43 datasets found
  1. US Highschool students dataset

    • kaggle.com
    zip
    Updated Apr 14, 2024
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    peter mushemi (2024). US Highschool students dataset [Dataset]. https://www.kaggle.com/datasets/petermushemi/us-highschool-students-dataset
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    zip(0 bytes)Available download formats
    Dataset updated
    Apr 14, 2024
    Authors
    peter mushemi
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    The dataset is related to student data, from an educational research study focusing on student demographics, academic performance, and related factors. Here’s a general description of what each column likely represents:

    Sex: The gender of the student (e.g., Male, Female). Age: The age of the student. Name: The name of the student. State: The state where the student resides or where the educational institution is located. Address: Indicates whether the student lives in an urban or rural area. Famsize: Family size category (e.g., LE3 for families with less than or equal to 3 members, GT3 for more than 3). Pstatus: Parental cohabitation status (e.g., 'T' for living together, 'A' for living apart). Medu: Mother's education level (e.g., Graduate, College). Fedu: Father's education level (similar categories to Medu). Mjob: Mother's job type. Fjob: Father's job type. Guardian: The primary guardian of the student. Math_Score: Score obtained by the student in Mathematics. Reading_Score: Score obtained by the student in Reading. Writing_Score: Score obtained by the student in Writing. Attendance_Rate: The percentage rate of the student’s attendance. Suspensions: Number of times the student has been suspended. Expulsions: Number of times the student has been expelled. Teacher_Support: Level of support the student receives from teachers (e.g., Low, Medium, High). Counseling: Indicates whether the student receives counseling services (Yes or No). Social_Worker_Visits: Number of times a social worker has visited the student. Parental_Involvement: The level of parental involvement in the student's academic life (e.g., Low, Medium, High). GPA: The student’s Grade Point Average, a standard measure of academic achievement in schools.

    This dataset provides a comprehensive look at various factors that might influence a student's educational outcomes, including demographic factors, academic performance metrics, and support structures both at home and within the educational system. It can be used for statistical analysis to understand and improve student success rates, or for targeted interventions based on specific identified needs.

  2. d

    USA High School Student Marketing Database by ASL Marketing

    • datarade.ai
    Updated Dec 19, 2019
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    ASL Marketing (2019). USA High School Student Marketing Database by ASL Marketing [Dataset]. https://datarade.ai/data-products/high-school-student-data
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    Dataset updated
    Dec 19, 2019
    Dataset provided by
    American Student List, LLC
    Authors
    ASL Marketing
    Area covered
    United States
    Description

    Database is provided by ASL Marketing and covers the United States of America. With ASL Marketing Reaching GenZ has never been easier. Current high school student data customized by: Class year Date of Birth Gender GPA Geo Household Income Ethnicity Hobbies College-bound Interests College Intent Email

  3. o

    US Public Schools

    • public.opendatasoft.com
    • data.smartidf.services
    csv, excel, geojson +1
    Updated Jan 6, 2023
    + more versions
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    (2023). US Public Schools [Dataset]. https://public.opendatasoft.com/explore/dataset/us-public-schools/
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    csv, json, excel, geojsonAvailable download formats
    Dataset updated
    Jan 6, 2023
    License

    https://en.wikipedia.org/wiki/Public_domainhttps://en.wikipedia.org/wiki/Public_domain

    Area covered
    United States
    Description

    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 2017-2018 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 3065 new records, modifications to the spatial location and/or attribution of 99,287 records, and removal of 2996 records not present in the NCES CCD data.

  4. d

    2020 - 2021 Diversity Report

    • catalog.data.gov
    • data.cityofnewyork.us
    • +1more
    Updated Nov 29, 2024
    + more versions
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    data.cityofnewyork.us (2024). 2020 - 2021 Diversity Report [Dataset]. https://catalog.data.gov/dataset/2020-2021-diversity-report
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    Dataset updated
    Nov 29, 2024
    Dataset provided by
    data.cityofnewyork.us
    Description

    Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students

  5. s

    US Private Schools

    • data.smartidf.services
    • public.opendatasoft.com
    csv, excel, geojson +1
    Updated Jul 9, 2024
    + more versions
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    (2024). US Private Schools [Dataset]. https://data.smartidf.services/explore/dataset/us-private-schools/
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    geojson, excel, json, csvAvailable download formats
    Dataset updated
    Jul 9, 2024
    License

    https://en.wikipedia.org/wiki/Public_domainhttps://en.wikipedia.org/wiki/Public_domain

    Area covered
    United States
    Description

    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.

  6. o

    US Colleges and Universities

    • public.opendatasoft.com
    • data.smartidf.services
    • +2more
    csv, excel, geojson +1
    Updated Jul 6, 2025
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    (2025). US Colleges and Universities [Dataset]. https://public.opendatasoft.com/explore/dataset/us-colleges-and-universities/
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    json, excel, geojson, csvAvailable download formats
    Dataset updated
    Jul 6, 2025
    License

    https://en.wikipedia.org/wiki/Public_domainhttps://en.wikipedia.org/wiki/Public_domain

    Area covered
    United States
    Description

    The Colleges and Universities feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Integrated Post Secondary Education System (IPEDS, http://nces.ed.gov/ipeds/), National Center for Education Statistics (NCES, https://nces.ed.gov/), US Department of Education for the 2018-2019 school year. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories. This feature class contains all MEDS/MEDS+ as approved by the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program (HSIP) Team. 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 feature class does not have a relationship class but is related to Supplemental Colleges. Colleges and Universities that are not included in the NCES IPEDS data are added to the Supplemental Colleges feature class when found. This release includes the addition of 175 new records, the removal of 468 no longer reported by NCES, and modifications to the spatial location and/or attribution of 6682 records.

  7. High School Longitudinal Study, 2009-2013 [United States]

    • icpsr.umich.edu
    ascii, delimited +5
    Updated May 12, 2016
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    United States Department of Education. Institute of Education Sciences. National Center for Education Statistics (2016). High School Longitudinal Study, 2009-2013 [United States] [Dataset]. http://doi.org/10.3886/ICPSR36423.v1
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    sas, delimited, spss, excel, ascii, stata, rAvailable download formats
    Dataset updated
    May 12, 2016
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    United States Department of Education. Institute of Education Sciences. National Center for Education Statistics
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/36423/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/36423/terms

    Time period covered
    2009 - 2013
    Area covered
    United States
    Description

    The High School Longitudinal Study of 2009 (HSLS:09) is nationally representative, longitudinal study of 9th graders who were followed through their secondary and postsecondary years, with an emphasis on understanding students' trajectories from the beginning of high school into postsecondary education, the workforce, and beyond. What students decide to pursue when, why, and how are crucial questions for HSLS:09. The HSLS:09 focuses on answering the following questions: How do parents, teachers, counselors, and students construct choice sets for students, and how are these related to students' characteristics, attitudes, and behavior? How do students select among secondary school courses, postsecondary institutions, and possible careers? How do parents and students plan financing for postsecondary experiences? What sources inform these plans? What factors influence students' decisions about taking STEM courses and following through with STEM college majors? Why are some students underrepresented in STEM courses and college majors? How students' plans vary over the course of high school and how decisions in 9th grade impact students' high school trajectories. When students are followed up in the spring of 11th grade and later, their planning and decision-making in 9th grade may be linked to subsequent behavior. This data collection also provides data for some arts-related topics, including the following: student participation in outside of schools arts activities; credit hours of arts classes taken; GPA from arts classes; and parent-led arts experiences. For the public-use file, a total of 23,503 students responded from over 900 high schools both public and private.

  8. Public School Characteristics - Current

    • catalog.data.gov
    • s.cnmilf.com
    • +3more
    Updated Oct 21, 2024
    + more versions
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    National Center for Education Statistics (NCES) (2024). Public School Characteristics - Current [Dataset]. https://catalog.data.gov/dataset/public-school-characteristics-current-340b1
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    Dataset updated
    Oct 21, 2024
    Dataset provided by
    National Center for Education Statisticshttps://nces.ed.gov/
    Description

    The National Center for Education Statistics' (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated point locations (latitude and longitude) for public elementary and secondary schools included in the NCES Common Core of Data (CCD). The CCD program annually collects administrative and fiscal data about all public schools, school districts, and state education agencies in the United States. The data are supplied by state education agency officials and include basic directory and contact information for schools and school districts, as well as characteristics about student demographics, number of teachers, school grade span, and various other administrative conditions. CCD school and agency point locations are derived from reported information about the physical location of schools and agency administrative offices. The point locations and administrative attributes in this data layer represent the most current CCD collection. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations. For more information about these CCD attributes, as well as additional attributes not included, see: https://nces.ed.gov/ccd/files.asp.Notes:-1 or MIndicates that the data are missing.-2 or NIndicates that the data are not applicable.-9Indicates that the data do not meet NCES data quality standards.Collections are available for the following years:2022-232021-222020-212019-202018-192017-18All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data. Collections are available for the following years:

  9. o

    RANKING: National Center for Education Statistics. Public...

    • explore.openaire.eu
    Updated Jan 1, 2018
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    National Center For Education Statistics (2018). RANKING: National Center for Education Statistics. Public Elementary/Secondary School Summary (CCD): School Count - Regular Schools, 2012. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 017-002-002National Center for Education Statistics. Public Elementary/Secondary School Summary (CCD): School Count - Charter School, 2012. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 017-002-020 [Dataset]. http://doi.org/10.6068/dp1626dc6fb324
    Explore at:
    Dataset updated
    Jan 1, 2018
    Authors
    National Center For Education Statistics
    Description

    National Center for Education Statistics. Public Elementary/Secondary School Summary (CCD): School Count - Regular Schools, 2012. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 017-002-002 Dataset: A public elementary/secondary school providing instruction and education services that does not focus primarily on special education, vocational/technical education, or alternative education, or on any of the particular themes associated with magnet/special program emphasis schools. Data are from the Common Core of Data (CCD), a program of the U.S. Department of Education's National Center for Education Statistics that annually collects fiscal and non-fiscal data about all public schools, public school districts and state education agencies in the United States. The data are supplied by state education agency officials. http://nces.ed.gov/ccd/ccddata.asp Category: Education Subject: Secondary Schools, Junior High Schools, High Schools, Public Schools, Elementary Schools Source: National Center for Education Statistics The National Center for Education Statistics (NCES) is the primary federal entity in the United States for collecting and analyzing data related to education in the US and other nations. NCES is located within the US Department of Education and the Institute of Education Sciences. The NCES fulfills a congressional mandate to collect, collate, analyze, and report complete statistics on the condition of US education; conduct and publish reports; and review and report on education activities internationally. The NCES is one of four centers (along with the National Center for Education Research, the National Center for Education Evaluation and Regional Assistance, and the National Center for Special Education Research) charged with carrying out the work of the Institute of Education Sciences. http://nces.ed.gov/National Center for Education Statistics. Public Elementary/Secondary School Summary (CCD): School Count - Charter School, 2012. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 017-002-020 Dataset: A school providing free public elementary and/or secondary education to eligible students under a specific charter granted by the state legislature or other appropriate authority, and designated by such authority to be a charter school. Data are from the Common Core of Data (CCD), a program of the U.S. Department of Education's National Center for Education Statistics that annually collects fiscal and non-fiscal data about all public schools, public school districts and state education agencies in the United States. The data are supplied by state education agency officials. http://nces.ed.gov/ccd/ccddata.asp Category: Education Subject: Secondary Schools, Junior High Schools, High Schools, Elementary Schools, School Choice, Charter Schools Source: National Center for Education Statistics The National Center for Education Statistics (NCES) is the primary federal entity in the United States for collecting and analyzing data related to education in the US and other nations. NCES is located within the US Department of Education and the Institute of Education Sciences. The NCES fulfills a congressional mandate to collect, collate, analyze, and report complete statistics on the condition of US education; conduct and publish reports; and review and report on education activities internationally. The NCES is one of four centers (along with the National Center for Education Research, the National Center for Education Evaluation and Regional Assistance, and the National Center for Special Education Research) charged with carrying out the work of the Institute of Education Sciences. http://nces.ed.gov/

  10. d

    K-12 Education Marketing Data | 3M Records | District, Elementary, Middle,...

    • datarade.ai
    .xml, .csv, .xls
    Updated Jan 15, 2025
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    McGRAW (2025). K-12 Education Marketing Data | 3M Records | District, Elementary, Middle, Highschool, and Curriculum Professionals [Dataset]. https://datarade.ai/data-products/k-12-education-marketing-data-3m-records-district-elemen-mcgraw
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    .xml, .csv, .xlsAvailable download formats
    Dataset updated
    Jan 15, 2025
    Dataset authored and provided by
    McGRAW
    Area covered
    United States of America
    Description

    Seeking a comprehensive database that encompasses high school students, college attendees, young professionals, or individuals interested in continuing education opportunities?

    We offer unparalleled access to premium student data lists, including detailed information on students by name, their parents, college attendees, graduates, and much more. Whether you're aiming to spearhead a direct mail initiative for college readiness programs, further education courses, or even school dance attire, our comprehensive database positions you to seamlessly connect with your ideal customer.

    What sort of data do we have?

    • College Bound HS Students
    • K-12 Data
    • College Student Mailing lists
    • Homeschool Mailing Lists

    We understand the challenges marketers face when reaching prospective students. Our solutions provide a data-driven, results-oriented roadmap to enrollment success. Accurate, demographics-rich student marketing data is critical to your school’s successful marketing plan, especially in today’s competitive environment. Our data alliances enable us to bring to market the most robust portfolio of data lists, including students and their parents, young adults, and working professionals for continuing education programs.

    Why Buy Leads From Us? With McGRAW’s student leads, you can build a robust pipeline, drive enrollment growth, and achieve your institution's educational and financial objectives. Our education leads offer:

    Targeted Outreach: Connect with students interested in specific programs and fields of study. Comprehensive Data: Gain insights into students' academic interests, career goals, and preferred locations. High Engagement Rates: Reach students who are actively exploring educational options, ensuring higher response rates. Scalable Solutions: Access a wide range of leads to match your institution's enrollment goals and capacity. Quick Integration: Seamlessly integrate leads into your CRM for efficient follow-up and management. Compliance and Accuracy: Ensure all leads are generated through compliant and ethical methods, providing accurate and reliable data. What other industries can utilize the data? There are obvious ways to utilize education data and leads, but there may be some additional industries that could benefit.

    Book publishers Colleges Universities Religious Organizations Education Supply Companies Office Supply Companies Fundraising Product Companies

  11. College enrollment in public and private institutions in the U.S. 1965-2031

    • statista.com
    • ai-chatbox.pro
    Updated Mar 25, 2025
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    Statista (2025). College enrollment in public and private institutions in the U.S. 1965-2031 [Dataset]. https://www.statista.com/statistics/183995/us-college-enrollment-and-projections-in-public-and-private-institutions/
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    Dataset updated
    Mar 25, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    There were approximately 18.58 million college students in the U.S. in 2022, with around 13.49 million enrolled in public colleges and a further 5.09 million students enrolled in private colleges. The figures are projected to remain relatively constant over the next few years.

    What is the most expensive college in the U.S.? The overall number of higher education institutions in the U.S. totals around 4,000, and California is the state with the most. One important factor that students – and their parents – must consider before choosing a college is cost. With annual expenses totaling almost 78,000 U.S. dollars, Harvey Mudd College in California was the most expensive college for the 2021-2022 academic year. There are three major costs of college: tuition, room, and board. The difference in on-campus and off-campus accommodation costs is often negligible, but they can change greatly depending on the college town.

    The differences between public and private colleges Public colleges, also called state colleges, are mostly funded by state governments. Private colleges, on the other hand, are not funded by the government but by private donors and endowments. Typically, private institutions are  much more expensive. Public colleges tend to offer different tuition fees for students based on whether they live in-state or out-of-state, while private colleges have the same tuition cost for every student.

  12. State and District High School Graduation Rates

    • educationtocareer.data.mass.gov
    application/rdfxml +5
    Updated Apr 22, 2025
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    Department of Elementary and Secondary Education (2025). State and District High School Graduation Rates [Dataset]. https://educationtocareer.data.mass.gov/Assessment-and-Accountability/State-and-District-High-School-Graduation-Rates/u57w-6nby
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    csv, xml, json, application/rdfxml, application/rssxml, tsvAvailable download formats
    Dataset updated
    Apr 22, 2025
    Dataset provided by
    Missouri Department of Elementary and Secondary Educationhttps://dese.mo.gov/
    Authors
    Department of Elementary and Secondary Education
    Description

    This dataset shows the percentage of students who graduated from Massachusetts public schools with a regular high school diploma within 4 or 5 years. It is a long file that contains multiple rows for each school and district, with rows for different years and different student groups.

    Note: Data is currently available at the school level only, as well as the state overall. For district-level graduation rates, please see the High School Graduation Rates dataset, or the High School Graduation Rates report on our DESE Profiles site.

    Economically Disadvantaged was used 2015-2021. Low Income was used prior to 2015, and a different version of Low Income has been used since 2022. Please see the DESE Researcher's Guide for more information. 

    For more data about student experiences and outcomes in high school and beyond, please see the main DART: Success After High School dataset and dashboard.

  13. p

    Trends in Total Students (1987-2023): U. S. Grant High School

    • publicschoolreview.com
    + more versions
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    Public School Review, Trends in Total Students (1987-2023): U. S. Grant High School [Dataset]. https://www.publicschoolreview.com/u-s-grant-high-school-profile
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    Dataset authored and provided by
    Public School Review
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset tracks annual total students amount from 1987 to 2023 for U. S. Grant High School

  14. Students' Academic Performance Dataset

    • kaggle.com
    zip
    Updated Nov 25, 2016
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    Ibrahim Aljarah (2016). Students' Academic Performance Dataset [Dataset]. https://www.kaggle.com/aljarah/xAPI-Edu-Data
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    zip(6103 bytes)Available download formats
    Dataset updated
    Nov 25, 2016
    Authors
    Ibrahim Aljarah
    License

    Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
    License information was derived automatically

    Description

    Description

    Student's Academic Performance Dataset (xAPI-Edu-Data)

    Data Set Characteristics: Multivariate

    Number of Instances: 480

    Area: E-learning, Education, Predictive models, Educational Data Mining

    Attribute Characteristics: Integer/Categorical

    Number of Attributes: 16

    Date: 2016-11-8

    Associated Tasks: Classification

    Missing Values? No

    File formats: xAPI-Edu-Data.csv

    Source:

    Elaf Abu Amrieh, Thair Hamtini, and Ibrahim Aljarah, The University of Jordan, Amman, Jordan, http://www.Ibrahimaljarah.com www.ju.edu.jo

    Dataset Information:

    This is an educational data set which is collected from learning management system (LMS) called Kalboard 360. Kalboard 360 is a multi-agent LMS, which has been designed to facilitate learning through the use of leading-edge technology. Such system provides users with a synchronous access to educational resources from any device with Internet connection.

    The data is collected using a learner activity tracker tool, which called experience API (xAPI). The xAPI is a component of the training and learning architecture (TLA) that enables to monitor learning progress and learner’s actions like reading an article or watching a training video. The experience API helps the learning activity providers to determine the learner, activity and objects that describe a learning experience. The dataset consists of 480 student records and 16 features. The features are classified into three major categories: (1) Demographic features such as gender and nationality. (2) Academic background features such as educational stage, grade Level and section. (3) Behavioral features such as raised hand on class, opening resources, answering survey by parents, and school satisfaction.

    The dataset consists of 305 males and 175 females. The students come from different origins such as 179 students are from Kuwait, 172 students are from Jordan, 28 students from Palestine, 22 students are from Iraq, 17 students from Lebanon, 12 students from Tunis, 11 students from Saudi Arabia, 9 students from Egypt, 7 students from Syria, 6 students from USA, Iran and Libya, 4 students from Morocco and one student from Venezuela.

    The dataset is collected through two educational semesters: 245 student records are collected during the first semester and 235 student records are collected during the second semester.

    The data set includes also the school attendance feature such as the students are classified into two categories based on their absence days: 191 students exceed 7 absence days and 289 students their absence days under 7.

    This dataset includes also a new category of features; this feature is parent parturition in the educational process. Parent participation feature have two sub features: Parent Answering Survey and Parent School Satisfaction. There are 270 of the parents answered survey and 210 are not, 292 of the parents are satisfied from the school and 188 are not.

    (See the related papers for more details).

    Attributes

    1 Gender - student's gender (nominal: 'Male' or 'Female’)

    2 Nationality- student's nationality (nominal:’ Kuwait’,’ Lebanon’,’ Egypt’,’ SaudiArabia’,’ USA’,’ Jordan’,’ Venezuela’,’ Iran’,’ Tunis’,’ Morocco’,’ Syria’,’ Palestine’,’ Iraq’,’ Lybia’)

    3 Place of birth- student's Place of birth (nominal:’ Kuwait’,’ Lebanon’,’ Egypt’,’ SaudiArabia’,’ USA’,’ Jordan’,’ Venezuela’,’ Iran’,’ Tunis’,’ Morocco’,’ Syria’,’ Palestine’,’ Iraq’,’ Lybia’)

    4 Educational Stages- educational level student belongs (nominal: ‘lowerlevel’,’MiddleSchool’,’HighSchool’)

    5 Grade Levels- grade student belongs (nominal: ‘G-01’, ‘G-02’, ‘G-03’, ‘G-04’, ‘G-05’, ‘G-06’, ‘G-07’, ‘G-08’, ‘G-09’, ‘G-10’, ‘G-11’, ‘G-12 ‘)

    6 Section ID- classroom student belongs (nominal:’A’,’B’,’C’)

    7 Topic- course topic (nominal:’ English’,’ Spanish’, ‘French’,’ Arabic’,’ IT’,’ Math’,’ Chemistry’, ‘Biology’, ‘Science’,’ History’,’ Quran’,’ Geology’)

    8 Semester- school year semester (nominal:’ First’,’ Second’)

    9 Parent responsible for student (nominal:’mom’,’father’)

    10 Raised hand- how many times the student raises his/her hand on classroom (numeric:0-100)

    11- Visited resources- how many times the student visits a course content(numeric:0-100)

    12 Viewing announcements-how many times the student checks the new announcements(numeric:0-100)

    13 Discussion groups- how many times the student participate on discussion groups (numeric:0-100)

    14 Parent Answering Survey- parent answered the surveys which are provided from school or not (nominal:’Yes’,’No’)

    15 Parent School Satisfaction- the Degree of parent satisfaction from school(nominal:’Yes’,’No’)

    16 Student Absence Days-the number of absence days for each student (nominal: above-7, under-7)

    The students are classified into three numerical intervals based on their total grade/mark:

    Low-Level: interval includes values from 0 to 69,

    Middle-Level: interval includes values from 70 to 89,

    High-Level: interval includes values from 90-100.

    Relevant Papers:

    -Amrieh, E. A., Hamtini, T., & Aljarah, I. (2016). Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods. International Journal of Database Theory and Application, 9(8), 119-136.

    -Amrieh, E. A., Hamtini, T., & Aljarah, I. (2015, November). Preprocessing and analyzing educational data set using X-API for improving student's performance. In Applied Electrical Engineering and Computing Technologies (AEECT), 2015 IEEE Jordan Conference on (pp. 1-5). IEEE.

    Citation Request:

    Please include these citations if you plan to use this dataset:

    • Amrieh, E. A., Hamtini, T., & Aljarah, I. (2016). Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods. International Journal of Database Theory and Application, 9(8), 119-136.

    -Amrieh, E. A., Hamtini, T., & Aljarah, I. (2015, November). Preprocessing and analyzing educational data set using X-API for improving student's performance. In Applied Electrical Engineering and Computing Technologies (AEECT), 2015 IEEE Jordan Conference on (pp. 1-5). IEEE.

  15. N

    School Point Locations

    • data.cityofnewyork.us
    • s.cnmilf.com
    • +3more
    application/rdfxml +5
    Updated Sep 22, 2011
    + more versions
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    Department of Education (DOE) (2011). School Point Locations [Dataset]. https://data.cityofnewyork.us/Education/School-Point-Locations/jfju-ynrr
    Explore at:
    tsv, application/rssxml, xml, csv, application/rdfxml, jsonAvailable download formats
    Dataset updated
    Sep 22, 2011
    Dataset authored and provided by
    Department of Education (DOE)
    Description

    This is an ESRI shape file of school point locations based on the official address. It includes some additional basic and pertinent information needed to link to other data sources. It also includes some basic school information such as Name, Address, Principal, and Principal’s contact information.

  16. Percentage of high school students participating in physical activity (60+...

    • healthdata.nj.gov
    • data.wu.ac.at
    application/rdfxml +5
    Updated Sep 18, 2020
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    Student Health Survey, Office of Student Support Services, New Jersey Department of Education; (2020). Percentage of high school students participating in physical activity (60+ minutes, 5+ days), New Jersey, by year: Beginning 2009 (odd years only) [Dataset]. https://healthdata.nj.gov/dataset/Percentage-of-high-school-students-participating-i/mfhn-qewg
    Explore at:
    xml, csv, application/rdfxml, tsv, json, application/rssxmlAvailable download formats
    Dataset updated
    Sep 18, 2020
    Dataset provided by
    New Jersey Department of Educationhttp://www.state.nj.us/education/
    Authors
    Student Health Survey, Office of Student Support Services, New Jersey Department of Education;
    Area covered
    New Jersey
    Description

    Ratio: Percent of public high school students surveyed who exercised or participated in physical activity.

    Definition: The percentage of public high school students who participated in physical activities that increased their heart rate and made them breathe hard some of the time for a total of at least 60 minutes per day on five or more of the past seven days.

    Data Sources:

    (1) Student Health Survey, Office of Student Support Services, New Jersey Department of Education;

    (2) Youth Risk Behavior Surveillance System, Division of Adolescent and School Health, Centers for Disease Control and Prevention

  17. Data from: Condition of America's Public School Facilities, 1999

    • catalog.data.gov
    • data.amerigeoss.org
    • +1more
    Updated Aug 12, 2023
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    National Center for Education Statistics (NCES) (2023). Condition of America's Public School Facilities, 1999 [Dataset]. https://catalog.data.gov/dataset/condition-of-americas-public-school-facilities-1999-e21ab
    Explore at:
    Dataset updated
    Aug 12, 2023
    Dataset provided by
    National Center for Education Statisticshttps://nces.ed.gov/
    Description

    The Condition of America's Public School Facilities, 1999 (FRSS 73), is a study that is part of the Fast Response Survey System (FRSS) program; program data is available since 1998-99 at . FRSS 73 (https://nces.ed.gov/surveys/frss/) is a cross-sectional survey that collects and report data on key issues at public elementary and secondary schools in the United States. The sample for FRSS 73 included approximately 1000 public elementary, middle, and high schools. District personnel who were familiar with the condition of schools completed questionnaires for each sampled school in their districts. The study's weighted response rate was 91 percent. Key statistics produced from FRSS 73 provide information on the pervasiveness of air conditioning, the number of temporary classrooms, the number of days particular public schools were closed for repairs, planned construction, repairs, and additions, long range facilities plans, the age of public schools, overcrowding and practices used to address overcrowding, estimated costs for bringing facilities to a satisfactory condition, and the overall condition of roofs, floors, walls, plumbing, heating, electric facilities, and safety features.

  18. p

    Distribution of Students Across Grade Levels in U. S. Grant High School

    • publicschoolreview.com
    + more versions
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    Public School Review, Distribution of Students Across Grade Levels in U. S. Grant High School [Dataset]. https://www.publicschoolreview.com/u-s-grant-high-school-profile
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    Dataset authored and provided by
    Public School Review
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset tracks annual distribution of students across grade levels in U. S. Grant High School

  19. A

    Dual Enrollment Programs and Courses for High School Students, 2002-03

    • data.amerigeoss.org
    • datasets.ai
    • +2more
    zipped dat +1
    Updated Jul 24, 2019
    + more versions
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    United States[old] (2019). Dual Enrollment Programs and Courses for High School Students, 2002-03 [Dataset]. https://data.amerigeoss.org/hu/dataset/36569aa2-7628-45ec-b3dc-856b17bafadf
    Explore at:
    zipped dat, zipped sas7bdatAvailable download formats
    Dataset updated
    Jul 24, 2019
    Dataset provided by
    United States[old]
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Dual Enrollment Programs and Courses for High School Students, 2002-03 (PEQIS 14), is a study that is part of the Postsecondary Education Quick Information System (PEQIS) program; program data is available since 1997 at https://nces.ed.gov/surveys/peqis/. PEQIS 14 (https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2009045) is a cross-sectional survey that collected information on the topic of dual enrollment of high school students at postsecondary institutions. 1,600 Title IV degree-granting postsecondary institutions in the 50 United States and the District of Columbia were sampled. The study was conducted using online or paper surveys. The overall response rates were 92 percent weighted and 91 percent unweighted. Key statistics produced from PEQIS 14 were information on the prevalence of college course-taking by high school students at their institutions during the 2002-03 12-month academic year, both within and outside of dual enrollment programs. Among institutions with dual enrollment programs, additional information was obtained on the characteristics of programs, including course location and type of instructors, program and course curriculum, academic eligibility requirements, and funding.

  20. Percentage of high school students who consume recommended daily fruit and...

    • healthdata.nj.gov
    • data.wu.ac.at
    application/rdfxml +5
    Updated Feb 26, 2016
    Share
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    Student Health Survey, Office of Student Support Services, New Jersey Department of Education (2016). Percentage of high school students who consume recommended daily fruit and vegetable servings (5+ a day), New Jersey, by year: Beginning 2009 (odd years only) [Dataset]. https://healthdata.nj.gov/dataset/Percentage-of-high-school-students-who-consume-rec/k7tr-ghjx
    Explore at:
    xml, tsv, application/rssxml, json, application/rdfxml, csvAvailable download formats
    Dataset updated
    Feb 26, 2016
    Dataset provided by
    New Jersey Department of Educationhttp://www.state.nj.us/education/
    Authors
    Student Health Survey, Office of Student Support Services, New Jersey Department of Education
    Area covered
    New Jersey
    Description

    Ratio: Percent of students surveyed consuming daily recommended servings of fruits and vegetables.

    Definition: The percentage of surveyed adolescents in grades 9 to 12 who reported consuming five or more daily servings of fruits and vegetables (including legumes).

    Data Source: Student Health Survey, Office of Student Support Services, New Jersey Department of Education

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peter mushemi (2024). US Highschool students dataset [Dataset]. https://www.kaggle.com/datasets/petermushemi/us-highschool-students-dataset
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US Highschool students dataset

Explore at:
22 scholarly articles cite this dataset (View in Google Scholar)
zip(0 bytes)Available download formats
Dataset updated
Apr 14, 2024
Authors
peter mushemi
License

MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically

Description

The dataset is related to student data, from an educational research study focusing on student demographics, academic performance, and related factors. Here’s a general description of what each column likely represents:

Sex: The gender of the student (e.g., Male, Female). Age: The age of the student. Name: The name of the student. State: The state where the student resides or where the educational institution is located. Address: Indicates whether the student lives in an urban or rural area. Famsize: Family size category (e.g., LE3 for families with less than or equal to 3 members, GT3 for more than 3). Pstatus: Parental cohabitation status (e.g., 'T' for living together, 'A' for living apart). Medu: Mother's education level (e.g., Graduate, College). Fedu: Father's education level (similar categories to Medu). Mjob: Mother's job type. Fjob: Father's job type. Guardian: The primary guardian of the student. Math_Score: Score obtained by the student in Mathematics. Reading_Score: Score obtained by the student in Reading. Writing_Score: Score obtained by the student in Writing. Attendance_Rate: The percentage rate of the student’s attendance. Suspensions: Number of times the student has been suspended. Expulsions: Number of times the student has been expelled. Teacher_Support: Level of support the student receives from teachers (e.g., Low, Medium, High). Counseling: Indicates whether the student receives counseling services (Yes or No). Social_Worker_Visits: Number of times a social worker has visited the student. Parental_Involvement: The level of parental involvement in the student's academic life (e.g., Low, Medium, High). GPA: The student’s Grade Point Average, a standard measure of academic achievement in schools.

This dataset provides a comprehensive look at various factors that might influence a student's educational outcomes, including demographic factors, academic performance metrics, and support structures both at home and within the educational system. It can be used for statistical analysis to understand and improve student success rates, or for targeted interventions based on specific identified needs.

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