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

Yearly data of Quality Review ratings from 2005 to 2017

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.

This indicator includes the high school graduation rates by district for the following districts with high schools within Champaign County: Champaign Community Unit School District #4, Fisher Community Unit School District #1, Mahomet-Seymour Community Unit School District #3, Rantoul Township High School District #193, St. Joseph-Ogden Community High School District #305, Tolono Community Unit School District #7, and Urbana School District #116.

Between 2010 and 2024, the graduation rates of the different districts fluctuated independently of each other, with no trend prevalent across the board. The Illinois Report Card states that there is a possible data impact on the 2020 and 2021 graduation rates due to the COVID-19 pandemic. This could explain the uncharacteristically low graduation rate in Tolono District #7 in 2021 compared to previous years. However, the graduation rate in Champaign Unit #4 and Urbana District #116 increased from 2019 to 2021, and the graduation rate in St. Joseph-Ogden District #305 was the same in 2019 and 2021.

The average graduation rate across all Champaign County high schools increased from 87.7% in 2019 before the COVID-19 pandemic to 88.1% in 2023 when the pandemic emergency ended. This rate increased again in 2024 to 89.2%. High school graduation rates are an apt measure of pre-college academic achievement in the county, and provide context for the other indicators in the education category.

This data, along with a variety of other school district data, is available on the Illinois Report Card, an Illinois State Board of Education and Northern Illinois University website.

Sources: Illinois Report Card. (2023-2024). Champaign CUSD 4. Illinois State Board of Education. (Accessed 6 December 2024). Illinois Report Card. (2023-2024). Fisher CUSD 1. Illinois State Board of Education. (Accessed 6 December 2024). Illinois Report Card. (2023-2024). Mahomet-Seymour CUSD 3. Illinois State Board of Education. (Accessed 6 December 2024). Illinois Report Card. (2023-2024). Rantoul Township HSD 193. Illinois State Board of Education. (Accessed 6 December 2024). Illinois Report Card. (2023-2024). St. Joseph Ogden CHSD 305. Illinois State Board of Education. (Accessed 6 December 2024). Illinois Report Card. (2023-2024). Tolono CUSD 7. Illinois State Board of Education. (Accessed 6 December 2024). Illinois Report Card. (2023-2024). Urbana SD 116. Illinois State Board of Education. (Accessed 6 December 2024).

This dataset includes all identifiable DCPS public elementary schools, middle schools, education campuses, high schools, and special education schools, as well as learning centers. This dataset does not include private or charter schools. School locations were identified from a database from the District of Columbia Public Schools, Office of Facilities Management.

Student Performance Dataset 2024 Overview This dataset comprises detailed information about high school students in China, collected from various universities and schools. It is designed to analyze the factors influencing student performance, well-being, and engagement. The data includes a wide range of features such as demographic details, academic performance, health status, parental support, and more. The participating institutions include prominent universities such as Tsinghua University, Peking University, Fudan University, Shanghai Jiao Tong University, and Zhejiang University.

Dataset Description Features Student ID: Unique identifier for each student. Gender: Gender of the student (Male/Female). Age: Age of the student. Grade Level: The grade level of the student (e.g., 9, 10, 11, 12). Attendance Rate: The percentage of days the student attended school. Study Hours: Average number of hours the student spends studying daily. Parental Education Level: The highest level of education attained by the student's parents. Parental Involvement: The level of parental involvement in the student's education (High, Medium, Low). Extracurricular Activities: Whether the student participates in extracurricular activities (Yes/No). Socioeconomic Status: Socioeconomic status of the student's family (High, Medium, Low). Previous Academic Performance: Previous academic performance level (High, Medium, Low). Class Participation: The level of participation in class (High, Medium, Low). Health Status: General health status of the student (Good, Average, Poor). Access to Learning Resources: Whether the student has access to necessary learning resources (Yes/No). Internet Access: Whether the student has access to the internet (Yes/No). Learning Style: Preferred learning style of the student (Visual, Auditory, Kinesthetic). Teacher-Student Relationship: Quality of the relationship between the student and teachers (Positive, Neutral, Negative). Peer Influence: Influence of peers on the student's behavior and performance (Positive, Neutral, Negative). Motivation Level: Student's level of motivation (High, Medium, Low). Hours of Sleep: Average number of hours the student sleeps per night. Diet Quality: Quality of the student's diet (Good, Average, Poor). Transportation Mode: Mode of transportation used by the student to commute to school (Bus, Car, Walk, Bike). School Type: Type of school attended by the student (Public, Private). School Location: Location of the school (Urban, Rural). Homework Completion Rate: The rate at which the student completes homework assignments. Reading Proficiency: Proficiency level in reading. Math Proficiency: Proficiency level in mathematics. Science Proficiency: Proficiency level in science. Language Proficiency: Proficiency level in language. Physical Activity Level: The level of physical activity (High, Medium, Low). Screen Time: Average daily screen time in hours. Bullying Incidents: Number of bullying incidents the student has experienced. Special Education Services: Whether the student receives special education services (Yes/No). Counseling Services: Whether the student receives counseling services (Yes/No). Learning Disabilities: Whether the student has any learning disabilities (Yes/No). Behavioral Issues: Whether the student has any behavioral issues (Yes/No). Attendance of Tutoring Sessions: Whether the student attends tutoring sessions (Yes/No). School Climate: Overall perception of the school's environment (Positive, Neutral, Negative). Parental Employment Status: Employment status of the student's parents (Employed, Unemployed). Household Size: Number of people living in the student's household. Performance Score: Overall performance score of the student (Low, Medium, High).

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).

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 .

Historical Dataset of Watauga County Schools School District is provided by PublicSchoolReview and contain statistics on metrics:Comparison of Diversity Score Trends,Total Revenues Trends,Total Expenditure Trends,Average Revenue Per Student Trends,Average Expenditure Per Student Trends,Reading and Language Arts Proficiency Trends,Math Proficiency Trends,Science Proficiency Trends,Graduation Rate Trends,Overall School District Rank Trends,Asian Student Percentage Comparison Over Years (1997-2023),Hispanic Student Percentage Comparison Over Years (1999-2023),Black Student Percentage Comparison Over Years (1991-2023),White Student Percentage Comparison Over Years (1991-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Comparison of Students By Grade Trends

This Dataset shows schools ranking according in the order of performance in National Form Six Examination from 2015 to date

This dataset includes the attendance rate for public school students PK-12 by student group and by district during the 2022-2023 school year.

Student groups include:

Students experiencing homelessness Students with disabilities Students who qualify for free/reduced lunch English learners All high needs students Non-high needs students Students by race/ethnicity (Hispanic/Latino of any race, Black or African American, White, All other races)

Attendance rates are provided for each student group by district and for the state. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch.

When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

This dataset includes publicly available data published primarily by the Pennsylvania Department of Education and the Pennsylvania Office of Safe Schools. The dataset was created by combining several publications by the Pennsylvania Department of Education, including the 2017 School Fast Fact database, 2016-2017 Academic Performance database, and the 2017 Keystone Score database. The dataset includes institutional (school-wide) variables for every public high school in Pennslyvania (n = 407 ). The data includes information surrounding each institution's socio-economic status, racial composition, academic performance, and type of and total use of exclusionary discipline (in-school suspension, out-of-school suspension, and expulsion) for the school year 2016-2017. The dataset also includes neighborhood information for each school location. This data was collected from AreaVibes, a website known for its ability to guide individuals in their search for ideal residential areas in the United States and Canada. AreaVibes deploys a unique algorithm that evaluates multiple different data points for each location, including amenities, cost of living, crime rates, employment, housing, schools, and user ratings. This dataset deployed AreaVibes to input the physical addresses of each high school in order to retrieve the livability score for the surrounding neighborhoods of these educational institutions. Furthermore, the website was instrumental in collecting neighborhood crime scores, offering valuable insights into the levels of criminal activity within specific geographic zones. The crime score takes into account both violent crime and property crime. However, higher weights are given to violent crimes (65%) than property crime (35%) as they are more severe. Data for calculation by Areavibes is derived from FBI Uniform Crime Report.School discipline is crucial for ensuring safety, well-being, and academic success. However, the continued use of exclusionary discipline practices, such as suspension and expulsion, has raised concerns due to their ineffectiveness and harmful effects on students. Despite compelling evidence against these practices, many educational institutions persist in relying on them. This persistence has led to a troubling reality—a racial and socioeconomic discipline gap in schools. This data is used to explore the evident racial and socioeconomic disparities within high school discipline frameworks, shedding light on the complex web of factors that contribute to these disparities and exploring potential solutions. Drawing from social disorganization theory, the data explores the interplay between neighborhood and school characteristics, emphasizing the importance of considering the social context of schools.

2015 school progress report ratings for all Chicago Public Schools.

This dataset includes the attendance rate for public school students PK-12 by town during the 2020-2021 school year. Attendance rates are provided for each town for the overall student population and for the high needs student population. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch. When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

Locations of educational units in the Chicago Public Schools District for school year 2023-2024. 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.

Historical Dataset of State Specialty Schools II- Zest Preparatory Academy School District is provided by PublicSchoolReview and contain statistics on metrics:Comparison of Students By Grade Trends

Historical Dataset of Ann Arbor School District is provided by PublicSchoolReview and contain statistics on metrics:Comparison of Diversity Score Trends,Total Revenues Trends,Total Expenditure Trends,Average Revenue Per Student Trends,Average Expenditure Per Student Trends,Reading and Language Arts Proficiency Trends,Math Proficiency Trends,Science Proficiency Trends,Graduation Rate Trends,Overall School District Rank Trends,American Indian Student Percentage Comparison Over Years (1991-2009),Asian Student Percentage Comparison Over Years (1991-2023),Hispanic Student Percentage Comparison Over Years (1991-2023),Black Student Percentage Comparison Over Years (1991-2023),White Student Percentage Comparison Over Years (1991-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Comparison of Students By Grade Trends

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

This dataset tracks annual graduation rate from 2013 to 2023 for Oxford School District vs. Massachusetts

Dataset

This dataset was created by Yipeng

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