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).

School progress report, district scorecard, PSSA & Keystone, district graduation rate, school graduation rate, aimsweb-star, attendance, out-of-school suspensions, serious incidents, NSC student tracker reports, college matriculation, end-of-year report

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.

This dataset contains the school performance indices (SPIs) for 2009-10 (2010), 2010-11 (2011), and 2011-12 (2012) for all schools that administered the Connecticut Mastery Test (CMT). These data were published in the School Performance Reports released by the CT State Department of Education (CSDE) in December 2013 (see http://www.csde.state.ct.us/public/performancereports/20122013reports.asp) Note: Cells are left blank if there is no SPI, which happens when there are small N sizes for a particular subgroup or subject.

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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 the details of key school performance indicators like the drop-out rate, retention rate, repetition rate, and the promotion rate by levels of education for all schools.

Title: Kenyan Secondary School Student Performance Data

Description:
This dataset captures fictionalized but representative performance data for students in a Kenyan secondary school. It includes academic performance, attendance records, and gender information, providing a comprehensive view of individual and collective achievements across various subjects and terms. This dataset suits educational data analysis, machine learning models, and dashboard development.

Features:
- studentname: The name of the student.
- gender: Gender of the student (Male or Female).
- form: Class level the student is in (1, 2, 3, 4).
- dob: Date of birth of the student.
- class_teacher: Class teacher of the class/form.
- term: The academic term (1, 2, 3, 4).
- Maths, English, Kiswahili, History, Biology, Business, HomeScience, Physics, Chemistry, Biology, cre, Agriculture, Computer: Scores in various subjects, ranging from 40 to 100.
- attendance: student attendance out of 20.
- attendance (%): student attendance in %.
- average: The average score is calculated across all subjects for each student.
- grade: student grade based on the scale below.

grade scale

0 - 29 E 30 - 34 D- 35 - 39 D 40 - 44 D+ 45 - 49 C- 50 - 54 C 55 - 59 C+ 60 - 64 B- 65 - 69 B 70 - 74 B 75 - 79 A-

80+ A

Potential Use Cases:
1. Education Analytics: Understand trends in student performance across subjects, terms, and classes.
2. Machine Learning: Build predictive models for student performance based on attendance and demographic factors.
3. Dashboard Development: Create interactive visualizations and tools for schools to monitor student performance.
4. Policy Analysis: Use the data to simulate educational policies and their impacts on performance.

Key Insights:
This dataset allows for the analysis of:
- Gender disparities in performance.
- Subject-wise strengths and weaknesses.
- Impact of attendance on academic success.
- Comparative performance across forms and terms.

Acknowledgment:
This is a fictional dataset inspired by the structure and challenges of Kenyan secondary schools. It is not derived from student data and should be used strictly for educational and analytical purposes.

SCHOOL PROFICIENCY INDEXSummaryThe school proficiency index uses school-level data on the performance of 4th grade students on state exams to describe which neighborhoods have high-performing elementary schools nearby and which are near lower performing elementary schools. The school proficiency index is a function of the percent of 4th grade students proficient in reading (r) and math (m) on state test scores for up to three schools (i=1,2,3) within 1.5 miles of the block-group centroid. S denotes 4th grade school enrollment:Elementary schools are linked with block-groups based on a geographic mapping of attendance area zones from School Attendance Boundary Information System (SABINS), where available, or within-district proximity matches of up to the three-closest schools within 1.5 miles. In cases with multiple school matches, an enrollment-weighted score is calculated following the equation above. Please note that in this version of the data (AFFHT0004), there is no school proficiency data for jurisdictions in Kansas, West Virginia, and Puerto Rico because no data was reported for jurisdictions in these states in the Great Schools 2013-14 dataset. InterpretationValues are percentile ranked and range from 0 to 100. The higher the score, the higher the school system quality is in a neighborhood. Data Source: Great Schools (proficiency data, 2015-16); Common Core of Data (4th grade school addresses and enrollment, 2015-16); Maponics (attendance boundaries, 2016).Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 7.

To learn more about the School Proficiency Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020

Information for this dataset is sourced from the Department for Education. Data is held for primary, secondary and special schools in England. Data is calculated at different aggregation levels: school, local authority and national. The school performance data consists of exam, test and teacher assessment data. Along with attainment measures these are used to calculated progress measures.

This dataset contains ranking information of primary schools according to performance in primary school leaving certificate examinations.

Attainment data for children at various stages in early years and primary education: Early years foundation stage profile (EYFSP); Year 1 Phonics; Key Stage 1 (KS1); Key Stage 2 (KS2). The data is by school location, rather than by pupil residence, in determining which ward the data relates to. A list of schools by wards is also provided. The data source is the National Consortium of Examination Results (NCER). A summary of Calderdale school performance can be found on the Council website: School performance tables . School performance for individual schools can be found at Compare school performance .

Key Stage 2 (KS2) data for year 6 primary school pupils who met or exceeded the Expected Standard (EXS+) by School Ward for the period 2016 onwards. The data is by school location, rather than by pupil residence. In determining, which ward the data relates to, a Schools list by wards is available. The data source is the National Consortium of Examination Results (NCER) but the figures come from the Department of Education (DfE). A summary of Calderdale school performance can be found on the Council website: School performance tables. School performance for individual schools can be found at Compare school performance. Please note some DFE numbers might have changed please see previous DFE code on Schools list.

Key Stage 1 (KS1) data for primary school pupils in Year 2 who met or exceeded the Expected Standard (EXS+) by School Ward for the 2016 onwards. The data is by school location, rather than by pupil residence. In determining, which ward the data relates to, a Schools list by wards is available. The data source is the National Consortium of Examination Results (NCER). A summary of Calderdale school performance can be found on the Council website: School performance tables. School performance for individual schools can be found at Compare school performance. Please note some DFE numbers might have changed please see previous DFE code on Schools list.

Interactive resource provided by the Department for Education showing individual school performance for every school in Barnet.

Reference information on individual schools - Explore Education Statistics data set Information about schools from Key stage 4 performance

Year 1 (Y1) primary school pupils phonics data who met the Expected Standard for Phonics by School Ward for the period 2013 onwards.The data is by school location, rather than by pupil residence. In determining, which ward the data relates to, a Schools list by wards is available. The data source is the National Consortium of Examination Results (NCER). A summary of Calderdale school performance can be found on the Council website: School performance tables. School performance for individual schools can be found at Compare school performance. Please note some DFE numbers might have changed please see previous DFE code on Schools list.

This file contains data on the attainment of primary schools at institution level.

The School Quality Reports share information about school performance, set expectations for schools, and promote school improvement. Due to size constraints only partial data is reflected, to view entire data open up the excel file that shown with data set name. These reports include information from multiple sources, including Quality Reviews, the NYC School Survey, and student performance. The School Quality Reports are organized around the Framework for Great Schools, which include six elements Rigorous Instruction, Collaborative Teachers, Supportive Environment, Effective School Leadership, Strong FamilyCommunity Ties, and Trust—that drive student achievement and school improvement.

Performance of NYC High Schools

The Quality Review is a process that evaluates how well schools are organized to support student learning and teacher practice. It was developed to assist New York City Department of Education (NYCDOE) schools in raising student achievement by looking behind a school’s performance statistics to ensure that the school is engaged in effective methods of accelerating student learning.