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).

This dataset denotes values of the School Proficiency Index. The index illustrates school-level data to describe neighborhoods relative to nearby school performance. Specifically, the data is 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.

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.

2009/10 Progress Report results for all schools (data as of 2/2/2011)

Peer indexes are calculated differently depending on School Level. Schools are only compared to other schools in the same School Level (e.g., Elementary, K-8, Middle, High, Transfer)

1) Elementary, K-8, K-3, K-2 - peer index is a value from 0-100. We use a composite demographic statistic based on % ELL, % SpEd, % Title I free lunch, and % Black/Hispanic. Higher values indicate student populations with higher need.

2) Middle - peer index is a value from 1.00-4.50. For middle schools, we use the average 4th grade proficiency ratings in ELA and Math and the % SpEd. Lower values indicate student populations with higher need.

3) High School - peer index is a value from 1.00-4.50. For high schools, we use the average 8th grade proficiency, the % SpEd, the % Self-contained, and the % overage. Lower values indicate student populations with higher need.

This dataset tracks annual distribution of students across grade levels in Columbus Performance Academy School District and average distribution per school district in Ohio

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

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 includes 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 Farm to School Census measures USDA's progress toward improving access to local foods in schools. The web-based interface allows users to run customized searches using data from the Farm to School Census. From a total of 18,104 public, private, and charter school districts in the target list frame, 12,585 schools and school districts completed usable responses for a response rate of 70%. Visualizations display national and state level data, and explanatory notes for each portion of the survey questionnaire are provided. Users can focus their search by location/state/school district/zip code, participation level, local food purchased category (fruit, vegetables, fluid milk, other dairy, meat/poultry, eggs, seafood, plant-based protein, grains/flour, baked goods, herbs), and sources (purchased directly or through intermediary). Resources in this dataset:Resource Title: Census Data Explorer | USDA-FNS Farm to School Census. File Name: Web Page, url: https://farmtoschoolcensus.fns.usda.gov/census-results/census-data-explorer This searchable database allows users to run customized searches using data from the Farm to School Census.

This dataset tracks annual distribution of students across grade levels in Performance Academy Eastland School District and average distribution per school district in Ohio

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.

The School Quality Reports share information about school performance, set expectations for schools, and promote school improvement. 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 includes six elements—Rigorous Instruction, Collaborative Teachers, Supportive Environment, Effective School Leadership, Strong FamilyCommunity Ties, and Trust—that drive student achievement and school improvement.

Analysis of ‘2013-2014 School Quality Reports Results For Elementary, Middle and K-8 Schools’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ea995a76-f4d9-4e1e-a80a-32d9c90027bb on 13 November 2021.

--- Dataset description provided by original source is as follows ---

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.

--- Original source retains full ownership of the source dataset ---

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 Academic Performance Test (CAPT). 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.

Dataset including performance scores, quality metrics, zone information, and program details for NYC public elementary and middle schools, primarily sourced from New York City Department of Education InfoHub and Open Data NY.

Cumulation of the weekly release of COVID-19 data for Maricopa County by Elementary School District. Includes PCR Test Percent Positivity as viewed on the Maricopa County School Reopening Dashboard map by week. For more information about the data, visit: https://www.maricopa.gov/5594/School-Metrics.

Data includes: board and school information, grade 3 and 6 EQAO student achievements for reading, writing and mathematics, and grade 9 mathematics EQAO and OSSLT. Data excludes private schools, Education and Community Partnership Programs (ECPP), summer, night and continuing education schools.

How Are We Protecting Privacy?

Results for OnSIS and Statistics Canada variables are suppressed based on school population size to better protect student privacy. In order to achieve this additional level of protection, the Ministry has used a methodology that randomly rounds a percentage either up or down depending on school enrolment. In order to protect privacy, the ministry does not publicly report on data when there are fewer than 10 individuals represented.

* Percentages depicted as 0 may not always be 0 values as in certain situations the values have been randomly rounded down or there are no reported results at a school for the respective indicator. * Percentages depicted as 100 are not always 100, in certain situations the values have been randomly rounded up.

The information in the School Information Finder is the most current available to the Ministry of Education at this time, as reported by schools, school boards, EQAO and Statistics Canada. The information is updated as frequently as possible.

This information is also available on the Ministry of Education's School Information Finder website by individual school.

Descriptions for some of the data types can be found in our glossary.

School/school board and school authority contact information are updated and maintained by school boards and may not be the most current version. For the most recent information please visit: https://data.ontario.ca/dataset/ontario-public-school-contact-information.

1) Data Introduction • The Student Performance Dataset is a survey of secondary school mathematics students and is a dataset containing a variety of information in a table format, including student demographics, family environment, parents' education and occupation, health, family relationships, and grades.

2) Data Utilization (1) Student Performance Dataset has characteristics that: • Each row contains a total of 33 different characteristics, including school ID, gender, age, family size, parents' educational level and occupation, family relationship, health status, and grades. • It is suitable for a variety of data analysis and prediction exercises, including regression analysis and categorical variable imbalance analysis, including the target variable Grade. (2) Student Performance Dataset can be used to: • Analyzing academic achievement prediction and influencing factors: It can be used to analyze the impact of various factors such as student's background, family environment, and parental characteristics on grades and to develop a grade prediction model. • Establishing educational policies and customized support strategies: Based on student-specific characteristics and grade data, it can be applied to establishing educational policies such as closing educational gaps, supporting vulnerable student groups, and providing customized learning guidance.

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.

This dataset tracks annual white student percentage from 2009 to 2023 for Performance Academy Eastland School District vs. Ohio