Institution performance by cohort and disadvantage status. Cohorts include academic, A level, applied general, tech level, and technical certificate students, as well as those eligible for inclusion in English and maths specific measures.

Subject entries and grades achieved in schools/colleges in the performance tables data for 2024/25.

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.

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).

The Iowa School Performance Profiles is an online tool showing how public schools performed on required measures. The website was developed to meet both federal and state requirements for publishing online school report cards: The federal Every Student Succeeds Act and House File 215, adopted by Iowa lawmakers in 2013. The website includes: Scores on school accountability measures required under ESSARatings based on those scores: Exceptional, High Performing, Commendable, Acceptable, Needs Improvement, and PriorityIdentification of schools for support and improvement based on accountability scores (Comprehensive and Targeted schools)Additional education data that must be reported by law but do not figure into school accountability scores To learn more about school scores, measures, rankings and other data, visit the “Help” section for a user guide, technical guide and other resources.

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.

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

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 and college performance tables data in 2024/25.

Performance of NYC High Schools

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

Information about the schools included in the school and college performance tables data in 2023/24, includes the Progress 8 banding.

National level headline performance measures in state-funded schools broken down by pupil and school characteristics since 2018/19.

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 the school classifications, school performance indices (SPIs), and SPI target attainment status for 2012-13 for all schools that administered the Connecticut Academic Performance Test (CAPT). It also includes school classifications assigned to high schools with non-tested grades. 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: Target attainment status will say “n/a” if there is no 2012-13 SPI target or if there is no 2012-13 SPI, which happens when there are small N sizes for a particular subgroup or subject.

About the Math-Students Dataset

This dataset, originally sourced from the UCI Machine Learning Repository, offers a rich collection of data on student performance in a math program. It provides detailed insights into both the academic achievements and the socio-demographic backgrounds of the students, making it an excellent resource for educational data mining and predictive analytics.

Key Features & Attributes

• Demographics & Background:

  • School: Identifies the student's school (e.g., Gabriel Pereira or Mousinho da Silveira).
  • Sex & Age: Basic demographic information to help explore performance trends among different groups.
  • Address & Family Size: Details about the student’s home environment, including whether they live in an urban or rural area and their family size.

• Parental & Household Information:

  • Parental Cohabitation & Education: Data on whether parents live together and their education levels, which can correlate with student support and academic outcomes.
  • Parental Occupation: Information on the mother’s and father’s jobs, providing further context on socioeconomic factors.

• Educational & Behavioral Variables:

  • Study Time & Failures: Weekly study time and history of past class failures help gauge academic dedication and potential challenges.
  • Support & Extracurricular Activities: Records on whether the student has received extra educational support or participates in extracurricular activities, which can influence overall performance.
  • School-Related Factors: Travel time to school, attendance (absences), and participation in additional paid classes contribute to a holistic view of the educational environment.

• Lifestyle & Social Factors:

  • Internet Access, Free Time & Socializing: Variables like internet availability, free time, and how often students go out with friends help capture lifestyle and behavioral patterns.
  • Health & Well-being: Self-reported health status and alcohol consumption patterns during weekdays and weekends offer insights into personal well-being, which may impact academic performance.

• Academic Performance:

  • Grades: The dataset includes three key assessments—G1 (first period grade), G2 (second period grade), and G3 (final grade). G3, the final grade, serves as the primary target variable for predictive models.

Potential Applications

• Predictive Modeling:
  Researchers and data scientists can build regression models to predict final grades (G3) based on the numerous socio-demographic and educational features.
• Exploratory Data Analysis:
  The dataset is ideal for exploring relationships between family background, lifestyle choices, and academic success. For example, one could analyze how study time or parental education levels correlate with performance.
• Educational Interventions:
  By identifying key factors that contribute to academic outcomes, educators and policymakers can develop targeted interventions to support at-risk students.
• Comparative Studies:
  While this dataset focuses on math scores, its structure is similar to the Portuguese language course dataset. This similarity provides opportunities for cross-domain comparisons in educational research.

Additional Insights

• Data Complexity & Quality:
  Despite its moderate size, the dataset is rich in both categorical and numerical variables. This diversity requires careful preprocessing and feature engineering but also offers the chance to uncover complex interactions between various factors.
• Research Impact:
  The dataset has been widely used in the field of educational data mining. Its comprehensive nature has provided a basis for numerous studies examining the interplay between academic performance and a range of external factors.
• Historical Context:
  Originating from a study presented at the 5th FUBUTEC 2008 conference, the dataset has contributed valuable insights into secondary school performance and continues to serve as a benchmark for educational analytics research.