The Federal School Code List contains the unique codes assigned by the Department of Education for schools participating in the Title IV federal student aid programs. Students can enter these codes on the Free Application for Federal Student Aid (FAFSA) to indicate which postsecondary schools they want to receive their financial application results. The Federal School Code List is a searchable document in Excel format. The list will be updated on the first of February, May, August, and November of each calendar year.

This dataset provides a list of school codes......

This paper estimates the long-term impact of growing up in better neighborhoods and attending better schools on educational attainment. First, I use a spatial regression-discontinuity design to estimate school effects. Second, I study students who move across neighborhoods in Montreal during childhood to estimate the causal effect of growing up in a better area (total exposure effects). I find large effects for both dimensions. Combining both research designs in a decomposition framework, and under key assumptions, I estimate that 50%-70% of the benefits of moving to a better area on educational attainment are due to access to better schools.

List of school codes in Taipei City...............

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2675 new records, modifications to the spatial location and/or attribution of 19836 records, the removal of 254 records no longer applicable.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.

Context

This Public Schools feature dataset is composed of all Public elementary and secondary education facilities in the United States.

Content

This Public Schools feature dataset is composed of all Public elementary and secondary education facilities in the United States as defined by the Common Core of Data(CCD, https://nces.ed.gov/ccd/ ), National Center for Education Statistics (NCES, https://nces.ed.gov ), US Department of Education for the 2014-2015 school year. This includes all Kindergarten through 12th grade schools as tracked by the Common Core of Data. Included in this dataset are military schools in US territories and referenced in the city field with an APO or FPO address. DOD schools represented in the NCES data that are outside of the United States or US territories have been omitted. 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 1830 new records and modifications to the spatial location and/or attribution of 100540 records. The ADDRESS2 field has been removed. Where applicable, values previously in ADDRESS2 have been concatenated to ADDRESS. This feature class does not have a relationship class.

Acknowledgements

This dataset was downloaded on March 23, 2019 from: https://hifld-geoplatform.opendata.arcgis.com/datasets/87376bdb0cb3490cbda39935626f6604_0

This dataset is provided by the Homeland Infrastructure Foundation-Level Data (HIFLD) without a license and for Public Use.

HIFLD Open GP - Education Shared By: jrayer_geoplatform Data Source: services1.arcgis.com

Users are advised to read the data set's metadata thoroughly to understand appropriate use and data limitations.

Provide national unified school numbers. This data is derived from the records of the National Taxation Bureau or groups applying for withholding unit unified number establishment (change) registration, and provides a search for national unified school numbers. It is for reference only. For other information not included in this dataset, please refer to the Ministry of Education website. The data link was adjusted on June 22, 2020 to https://eip.fia.gov.tw/data/BGMOPEN99X.csv.

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.

With data on school locations, categories, and contact information, analysts can explore various aspects of public school distribution, accessibility, and resource allocation. The geographical data allows for mapping and spatial analysis, which can help identify areas with higher concentrations of schools or regions that may lack adequate public education facilities. This dataset's uniform structure makes it suitable for integration with other demographic or socioeconomic datasets, enabling more nuanced analysis of educational accessibility and equity. Several analyses can be performed using this dataset: - Descriptive Statistics: To provide a summary of the dataset, including the number of schools by category, average number of schools per ZIP code, and other basic statistics. - Cluster Analysis: To group schools based on similar characteristics such as location, school type (high, middle, elementary), and size to identify patterns in school distribution. - Accessibility Analysis: To evaluate the ease of access to public schools for students in different areas, considering factors such as distance to schools and availability of public transportation. - Demographic and Socioeconomic Impact Analysis: To understand how demographic and socioeconomic factors influence the distribution and accessibility of public schools.

The dataset contains information on the number of secondary school students divided by year of course, type of course, address and gender within the State Schools of the Municipality of Milan. The data that are specifically located within the dataset are: * School Year: Numerical School year of reference school registry; * CodeSchool: School code (plexus); * AddressSchool:School address * Name InstituteReference: Name (name) of the reference institution of the school * NameSchool: Name (name) of the school (plexus) * Path : Indicates the active path in the school. The "Iefp" category corresponds to vocational education and training courses of regional competence that remain aimed at obtaining the three-year qualification / four-year diploma * School Address: Text depicting the school address undertaken; * PupilsMen : Number of male pupils * PupilsFemale: Number of female pupils * ZIP code: Postal code *MUNICIPALITY: City Hall * ID_NIL: Local Identity Core Identifier * NIL:Local Identity Unit * LONG_X_4326: Longitude * LAT_Y_4326: Latitude * Location: Latitude and Longitude

This dataset tracks annual distribution of students across grade levels in New Code Academy High School

This dataset tracks annual distribution of students across grade levels in Code Elementary School

CSV Table. This table includes coded descriptions for School Districts in St. Louis County, Missouri. Link to Metadata.

Updated yearly using enrollment data, employment data, information from websites, phone calls, and any other resources as available. At time of update fields were added to include employment data, enrollment data, building code, school code, TAZ08, and school website. Please verify information before use as it will be updated on an ongoing basis. Please contact COMPASS with any questions or any knowledge of updates, alterations or modifications that need to be made. FIELDS:UpdateBy: Name or initials of last person to update the recordUpdateOn: Date the record was last updated onSchoolName: Name of the school at the pointSchoolDist: School district the point physically is withinType: Describes the nature of the building and grade/age range of students enrolledValues:PRE K: Preschool &/or Nursery school & Day CareELEMENTARY: Traditional Kindergarten through 6thgradeK-8: Kindergarten through 8th gradeK-12: Kindergarten through 12th grade MIDDLE: 6thgrade through 8thgradeJUNIOR HS: 7thgrade through 9th gradeSENIOR HS: 9th through 12thgradePOST SR: College, University, Technical or Professional SchoolsOTHER: Irregular range of grades or ages ADMIN: Administrative Building/ServicesRETAIL-EDU: Retailor or seller of educational materials or suppliesSiteAddres: Physical address of the school or buildingSiteCity: City the school or building is located inSiteState: State the school or building is located inSiteZip: Zip code the school or building is located inSiteCounty: County the school or building is located inBuilding_Code: Building Code assigned to the school according to the 2012 Enrollment data sheet, where the number is not available or this does not apply the value used is ‘N/A’School_Code:School Code assigned to the school according to the 2012 Enrollment data sheet, where the number is not available or this does not apply the value used is ‘N/A’School_JoinID: Concatonated field of Building Code + School Code as a 7 digit code assigned by the 2012 Enrollment data sheet. If the School Code is only a three digit code an additional ‘0’ is added before the code to achieve the full seven digits necessary for the field. Where the number is not available or this does not apply the value used is ‘N/A’Notes: Any pertinent information that was not suited for another fieldEmploy13:Number of employees according to the 2013 employment final point fileTAZ08: TAZ08 in which the point liesType_II:Describes the nature of the school – public vs private runValues:PUBLIC: Owned, operated, funded, governed and sanctioned by the Idaho Department of EducationPRIVATE: Owned, operated & funded by private donors, foundation, trust or other source. May or may not meet State or Federal curriculum requirements/standardsOPT_ENROLL: Y/N field indicating if there is an open enrollment boundary for the schoolType_III:Any further information or description about the school. Values:AG PRODUCTION & RESEARCH: U of I extension campuses with specific research focus and use intentionALTERNATIVE: Any alternative learning environment, field may contain a ‘-_’ for a further description about what the alternative style is; teen parents, night school, at risk, ect…CHARTER: Any public school classified as a charter by the State Board of EducationCOLLEGE, UNIVERSITY, TRADE SCHOOL: Any post-secondary education institution, includes graduate programs, law schools and vocational training programs.COMMUNITY EDUCATION – ENVIRONMENTAL: Nontraditional classroom facilities which offer courses for the community (child and adult) to promote higher learning and understanding of the environment, care of the environment and environmental issues.CULTURAL: Any school which offers cultural enrichment or a multi-cultural learning environment. Field may also contain ‘-_’ to describe what the specific culture the school educates in.DURRING INCARCERATION: Schools are run through the Juvenile Detention Centers. These schools are acknowledged by the State Department of Education, and are recognized by the State. Available to students during the time of their incarceration. FAITH BASED: Any school run by or affiliated with a religious organization or faith based system of beliefs, and incorporates values and beliefs into the curriculum.FAITH BASED BOARDING: Any school run by or affiliated with a religious organization or faith based system of beliefs, and incorporates values and beliefs into the curriculum. These school also offer a live in facility option to students.HEADSTART: Formal pre-kindergarten education programsINTERNATIONAL BACCALAUREATE: School which offers programs for International Baccalaureate credit for studentsLANGUAGE AND CULTURE: Private (non-charter) language and culture focused schools. Field may also contain ‘-_’ to describe what the specific culture the school educates in.MAGNET: Any school with a particular subject area focus intended to draw students with natural aptitudes or specific interests, these schools have open enrollment boundaries with an application process, as long as the student resides within the school district to which the school is a part of. MONTESSORI: Private schools with a focus on experiential learning rather than traditional learning methods. MUSIC: Schools with an additional focus on musical aptitude and methodsONLINE OR HOME SCHOOL: Virtual or online classroom optionsSPECIAL NEEDS: Schools with facilities and resources for students with special needs or additional assistance and attention. Access: Indicates whether the point is the actual building location itself or an access point. Building locations are coded as "Loc" and access points are coded as "PV" for pedestrian/vehicle access.Main_Acc: Identifies if an access point is the main entrance/exit location for each school.Source: Where the numbers for the employment data and/or student enrollment were gathered from.Enrollment: # of students enrolled according to the 2012 enrollment data, or based on best information we were otherwise able to obtain (if not on the 2012 enrollment data).Website:Most recent URL if able to locate, if unable to locate indicated in field with “UTL”Status: Used to describe if the school is currently active, closed, or planned (used to query out inactive schools for performance monitoring purposes)UniqueID: Made by combining District number and building number in from DDDBBBB. _Updated Fall 2013 From School District WebsitesUpdated 9/11/11 From School District WebsitesJuly 2010 . Canyon County has since requested a new data structure to match their address points. The new schools file has the new structure. The point location of this file is identical to the new schools point file May 2010 - Edited the Ada County schools to align with school sites on NAIP imagery and confirmed schools against respective school district websites Jan - March 2010 - Worked with Jay Young over a several month period and several renditions to reconcile the Canyon County side of this file. December 2009 - Merged with Jay Young's Canyon point file in order to build a new data structure that meets Emergency Service data standards. Went through point by point to ensure alignement with buildings on NAIP imagery and attribute values.

Dataset

This dataset was created by Xuan Hui Ng

Contents

Data and Code to accompany the paper "Correlation Neglect in Student-to-School Matching."Abstract: We present results from three experiments containing incentivized school-choice scenarios. In these scenarios, we vary whether schools' assessments of students are based on a common priority (inducing correlation in admissions decisions) or are based on independent assessments (eliminating correlation in admissions decisions). The quality of students' application strategies declines in the presence of correlated admissions: application strategies become substantially more aggressive and fail to include attractive ``safety'' options. We provide a battery of tests suggesting that this phenomenon is at least partially driven by correlation neglect, and we discuss implications for the design and deployment of student-to-school matching mechanisms.

This dataset contains information on secondary school student performance collected from two Portuguese schools. It was originally introduced by **Cortez & Silva ** in the paper “Using Data Mining to Predict Secondary School Student Performance.”

The data was gathered through school reports and student questionnaires, covering demographic, social, and academic-related variables. Two separate datasets are provided:

• student-mat.csv → Math course performance
• student-por.csv → Portuguese language course performance

Number of instances: 649 (Mathematics) + 649 (Portuguese) Number of features: 30 input variables + 3 grade outputs (G1, G2, G3) Target variable: G3 (final grade, 0–20 scale) Missing values: None

Objective

The main goal is to predict student academic success, especially the final grade G3.

• Since G1 (first period grade) and G2 (second period grade) are highly correlated with G3, experiments can be designed with or without these features:

  • Easier task → Predicting G3 using G1 and G2
  • Harder task (more useful) → Predicting G3 without G1 and G2

This dataset is suitable for:

• Regression (predicting numeric grades)
• Classification (e.g., pass/fail, grade levels)

Features

The dataset includes 30 attributes from multiple categories:

• Demographics → sex, age, address, famsize, Pstatus
• Parental background → Medu, Fedu, Mjob, Fjob, guardian
• School-related → school, reason, traveltime, studytime, failures
• Support systems → schoolsup, famsup, paid, activities, nursery, higher, internet
• Lifestyle & social → romantic, freetime, goout, Dalc, Walc, health, absences
• Performance indicators → G1, G2, G3

Key Insights

• Strong G1/G2 ↔ G3 correlation: Final grade is heavily dependent on earlier grades.
• Student overlap: 382 students appear in both datasets (Math & Portuguese), identifiable by matching attributes.
• No missing data: Dataset is clean and ready for modeling.

Use Cases

• Predicting final grades for early intervention.
• Identifying at-risk students who may need extra support.
• Exploring socio-economic and lifestyle factors influencing education.
• Testing feature engineering and model comparison strategies.

Reference

• Cortez, P., & Silva, A. Using Data Mining to Predict Secondary School Student Performance. Proceedings of the 5th Annual Future Business Technology Conference.

This dataset is a playground for classification & regression tasks, ideal for experimenting with feature selection, ensemble methods, and interpretable ML approaches.

School code, district type, school type, grade span, county name, district name, school name, mailing and street addresses, legislative data pertaining to school, geocoding data, and other data for California public and charter schools.

School Districts are administrative units within which local officials provide public educational services for the area's residents. The Census Bureau obtains school district boundaries, names, local education agency codes, grade ranges, and school district levels annually from state education officials. The Census Bureau collects this information for the primary purpose of providing the U.S. Department of Education with annual estimates of the number of children aged 5 through 17 in families in poverty within each school district, county, and state. This information serves as the basis for the Department of Education to determine the annual allocation of Title I funding to states and school districtsThe Census Bureau tabulates data for four types of school districts: elementary, secondary, unified, and administrative. Each school district is assigned a five-digit code that is unique within state. School district codes are the local education agency number assigned by the Department of Education and are not necessarily in alphabetical order by school district name.Unified school districts provide education to children of all school ages. In general, if there is a unified school district, no elementary or secondary school district exists. If there is an elementary school district, the secondary school district may or may not exist. Administrative school districts were added in 2022 and provide administrative, planning, and educational services for all grade ranges. Currently, the Census Bureau maintains administrative school districts only in Vermont, and they represent supervisory unions and supervisory districts.The Census Bureau categorizes school districts based on the grade ranges for which the school district is financially responsible. These may or may not be the same as the grade ranges that a school district operates. A typical example would be a school district that operates schools for children in grades Kindergarten (KG)-8 and pays a neighboring school district to educate children in grades 9-12. The first school district is operationally responsible for grades KG-8, but financially responsible for grades KG-12. Therefore, the Census Bureau would define the grade range for that school district as KG-12. If an elementary school district is financially responsible for grades KG-12 or Pre-Kindergarten (PK)-12, there will be no secondary school district represented for that area. In cases, where an elementary school district is financially responsible for only lower grades, there is generally a secondary school district that is financially responsible for providing educational services for the upper grades.

The Development for Codes of Conduct in Online Classrooms of Vietnamese High School Students (CCOCVHSS) dataset includes 06 files with different formats (.doc, .cvs, .sav) to suit each step in the process of developing items of CCOCVHSS, specifically as follows: 1. Initial_Items_Pool.docx: presents 34 items developed by the research team based on the overview and analysis of research documents related to student behavior in the online learning environment in relation to teachers and other students with two main aspects: attitude and behavior, along with codes of conduct for students at general schools for online learning. 2. Experts_Judge_Results.xlsx: includes 07 columns and 35 rows, in which the columns correspond to data fields. Meanwhile, the rows show information about each item code, the content of that item, each expert's rating for that item, the total score of that item, and the analysis results of the proportions of the three rating levels. 3. Questionare_Of_CCOCVHSS.docx: is a questionnaire designed to serve the data collection with three parts: (1) Introduction and declaration of consent; (2) Demographic information; and (3) Questions. 4. CCOCVHSS _rawdata.csv: is the data used for analysis that has been cleaned from the raw data collected from the online survey.