Overall educational attainment measures the highest level of education attained by a given individual: for example, an individual counted in the percentage of the measured population with a master’s or professional degree can be assumed to also have a bachelor’s degree and a high school diploma, but they are not counted in the population percentages for those two categories. Overall educational attainment is the broadest education indicator available, providing information about the measured county population as a whole.

Only members of the population aged 25 and older are included in these educational attainment estimates, sourced from the U.S. Census Bureau American Community Survey (ACS).

Champaign County has high educational attainment: over 48 percent of the county's population aged 25 or older has a bachelor's degree or graduate or professional degree as their highest level of education. In comparison, the percentage of the population aged 25 or older in the United States and Illinois with a bachelor's degree in 2023 was 21.8% (+/-0.1) and 22.8% (+/-0.2), respectively. The population aged 25 or older in the U.S. and Illinois with a graduate or professional degree in 2022, respectively, was 14.3% (+/-0.1) and 15.5% (+/-0.2).

Educational attainment data was sourced from the U.S. Census Bureau’s American Community Survey 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Educational Attainment for the Population 25 Years and Over.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (16 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (29 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (6 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (4 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (4 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (13 September 2018). U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).

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.

US Census American Community Survey (ACS) 2016, 5-year estimates of the key social characteristics of Elementary School Districts geographic level in Orange County, California. The data contains 500 fields for the variable groups S01: Households by type (universe: total households, table X11, 17 fields); S02: Relationship (universe: population in households, table X9, 19 fields); S03: Marital status (universe: population 15 years and over, table X12, 13 fields); S04: Fertility (universe: women 15-50 years who had birth in the past 12 months, table X13, 11 fields); S05: Grandparents (universe: grandparents living or responsible for own grandchildren under 18 years, table X10, 18 fields); S06: School enrollment (universe: population 3 years old and over enrolled in school, table X14, 17 fields); S07: Educational attainment (universe: population 25 years and over, table X15, 25 fields); S08: Veteran status (universe: civilian population 18 years and over, table X21, 2 fields); S09: Disability status and type by sex and age (universe: total civilian non-institutionalized population, table X18, 77 fields); S10: Disability status by age and health insurance coverage (universe: civilian non-institutionalized population, table X18, 16 fields); S11: Residence 1 year ago (universe: population 1 year and over, table X7, 6 fields); S12: Place of birth (universe: total population, table X5, 27 fields); S13: Citizenship status by nativity in the US (universe: total population, table X5, 6 fields); S14: Year of entry (universe: population born outside the US, table X5, 21 fields); S15: World region of birth of foreign born population (universe: foreign born population, excluding population born at sea, table X5, 25 fields); S16: Language spoken in households (universe: total households, table X16, 6 fields); S17: Language spoken at home (universe: population 5 years and over, table X16, 67 fields); S18: Ancestry (universe: total population reporting ancestry, table X4, 114 fields), and; S19: Computers and internet use (universe: total population in households and total households, table X28, 13 fields). The US Census geodemographic data are based on the 2016 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

Analysis of ‘2015-16 Health Education MS Level’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/292f4aab-737a-4c71-8c12-5ac08cc29375 on 12 November 2021.

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

Local Law 14 (2016) requires that the NYCDOE provide citywide Health Education data, disaggregated by community school district, city council district and each individual school. This reports provides information about the number and percent of students receiving one semester of health education as defined in local law 14 as reported through 2015-2016 STARS database. It includes school level data on number of 6-8 graders that received a semester of health instruction as well as number of 8th graders meeting the middle school health requirements for 2015-16 school year. This regulation does not require students to receive health instruction at any particular grade level in middle school, only prior to completing 8th grade and a student may advance without completing the course.

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

In an impressive increase from years past, 39 percent of women in the United States had completed four years or more of college in 2022. This figure is up from 3.8 percent of women in 1940. A significant increase can also be seen in males, with 36.2 percent of the U.S. male population having completed four years or more of college in 2022, up from 5.5 percent in 1940.

4- and 2-year colleges

In the United States, college students are able to choose between attending a 2-year postsecondary program and a 4-year postsecondary program. Generally, attending a 2-year program results in an Associate’s Degree, and 4-year programs result in a Bachelor’s Degree.

Many 2-year programs are designed so that attendees can transfer to a college or university offering a 4-year program upon completing their Associate’s. Completion of a 4-year program is the generally accepted standard for entry-level positions when looking for a job.

Earnings after college

Factors such as gender, degree achieved, and the level of postsecondary education can have an impact on employment and earnings later in life. Some Bachelor’s degrees continue to attract more male students than female, particularly in STEM fields, while liberal arts degrees such as education, languages and literatures, and communication tend to see higher female attendance.

All of these factors have an impact on earnings after college, and despite nearly the same rate of attendance within the American population between males and females, men with a Bachelor’s Degree continue to have higher weekly earnings on average than their female counterparts.

Description

Student's Academic Performance Dataset (xAPI-Edu-Data)

Data Set Characteristics: Multivariate

Number of Instances: 480

Area: E-learning, Education, Predictive models, Educational Data Mining

Attribute Characteristics: Integer/Categorical

Number of Attributes: 16

Date: 2016-11-8

Associated Tasks: Classification

Missing Values? No

File formats: xAPI-Edu-Data.csv

Source:

Elaf Abu Amrieh, Thair Hamtini, and Ibrahim Aljarah, The University of Jordan, Amman, Jordan, http://www.Ibrahimaljarah.com www.ju.edu.jo

Dataset Information:

This is an educational data set which is collected from learning management system (LMS) called Kalboard 360. Kalboard 360 is a multi-agent LMS, which has been designed to facilitate learning through the use of leading-edge technology. Such system provides users with a synchronous access to educational resources from any device with Internet connection.

The data is collected using a learner activity tracker tool, which called experience API (xAPI). The xAPI is a component of the training and learning architecture (TLA) that enables to monitor learning progress and learner’s actions like reading an article or watching a training video. The experience API helps the learning activity providers to determine the learner, activity and objects that describe a learning experience. The dataset consists of 480 student records and 16 features. The features are classified into three major categories: (1) Demographic features such as gender and nationality. (2) Academic background features such as educational stage, grade Level and section. (3) Behavioral features such as raised hand on class, opening resources, answering survey by parents, and school satisfaction.

The dataset consists of 305 males and 175 females. The students come from different origins such as 179 students are from Kuwait, 172 students are from Jordan, 28 students from Palestine, 22 students are from Iraq, 17 students from Lebanon, 12 students from Tunis, 11 students from Saudi Arabia, 9 students from Egypt, 7 students from Syria, 6 students from USA, Iran and Libya, 4 students from Morocco and one student from Venezuela.

The dataset is collected through two educational semesters: 245 student records are collected during the first semester and 235 student records are collected during the second semester.

The data set includes also the school attendance feature such as the students are classified into two categories based on their absence days: 191 students exceed 7 absence days and 289 students their absence days under 7.

This dataset includes also a new category of features; this feature is parent parturition in the educational process. Parent participation feature have two sub features: Parent Answering Survey and Parent School Satisfaction. There are 270 of the parents answered survey and 210 are not, 292 of the parents are satisfied from the school and 188 are not.

(See the related papers for more details).

Attributes

1 Gender - student's gender (nominal: 'Male' or 'Female’)

2 Nationality- student's nationality (nominal:’ Kuwait’,’ Lebanon’,’ Egypt’,’ SaudiArabia’,’ USA’,’ Jordan’,’ Venezuela’,’ Iran’,’ Tunis’,’ Morocco’,’ Syria’,’ Palestine’,’ Iraq’,’ Lybia’)

3 Place of birth- student's Place of birth (nominal:’ Kuwait’,’ Lebanon’,’ Egypt’,’ SaudiArabia’,’ USA’,’ Jordan’,’ Venezuela’,’ Iran’,’ Tunis’,’ Morocco’,’ Syria’,’ Palestine’,’ Iraq’,’ Lybia’)

4 Educational Stages- educational level student belongs (nominal: ‘lowerlevel’,’MiddleSchool’,’HighSchool’)

5 Grade Levels- grade student belongs (nominal: ‘G-01’, ‘G-02’, ‘G-03’, ‘G-04’, ‘G-05’, ‘G-06’, ‘G-07’, ‘G-08’, ‘G-09’, ‘G-10’, ‘G-11’, ‘G-12 ‘)

6 Section ID- classroom student belongs (nominal:’A’,’B’,’C’)

7 Topic- course topic (nominal:’ English’,’ Spanish’, ‘French’,’ Arabic’,’ IT’,’ Math’,’ Chemistry’, ‘Biology’, ‘Science’,’ History’,’ Quran’,’ Geology’)

8 Semester- school year semester (nominal:’ First’,’ Second’)

9 Parent responsible for student (nominal:’mom’,’father’)

10 Raised hand- how many times the student raises his/her hand on classroom (numeric:0-100)

11- Visited resources- how many times the student visits a course content(numeric:0-100)

12 Viewing announcements-how many times the student checks the new announcements(numeric:0-100)

13 Discussion groups- how many times the student participate on discussion groups (numeric:0-100)

14 Parent Answering Survey- parent answered the surveys which are provided from school or not (nominal:’Yes’,’No’)

15 Parent School Satisfaction- the Degree of parent satisfaction from school(nominal:’Yes’,’No’)

16 Student Absence Days-the number of absence days for each student (nominal: above-7, under-7)

The students are classified into three numerical intervals based on their total grade/mark:

Low-Level: interval includes values from 0 to 69,

Middle-Level: interval includes values from 70 to 89,

High-Level: interval includes values from 90-100.

Relevant Papers:

-Amrieh, E. A., Hamtini, T., & Aljarah, I. (2016). Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods. International Journal of Database Theory and Application, 9(8), 119-136.

-Amrieh, E. A., Hamtini, T., & Aljarah, I. (2015, November). Preprocessing and analyzing educational data set using X-API for improving student's performance. In Applied Electrical Engineering and Computing Technologies (AEECT), 2015 IEEE Jordan Conference on (pp. 1-5). IEEE.

Citation Request:

Please include these citations if you plan to use this dataset:

• Amrieh, E. A., Hamtini, T., & Aljarah, I. (2016). Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods. International Journal of Database Theory and Application, 9(8), 119-136.

-Amrieh, E. A., Hamtini, T., & Aljarah, I. (2015, November). Preprocessing and analyzing educational data set using X-API for improving student's performance. In Applied Electrical Engineering and Computing Technologies (AEECT), 2015 IEEE Jordan Conference on (pp. 1-5). IEEE.

Summary

dataset created from a higher education institution (acquired from several disjoint databases) related to students enrolled in different undergraduate degrees, such as agronomy, design, education, nursing, journalism, management, social service, and technologies. The dataset includes information known at the time of student enrollment (academic path, demographics, and social-economic factors) and the students' academic performance at the end of the first and second semesters. The data is used to build classification models to predict students' dropout and academic sucess. The problem is formulated as a three category classification task, in which there is a strong imbalance towards one of the classes.

Introduction

This dataset delves into the correlation between dropout rates and student success in various educational settings. It includes comprehensive information on student demographics, academic performance, and factors contributing to dropout incidents. The dataset aims to provide valuable insights for educators, policymakers, and researchers to enhance strategies for fostering student retention and academic achievement.

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Dataset

The dataset includes information known at the time of student enrollment – academic path, demographics, and social-economic factors.

- Marital status: Categorical variable indicating the marital status of the individual. (1 – single 2 – married 3 – widower 4 – divorced 5 – facto union 6 – legally separated)

- Application mode: Categorical variable indicating the mode of application. (1 - 1st phase - general contingent 2 - Ordinance No. 612/93 5 - 1st phase - special contingent (Azores Island) 7 - Holders of other higher courses 10 - Ordinance No. 854-B/99 15 - International student (bachelor) 16 - 1st phase - special contingent (Madeira Island) 17 - 2nd phase - general contingent 18 - 3rd phase - general contingent 26 - Ordinance No. 533-A/99, item b2) (Different Plan) 27 - Ordinance No. 533-A/99, item b3 (Other Institution) 39 - Over 23 years old 42 - Transfer 43 - Change of course 44 - Technological specialization diploma holders 51 - Change of institution/course 53 - Short cycle diploma holders 57 - Change of institution/course (International)).

- Application order: Numeric variable indicating the order of application. (between 0 - first choice; and 9 last choice).

- Course: Categorical variable indicating the chosen course. (33 - Biofuel Production Technologies 171 - Animation and Multimedia Design 8014 - Social Service (evening attendance) 9003 - Agronomy 9070 - Communication Design 9085 - Veterinary Nursing 9119 - Informatics Engineering 9130 - Equinculture 9147 - Management 9238 - Social Service 9254 - Tourism 9500 - Nursing 9556 - Oral Hygiene 9670 - Advertising and Marketing Management 9773 - Journalism and Communication 9853 - Basic Education 9991 - Management (evening attendance)).

- evening attendance: Binary variable indicating whether the individual attends classes during the daytime or evening. (1 for daytime, 0 for evening).

- Previous qualification: Numeric variable indicating the level of the previous qualification. (1 - Secondary education 2 - Higher education - bachelor's degree 3 - Higher education - degree 4 - Higher education - master's 5 - Higher education - doctorate 6 - Frequency of higher education 9 - 12th year of schooling - not completed 10 - 11th year of schooling - not completed 12 - Other - 11th year of schooling 14 - 10th year of schooling 15 - 10th year of schooling - not completed 19 - Basic education 3rd cycle (9th/10th/11th year) or equiv. 38 - Basic education 2nd cycle (6th/7th/8th year) or equiv. 39 - Technological specialization course 40 - Higher education - degree (1st cycle) 42 - Professional higher technical course 43 - Higher education - master (2nd cycle)).

- Nationality: Categorical variable indicating the nationality of the individual. (1 - Portuguese; 2 - German; 6 - Spanish; 11 - Italian; 13 - Dutch; 14 - English; 17 - Lithuanian; 21 - Angolan; 22 - Cape Verdean; 24 - Guinean; 25 - Mozambican; 26 - Santomean; 32 - Turkish; 41 - Brazilian; 62 - Romanian; 100 - Moldova (Republic of); 101 - Mexican; 103 - Ukrainian; 105 - Russian; 108 - Cuban; 109 - Colombian).

- Mother's qualification: Numeric variable indicating the level of the mother's qualification.
(1 - Secondary Education - 12th Year of Schooling or Eq. 2 - Higher Education - Bachelor's Degree 3 - Higher Education - Degree 4 - Higher Education - Master's 5 - Higher Education - Doctorate 6 - Frequency of Higher Education 9 - 12th Year of Schooling - Not Completed 10 - 11th Year of Schooling - Not Completed 11 - 7th Year (...

Abstract copyright UK Data Service and data collection copyright owner.

The Young Lives survey is an innovative long-term project investigating the changing nature of childhood poverty in four developing countries. The study is being conducted in Ethiopia, India, Peru and Vietnam and has tracked the lives of 12,000 children over a 20-year period, through 5 (in-person) survey rounds (Round 1-5) and, with the latest survey round (Round 6) conducted over the phone in 2020 and 2021 as part of the Listening to Young Lives at Work: COVID-19 Phone Survey.
Round 1 of Young Lives surveyed two groups of children in each country, at 1 year old and 5 years old. Round 2 returned to the same children who were then aged 5 and 12 years old. Round 3 surveyed the same children again at aged 7-8 years and 14-15 years, Round 4 surveyed them at 12 and 19 years old, and Round 5 surveyed them at 15 and 22 years old. Thus the younger children are being tracked from infancy to their mid-teens and the older children through into adulthood, when some will become parents themselves.

The 2020 phone survey consists of three phone calls (Call 1 administered in June-July 2020; Call 2 in August-October 2020 and Call 3 in November-December 2020) and the 2021 phone survey consists of two additional phone calls (Call 4 in August 2021 and Call 5 in October-December 2021) The calls took place with each Young Lives respondent, across both the younger and older cohort, and in all four study countries (reaching an estimated total of around 11,000 young people).
The Young Lives survey is carried out by teams of local researchers, supported by the Principal Investigator and Data Manager in each country.

Further information about the survey, including publications, can be downloaded from the Young Lives website.

School Survey:
A school survey was introduced into Young Lives in 2010, following the third round of the household survey, in order to capture detailed information about children's experiences of schooling, and to improve our understanding of:

• the relationships between learning outcomes, and children's home backgrounds, gender, work, schools, teachers and class and school peer-groups
• school effectiveness, by analysing factors explaining the development of cognitive and non-cognitive skills in school, including value-added analysis of schooling and comparative analysis of school-systems
• equity issues (including gender) in relation to learning outcomes and the evolution of inequalities within education

The survey allows researchers to link longitudinal information on household and child characteristics from the household survey with data on the schools attended by the Young Lives children and children's achievements inside and outside the school. It provides policy-relevant information on the relationship between child development (and its determinants) and children's experience of school, including access, quality and progression. This combination of household, child and school-level data over time constitutes the comparative advantage of Young Lives.

A further round of school surveys took place during the 2016-2017 school year. The key focus areas for these were:

• benchmarking levels of student attainment and progress in key learning domains
• effects of school and teacher quality, and school effectiveness
• educational transitions at age 15

The 2016-2017 school surveys focused on the level of schooling accessed by 15-year-olds in each country, so including Grade 7 and 8 students in Ethiopia (upper primary level), Grade 9 students in India (lower secondary level), and Grade 10 students in Vietnam (upper secondary level).

The School Survey data are held separately for each country. The Ethiopia data are available from the UK Data Archive under SN 7823 and SN 8359, the Vietnam data are available from SN 7663 and SN 8360, and the Peru data have been archived under SN 7479 (no 2016-2017 survey).

Further information is available from the Young Lives School Survey webpages.


Main Topics:

The India survey included data collection at the school, class and pupil level, and involved the Director / Head teacher, the Maths and English teachers, and the Young Lives child. The instruments included in the survey were:

• Principal questionnaire - collected background data on the principal and the school (including school management practices)
• Teacher questionnaire - collected background data on Class 9 Maths and English teachers (including teacher motivation and class-level information)
• Student questionnaire - collected background data on Grade 10 students (including academic support within and beyond school, and psychosocial measures)
• School facilities observation - collected data on school infrastructure
• Teacher professional knowledge questionnaire - collected...

Unemployment rate, participation rate, and employment rate by educational attainment, gender and age group, annual.

BackgroundThe obesity epidemic presents a major public health challenge, and a poor diet quality has been identified as one of the most important contributing factors. Whereas a sufficient fruit and vegetable consumption has been associated with several positive health outcomes, the long-term effect on overweight and obesity is unclear. Thus, the aims of this study were to investigate if one year with free school fruit had any effect on weight status 14 years later, and if it affected the birth weight of the participants’ children.MethodsIn 2001, 10 -12-year old Norwegian children, received one year of free school fruit in the intervention study “Fruits and Vegetables Make the Marks” (FVMM) and in 2016, a total of 1081 participants of 2049 eligible responded to a follow-up survey. Multilevel logistic regression was used to investigate if one year of free school fruit was associated with weight status and with birthweight status of the offspring. The analyses were adjusted for gender, educational level, and the offspring analysis also for parents’ weight status, and the nested design (child/parent).ResultsThe odds ratios of being overweight (OR: 0.93, 95% CI: 0.70, 1.24) or having a child with high or low birth weight (OR: 0.52, 95% CI: 0.21, 1.30) in the intervention group compared to the control group were not statistically significant, 14 years after the intervention period.ConclusionsOne year of free school fruit did not have an effect on weight status on the participants or birth weight of their offspring, 14 years after the intervention period. Although, results from the present study contribute to fill the knowledge gaps concerning long-term effects of public health efforts on weight status, more follow-up studies with larger samples are warranted.

Abstract (en): Summary File 3 contains sample data, which is the information compiled from the questions asked of a sample of all people and housing units in the United States. Population items include basic population totals as well as counts for the following characteristics: urban and rural, households and families, marital status, grandparents as caregivers, language and ability to speak English, ancestry, place of birth, citizenship status, year of entry, migration, place of work, journey to work (commuting), school enrollment and educational attainment, veteran status, disability, employment status, industry, occupation, class of worker, income, and poverty status. Housing items include basic housing totals and counts for urban and rural, number of rooms, number of bedrooms, year moved into unit, household size and occupants per room, units in structure, year structure built, heating fuel, telephone service, plumbing and kitchen facilities, vehicles available, value of home, and monthly rent and shelter costs. The Summary File 3 population tables are identified with a "P" prefix and the housing tables are identified with an "H," followed by a sequential number. The "P" and "H" tables are shown for the block group and higher level geography, while the "PCT" and "HCT" tables are shown for the census tract and higher level geography. There are 16 "P" tables, 15 "PCT" tables, and 20 "HCT" tables that bear an alphabetic suffix on the table number, indicating that they are repeated for nine major race and Hispanic or Latino groups. There are 484 population tables and 329 housing tables for a total of 813 unique tables. All persons in housing units in Kentucky in 2000. 2006-01-12 All files were removed from dataset 82 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 81 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 80 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 79 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 78 and flagged as study-level files, so that they will accompany all downloads. (1) The data are provided in 77 segments [files] per state. The segments are the Geographic Header, Tables P1-P14, P15-P24, P25-P37, P38-P46, P47-P50, P51-P67, P68-P91, P92-P138, P139-P145C, P145D-P145H, P145I-P146F, P146G-P147I, P148A-P149D, P149E-P150I, P151A-P154D, P154E-P159G, P159H-P160E, P16OF-P160I, PCT1-PCT8, PCT9-PCT15, PCT16-PCT17, PCT18-PCT19, PCT20-PCT24, PCT25-PCT27, PCT28-PCT32, PCT33-PCT34, PCT35-PCT37, PCT38-PCT43, PCT44-PCT48, PCT49-PCT51, PCT52-PCT56, PCT57-PCT61, PCT62A-PCT63C, PCT63D-PCT64H, PCT64I-PCT66C, PCT66D-PCT67E, PCT67F-PCT68C, PCT68D-PCT68H, PCT68I-PCT69I, PCT70A-PCT70I, PCT71A-PCT71E, PCT71F-PCT71I, PCT72A-PCT72B, PCT72C-PCT72D, PCT72E-PCT72F, PCT72G-PCT72H, PCT72I-PCT73A, PCT73B-PCT73C, PCT73D-PCT73E, PCT73F-PCT73G, PCT73H-PCT73I, PCT74A-PCT75C, PCT75D-PCT75G, PCT75H-PCT76D, PCT76E-PCT76I, H1-H18, H19-H26, H27-H44, H45-H68, H69-H86, H87-H104, H105-H121, HCT1-HCT3, HCT4, HCT5, HCT6-HCT7, HCT8-HCT14, HCT15-HCT17, HCT18-HCT23, HCT24-HCT31C, HCT31D-HCT36D, HCT36E-HCT40I, HCT41A-HCT43I, HCT44A-HCT44G, HCT44H-HCT47F, and HCT47G-HCT48I. The Geographic Header is in fixed-format ASCII and the tables are in comma-delimited ASCII format. (2) The codebook is provided by the principal investigator as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided on the ICPSR Web site.

US Census American Community Survey (ACS) 2014, 5-year estimates of the key social characteristics of Unified School Districts geographic level in Orange County, California. The data contains 500 fields for the variable groups S01: Households by type (universe: total households, table X11, 17 fields); S02: Relationship (universe: population in households, table X9, 19 fields); S03: Marital status (universe: population 15 years and over, table X12, 13 fields); S04: Fertility (universe: women 15-50 years who had birth in the past 12 months, table X13, 11 fields); S05: Grandparents (universe: grandparents living or responsible for own grandchildren under 18 years, table X10, 18 fields); S06: School enrollment (universe: population 3 years old and over enrolled in school, table X14, 17 fields); S07: Educational attainment (universe: population 25 years and over, table X15, 25 fields); S08: Veteran status (universe: civilian population 18 years and over, table X21, 2 fields); S09: Disability status and type by sex and age (universe: total civilian non-institutionalized population, table X18, 77 fields); S10: Disability status by age and health insurance coverage (universe: civilian non-institutionalized population, table X18, 16 fields); S11: Residence 1 year ago (universe: population 1 year and over, table X7, 6 fields); S12: Place of birth (universe: total population, table X5, 27 fields); S13: Citizenship status by nativity in the US (universe: total population, table X5, 6 fields); S14: Year of entry (universe: population born outside the US, table X5, 21 fields); S15: World region of birth of foreign born population (universe: foreign born population, excluding population born at sea, table X5, 25 fields); S16: Language spoken in households (universe: total households, table X16, 6 fields); S17: Language spoken at home (universe: population 5 years and over, table X16, 67 fields); S18: Ancestry (universe: total population reporting ancestry, table X4, 114 fields), and; S19: Computers and internet use (universe: total population in households and total households, table X28, 13 fields). The US Census geodemographic data are based on the 2014 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

Analysis of ‘2016-2017 Discharge Reporting by School Level - MS’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a54cc4f8-b14e-4498-93d2-9c8452971e94 on 26 January 2022.

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

This report provides data regarding students enrolled in New York City schools during the 2015-2016 school year, according to the guidelines set by Local Law 2011/042. At the citywide, borough and district levels, the DOE is required to report discharge, transfer and graduation counts by grade level (middle school only), cohort (high school only) and disability status. At the school level, the DOE is required to report discharge and transfer counts by grade level (middle school only), cohort (high school only), disability status broken down by, age as of 12/31 of the previous calendar year age, race/ethnicity, and gender. Citywide, Borough, and District results represent the last discharge or transfer for each student. School level results represent all events for all students. District 79 programs are included in the Citywide, Borough and District results, but not shown in the school-level spreadsheets.

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

Analysis of ‘Student Performance Data Set’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/impapan/student-performance-data-set on 13 February 2022.

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

About this dataset

This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd period grades.

Attribute Information:

# Attributes for both student-mat.csv (Math course) and student-por.csv (Portuguese language course) datasets:
1 school - student's school (binary: 'GP' - Gabriel Pereira or 'MS' - Mousinho da Silveira)
2 sex - student's sex (binary: 'F' - female or 'M' - male)
3 age - student's age (numeric: from 15 to 22)
4 address - student's home address type (binary: 'U' - urban or 'R' - rural)
5 famsize - family size (binary: 'LE3' - less or equal to 3 or 'GT3' - greater than 3)
6 Pstatus - parent's cohabitation status (binary: 'T' - living together or 'A' - apart)
7 Medu - mother's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
8 Fedu - father's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
9 Mjob - mother's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other')
10 Fjob - father's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other')
11 reason - reason to choose this school (nominal: close to 'home', school 'reputation', 'course' preference or 'other')
12 guardian - student's guardian (nominal: 'mother', 'father' or 'other')
13 traveltime - home to school travel time (numeric: 1 - <15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - >1 hour)
14 studytime - weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours)
15 failures - number of past class failures (numeric: n if 1<=n<3, else 4)
16 schoolsup - extra educational support (binary: yes or no)
17 famsup - family educational support (binary: yes or no)
18 paid - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)
19 activities - extra-curricular activities (binary: yes or no)
20 nursery - attended nursery school (binary: yes or no)
21 higher - wants to take higher education (binary: yes or no)
22 internet - Internet access at home (binary: yes or no)
23 romantic - with a romantic relationship (binary: yes or no)
24 famrel - quality of family relationships (numeric: from 1 - very bad to 5 - excellent)
25 freetime - free time after school (numeric: from 1 - very low to 5 - very high)
26 goout - going out with friends (numeric: from 1 - very low to 5 - very high)
27 Dalc - workday alcohol consumption (numeric: from 1 - very low to 5 - very high)
28 Walc - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high)
29 health - current health status (numeric: from 1 - very bad to 5 - very good)
30 absences - number of school absences (numeric: from 0 to 93)

# these grades are related with the course subject, Math or Portuguese:
31 G1 - first period grade (numeric: from 0 to 20)
31 G2 - second period grade (numeric: from 0 to 20)
32 G3 - final grade (numeric: from 0 to 20, output target)

Acknowledgements

If you use this dataset in your research, please credit the authors

Citations

P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.

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

This table contains the number of first-year and senior students at government-funded higher education institutions by type of education, field of study, country of origin and country of birth (parents). Statistics Netherlands is switching to a new classification of the population by origin. From now on, where someone was born is more decisive than where someone's parents were born. The word migration background is no longer used. The main division western/non-western is replaced by a division based on continents and common immigration countries. This classification is gradually being introduced in tables and publications with population by origin. From academic year 2015/'16, this table also contains the number of students enrolled at a number of designated institutions: the theological universities and the University for Humanistic Studies. From the 2016/'17 academic year, the Transnational University of Limburg has also been included. This table uses the ISCED-F2013 format. This table shows the distribution of students across the various fields of study per year. Due to clustering and splitting of studies from year to year, this table is less suitable for a comparison of study options over the years. Data available from: academic year 2011/'12 . Status of the figures: The figures for the academic years up to and including 2021/'22 are final and the figures for the academic year 2022/'23 are provisional. Changes as of April 14, 2023: The final figures for the academic year 2021/'22 have been added. When will the new numbers come out? The provisional figures for the academic year 2023/'24 will be available in the second quarter of 2024.

MX：教育程度：至少完成高中：25岁以上人口：累计百分比：男性在12-01-2016达34.434%，相较于12-01-2015的34.684%有所下降。MX：教育程度：至少完成高中：25岁以上人口：累计百分比：男性数据按年更新，12-01-1990至12-01-2016期间平均值为32.522%，共14份观测结果。该数据的历史最高值出现于12-01-2012，达35.634%，而历史最低值则出现于12-01-2000，为22.317%。CEIC提供的MX：教育程度：至少完成高中：25岁以上人口：累计百分比：男性数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的墨西哥 – 表 MX.世行.WDI：教育统计。

Integrated computing uses computing tools and concepts to support learning in other disciplines while giving all students opportunities to experience computer science. Integrated computing is often motivated as a way to introduce computing to students in a low-stakes environment, reducing barriers to learning computer science, often especially for underrepresented groups. This dataset examined integrated computing activities implemented in US schools to examine which programming and CT concepts they teach and whether those concepts differed across contexts. We gathered data on 262 integrated computing activities from in-service K-12 teachers and 20 contextual factors related to the classroom (i.e., primary discipline, grade level, programming paradigm, programming language, minimum amount of time the lesson takes, source of the lesson plan), the teacher (i.e., years teaching, current role (classroom teacher, tech specialist, STEM specialist, etc.), grade levels taught, disciplines taught, degrees and certifications, institutional support received for integrated computing, gender, race, self-efficacy), and the school (e.g., socioeconomic status of students, racial composition, number of CS courses offered, number of CS teachers, years CS courses have been taught, number of students, school location (urban, suburban, rural)). Methods Procedure Data about integrated computing lessons in non-CS classrooms were collected from in-service K-12 teachers in the United States via an online survey, and 262 surveys were completed. Participants were recruited first through teacher networks and districts to include diverse populations and then through LinkedIn. Teachers received a $100 gift card upon completion of the survey, which took approximately 30 minutes. Due to the incentive, submissions were screened during data collection to ensure eligibility (i.e., having a valid school district email) and quality (described below).

Instrument The survey asked about the programming and CT concepts taught in the activities and 20 factors related to classroom, teacher, and school context. The programming concepts included were based on a framework developed by Margulieux et al., 2023. A full list of concepts and contextual factors can be found below. Due to the large sample size, the survey was designed to be primarily quantitative but included a few qualitative questions (e.g., "Please describe in 1-2 sentences the computing learning objective of this activity") and requested teachers to submit their lesson plans. The research team used these qualitative elements to verify data quality, such as by ensuring the lesson included computing and comparing elements of the lesson plans to the quantitative data provided by the teachers. Overall, we found, and excluded, very few instances of low-quality data.

Survey Questions and Descriptive Statistics Qualitative Questions: Title of lesson plan One sentence describing the activity topic (e.g., In this activity, students apply their computational thinking skills to explore the life cycle of a butterfly.) One sentence describing the disciplinary learning objective (e.g., The primary learning goal is to model the life cycle of a butterfly.) One sentence describing the computing learning objective (e.g., Students will conditionals to match body features to life stages.) 1-3 sentences describing the instructional paradigm (e.g., Students will discuss butterflies and life cycles with their partners. Then they will modify the program and use conditionals to create the model.)

Quantitative Question Topic: Response Options (descriptive statistics in parentheses)

Programming and CT Concepts Programming paradigm: Select one: No Programming (80), Unplugged (87), Block-based (69), Text-based (26) Programming language: Open-ended Programming concepts: Select all that apply: Operator-arithmetic, Operator-Boolean, Operator-relational, Conditional-if-else, Conditional-if-then, Loop-for loop, Loop-while loop, Loop-loop index variable, Function-define/call, Function-parameter, Variable, Data types (string, integer, etc.), List, Multimedia component (sprite, sound, button, etc.), Multimedia properties (color, location, etc.), Multimedia movement (forward, back, turn), Output-string, Output-variable, User input, Event (M = 3.2, SD = 2.7) CT concepts: Select all that apply: Algorithms–sequences (158), Algorithms–parallelism (10), Pattern recognition (142), Abstraction (84), Decomposition (89), Debugging (40), Automation (40) (M = 2.1, SD = 1.1)

Classroom Context Integrated discipline: Select one: Art (5), Language arts (37), Foreign language (2), Math (67), Music (3), Science (61), Social Studies (13) Grades taught in lesson: Select all that apply: Kindergarten through 12th grade (activities that spanned K-5 = 107, 6-8 = 53, 9-12 = 93, K-12 = 9) Minimum amount of time the lesson takes: Select one: < 1 hour (90), 1-3 hours (126), 3-8 hours (32), 8+ hours (14) Source of the lesson plan: Select all that apply: Colleague (16), Online search (18), Professional development (20), Professional organization (23), Created based on an external source by myself or with colleagues (28), Modified from an external source (33), Created by myself or with colleagues (124)

Teacher Information Number of years teaching: Open-ended, M = 14.11, SD = 7.6 Current role: Select one: Teacher (220), STEM/Tech specialist (24), Librarian (9), Computer lab director (1), Other (8) Grade levels taught: Select all that apply: K-2, 3-5, 6-8, 9-10, 11-12 (grade levels that spanned K-5 = 79, 6-8 = 45, 9-12 = 93, K-12 = 45) Disciplines taught: Select all that apply: Art (13), Language arts (71), Foreign language (5), Math (134), Music (4), Science (100), Social Studies (54), Computer science (80), Technology (78), Other (8) Degrees, Certs, endorsements, etc. attained: Select all that apply: Teaching certificate in primary discipline(s) (164), Teaching certificate in CS (17), Bachelor’s degree in primary discipline education (129), Bachelor’s degree in CS or CS education (4), Master’s degree in primary discipline education (163), Master’s degree in CS or CS education (0), Endorsement in computer science education (47), EdD or PhD in education (17), Other (86) Support for integrated CS/CT development and implementation: Select all that apply: Professional development through my school/district/LEA/RESA (157), Professional development through external organizations (117), Peer/colleague/department collaboration in my school/district/LEA/RESA (130), Peer/colleague collaboration in external organizations (73), Funding for software licensing, hardware, or curricula (69) Self-efficacy: Views of CT and self-efficacy scale from Yadav, Caeli, Ocak, and Macann, 2022 (M = 4.23 out of 5, SD = 0.60) Gender: Select one: Man (60), Woman (198), Non-binary/third gender (2), Prefer not to say (2) Race: Select one: African American or Black (31), American Indian or Indigenous (1), Asian (13), Caucasian or White (193), Latino/a/x or Hispanic (10), Middle Eastern (0), Pacific Islander (0), Other (14)

School Context Number of students: Open-ended (M = 1179, SD = 741) Number of CS teachers: Open-ended (M = 1.6, SD = 1.4) Number of CS courses: Open-ended (M = 2.1, SD = 2.0) Number of years CS courses taught: Open-ended (M = 3.0, SD = 2.1) Racial composition: Give % of each race: American Indian or Native American (M = 1.8%), Asian (M = 4.5%), Black or African American (M = 23.3%), Hispanic or Latino (M = 17.2%), White or Caucasian (M = 47.5%), Other (M = 2.4%) % of students eligible for free or reduced lunch: Open-ended (M = 56%, SD = 34%) Type of area: Select one: Rural (90), Suburban (122), Urban (50)

As of June 2022, there were 153 school shootings in total in the United States in that year. Of these incidents, only two were active shooter incidents. The largest number of active shooter incidents in schools was in 2018, with 11 active shooters.

The source defines a shooting as any time a gun is brandished, fired, or a bullet hits school property for any reason.

教育程度：至少完成初中：25岁以上人口：累计百分比：男性在12-01-2022达99.267%，相较于12-01-2021的99.336%有所下降。教育程度：至少完成初中：25岁以上人口：累计百分比：男性数据按年更新，12-01-2001至12-01-2022期间平均值为98.743%，共14份观测结果。该数据的历史最高值出现于12-01-2019，达99.633%，而历史最低值则出现于12-01-2016，为88.283%。CEIC提供的教育程度：至少完成初中：25岁以上人口：累计百分比：男性数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的亚美尼亚 – Table AM.World Bank.WDI: Social: Education Statistics。

E-learning plays an important role in achieving the Sustainable Development Goals (SDGs). This research aimed to E-Learning’s impact on attaining SDGs in Jordanian Higher Education with a primary focus on the University of Jordan as a case study. The study was conducted on a sample of 3,000 students at the University of Jordan from various majors and academic levels and for both genders. The study adopted the quantitative statistical analysis method where a questionnaire was distributed electronically to students through the official platforms approved by the university. The results of the research showed that there is a positive role for E-learning in Higher Education institutions in achieving the sustainable development goals in Jordan, especially SDGs (1, 2, 4, 5, 7, 8, 9, 11, 12, 15, 16 and 17). Through the university’s efforts to develop the skills of students and faculty members in the field of technology and innovation, and holding seminars and conferences via E-learning platforms that enable universities to disseminate valuable information, participate in open dialogues, and raise awareness about SDGs and how to achieve them. Despite these efforts, more remain required to work towards the achievement of SDGs (3, 6, 10, 13, and 14).