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If this Data Set is useful, and upvote is appreciated. 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).
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TwitterDataset showing Secondary Schools located with the Councils administrative area. Individual Secondary Schools are recorded as points.
Upon accessing this Licensed Data you will be deemed to have accepted the terms of the Public Sector End User Licence - INSPIRE
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TwitterOpen Government Licence 2.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/2/
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Secondary Schools' Location in York. *Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset.The date shown in the "Last Updated" field of each GIS resource reflects when the data was first published.
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👏 Upvote this dataset if you find it interesting !
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.
| Variable | Description |
|---|---|
school | student's school (binary: 'GP' - Gabriel Pereira or 'MS' - Mousinho da Silveira) |
sex | student's sex (binary: 'F' - female or 'M' - male) |
age | student's age (numeric: from 15 to 22) |
address | student's home address type (binary: 'U' - urban or 'R' - rural) |
famsize | family size (binary: 'LE3' - less or equal to 3 or 'GT3' - greater than 3) |
Pstatus | parent's cohabitation status (binary: 'T' - living together or 'A' - apart) |
Medu | mother's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education) |
Fedu | father's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education) |
Mjob | mother's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other') |
Fjob | father's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other') |
reason | reason to choose this school (nominal: close to 'home', school 'reputation', 'course' preference or 'other') |
guardian | student's guardian (nominal: 'mother', 'father' or 'other') |
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) |
studytime | weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours) |
failures | number of past class failures (numeric: n if 1<=n<3, else 4) |
schoolsup | extra educational support (binary: yes or no) |
famsup | family educational support (binary: yes or no) |
paid | extra paid classes within the course subject (Math or Portuguese) (binary: yes or no) |
activities | extra-curricular activities (binary: yes or no) |
nursery | attended nursery school (binary: yes or no) |
higher | wants to take higher education (binary: yes or no) |
internet | Internet access at home (binary: yes or no) |
romantic | with a romantic relationship (binary: yes or no) |
famrel | quality of family relationships (numeric: from 1 - very bad to 5 - excellent) |
freetime | free time after school (numeric: from 1 - very low to 5 - very high) |
goout | going out with friends (numeric: from 1 - very low to 5 - very high) |
Dalc | workday alcohol consumption (numeric: from 1 - very low to 5 - very high) |
Walc | weekend alcohol consumption (numeric: from 1 - very low to 5 - very high) |
health | current health status (numeric: from 1 - very bad to 5 - very good) |
absences | number of school absences (numeric: from 0 to 93) |
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)
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This Public Schools feature dataset is composed of all Public elementary and secondary education facilities in the United States as defined by the Common Core of Data (CCD, https://nces.ed.gov/ccd/ ), National Center for Education Statistics (NCES, https://nces.ed.gov ), US Department of Education for the 2017-2018 school year. This includes all Kindergarten through 12th grade schools as tracked by the Common Core of Data. Included in this dataset are military schools in US territories and referenced in the city field with an APO or FPO address. DOD schools represented in the NCES data that are outside of the United States or US territories have been omitted. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 3065 new records, modifications to the spatial location and/or attribution of 99,287 records, and removal of 2996 records not present in the NCES CCD data.
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TwitterNumber of teachers in government and non-government secondary schools at school level by school location, school ownership and gender, as of March 2016.
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TwitterOpen Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
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Number of schools graded 1 and 2 on behaviour as a percentage of the total number of schools inspected in the 2005-2008 cycle. Source: Department for Children Schools and Families (DCSF) Publisher: Department for Children Schools and Families (DCSF) Geographies: County/Unitary Authority, Government Office Region (GOR), National Geographic coverage: England Time coverage: 2006 to 2008
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TwitterThis dataset describes number of teachers by gander in secondary school .Also shows number of qualified across various district and region.
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TwitterOpen Government Licence 2.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/2/
License information was derived automatically
Catchment areas for secondary schools in York. For further information about secondary school catchment areas please visit the City of York Council website. *Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset.The date shown in the "Last Updated" field of each GIS resource reflects when the data was first published.
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TwitterDesignated institution to provide learning spaces and learning environments for the teaching of students
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TwitterThis datasets contains the boundaries of the areas of first priortiy for secondary schools. Each school is given an area of priority and children that live within that area are given priority placement in that school.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Historical Dataset of Cedar Springs High School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1987-2023),Total Classroom Teachers Trends Over Years (1990-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1995-2023),American Indian Student Percentage Comparison Over Years (1995-2023),Asian Student Percentage Comparison Over Years (1989-2023),Hispanic Student Percentage Comparison Over Years (1993-2023),Black Student Percentage Comparison Over Years (1993-2023),White Student Percentage Comparison Over Years (1991-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (1991-2023),Free Lunch Eligibility Comparison Over Years (1993-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2003-2023),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2012-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2012-2023),Graduation Rate Comparison Over Years (2013-2023)
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Percentage of Teachers in Secondary Schools by Gender and Age Group (2021). Data as of 31st January 2021
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Historical Dataset of Manhasset Secondary School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1987-2023),Total Classroom Teachers Trends Over Years (1990-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1990-2023),Asian Student Percentage Comparison Over Years (1993-2023),Hispanic Student Percentage Comparison Over Years (1993-2023),Black Student Percentage Comparison Over Years (1993-2023),White Student Percentage Comparison Over Years (1993-2023),Two or More Races Student Percentage Comparison Over Years (2015-2023),Diversity Score Comparison Over Years (1993-2023),Free Lunch Eligibility Comparison Over Years (1995-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2003-2023),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2012-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2012-2023),Graduation Rate Comparison Over Years (2013-2023)
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TwitterCC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
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The Ministry of Educations' - Basic Education Statistical Booklet captures national statistics for the Education Sector in totality. This dataset highlights number of secondary schools based on gender Source: Table 65: Number of Secondary Schools by Gender
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This project investigated UK secondary school students’ views of inequality and their sense of agency concerning their occupational prospects, using questionnaire and interview data. The data came from 78 students from three secondary schools in England between Year 7 and Year 13 who were aged between 12 and 19. The three schools were in areas with different socioeconomic characteristics – an affluent town in the London commuter belt (School A), a city in the east of England (School B) and a town to the east of London (School C). School A had a lower than national average free school meals (FSM) rate, whereas both School B and School C had a higher than national average FSM rate.
18 participants were from School A, 38 from School B and 22 from School C. While all 18 students in School A and all 22 students in School C participated in both the questionnaire and follow-up interview stages, in School B 37 participants filled in the questionnaire and, of these, 22 took part in the interviews. One student from School B who did not fill in a questionnaire took part in the interview, making the total interviews from School B 23. One student from School C did not want to have their interview audio-recorded; therefore, their interview transcript does not exist.
As a result, the dataset in total contains 77 questionnaires and 62 interview transcripts. The PDF files are questionnaire files and the word document files are interview transcripts. A file name (for both the pdf files and word document files) begins with ‘Y’ that is followed by a number which indicates a school year and this is followed by two letters that indicate a code for an individual participant, while the letter A, B or C immediately after a hyphen indicates School A, B or C respectively.
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TwitterThis dataset mainly contains the overall ability characteristics of international high school students under the AP course system, which can be used to evaluate the overall ability of each student, so as to provide students and teachers with a guide for future career and professional direction
Student's features: - Gender - Highest GPA - School Rank - AP Courses for 10th grade - AP Courses for 11th grade - Academic competitions - Academic papers publications - Hours of community service - Summer schools - Student clubs - Personal big projects - Personal interests and hobbies - Self - learning ability - Communication ability - Adaptive ability - Team work ability - Problem - solving ability - Creativity ability - Information processing ability - EQ - Interested majors - Interested jobs - Interested colleges - Recommendation letters from renown people
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TwitterDesignated institution to provide learning spaces and learning environments for the teaching of students
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TwitterDesignated institution to provide learning spaces and learning environments for the teaching of students
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Twitterhttps://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
If this Data Set is useful, and upvote is appreciated. 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).