The Ministry of Educations' 2014 Basic Education Statistical Booklet captures national statistics for the education sector in that year. This Dataset shows the number of permanent and temporary classroom and the average class size in the 47 counties. Source - The Ministry of Educations' 2014 Basic Education Statistical Booklet, Table 86: Secondary Classroom Status.

Vietnam Grade School: Class: Lower Secondary data was reported at 153.600 Unit th in 2017. This records an increase from the previous number of 151.700 Unit th for 2016. Vietnam Grade School: Class: Lower Secondary data is updated yearly, averaging 150.000 Unit th from Sep 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 170.900 Unit th in 2004 and a record low of 73.300 Unit th in 1991. Vietnam Grade School: Class: Lower Secondary data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.G050: Education Statistics.

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. It is more difficult to predict G3 without G2 and G1, but such prediction is much more useful (see paper source for more details).

Attributes for both Maths.csv (Math course) and Portuguese.csv (Portuguese language course) datasets:

These grades are related with the course subject, Math or Portuguese:

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Vietnam Grade School: Class: Upper Secondary data was reported at 65.800 Unit th in 2017. This records an increase from the previous number of 65.100 Unit th for 2016. Vietnam Grade School: Class: Upper Secondary data is updated yearly, averaging 59.900 Unit th from Sep 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 68.600 Unit th in 2007 and a record low of 13.500 Unit th in 1991. Vietnam Grade School: Class: Upper Secondary data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.G050: Education Statistics.

Additional Information 1: student's school (binary: 0 - Mousinho da Silveira or 1 - Gabriel Pereira) 2: student's sex (binary: 0 - male or 1 - female) 3: student's age (numeric: from 15 to 22) 4: student's home address type (binary: 0 - rural or 1 - urban) 5: family size (binary: 0 - greater than 3 or 1 - less or equal to 3 ) 6: parent's cohabitation status (binary: 0 - apart or 1 - living together) 7: mother's education (numeric: 0 - none, 1 - primary education (4th grade), 2 - 5th to 9th grade, 3 - secondary education or 4 - higher education) 8: father's education (numeric: 0 - none, 1 - primary education (4th grade), 2 - 5th to 9th grade, 3 - secondary education or 4 - higher education) 9: home to school travel time (numeric: 1 -15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - >1 hour) 10: weekly study time (numeric: 1 - 2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours) 11: number of past class failures (numeric: n if if 1<=n<3, else 4) 12: extra educational support (binary: 0 - no or 1 - yes) 13: family educational support (binary: 0 - no or 1 - yes) 14: extra paid classes within the course subject (Math or Portuguese) (binary: 0 - no or 1 - yes) 15: extra-curricular activities (binary: 0 - no or 1 - yes) 16: attended nursery school (binary: 0 - no or 1 - yes) 17: wants to take higher education (binary: 0 - no or 1 - yes) 18: Internet access at home (binary: 0 - no or 1 - yes) 19: with a romantic relationship (binary: 0 - no or 1 - yes) 20: quality of family relationships (numeric: from 1 - very bad to 5 - excellent) 21: free time after school (numeric: from 1 - very low to 5 - very high) 22: going out with friends (numeric: from 1 - very low to 5 - very high) 23: workday alcohol consumption (numeric: from 1 - very low to 5 - very high) 24: weekend alcohol consumption (numeric: from 1 - very low to 5 - very high) 25: current health status (numeric: from 1 - very bad to 5 - very good) 26: number of school absences (numeric: from 0 to 93) 27: first period grade (numeric: from 0 to 20) 28: second period grade (numeric: from 0 to 20) 29: final grade (numeric: from 0 to 20, output target) 30: student mat pass or fail (binary: 0 - fail, 1 - pass)

Context

This dataset is used for predicting student dropout rates, making it a valuable resource for classification problems. It is a real-world dataset with various features related to student demographics, academic performance, and socio-economic factors. The dataset provides a comprehensive view of student enrollment and academic progress, making it a great practice dataset for classification tasks.

Features

• Marital status: Marital status of the student.
• Application mode: Mode of application (e.g., online, in-person).
• Application order: Order of application submission.
• Course: Course or program the student applied for.
• Daytime/evening attendance: Indicates whether the student is attending classes during the day or evening.
• Previous qualification: Previous academic qualification of the student.
• Previous qualification (grade): Grade achieved in the previous qualification.
• Nacionality: Nationality of the student.
• Mother's qualification: Academic qualification of the student's mother.
• Father's qualification: Academic qualification of the student's father.
• Mother's occupation: Occupation of the student's mother.
• Father's occupation: Occupation of the student's father.
• Admission grade: Grade achieved upon admission.
• Displaced: Indicates if the student is displaced.
• Educational special needs: Indicates if the student has special educational needs.
• Debtor: Indicates if the student is in debt.
• Tuition fees up to date: Indicates if the student's tuition fees are up to date.
• Gender: Gender of the student.
• Scholarship holder: Indicates if the student holds a scholarship.
• Age at enrollment: Age of the student at the time of enrollment.
• International: Indicates if the student is an international student.
• Curricular units 1st sem (credited): Number of curricular units credited in the first semester.
• Curricular units 1st sem (enrolled): Number of curricular units enrolled in during the first semester.
• Curricular units 1st sem (evaluations): Number of curricular units evaluated in the first semester.
• Curricular units 1st sem (approved): Number of curricular units approved in the first semester.
• Curricular units 1st sem (grade): Average grade for curricular units in the first semester.
• Curricular units 1st sem (without evaluations): Number of curricular units without evaluations in the first semester.
• Curricular units 2nd sem (credited): Number of curricular units credited in the second semester.
• Curricular units 2nd sem (enrolled): Number of curricular units enrolled in during the second semester.
• Curricular units 2nd sem (evaluations): Number of curricular units evaluated in the second semester.
• Curricular units 2nd sem (approved): Number of curricular units approved in the second semester.
• Curricular units 2nd sem (grade): Average grade for curricular units in the second semester.
• Curricular units 2nd sem (without evaluations): Number of curricular units without evaluations in the second semester.
• Unemployment rate: Local unemployment rate.
• Inflation rate: Local inflation rate.
• GDP: Local GDP.
• Target Target: Multiclass classification problem with the following classes: Graduate: Indicates if the student has graduated. Dropout: Indicates if the student has dropped out. Enrolled: Indicates if the student is still enrolled.

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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.csv) and Portuguese language (por.csv). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks.

Dataset Description

Attributes for both student-mat.csv (Math course) and student-por.csv (Portuguese language course) datasets:

these grades are related with the course subject, Math or Portuguese:

G1 - first period grade (numeric: from 0 to 20) G2 - second period grade (numeric: from 0 to 20) G3 - final grade (numeric: from 0 to 20, output target)

This dataset contains student achievement data for two Portuguese high schools. The data was collected using school reports and questionnaires, and includes student grades, demographics, social, parent, and school-related features.

Two datasets are provided regarding performance in two distinct subjects: Mathematics and Portuguese language. I have cleaned the original datasets so that they are easier to read and use.

Attributes for both student_math_cleaned.csv (Math course) and student_portuguese_cleaned.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_type - student's home address type (binary: "Urban" or "Rural")
5. family_size - family size (binary: "Less or equal to 3" or "Greater than 3")
6. parent_status - parent's cohabitation status (binary: "Living together" or "Apart")
7. mother_education - mother's education (ordinal: "none", "primary education (4th grade)", "5th to 9th grade", "secondary education" or "higher education")
8. father_education - father's education (ordinal: "none", "primary education (4th grade)", "5th to 9th grade", "secondary education" or "higher education")
9. mother_job - mother's job (nominal: "teacher", "health" care related, civil "services" (e.g. administrative or police), "at_home" or "other")
10. father_job - 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. travel_time - home to school travel time (ordinal: "<15 min.", "15 to 30 min.", "30 min. to 1 hour", or 4 - ">1 hour")
14. study_time - weekly study time (ordinal: 1 - "<2 hours", "2 to 5 hours", "5 to 10 hours", or ">10 hours")
15. class_failures - number of past class failures (numeric: n if 1<=n<3, else 4)
16. school_support - extra educational support (binary: yes or no)
17. family_support - family educational support (binary: yes or no)
18. extra_paid_classes - 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_ed - wants to take higher education (binary: yes or no)
22. internet - Internet access at home (binary: yes or no)
23. romantic_relationship - with a romantic relationship (binary: yes or no)
24. family_relationship - quality of family relationships (numeric: from 1 - very bad to 5 - excellent)
25. free_time - free time after school (numeric: from 1 - very low to 5 - very high)
26. social - going out with friends (numeric: from 1 - very low to 5 - very high)
27. weekday_alcohol - workday alcohol consumption (numeric: from 1 - very low to 5 - very high)
28. weekend_alcohol - 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:

1. grade_1 - first period grade (numeric: from 0 to 20)
2. grade_2 - second period grade (numeric: from 0 to 20)
3. final_grade - final grade (numeric: from 0 to 20, output target)

Important note: the target attribute final_grade has a strong correlation with attributes grade_2 and grade_1. This occurs because final_grade is the final year grade (issued at the 3rd period), while grade_1 and grade_2 correspond to the 1st and 2nd period grades. It is more difficult to predict final_grade without grade_2 and grade_1, but these predictions will be much more useful.

Additional note: there are 382 students that belong to both datasets, though the ID's do not match. These students can be identified by searching for identical attributes that characterize each student.

Please include this citation if you plan to use this database: 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.

The aim of the upper secondary school student barometer is to collect information on the studies and everyday life of upper secondary school students. The survey covers topics such as applying to upper secondary school, current studies, study environment, teaching and guidance, study skills, study progress, thoughts about the future, well-being, livelihood, housing, inappropriate conduct, and values and attitudes. The survey was carried out by the Research Foundation for Studies and Education (Otus) and the Union of Upper Secondary School Students in Finland with funding from the Ministry of Education and Culture. First, respondents were asked, among other things, about their reasons for applying to upper secondary school. They were also asked how well the studies at upper secondary school corresponded to their expectations and how many hours per week they spent studying. Regarding the study environment, various statements were made about the facilities and the learning materials and equipment. They were also asked about the impact of smartphones on learning. Afterwards, the respondents were asked what they thought about the teaching and guidance provided by the institution and were asked to answer questions about their study skills. They were also asked about the progress of their studies and whether something had been holding their studies back. Respondents were also asked whether they thought they would graduate. In addition, respondents were asked whether they had considered transferring to a vocational school or dropping out of studies. This was followed by questions about the matriculation exams, for example which subjects the respondent intended to take in the matriculation exams and what influenced these choices. Questions about the future included for example, whether they plan to continue their studies after upper secondary school and what disciplines they are interested in. They were also asked about the kind of things they would like to have in their future working life. In terms of well-being, the respondent's perceived state of health was asked and general statements about well-being were made. This was followed by questions on the respondents' sources of income, employment and housing. Finally, respondents were asked whether they had experienced inappropriate conduct in upper secondary school and, through general current social statements, about their values and attitudes. Background variables include gender, age, mother tongue, parents' work status, parents' educational level, whether they experience a minority status, number of years of study, size of the educational institution, municipality and admission level of the educational institution.

All Indian Schools Dataset with 1572549 unique Schools

Deparment of School Education and Literacy, Government of India

The dataset contains information about schools in India. It includes details such as school name, location, category, type, management, and more. The data is sourced from the Department of School Education and Literacy, Government of India.

Content

The dataset contains the following columns:

• schoolId: Unique identifier for the school
• udiseSchCode: Unique code assigned to the school by UDISE
• schoolName: Name of the school
• pincode: Pincode of the school location
• locality: Locality of the school (Rural or Urban)
• schLocDesc: Description of the locality
• category: Category of the school
• categoryDesc: Description of the category
• type: Type of the school
• typeDesc: Description of the type
• classFrom: Starting class in the school
• classTo: Ending class in the school
• status: Status of the school
• mergedYear: Year of merging (if applicable)
• state: State where the school is located
• district: District where the school is located
• block: Block where the school is located
• cluster: Cluster where the school is located
• village: Village where the school is located
• management: Management of the school
• isOperational201819: Operational status for 2018-19
• isOperational201920: Operational status for 2019-20
• isOperational202021: Operational status for 2020-21
• isOperational202122: Operational status for 2021-22
• isOperational202223: Operational status for 2022-23

This longitudinal data collection supplies information on the educational, vocational, and personal development of young people who were high school seniors in 1972 and examines the kinds of factors -- personal, familial, social, institutional, and cultural -- that may affect that development. The collection provides a broad spectrum of information on each student and covers areas such as ability, socioeconomic status, home background, community environment, ethnicity, significant others, current activity at time of survey, educational attainment, school experiences, school performance, work status, work performance and satisfaction, goal orientations, marriage and the family, and military experience. Data collected in the base-year (1972) focus on factors relating to the student's personal/family background, education and work experiences, plans, aspirations, attitudes, and opinions. The first follow-up, which was conducted in 1973, offers information on the respondent's activity state (education, work, etc.), socioeconomic status, work and educational experience since leaving high school, future plans, and expectations. The second follow-up (1974) probes respondents on similar measures but is augmented by additional variables pertaining to work and education. The third follow-up (1976) contains additional items on graduate school application and entry, job supervision, sex roles, sex and race biases, and a subjective rating of high school experiences. The fourth follow-up (1979) offers data similar to the other follow-ups but includes some variables that were modified to elicit unique information. For the fifth follow-up, the sample members averaged 32 years of age and had been out of high school for 14 years. In addition to covering the same subject areas as the previous surveys, this follow-up includes additional questions on marital history, divorce, child support, and economic relationships in modern families. Part 1 of this collection contains base-year data as well as data collected during four subsequent follow-ups undertaken in 1973, 1974, 1976, and 1979, while Part 12 contains fifth follow-up data for 1986. Part 2, the School File, contains information obtained from the respondent's high school and also from high school counselors. Data are available on school organization and enrollment, course offerings, special services and programs, library and other resources, time scheduling, and grading systems. Counselor information is supplied on work loads, counseling practices and facilities, experience with student financial aid programs, age, ethnicity, training, and experience. A supplementary School District Census File, Part 3, contains 1970 Census data tabulated by school district boundaries. In addition, the collection includes an FICE Code File and a CEEB Institutional Data Base File that can be used in conjunction with the student file to supply contextual information about respondents' colleges. The Institutional Data Base File offers data for colleges and universities on items such as enrollment, income and revenues, expenses, tuition and fees, and median student scores on standardized tests. Parts 6, 7, 9, and 10 contain transcript data from each postsecondary institution reported by sample members in the first through fourth follow-up surveys. Data are available for several types of postsecondary institutions, ranging from short-term vocational or occupational programs through major universities with graduate programs and professional schools. Data in these four rectangular files -- Student, Transcript, Term, and Course Files -- are organized to be used in combination hierarchically. Information is available on terms of attendance, fields of study, specific courses taken, and grades and credits earned. The Fifth Follow-Up Teaching Supplement (Parts 15-17) surveyed those members of the original 1972 sample who had obtained teaching certificates and/or who had teaching experience. Respondents were asked questions about their qualifications, experience, and attitudes toward teaching.

Predict students' dropout and academic success

Investigating the Impact of Social and Economic Factors

By [source]

About this dataset

This dataset provides a comprehensive view of students enrolled in various undergraduate degrees offered at a higher education institution. It includes demographic data, social-economic factors and academic performance information that can be used to analyze the possible predictors of student dropout and academic success. This dataset contains multiple disjoint databases consisting of relevant information available at the time of enrollment, such as application mode, marital status, course chosen and more. Additionally, this data can be used to estimate overall student performance at the end of each semester by assessing curricular units credited/enrolled/evaluated/approved as well as their respective grades. Finally, we have unemployment rate, inflation rate and GDP from the region which can help us further understand how economic factors play into student dropout rates or academic success outcomes. This powerful analysis tool will provide valuable insight into what motivates students to stay in school or abandon their studies for a wide range of disciplines such as agronomy, design, education nursing journalism management social service or technologies

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How to use the dataset

This dataset can be used to understand and predict student dropouts and academic outcomes. The data includes a variety of demographic, social-economic and academic performance factors related to the students enrolled in higher education institutions. The dataset provides valuable insights into the factors that affect student success and could be used to guide interventions and policies related to student retention.

Using this dataset, researchers can investigate two key questions: - which specific predictive factors are linked with student dropout or completion? - how do different features interact with each other? For example, researchers could explore if there any demographic characteristics (e.g., gender, age at enrollment etc.) or immersion conditions (e.g., unemployment rate in region) are associated with higher student success rates, as well as understand what implications poverty has for educational outcomes. By answering these questions, research insight is generated which can provide critical information for administrators on formulating strategies that promote successful degree completion among students from diverse backgrounds in their institutions.

In order to use this dataset effectively it is important that scientists familiarize themselves with all variables provided in the dataset including categorical (qualitative) variables such as gender or application mode; numerical variables such as number of curricular units at the beginning of semesters or age at enrollment; ordinal data measurement type variables such as marital status; studied trends over time such as inflation rate or GDP; frequency measurements variables like percentage of scholarship holders; etc.. Additionally scientists should make sure they aware off all potential bias included in the data prior running analysis–for example understanding if one population is underrepresented compared another -as this phenomenon could lead unexpected results if not taken into consideration while conducting research undertaken using this data set.. Finally it would be important for practitioners realize that this current Kaggle Dataset contains only one semester-worth information on each admission intake whereas additional studies conducted for a longer time period might be able provide more accurate results related selected topic area due further deterioration retention achievement coefficients obtained from those gradually accurate experiments unfolding different year-long admissions seasons

Research Ideas

• Prediction of Student Retention: This dataset can be used to develop predictive models that can identify student risk factors for dropout and take early interventions to improve student retention rate.
• Improved Academic Performance: By using this data, higher education institutions could better understand their students' academic progress and identify areas of improvement from both an individual and institutional perspective. This will enable them to develop targeted courses, activities, or initiatives that enhance academic performance more effectively and efficiently.
• Accessibility Assistance: Using the demographic information included in the dataset, institutions could develop s...

The data examined the well-being of Finnish schoolchildren in grades seven to nine of basic education. The themes of the survey included school conditions, social relations, possibilities of self-actualisation and state of health. The questions were presented as attitudinal statements and multiple-choice questions. The statements were evaluated on a five-level scale ranging from "strongly agree" to "strongly disagree". Questions charting conditions at the school covered school facilities and activities. The respondents were asked, for instance, about satisfaction with the classroom's size, temperature and ventilation, the school's toilet facilities as well as the cleanliness and safety of the school buildings and yard areas. They were also asked to evaluate if it was possible to work in peace in the classroom, if the amount of schoolwork was appropriate, if rules and disciplinary actions at the school were sensible, and if it was easy to get to visit the school nurse or counselor. Some statements examined social relations between classmates as well as between the pupils and teachers, along with parents' attitudes toward studying. The respondents were asked, for example, if groupwork was successful, if classmates offered help, if teachers were friendly, and if parents respected their schoolwork, helped with homework and participated in parent-teacher meetings. It was also charted whether the respondents had been bullied at school during the current semester and whether they had themselves bullied someone. Some questions pertained to self-actualisation. These questions covered e.g. whether teachers listened to the respondents' opinions, if pupils' opinions were taken into consideration in the school's decision-making, whether the respondents had found a suitable study method and took care of their school duties, if they were thanked for good work, if they felt that school is important and whether they received help from the teacher when needed. Questions concerning the respondents' general health examined if they had suffered from a variety of symptoms during the current school term (e.g. stomach pains, difficulty falling asleep or waking up at night, headaches, feeling sad, fears). Background variables included gender, age and grade.

This dataset presents the management indicators H/E and I/S at the start of the school year , by institution and level of training as well as the totals by department and by academy.

H/E indicator: weekly teaching hours per pupil (number of hours per pupil) This indicator corresponds to the ratio of the number of weekly teaching hours delivered by teachers at a given level of training to the number of school-status pupils at that level of training. It depends in particular on the schedules of the programmes and the sizes of the divisions (or classes) in which the lessons are taught. An H/E indicator of 1.5 means that for every 100 pupils, 150 teaching hours of teachers are mobilised. H corresponds to the number of hours of weekly instruction provided in front of pupils E is the sum of the students in the entire class

The observed differences in H/E must be compared with the number and size of schools (the smaller a school and therefore the more small a department has, the higher the H/E) and the distribution of education levels (the more vocational training is present, the higher the H/E). In addition, the numbers taken into account in the H/E are those of the classes. However, many lessons are taught in front of groups of students.

A second indicator is therefore used: the average number of pupils per structure (whole class or group), I/O. This makes it possible in particular to analyse the differences in the means allocated per class (I/O) to the average number of pupils per given structure.

I/O indicator: pupils per I/O structure (average number of pupils per structure) Average number of staff in the structures (whole classes or groups) weighted by the number of teaching hours provided in each structure. It makes it possible to estimate the number of pupils with school status for whom a teacher is responsible on average for one hour of lessons. It is lower when students attend classes in small groups than when classes are delivered in front of entire classes. E: number of pupils in a structure weighted by the number of teaching hours provided in that structure S: Number of weekly teaching hours in front of pupils

NB: If the headcount of a structure is less than 2, the numerator and denominator of the I/O indicator shall be noted as missing for that structure. Thus, the sum of the number of teaching hours per institution and level of training used for the I/O calculation may differ slightly from that used for the H/E calculation. Indicators can take the value "ns" when they are not significant, usually due to a very low number of students in the level of training. The date of observation shall be 1 November of each year. This date varies between institutions and from year to year from 1 September to 15 December.

US Census American Community Survey (ACS) 2018, 5-year estimates of the key economic characteristics of Secondary School Districts geographic level in Orange County, California. The data contains 397 fields for the variable groups E01: Employment status (universe: population 16 years and over, table X23, 7 fields); E02: Work status by age of worker (universe: population 16 years and over, table X23, 36 fields); E03: Commuting to work (universe: workers 16 years and over, table X8, 8 fields); E04: Travel time to work (universe: workers 16 years and over who did not work at home, table X8, 14 fields); E05: Number of vehicles available for workers (universe: workers 16 years and over in households, table X8, 8 fields); E06: Median age by means of transportation to work (universe: median age, workers 16 years and over, table X8, 7 fields); E07: Means of transportation to work by race (universe: workers 16 years and over, table X8, 64 fields); E08: Occupation (universe: civilian employed population 16 years and over, table X24, 53 fields); E09: Industry (universe: civilian employed population 16 years and over, table X24, 43 fields); E10: Class of worker (universe: civilian employed population 16 years and over, table X24, 19 fields); E11: Household income and earnings in the past 12 months (universe: total households, table X19, 37 fields); E12: Income and earnings in dollars (universe: inflation-adjusted dollars, tables X19-X20, 31 fields); E13: Family income in dollars (universe: total families, table X19, 17 fields); E14: Health insurance coverage (universe: total families, table X19, 17 fields); E15: Ratio of income to Poverty level (universe: total population for whom Poverty level is determined, table X17, 8 fields); E16: Poverty in population in the past 12 months (universe: total population for whom Poverty level is determined, table X17, 7 fields); E17: Poverty in households in the past 12 months (universe: total households, table X17, 9 fields); E18: Percentage of families and people whose income in the past 12 months is below the poverty level (universe: families, population, table X17, 8 fields), and; X19: Poverty and income deficit (dollars) in the past 12 months for families (universe: families with income below Poverty level in the past 12 months, table X17, 4 fields). The US Census geodemographic data are based on the 2018 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).

Abstract This study aimed to examine the association between clusters of physical activity (PA), diet, and television viewing (TV) with weight status among a representative sample of Brazilian students. Data from the National Health School-based Survey (PeNSE) 2015 were analyzed (n = 16,521; mean age 14.8, standard deviation 0.03 year). PA (minutes/week spent in leisure-time, and commuting to/from school), TV (hours/day), and weekly consumption of deep-fried empanadas, candies, sodas, ultra-processed foods, fast foods, green salads or vegetables, and fruits were self-reported on the validated PeNSE questionnaire. Latent class analysis defined behavior classes, and binary logistic regression assessed the association between clustering and weight status. Six classes’ types with positive and negative behaviors were identified. Adolescents belonging to the “low TV time and high healthy diet” class had higher chances of being overweight (including obesity) compared to their peers in the “moderate PA and mixed diet” class. No associations were found in the other clusters. Mixed classes with healthy and unhealthy behaviors characterized adolescents’ lifestyles and these profiles were related to weight status.

The Higher Education General Information Survey (HEGIS) series was designed to provide comprehensive information on various aspects of postsecondary education in the United States and its territories (American Samoa, Guam, Puerto Rico, the Virgin Islands, and the Marshall Islands) and Department of Defense schools outside the United States. Data are available for both public and private two-year and four-year institutions. The HEGIS Fall Enrollment component for 1972 sought enrollment data from 2,945 institutions of higher education. Key data elements, presented for up to three record types for each institution, include total enrollments of degree-credit students by class level, sex, and attendance status (full-time versus part-time) and enrollments of resident students, extension students, and first-time students. In addition, data are provided on number of non-bachelor's-degree credit students and total number of students or head counts.