The data here is from the report entitled Trends in Enrollment, Credit Attainment, and Remediation at Connecticut Public Universities and Community Colleges: Results from P20WIN for the High School Graduating Classes of 2010 through 2016. The report answers three questions: 1. Enrollment: What percentage of the graduating class enrolled in a Connecticut public university or community college (UCONN, the four Connecticut State Universities, and 12 Connecticut community colleges) within 16 months of graduation? 2. Credit Attainment: What percentage of those who enrolled in a Connecticut public university or community college within 16 months of graduation earned at least one year’s worth of credits (24 or more) within two years of enrollment? 3. Remediation: What percentage of those who enrolled in one of the four Connecticut State Universities or one of the 12 community colleges within 16 months of graduation took a remedial course within two years of enrollment? Notes on the data: School Credit: % Earning 24 Credits is a subset of the % Enrolled in 16 Months. School Remediation: % Enrolled in Remediation is a subset of the % Enrolled in 16 Months.

About this dataset

Context

The dataset tabulates the population of College Springs by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of College Springs across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of male population, with 56.68% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the College Springs is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of College Springs total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for College Springs Population by Race & Ethnicity. You can refer the same here

The comparing of three years of data of students graduating,

Course Success rate is the percent of students obtaining grades A‐C and P out of the total number of students enrolled at the beginning of the term. Course success is the building block toward student program completion. Without successful completion of courses, City Colleges of Chicago students will not be able to earn credits toward a degree or certificate, nor will they progress from remedial to college-level coursework.

Patterns of educational attainment vary greatly across countries, and across population groups within countries. In some countries, virtually all children complete basic education whereas in others large groups fall short. The primary purpose of this database, and the associated research program, is to document and analyze these differences using a compilation of a variety of household-based data sets: Demographic and Health Surveys (DHS); Multiple Indicator Cluster Surveys (MICS); Living Standards Measurement Study Surveys (LSMS); as well as country-specific Integrated Household Surveys (IHS) such as Socio-Economic Surveys.As shown at the website associated with this database, there are dramatic differences in attainment by wealth. When households are ranked according to their wealth status (or more precisely, a proxy based on the assets owned by members of the household) there are striking differences in the attainment patterns of children from the richest 20 percent compared to the poorest 20 percent.In Mali in 2012 only 34 percent of 15 to 19 year olds in the poorest quintile have completed grade 1 whereas 80 percent of the richest quintile have done so. In many countries, for example Pakistan, Peru and Indonesia, almost all the children from the wealthiest households have completed at least one year of schooling. In some countries, like Mali and Pakistan, wealth gaps are evident from grade 1 on, in other countries, like Peru and Indonesia, wealth gaps emerge later in the school system.The EdAttain website allows a visual exploration of gaps in attainment and enrollment within and across countries, based on the international database which spans multiple years from over 120 countries and includes indicators disaggregated by wealth, gender and urban/rural location. The database underlying that site can be downloaded from here.

The percentage of 1st through 12th graders who change schools out of all students in a school year. Students must have attended both schools for which they were registered for at least one day. Additionally, this indicator only identifies the share of students that change schools for any reasons and not the frequency, number of school switches, or change in residences in a school year. The percentage reflects the last home address available for the student who changed schools. This may or may not be the home address provided for the first school that they are registered to attend. Source: Baltimore City Public School System Years Available: 2010-2011, 2011-2012, 2012-2013, 2013-2014, 2014-2015, 2015-2016, 2018-2019, 2019-2020

This paper explores a unique dataset of all the SET ratings provided by students of one university in Poland at the end of the winter semester of the 2020/2021 academic year. The SET questionnaire used by this university is presented in Appendix 1. The dataset is unique for several reasons. It covers all SET surveys filled by students in all fields and levels of study offered by the university. In the period analysed, the university was entirely in the online regime amid the Covid-19 pandemic. While the expected learning outcomes formally have not been changed, the online mode of study could have affected the grading policy and could have implications for some of the studied SET biases. This Covid-19 effect is captured by econometric models and discussed in the paper. The average SET scores were matched with the characteristics of the teacher for degree, seniority, gender, and SET scores in the past six semesters; the course characteristics for time of day, day of the week, course type, course breadth, class duration, and class size; the attributes of the SET survey responses as the percentage of students providing SET feedback; and the grades of the course for the mean, standard deviation, and percentage failed. Data on course grades are also available for the previous six semesters. This rich dataset allows many of the biases reported in the literature to be tested for and new hypotheses to be formulated, as presented in the introduction section. The unit of observation or the single row in the data set is identified by three parameters: teacher unique id (j), course unique id (k) and the question number in the SET questionnaire (n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9} ). It means that for each pair (j,k), we have nine rows, one for each SET survey question, or sometimes less when students did not answer one of the SET questions at all. For example, the dependent variable SET_score_avg(j,k,n) for the triplet (j=Calculus, k=John Smith, n=2) is calculated as the average of all Likert-scale answers to question nr 2 in the SET survey distributed to all students that took the Calculus course taught by John Smith. The data set has 8,015 such observations or rows. The full list of variables or columns in the data set included in the analysis is presented in the attached filesection. Their description refers to the triplet (teacher id = j, course id = k, question number = n). When the last value of the triplet (n) is dropped, it means that the variable takes the same values for all n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9}.Two attachments:- word file with variables description- Rdata file with the data set (for R language).Appendix 1. Appendix 1. The SET questionnaire was used for this paper. Evaluation survey of the teaching staff of [university name] Please, complete the following evaluation form, which aims to assess the lecturer’s performance. Only one answer should be indicated for each question. The answers are coded in the following way: 5- I strongly agree; 4- I agree; 3- Neutral; 2- I don’t agree; 1- I strongly don’t agree. Questions 1 2 3 4 5 I learnt a lot during the course. ○ ○ ○ ○ ○ I think that the knowledge acquired during the course is very useful. ○ ○ ○ ○ ○ The professor used activities to make the class more engaging. ○ ○ ○ ○ ○ If it was possible, I would enroll for the course conducted by this lecturer again. ○ ○ ○ ○ ○ The classes started on time. ○ ○ ○ ○ ○ The lecturer always used time efficiently. ○ ○ ○ ○ ○ The lecturer delivered the class content in an understandable and efficient way. ○ ○ ○ ○ ○ The lecturer was available when we had doubts. ○ ○ ○ ○ ○ The lecturer treated all students equally regardless of their race, background and ethnicity. ○ ○

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.

About this dataset

Context

The dataset tabulates the College Corner population by age. The dataset can be utilized to understand the age distribution and demographics of College Corner.

Content

The dataset constitues the following three datasets

• College Corner, OH Age Group Population Dataset: A complete breakdown of College Corner age demographics from 0 to 85 years, distributed across 18 age groups
• College Corner, OH Age Cohorts Dataset: Children, Working Adults, and Seniors in College Corner - Population and Percentage Analysis
• College Corner, OH Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

The percentage of high school students who have successfully passed the H.S.A. exams out of all high school students that took the exam in the school year (considering only the highest score per subject area). In Maryland, all students who entered 9th grade in or after 2005 are required to take and pass the High School Assessments (H.S.A.) in order to graduate, including students in special education, English language learners (ELLs), and students with 504 plans. There are currently three H.S.A. exams: English, Algebra/Data Analysis; and Biology (a H.S.A. in Government has since been discontinued). Students can retake the HSAs as many times as necessary to pass. Source: Baltimore City Public Schools Years Available: 2009-2010, 2010-2011, 2012-2013, 2013-2014

This dataset includes the attendance rate for public school students PK-12 by student group and by district during the 2022-2023 school year.

Student groups include:

Students experiencing homelessness Students with disabilities Students who qualify for free/reduced lunch English learners All high needs students Non-high needs students Students by race/ethnicity (Hispanic/Latino of any race, Black or African American, White, All other races)

Attendance rates are provided for each student group by district and for the state. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch.

When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

Local Law 174 enacted in 2016 requires the Department of Education of the New York City School District to submit to the Council an annual report concerning Career and Technical Education programs in New York city schools.

This report provides information about Career and Technical Education (CTE) programs and students in CTE programs and CTE-designated high schools, as defined in Local Law 174 as reported through the 2018-2019 STARS database. CTE Designated High Schools are those in which all students are engaged in NYC DOE-approved CTE sequences of instruction that integrate rigorous academic study with workforce skills in specific career pathways. It is important to note that schools self-report their scheduling information in STARS. The report also includes information regarding the number and ratio of certified CTE instructors.

This report consists of seven tabs:

1. CTE Program Characteristics and Enrollment, including number of staff that attended a CTE PD
2. Number and percentage of students at each high school in a CTE program
3. Number and percentage of students in each Community School District in a CTE program
4. CTE application and admission - CTE designated schools only
5. Graduation rates - CTE designated schools only
6. Graduation rates - CTE designated schools only (by Community School District)

7. CTE Full-Time and Part-Time Teachers

8. CTE Program Characteristics and Enrollment

"This tab includes the following information for each high school-level CTE program: - high school name - CTE designation - name of the program - the industry for which the program prepares students - the number of industry partners associated with the program - CTE program approval status through the New York state department of education’s CTE approval process - grade levels served by such program - number of students enrolled in such program - number of school staff attending professional development events held by CTE"

1. Number and percentage of students at each high school in a CTE program

This tab includes the number and percentage of students at each high school with a CTE program, disaggregated by: student race and ethnicity; student gender; student special education status; student English Language Learner status; student economic need status (poverty); and communicty school district. Data on students with disabilities and English language learners are as of the end of the 2018-19 school year.

Increase the percentage of students with disabilities receiving instruction in general education classrooms for the majority of the school day from 65.10% in 2014 to 68% by 2018.

Analysis of ‘College Basketball Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/andrewsundberg/college-basketball-dataset on 28 January 2022.

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

Content

Data from the 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, and 2021 Division I college basketball seasons.

cbb.csv has seasons 2013-2019 combined

The 2020 season's data set is kept separate from the other seasons, because there was no postseason due to the Coronavirus.

The 2021 data is from 3/15/2021 and will be updated and added to cbb.csv after the tournament

Variables

RK (Only in cbb20): The ranking of the team at the end of the regular season according to barttorvik

TEAM: The Division I college basketball school

CONF: The Athletic Conference in which the school participates in (A10 = Atlantic 10, ACC = Atlantic Coast Conference, AE = America East, Amer = American, ASun = ASUN, B10 = Big Ten, B12 = Big 12, BE = Big East, BSky = Big Sky, BSth = Big South, BW = Big West, CAA = Colonial Athletic Association, CUSA = Conference USA, Horz = Horizon League, Ivy = Ivy League, MAAC = Metro Atlantic Athletic Conference, MAC = Mid-American Conference, MEAC = Mid-Eastern Athletic Conference, MVC = Missouri Valley Conference, MWC = Mountain West, NEC = Northeast Conference, OVC = Ohio Valley Conference, P12 = Pac-12, Pat = Patriot League, SB = Sun Belt, SC = Southern Conference, SEC = South Eastern Conference, Slnd = Southland Conference, Sum = Summit League, SWAC = Southwestern Athletic Conference, WAC = Western Athletic Conference, WCC = West Coast Conference)

G: Number of games played

W: Number of games won

ADJOE: Adjusted Offensive Efficiency (An estimate of the offensive efficiency (points scored per 100 possessions) a team would have against the average Division I defense)

ADJDE: Adjusted Defensive Efficiency (An estimate of the defensive efficiency (points allowed per 100 possessions) a team would have against the average Division I offense)

BARTHAG: Power Rating (Chance of beating an average Division I team)

EFG_O: Effective Field Goal Percentage Shot

EFG_D: Effective Field Goal Percentage Allowed

TOR: Turnover Percentage Allowed (Turnover Rate)

TORD: Turnover Percentage Committed (Steal Rate)

ORB: Offensive Rebound Rate

DRB: Offensive Rebound Rate Allowed

FTR : Free Throw Rate (How often the given team shoots Free Throws)

FTRD: Free Throw Rate Allowed

2P_O: Two-Point Shooting Percentage

2P_D: Two-Point Shooting Percentage Allowed

3P_O: Three-Point Shooting Percentage

3P_D: Three-Point Shooting Percentage Allowed

ADJ_T: Adjusted Tempo (An estimate of the tempo (possessions per 40 minutes) a team would have against the team that wants to play at an average Division I tempo)

WAB: Wins Above Bubble (The bubble refers to the cut off between making the NCAA March Madness Tournament and not making it)

POSTSEASON: Round where the given team was eliminated or where their season ended (R68 = First Four, R64 = Round of 64, R32 = Round of 32, S16 = Sweet Sixteen, E8 = Elite Eight, F4 = Final Four, 2ND = Runner-up, Champion = Winner of the NCAA March Madness Tournament for that given year)

SEED: Seed in the NCAA March Madness Tournament

YEAR: Season

Acknowledgements

This data was scraped from from http://barttorvik.com/trank.php#. I cleaned the data set and added the POSTSEASON, SEED, and YEAR columns

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

UA: Over-Age Students: Primary: Female: % of Female Enrollment data was reported at 7.058 % in 2016. This records an increase from the previous number of 7.028 % for 2015. UA: Over-Age Students: Primary: Female: % of Female Enrollment data is updated yearly, averaging 8.766 % from Dec 2002 (Median) to 2016, with 15 observations. The data reached an all-time high of 15.713 % in 2005 and a record low of 7.028 % in 2015. UA: Over-Age Students: Primary: Female: % of Female Enrollment data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ukraine – Table UA.World Bank.WDI: Education Statistics. Over-age students are the percentage of those enrolled who are older than the official school-age range for primary education.; ; UNESCO Institute for Statistics; ;

The number of students enrolled at Spanish universities maintained steadily above 1.4 million over the past decade. The number of university students fluctuated over the period under review, with figures registering the lowest point during the 2008/2009 academic year at over 1.4 million students and peaking during the 2022/2023 academic year at over 1.72 million. Most of the university students in Spain opted for public institutions to complete their studies, with approximately 1.3 million enrolled during the 2022/2023 academic year.

Spain and its university scene As of 2023, Spain had over 89 universities, most of them located in its capital autonomous community Madrid, which was serviced by 18 higher education institutions. Catalonia and Andalusia ranked second and third with 12 and 11 universities respectively. The latest data revealed that most students were registered for undergraduate degrees, with over one million future graduates during the 2022/23 academic year. Not all fields of study are however equally popular among male and female students – male students made up over 72 percent of engineering and architecture degrees in 2022/23, whereas over 72 percent of all health science students were female.

Educational attainment in Spain Over the past decade, Spain saw a great improvement in the proportion of population that attained an upper secondary or tertiary education. According to the latest studies, by 2022 over 64 percent of those aged 25 and over completed their secondary or tertiary education. This figure increased constantly over the period under review, with results showing a much smaller proportion in 2007, when the share of Spaniards aged 25 and over that had attained this educational level stood only at 50 percent. Just as the most recent data revealed, the number of years the average person is expected to dedicate to their education in Spain stood at 19 in 2021.

Share Percentage of Dalit Student Enrollment in School Education by Province in 2074 BS

This is a source dataset for a Let's Get Healthy California indicator at https://letsgethealthy.ca.gov/. The California Tobacco Control Program coordinates statewide tobacco control efforts and funds the California Student Tobacco Survey (CSTS). The data table shows the current smoking prevalence from 2001-2002 to 2015-2016 for California high school youth by selected demographics. Current cigarette smoking was defined as having smoked on one or more days during the past 30 days prior to the survey. In statistics, a confidence interval is a measure of the reliability of an estimate. It is a type of interval estimate of a population parameter. The CSTS is a large-scale biennial survey, in-school student survey administered to middle (grades 8) and high school (grades 10 and 12) students. Topics of the survey include awareness of and use of different tobacco products; history and patterns of tobacco use; tobacco purchasing patterns; knowledge and participation in school tobacco prevention or cessation programs; perceptions of tobacco use (i.e. social norms); awareness of advertising; and susceptibility to future tobacco use.

This data tracks four-year graduation rates from high schools located within the City of Tempe with data publicly available through the Arizona Department of Education.Values of “8888” are used when there is too few to count and values of “9999” are used where there is no data available.This page provides data for the High School Graduation Rate performance measure. The performance measure dashboard is available at 3.08 High School Graduation Rates.Additional InformationSource: Contact: Marie RaymondContact E-Mail: Marie_Raymond@tempe.govContact Phone: 480-585-7818Data Source: Tempe High School DistrictData Source Type: ExcelPreparation Method: Arizona Department of Education (ADE) generated Excel Spreadsheets- available at https://www.azed.gov/accountability-research/data/Publish Frequency: AnnuallyPublish Method: ManualData Dictionary

This dataset contains yearly certified enrollment for all public school districts (with physical boundaries) in Wisconsin for the 2023-2024 school year. This data is also available in the WISEdash Public Portal. This dataset is derived from publicly available files on the WISEdash Download Page. Enrollment Count is the number of students enrolled on specific dates as determined by school enrollment/exit dates that cover those dates. Percent Enrollment by Student Group is a percent of the enrollment count for all student groups combined. Reporting Disability is indicated in the pupil’s individualized education program (IEP) or individualized service plan (ISP). A person's race or ethnicity is the racial and/or ethnic group to which the person belongs or with which he or she most identifies. Ethnicity is self-reported as either Hispanic/Not Hispanic. Race is self-reported as any of the following 5 categories: Asian, American Indian or Alaskan Native, Black or African American, Native Hawaiian or other Pacific Islander, or White. The data displayed reflects the race/ethnicity that is reported by school districts to DPI.An economically disadvantaged student is one who is identified by Direct Certification (only if participating in the National School Lunch Program) OR a member of a household that meets the income eligibility guidelines for free or reduced-price meals (less than or equal to 185 percent of Federal Poverty Guidelines) under the National School Lunch Program (NSLP) OR identified by an alternate mechanism, such as the alternate household income form.English Learner status is any student whose first language, or whose parents' or guardians' first language, is not English and whose level of English proficiency requires specially designed instruction, either in English or in the first language or both, in order for the student to fully benefit from classroom instruction and to be successful in attaining the state's high academic standards expected of all students at their grade level.A child is eligible for the Migrant Education Program (MEP) (and thereby eligible to receive MEP services) if the child: meets the definition of “migratory child” in section 1309(3) of the ESEA,[1] and is an “eligible child” as the term is used in section 1115(c)(1)(A) of the ESEA and 34 C.F.R. § 200.103; and has the basis for the State’s determination that the child is a “migratory child” properly recorded on the national Certificate of Eligibility (COE). Eligibility determination is made by a Wisconsin state migrant recruiter during a face-to-face family interview.