The credits are counted based on where they are taken, even if that is an institution of higher ed (IHE) outside of the student's designated Early College partnership. Credits from Fall and Spring semester are counted; summer credits are not counted. Data includes any credits at public IHEs (Wentworth Institute of Technology data have not been submitted as of July 2024), and '22-23 data from private IHEs that are part of an Early College designated partnership.

The dataset is updated in the fall of each year to add in the previous year's credits. Credit counts are suppressed (hidden) for groups in which there are fewer than 6 students to protect student privacy, though those credits are still counted in totals.

The data here are the same as the credit data in the Early College Dashboard.

Dataset Description This dataset consists of academic and demographic information about 300 students from a university, which can be used for predicting academic outcomes, such as probation status. The dataset was simulated to represent a variety of student attributes across multiple categories like personal data, academic history, and other related information. The primary goal of this dataset is to analyze factors contributing to academic performance and identify students at risk of probation.

Column Descriptions Student No.: (Numeric) A unique identifier for each student. In this dataset, each student has a different ID number, making it a 100% unique column. Cohort: (Numeric) The year a student enrolled in the university. No missing values and consistent across the dataset. College: (Nominal) The name of the college the student belongs to. Examples include "Engineering," "Science," etc. No missing values. College Code: (Nominal) A numerical or alphanumerical code representing the college. This is an alternative representation of the "College" column. Major: (Nominal) The major field of study of the student. Some missing values (23%) represent students who haven’t declared a major or are in an undeclared status. Major Code: (Nominal) A code representing the major subject. Similar to the "Major" column, this has 23% missing values due to undeclared majors. Minor: (Nominal) The minor subject, if any, chosen by the student. This column has a high percentage of missing data (91%) since most students do not have minors. Spec: (Nominal) Specialization within the major field of study. Like the "Minor" column, this has 93% missing data as most students do not declare a specialization. Degree: (Numeric) The type of degree the student is pursuing (e.g., Bachelor's). In this dataset, all students are pursuing the same degree, so there are no missing values. Status: (Nominal) The current academic standing of the student (e.g., "Active," "Inactive"). No missing values. Load Status: (Nominal) The academic load status (e.g., "Full-time," "Part-time"). This column has very few missing values (1%). Gender: (Nominal) The gender of the student (e.g., "Male," "Female"). No missing values. Country: (Nominal) The country of origin of the student. Only 2 missing values, making it nearly complete. Governorate: (Nominal) The administrative region (governorate) the student comes from. This column has a small percentage of missing values (1%). Wellayah: (Nominal) The district or locality within the governorate. Around 1% of the data is missing. CGPA: (Numeric) The cumulative grade point average (CGPA) of the student. This field has 145 missing values, representing students without available CGPA records. Estimated Graduation Year: (Numeric) The expected year in which the student will graduate. No missing values. From HEAC: (Nominal) Indicates whether the student was admitted through the Higher Education Admission Center (HEAC). This column has 4% missing values. Admission Category: (Nominal) The category of admission (e.g., scholarship, self-funded). This column has a significant amount of missing data (98%), indicating that admission category data is either unavailable or irrelevant for most students. Birth Date: (Nominal) The birth date of the student. The dataset includes very few missing values (0%) and has been replaced by the derived feature "Age." Actual Graduation Date: (Nominal) The actual date on which a student graduates. More than half of the values are missing (54%), representing students who haven’t graduated yet. Withdrawal: (Nominal) Indicates whether the student has withdrawn from the university. This column has 89% missing data since the majority of students haven’t withdrawn. Marital Status: (Nominal) The marital status of the student (e.g., "Single," "Married"). No missing values. SQU Hostel: (Nominal) Indicates whether the student lives in the university hostel. No missing values. Percentage (Secondary School Score): (Nominal) The student’s percentage score from secondary school. No missing values. Probation Student: (Nominal) Indicates whether the student is under academic probation. This is the target variable for classification, with no missing values.

Record Details Total Records: 300 Total Attributes: 26 Missing Values: Some columns have a significant proportion of missing data (e.g., Minor, Spec, Major Code), while others have very few or no missing values (e.g., Gender, Cohort, College). Missing values were handled using a placeholder for clarity in certain columns.

This table gives an overview of government expenditure on regular education in the Netherlands since 1900. All figures presented have been calculated according to the standardised definitions of the OECD.

Government expenditure on education consists of expenditure by central and local government on education institutions and education. Government finance schools, colleges and universities. It pays for research and development conducted by universities. Furthermore it provides student grants and loans, allowances for school costs, provisions for students with a disability and child care allowances to households as well as subsidies to companies and non-profit organisations.

Total government expenditure is broken down into expenditure on education institutions and education on the one hand and government expenditure on student grants and loans and allowances for school costs to households on the other. If applicable these subjects are broken down into pre-primary and primary education, special needs primary education, secondary education, senior secondary vocational and adult education, higher professional education and university education. Data are available from 1900. Figures for the Second World War period are based on estimations due to a lack of source material.

The table also includes the indicator government expenditure on education as a percentage of gross domestic product (GDP). This indicator is used to compare government expenditure on education internationally. The indicator is compounded on the basis of definitions of the OECD (Organisation for Economic Cooperation and Development). The indicator is also presented in the StatLine table education; Education expenditure and CBS/OECD indicators. Figures for the First World War and Second World War period are not available for this indicator due to a lack of reliable data on GDP for these periods.

The statistic on education spending is compiled on a cash basis. This means that the education expenditure and revenues are allocated to the year in which they are paid out or received. However, the activity or transaction associated with the payment or receipt can take place in a different year.

Statistics Netherlands published the revised National Accounts in June 2018. Among other things, GDP has been adjusted upwards as a result of the revision. The revision has not been extended to the years before 1995. In the indicator “Total government expenditure as % of GDP”, a break occurs between 1994 and 1995 as a result of the revision.

Data available from: 1900

Status of the figures: The figures from 1995 to 2020 are final. The 2021 figures are revised provisional, the 2022 figures are provisional.

Changes on 7 December 2023: The revised provisional figures of 2021 and the provisional figures of 2022 have been added.

When will new figures be published? The final figures for 2021 will be published in the first quarter of 2024. The final figures for 2022 and the provisional figures for 2023 will be published in December 2024.

Participation rate in education, population aged 18 to 34, by age group and type of institution attended, Canada, provinces and territories. This table is included in Section E: Transitions and outcomes: Transitions to postsecondary education of the Pan Canadian Education Indicators Program (PCEIP). PCEIP draws from a wide variety of data sources to provide information on the school-age population, elementary, secondary and postsecondary education, transitions, and labour market outcomes. The program presents indicators for all of Canada, the provinces, the territories, as well as selected international comparisons and comparisons over time. PCEIP is an ongoing initiative of the Canadian Education Statistics Council, a partnership between Statistics Canada and the Council of Ministers of Education, Canada that provides a set of statistical measures on education systems in Canada.

This table gives an overview of expenditure on regular education within the Netherlands.

Government finance schools, colleges and universities. It pays for research which is done by universities on its behalf. Furthermore it provides student grants and loans, allowances for school costs, provisions for students with a disability and child care allowances as well as subsidies to companies and non-profit organisations. The government reclaims unjustified payments for student grants and loans and allowances for school costs. It also receives interest and repayments on student loans as well as EU grants for education.

Parents and/or students have to pay tuition fees for schools, colleges and universities, parent contributions and contributions for school activities. They also have to purchase books and materials, pay for transport from home to school and back for students who are not eligible for subsidised transport, pay for private tutoring, pay interest and repayments on student loans, and repay wrongfully received student grants, loans and allowances for school costs. Parents and/or students receive child care allowances, provisions for students with a disability and an allowance for school costs as well as student grants and loans and scholarships of companies.

Companies and non-profit organisations incur costs for supervising trainees and apprentices who combine learning with work experience. They also contribute to the cost of work related education of their employees and spend money on research that is outsourced to colleges for higher professional education and universities. Furthermore they contribute to the childcare allowances given to households and provide scholarships to students. Companies receive subsidies and tax benefits for the creation of apprenticeship places and trainee placements and for providing transport for pupils.

Organisations abroad contract universities in the Netherlands to undertake research for them. The European Union provides funds and subsidies for education to schools, colleges and universities as well as to the Dutch government. Foreign governments contribute to international schools in the Netherlands that operate under their nationality.

The table also contains various indicators used nationally and internationally to compare expenditure on education and place it in a broader context. The indicators are compounded on the basis of definitions of Statistics Netherlands and/or the OECD (Organisation for Economic Cooperation and Development). All figures presented have been calculated according to the standardised definitions of the OECD.

In this table tertiary education includes research and development, except for the indicator Expenditure on education institutions per student, excluding R & D.

The statistic on education spending is compiled on a cash basis. This means that the education expenditure and revenues are allocated to the year in which they are paid out or received. However, the activity or transaction associated with the payment or receipt can take place in a different year.

Statistics Netherlands published the revised National Accounts in June 2018. Among other things, GDP and total government expenditures have been adjusted upwards as a result of the revision.

Data available from: 1995

Status of the figures: The figures from 1995 to 2020 are final. The 2021 figures are revised provisional, the 2022 figures are provisional.

Changes as of 7 December 2023: The revised provisional figures of 2021 and the provisional figures of 2022 have been added.

When will new figures be published? The final figures for 2021 will be published in the first quarter of 2024. The final figures for 2022 and the provisional figures for 2023 will be published in December 2024.

Higher education plays a critical role in driving an innovative economy by equipping students with knowledge and skills demanded by the workforce.While researchers and practitioners have developed data systems to track detailed occupational skills, such as those established by the U.S. Department of Labor (DOL), much less effort has been made to document which of these skills are being developed in higher education at a similar granularity.Here, we fill this gap by presenting Course-Skill Atlas -- a longitudinal dataset of skills inferred from over three million course syllabi taught at nearly three thousand U.S. higher education institutions. To construct Course-Skill Atlas, we apply natural language processing to quantify the alignment between course syllabi and detailed workplace activities (DWAs) used by the DOL to describe occupations. We then aggregate these alignment scores to create skill profiles for institutions and academic majors. Our dataset offers a large-scale representation of college education's role in preparing students for the labor market.Overall, Course-Skill Atlas can enable new research on the source of skills in the context of workforce development and provide actionable insights for shaping the future of higher education to meet evolving labor demands, especially in the face of new technologies.

Data from the Ministry of Colleges and Universities' University Enrolment Statistical Reporting system.

Provides aggregated key enrolment data for university students, such as:

• Fall term headcount enrolment by campus, credential pursued and level of study
• Fall term headcount enrolment by program and Classification of Instructional Program
• Fall term headcount enrolment by student status in Canada and country of citizenship by institution
• Fall term headcount enrolment by student demographics (e.g., gender, age, first language)

To protect privacy, numbers are suppressed (NA) in categories with less than 10 students.

Related

• College enrolment
• College enrolments - 1996 to 2011
• Enrolment by grade in secondary schools
• School enrolment by gender
• Second language course enrolment
• Course enrolment in secondary schools

• Enrolment by grade in elementary schools

  *[e.g.]: example

This dataset contains the total annual unduplicated enrollment headcount and percentages by race and gender for undergraduate and graduate students at public community colleges and state universities in Massachusetts since 2014.

This dataset is 1 of 2 datasets that is also published in the interactive Annual Enrollment dashboard on the Department of Higher Education Data Center:

Public Postsecondary Annual Enrollment Public Postsecondary Annual Enrollment by Race and Gender

Related datasets: Public Postsecondary Fall Enrollment Public Postsecondary Fall Enrollment by Race and Gender

Notes: - Data appear as reported to the Massachusetts Department of Higher Education. - Annual enrollment refers to a 12 month enrollment period over one fiscal year (July 1 through June 30). - Figures published by DHE may differ slightly from figures published by other institutions and organizations due to differences in timing of publication, data definitions, and calculation logic. - Data for the University of Massachusetts are not included due to unique reporting requirements. See Fall Enrollment for HEIRS data on UMass enrollment. -The most common measure of enrollment is headcount of enrolled students. Annual headcount enrollment is unduplicated, meaning any individual student is only counted once per institution and fiscal year, even if they are enrolled in multiple terms. Enrollment can also be measured as full-time equivalent (FTE) students, a calculation based on the sum of credits carried by all enrolled students. In a fiscal year, 30 undergraduate credits = 1 undergraduate FTE, and 24 graduate credits = 1 graduate FTE at a state university.