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

This layer serves as the authoritative geographic data source for California's K-12 public school locations during the 2024-25 academic year. Schools are mapped as point locations and assigned coordinates based on the physical address of the school facility. The school records are enriched with additional demographic and performance variables from the California Department of Education's data collections. These data elements can be visualized and examined geographically to uncover patterns, solve problems and inform education policy decisions.The schools in this file represent a subset of all records contained in the CDE's public school directory database. This subset is restricted to TK-12 public schools that were open in October 2024 to coincide with the official 2024-25 student enrollment counts collected on Fall Census Day in 2024 (first Wednesday in October). This layer also excludes nonpublic nonsectarian schools and district office schools.The CDE's California School Directory provides school location other basic school characteristics found in the layer's attribute table. The school enrollment, demographic and program data are collected by the CDE through the California Longitudinal Achievement System (CALPADS) and can be accessed as publicly downloadable files from the Data & Statistics web page on the CDE website. Schools are assigned X, Y coordinates using a quality controlled geocoding and validation process to optimize positional accuracy. Most schools are mapped to the school structure or centroid of the school property parcel and are individually verified using aerial imagery or assessor's parcels databases. Schools are assigned various geographic area values based on their mapped locations including state and federal legislative district identifiers and National Center for Education Statistics (NCES) locale codes.

This report shares information about school performance, sets expectations for schools, and promotes school improvement. School Quality Report Educator Guides can be found here.

Compiled school level data for the 2021 - 2022 school year sourced from the Virginia Department of Education.

• State Assessment (SOL) Pass Rates
• Chronic Absenteeism Rates
• Graduation Rates
• Free and Reduced Lunch Eligibility
• State and Federal Funding
• Teacher Quality, Education, and Licensure
• Demographic information

Dataset 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

Boston Public Schools (BPS) schools for the school year 2018-2019. Updated September 2018.

This data is published by the State of Ohio on their Report Card website. It includes per-pupil spending and total enrollment for all school districts in Ohio.

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 .

This meticulously curated dataset offers a panoramic view of education on a global scale , delivering profound insights into the dynamic landscape of education across diverse countries and regions. Spanning a rich tapestry of educational aspects, it encapsulates crucial metrics including out-of-school rates, completion rates, proficiency levels, literacy rates, birth rates, and primary and tertiary education enrollment statistics. A treasure trove of knowledge, this dataset is an indispensable asset for discerning researchers, dedicated educators, and forward-thinking policymakers, enabling them to embark on a transformative journey of assessing, enhancing, and reshaping education systems worldwide.

Key Features: - Countries and Areas: Name of the countries and areas. - Latitude: Latitude coordinates of the geographical location. - Longitude: Longitude coordinates of the geographical location. - OOSR_Pre0Primary_Age_Male: Out-of-school rate for pre-primary age males. - OOSR_Pre0Primary_Age_Female: Out-of-school rate for pre-primary age females. - OOSR_Primary_Age_Male: Out-of-school rate for primary age males. - OOSR_Primary_Age_Female: Out-of-school rate for primary age females. - OOSR_Lower_Secondary_Age_Male: Out-of-school rate for lower secondary age males. - OOSR_Lower_Secondary_Age_Female: Out-of-school rate for lower secondary age females. - OOSR_Upper_Secondary_Age_Male: Out-of-school rate for upper secondary age males. - OOSR_Upper_Secondary_Age_Female: Out-of-school rate for upper secondary age females. - Completion_Rate_Primary_Male: Completion rate for primary education among males. - Completion_Rate_Primary_Female: Completion rate for primary education among females. - Completion_Rate_Lower_Secondary_Male: Completion rate for lower secondary education among males. - Completion_Rate_Lower_Secondary_Female: Completion rate for lower secondary education among females. - Completion_Rate_Upper_Secondary_Male: Completion rate for upper secondary education among males. - Completion_Rate_Upper_Secondary_Female: Completion rate for upper secondary education among females. - Grade_2_3_Proficiency_Reading: Proficiency in reading for grade 2-3 students. - Grade_2_3_Proficiency_Math: Proficiency in math for grade 2-3 students. - Primary_End_Proficiency_Reading: Proficiency in reading at the end of primary education. - Primary_End_Proficiency_Math: Proficiency in math at the end of primary education. - Lower_Secondary_End_Proficiency_Reading: Proficiency in reading at the end of lower secondary education. - Lower_Secondary_End_Proficiency_Math: Proficiency in math at the end of lower secondary education. - Youth_15_24_Literacy_Rate_Male: Literacy rate among male youths aged 15-24. - Youth_15_24_Literacy_Rate_Female: Literacy rate among female youths aged 15-24. - Birth_Rate: Birth rate in the respective countries/areas. - Gross_Primary_Education_Enrollment: Gross enrollment in primary education. - Gross_Tertiary_Education_Enrollment: Gross enrollment in tertiary education. - Unemployment_Rate: Unemployment rate in the respective countries/areas.

Dataset Overview 📝

The dataset includes the following key indicators, collected for over 200 countries:

• Government Expenditure on Education (% of GDP): Shows the percentage of a country’s GDP allocated to education.
• Literacy Rate (Adult Total): Represents the percentage of the population aged 15 and above who can read and write.
• Primary Completion Rate: The percentage of children who complete their primary education within the official age group.
• Pupil-Teacher Ratio (Primary and Secondary Education): Indicates the average number of students per teacher at the primary and secondary levels.
• School Enrollment Rates (Primary, Secondary, Tertiary): Reflects the percentage of the relevant age group enrolled in schools across different education levels.

Data Source 🌐

World Bank: This dataset is compiled from the World Bank's educational database, providing reliable, updated statistics on educational progress worldwide.

Potential Use Cases 🔍 This dataset is ideal for anyone interested in:

Educational Research: Understanding how education spending and policies impact literacy, enrollment, and overall educational outcomes. Predictive Modeling: Building models to predict educational success factors, such as completion rates and literacy. Global Education Analysis: Analyzing trends in global education systems and how different countries allocate resources to education. Policy Development: Helping governments and organizations make data-driven decisions regarding educational reforms and funding.

Key Questions You Can Explore 🤔

How does government expenditure on education correlate with literacy rates and school enrollment across different regions? What are the trends in pupil-teacher ratios over time, and how do they affect educational outcomes? How do education indicators differ between low-income and high-income countries? Can we predict which countries will achieve universal primary education based on current trends?

Important Notes ⚠️ - Missing Data: Some values may be missing for certain years or countries. Consider using techniques like forward filling or interpolation when working with time series models. - Data Limitations: This dataset provides global averages and may not capture regional disparities within countries.

Philippines Enrolment Data

Examining Private and Public Schools

By Humanitarian Data Exchange [source]

About this dataset

This dataset provides an interesting insight into the enrolment numbers in public and private schools across the Philippines. It covers all levels of enrolment – elementary, secondary, and post-secondary – as well as gender and urban/rural distinctions. This information is an invaluable asset for anyone looking to gain a comprehensive understanding of educational enrolment trends within the country in order to make informed decisions regarding resource allocation or policy implementations. However, keep in mind that due to differences in methodology and data collection techniques, caution should be taken when using this data as there may be inaccuracies or vague definitions applicable to specific age groups or subpopulations. Regardless, this dataset still serves as a valuable source of information for anyone wanting a proper picture of educational dynamics within the Philippines

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How to use the dataset

This dataset provides enrolment figures in public and private schools by level in the Philippines. With this data, users can explore disparities between public and private school enrolment and other potential inequalities associated with educational access.

In order to use this Kaggle dataset to analyze educational inequality in the Philippines, firstly one must understand which columns are included:

• Country: The name of the Philippine country
• School Level (Grouped): Groupings of school levels within primary/elementary and secondary level
• Enrolment Type: Public or Private
• Year: Time period of data collection

Now that you have an understanding about what this dataset contains, here are few ways you could use it for your analysis!

• Compare enrollment rates between genders - Use the 'School Level' column grouped into Primary/Elementary or Secondary fields along with 'Enrolment Type' (public vs. private) to sort out male/female enrollment differences from 2007 - 2018 at each grade level.
• Investigate discrepancies between urban vs rural areas - Look at where most students attend as reflected through the different divisions within provinces as defined by Commission on Elections (COMELEC). Depending if pupils mainly take up residence in urban or rural areas make sure to supplement this data with available measures towards educational disparities between these two settings such as infrastructure, resources etc.
• Analyze expansion trends over time - Using all columns within this dataset one could see how trends have changed over time since its inception year 2007 till recent year 2018 spanning different area types (such as mindanao through CAR etc.), school levels and regions across governance such provinces(NCR etc.).One could get additional insights such patterns around funding allocations too.

Using all these different analyses offered one can gain a better understanding about evolving disparities around education access in particular region or even countrywide!

Research Ideas

• Comparing enrolment statistics between public and private schools to identify more effective approaches in either sector.
• Identifying regions or areas which may benefit from additional investment in education infrastructure and resources.
• Visualizing enrolment rates at different levels of schooling to understand the relative level of educational attainment within a certain geographical area or region over time

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: education-nscb-xls-1.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Humanitarian Data Exchange.

Dataset

This dataset was created by Shuhan Luo

Released under U.S. Government Works

Contents

2018 DC School Report Card. A school framework is a set of metrics and weighted domains based on the school's grade configuration or school designations. The STAR Framework contains four school frameworks: Elementary School Framework, Middle School Framework, High School Framework, and Alternative School Framework. The Elementary School Framework has two versions, one for schools with Pre-Kindergarten and one for schools without Pre-Kindergarten.Supplemental:Metric scores are not reported for n-sizes less than 10; metrics that have an n-size less than 10 are not included in calculation of STAR scores and ratings.At the state level, teacher data is reported on the DC School Report Card for all schools, high-poverty schools, and low-poverty schools. The definition for high-poverty and low-poverty schools is included in DC's ESSA State Plan. At the school level, teacher data is reported for the entire school, and at the LEA-level, teacher data is reported for all schools only.On the STAR Framework, 203 schools received STAR scores and ratings based on data from the 2017-18 school year. Of those 203 schools, 2 schools closed after the completion of the 2017-18 school year (Excel Academy PCS and Washington Mathematics Science Technology PCHS). Because those two schools closed, they do not receive a School Report Card and report card metrics were not calculated for those schools.Schools with non-traditional grade configurations may be assigned multiple school frameworks as part of the STAR Framework. For example, a K-8 school would be assigned the Elementary School Framework and the Middle School Framework. Because a school may have multiple school frameworks, the total number of school framework scores across the city will be greater than the total number of schools that received a STAR score and rating.Detailed information about the metrics and calculations for the DC School Report Card and STAR Framework can be found in the 2018 DC School Report Card and STAR Framework Technical Guide (https://osse.dc.gov/publication/2018-dc-school-report-card-and-star-framework-technical-guide).

This Private Schools feature dataset is composed of private elementary and secondary education facilities in the United States as defined by the Private School Survey (PSS, https://nces.ed.gov/surveys/pss/), National Center for Education Statistics (NCES, https://nces.ed.gov), US Department of Education for the 2017-2018 school year. This includes all prekindergarten through 12th grade schools as tracked by the PSS. 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 2675 new records, modifications to the spatial location and/or attribution of 19836 records, the removal of 254 records no longer applicable. Additionally, 10,870 records were removed that previously had a STATUS value of 2 (Unknown; not represented in the most recent PSS data) and duplicate records identified by ORNL.

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.

This dataset provides detailed information on the number of public schools and their students in Qatar, categorized by municipality, level of education, and type of school (Boys, Girls, or Mixed). The data allows for analysis of trends in education across various regions, school types, and educational levels, offering insights into the distribution of students and schools by gender and education level.

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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This schools dataset is composed of all Public elementary and secondary education in the United States as defined by the Common Core of Data, National Center for Education Statistics (NCES), US Department of Education. This includes all Kindergarten through 12th grade schools as tracked by the Common Core of Data. The data was filter by Westchester County. A new field with school district website links was added in August 2019. The data is up to date as of the year 2022. Two schools not listed on the NCES have been added. These schools do not have a unique NCESID.

Total numbers of surplus places for Maintained and Academy York Local Authority Schools (Excludes dual registered subsidiary pupils). All data is taken from the January School Census.

District-run high schools in CPS are organized into 4 Geographic Networks, which provide administrative support, strategic direction, and leadership development to the schools within each Network. ​​​​​ This dataset is in a forma​​t for spatial datasets that is inherently tabular but allows for a map as a derived view. Please click the indicated link below for such a map.

To export the data in either tabular or geographic format, please use the Export button on this dataset.