2006/07 Progress Report results for all schools (data as of 1/14/09)

Peer indices 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)

1) Elementary & K-8 - 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 & High - 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 for all their students that have 4th grade test scores. For high schools, we use the average 8th grade proficiency ratings in ELA and Math for all their students that have 8th grade test scores. Lower values indicate student populations with higher need

3) D84 / Charter Schools - the overall score does not include the results of the learning environment survey.

4) Schools for Transfer Students - consists of schools with large populations of high school students transferring from NYC High Schools or from out of state/country. No peer index value is assigned because this set of schools is its own peer group. The reports contain 3 categories with one additional credit section. Unlike the HS Progress Report, the Environment Category is only composed of Survey Results. Performance measures 6-year graduation rate and Progress captures student level improvements in attendance, credit accumulation and Regents passed. The additional credit section rewards schools demonstrating exceptional achievement (11 credits or more earned per year) among overage/under-credit populations.

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

2008/09 Progress Report results for all schools (data as of 3/23/2010) Peer indices 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 - 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 & High - 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 for all their students that have 4th grade test scores. For high schools, we use the average 8th grade proficiency ratings in ELA and Math for all their students that have 8th grade test scores, % SpEd, and % Overage. Lower values indicate student populations with higher need.

Collection of data on standardized tests per school year and grade.

Context

The Programme for International Student Assessment (PISA) is a test given every three years to 15-year-old students from around the world to evaluate their performance in mathematics, reading, and science. This test provides a quantitative way to compare the performance of students from different parts of the world. In this homework assignment, we will predict the reading scores of students from the United States of America on the 2009 PISA exam.

The datasets pisa2009train.csv and pisa2009test.csv contain information about the demographics and schools for American students taking the exam, derived from 2009 PISA Public-Use Data Files distributed by the United States National Center for Education Statistics (NCES). While the datasets are not supposed to contain identifying information about students taking the test, by using the data you are bound by the NCES data use agreement, which prohibits any attempt to determine the identity of any student in the datasets.

Each row in the datasets pisa2009train.csv and pisa2009test.csv represents one student taking the exam. The datasets have the following variables:

Content

grade: The grade in school of the student (most 15-year-olds in America are in 10th grade)

male: Whether the student is male (1/0)

raceeth: The race/ethnicity composite of the student

preschool: Whether the student attended preschool (1/0)

expectBachelors: Whether the student expects to obtain a bachelor's degree (1/0)

motherHS: Whether the student's mother completed high school (1/0)

motherBachelors: Whether the student's mother obtained a bachelor's degree (1/0)

motherWork: Whether the student's mother has part-time or full-time work (1/0)

fatherHS: Whether the student's father completed high school (1/0)

fatherBachelors: Whether the student's father obtained a bachelor's degree (1/0)

fatherWork: Whether the student's father has part-time or full-time work (1/0)

selfBornUS: Whether the student was born in the United States of America (1/0)

motherBornUS: Whether the student's mother was born in the United States of America (1/0)

fatherBornUS: Whether the student's father was born in the United States of America (1/0)

englishAtHome: Whether the student speaks English at home (1/0)

computerForSchoolwork: Whether the student has access to a computer for schoolwork (1/0)

read30MinsADay: Whether the student reads for pleasure for 30 minutes/day (1/0)

minutesPerWeekEnglish: The number of minutes per week the student spend in English class

studentsInEnglish: The number of students in this student's English class at school

schoolHasLibrary: Whether this student's school has a library (1/0)

publicSchool: Whether this student attends a public school (1/0)

urban: Whether this student's school is in an urban area (1/0)

schoolSize: The number of students in this student's school

readingScore: The student's reading score, on a 1000-point scale

Acknowledgements

MITx ANALYTIX

2007/08 Progress Report results for all schools (data as of 1/13/09) Peer indices 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) 1) Elementary & K-8 - 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 & High - 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 for all their students that have 4th grade test scores. For high schools, we use the average 8th grade proficiency ratings in ELA and Math for all their students that have 8th grade test scores, % SpEd, and % Overage. Lower values indicate student populations with higher need. 3) Schools for Transfer Students - peer index is a value from 1.00-4.50. We use the average 8th grade proficiency ratings in ELA and Math for all their students that have 8th grade test scores and the % Overage/Under credited. Lower values indicate student populations with higher need. Unlike Elementary, Middle, and High School Progress Reports, the Environment Category is only composed of Survey Results.

File includes demographic, socioeconomic, and academic data on public schools in California with school closures listed by individual cause per academic year. Demographic, socioeconomic, and academic data come from the California Department of Education. Data on school closures come from CalMatters’ Disaster Days dataset.

This paper studies the extent to which young children develop their cognitive ability in high and low quality schools. We use a representative panel data set containing cognitive test scores of 4-6 year olds in Dutch schools. School quality is measured by the school’s average achievement test score at age 12. Our results indicate that children in high-quality schools develop their skills substantially faster than those in low-quality schools. The results remain robust to the inclusion of initial ability, parental background, and neighborhood controls. Moreover, using proximity to higher-achieving schools as an instrument for school choice corroborates the results. The robustness of the results points toward a causal interpretation, although it is not possible to erase all doubt about unobserved confounding factors.

Analysis of ‘Average SAT Scores for NYC Public Schools’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nycopendata/high-schools on 28 January 2022.

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

Content

This dataset consists of a row for every accredited high school in New York City with its department ID number, school name, borough, building code, street address, latitude/longitude coordinates, phone number, start and end times, student enrollment with race breakdown, and average scores on each SAT test section for the 2014-2015 school year.

Acknowledgements

The high school data was compiled and published by the New York City Department of Education, and the SAT score averages and testing rates were provided by the College Board.

Inspiration

Which public high school's students received the highest overall SAT score? Highest score for each section? Which borough has the highest performing schools? Do schools with lower student enrollment perform better?

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

Content

This dataset consists of a row for every accredited high school in New York City with its department ID number, school name, borough, building code, street address, latitude/longitude coordinates, phone number, start and end times, student enrollment with race breakdown, and average scores on each SAT test section for the 2014-2015 school year.

Acknowledgements

The high school data was compiled and published by the New York City Department of Education, and the SAT score averages and testing rates were provided by the College Board.

Inspiration

Which public high school's students received the highest overall SAT score? Highest score for each section? Which borough has the highest performing schools? Do schools with lower student enrollment perform better?

Analysis of ‘2008 - 2009 School Progress Reports - All Schools’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/635b01e5-ae48-4e0c-b71a-8716b2bb4177 on 26 January 2022.

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

2008/09 Progress Report results for all schools (data as of 3/23/2010)

Peer indices 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 - 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 & High - 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 for all their students that have 4th grade test scores. For high schools, we use the average 8th grade proficiency ratings in ELA and Math for all their students that have 8th grade test scores, % SpEd, and % Overage. Lower values indicate student populations with higher need.

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

We estimate the impact of district-level schooling mode (in-person versus hybrid or virtual learning) in the 2020-21 school year on students' pass rates on standardized tests in Grades 3--8 across 11 states. Pass rates declined from 2019 to 2021: an average decline of 12.8 percentage points in math and 6.8 in English language arts (ELA). Focusing on within-state, within-commuting zone variation in schooling mode, we estimate districts with full in-person learning had significantly smaller declines in pass rates (13.4 p.p. in math, 8.3 p.p. in ELA). The value to in-person learning was larger for districts with larger populations of Black students.

These files contain information on how to obtain the data and produce the results found in "Differential Prediction for Disadvantaged Students and Schools:The Role of High School Characteristics."The abstract for the paper is found below:Validity studies of college-admissions tests have found that on average, students who are Black or Hispanic earn lower freshman grade-point averages (FGPAs) than predicted by these test scores. This differential prediction is used as a measure of bias. These studies, however, conflate student and school characteristics. The differential prediction affecting minoritized groups may arise in part because they attended high schools in which college enrollees, regardless of race, perform worse than predicted. Using data on students who graduated from New York City public high schools in 2011 and enrolled in the City University of New York, we examined this using college-admissions and high-school test scores. There was no differential prediction based on race/ethnicity among students within high schools when school characteristics were accounted for. Instead, overprediction of FGPA was associated with the school proportion of enrolled Black and Hispanic students. Overprediction was larger in models with high school test scores.

College-bound seniors are those students that complete the SAT Questionnaire when they register for the SAT and identify that they will graduate from high school in a specific year. For example, the 2010 college-bound seniors are those students that self-reported they would graduate in 2010.

Students are not required to complete the SAT Questionnaire in order to register for the SAT. Students who do not indicate which year they will graduate from high school will not be included in any college-bound senior report.

Students are linked to schools by identifying which school they attend when registering for a College Board exam. A student is only included in a school’s report if he/she self-reports being enrolled at that school.

For data science, this dataset offers a rich source for exploratory data analysis, predictive modeling, and statistical testing. Researchers can explore correlations between SAT scores and other factors like school resources, student-teacher ratios, or geographic locations.

• Exploratory Data Analysis (EDA): Data scientists can use descriptive statistics and visualization techniques to understand the distribution of scores, check for outliers, and identify patterns or anomalies in the data.
• Predictive Modeling: Building models to predict SAT scores based on various predictors, such as school demographics or previous academic performance. This could include regression analysis or more complex machine learning algorithms.
• Time Series Analysis: If data across multiple years were available, analyzing trends over time would be possible, helping in understanding improvements or declines in performance. Comparative Analysis: Comparing scores across different schools or districts to evaluate disparities in educational achievement.
• Statistical Testing: Conducting hypothesis tests to see if the differences in performances across groups (e.g., by geographic region or school type) are statistically significant.

The folder contains datasets of math and literature test scores of primary school grade 2 students in Vietnam in 2018.

We use the introduction of a means-tested voucher program in Florida to examine whether increased competitive pressure on public schools affects students' test scores. We find greater score improvements in the wake of the program introduction for students attending schools that faced more competitive private school markets prior to the policy announcement, especially those that faced the greatest financial incentives to retain students. These effects suggest modest benefits for public school students from increased competition. The effects are consistent across several geocoded measures of competition and isolate competitive effects from changes in student composition or resource levels in public schools.

Analysis of ‘2006-07 School Progress Reports - All Schools’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/c7712a82-1cb7-4120-a9ef-54e0d25e4d3f on 26 January 2022.

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

2006/07 Progress Report results for all schools (data as of 1/14/09)

Peer indices 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)

1) Elementary & K-8 - 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 & High - 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 for all their students that have 4th grade test scores. For high schools, we use the average 8th grade proficiency ratings in ELA and Math for all their students that have 8th grade test scores. Lower values indicate student populations with higher need

3) D84 / Charter Schools - the overall score does not include the results of the learning environment survey.

4) Schools for Transfer Students - consists of schools with large populations of high school students transferring from NYC High Schools or from out of state/country. No peer index value is assigned because this set of schools is its own peer group. The reports contain 3 categories with one additional credit section. Unlike the HS Progress Report, the Environment Category is only composed of Survey Results. Performance measures 6-year graduation rate and Progress captures student level improvements in attendance, credit accumulation and Regents passed. The additional credit section rewards schools demonstrating exceptional achievement (11 credits or more earned per year) among overage/under-credit populations.

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

The disparate impact of COVID-19 on student learning gains across students’ household income and parental education levels–unweighted.

This dataset includes test scores for over 40,000 students in 175 Irish primary schools that were selected and randomly assigned to a variety of testing treatments as part of a four-year study. The goal of this research effort was to assess the effects of standardized tests and test results on teachers, students, and parents, as well as on school policy. Northern Ireland was chosen because of its developed educational system (in which the English language is used) and its prior lack of standardized testing. During the course of this study, three main testing treatments were implemented in all classrooms in each primary school: (1) no testing was done, (2) norm referenced ability and attainment testing was done in basic curricular areas (English, Irish, and mathematics), but pupil performance data were not returned to the teachers, and (3) norm referenced ability and attainment testing was done, and pupils' raw scores, percentiles, and standard scores were returned to teachers. This dataset contains the norm referenced test scores gathered over the course of the four-year study for each of eight primary age-group cohorts. Parts 1-6 contain scores from students who were in grades 1-6, respectively, during the first year of the study. Part 7 contains scores from students who were in grade 2 in the fourth (last) year of the study, and Part 8 contains the scores from students who were in grade 3 during the last year of the study. Background variables for each student (e.g., treatment group, school type, sex served by school, location of school, size of school, type of administration of school, school identification number, and student's sex) are also included.

This collection represents the first stage of a major longitudinal effort to provide trend data about critical transitions experienced by students as they leave elementary school and progress through high school and into college or their careers. The 1988 eighth-grade cohort will be followed at two-year intervals as this group passes through high school and postsecondary education. The longitudinal data collected will yield policy-relevant information about educational processes and outcomes, early and later predictors of dropping out, and students' access to programs and equal opportunity. The study has four types of data files. The Parent Component was designed to collect information about the factors that influence educational attainment and participation, including questions exploring family background and socioeconomic conditions and character of the home educational system. The School Administrator component was designed to gather general descriptive information about the educational settings in which the surveyed students were enrolled in the winter and spring of 1988. These data were collected from the chief administrator of each base-year school and concern school characteristics, grading and testing structure, school culture and academic climate, program and facilities information, parental interactions and involvement, and teaching staff characteristics. The Student Component collected information on school work, aspirations, social relationships, and basic achievement areas such as reading, mathematics, science, and social studies. The Teacher Component provided data that could be used to analyze the behaviors and outcomes of the student sample. Teachers were surveyed about the base-year students' characteristics and performance in the classroom, curriculum and classes for eighth graders, and teacher demographics, professional characteristics, and relationships with other teachers, students, and parents.