62 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. Colleges and Universities

    • disasters-geoplatform.hub.arcgis.com
    • geodata.colorado.gov
    • +6more
    Updated Aug 26, 2020
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    Esri U.S. Federal Datasets (2020). Colleges and Universities [Dataset]. https://disasters-geoplatform.hub.arcgis.com/datasets/d257743c055e4206bd8a0f2d14af69fe
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    Dataset updated
    Aug 26, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri U.S. Federal Datasets
    Area covered
    Description

    Colleges and UniversitiesThis feature layer, utilizing data from the National Center for Education Statistics (NCES), displays colleges and universities in the U.S. and its territories. NCES uses the Integrated Postsecondary Education Data System (IPEDS) as the "primary source for information on U.S. colleges, universities, and technical and vocational institutions." According to NCES, this layer "contains directory information for every institution in the 2021-22 IPEDS universe. Includes name, address, city, state, zip code and various URL links to the institution's home page, admissions, financial aid offices and the net price calculator. Identifies institutions as currently active, institutions that participate in Title IV federal financial aid programs for which IPEDS is mandatory. It also includes variables derived from the 2021-22 Institutional Characteristics survey, such as control and level of institution, highest level and highest degree offered and Carnegie classifications."Gallaudet UniversityData currency: 2021Data source: IPEDS Complete Data FilesData modification: Removed fields with coded values and replaced with descriptionsFor more information: Integrated Postsecondary Education Data SystemSupport documentation: IPEDS Complete Data Files > Directory Information > DictionaryFor feedback, please contact: ArcGIScomNationalMaps@esri.comU.S. Department of Education (ED)Per ED, "ED's mission is to promote student achievement and preparation for global competitiveness by fostering educational excellence and ensuring equal access.ED was created in 1980 by combining offices from several federal agencies." ED's employees and budget "are dedicated to:Establishing policies on federal financial aid for education, and distributing as well as monitoring those funds.Collecting data on America's schools and disseminating research.Focusing national attention on key educational issues.Prohibiting discrimination and ensuring equal access to education."

  3. Colleges and Universities

    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    Updated Aug 26, 2020
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    Esri U.S. Federal Datasets (2020). Colleges and Universities [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/datasets/fedmaps::colleges-and-universities/explore?showTable=true
    Explore at:
    Dataset updated
    Aug 26, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri U.S. Federal Datasets
    Area covered
    Description

    Colleges and Universities This feature layer, utilizing data from the National Center for Education Statistics (NCES), displays colleges and universities in the U.S. and its territories. NCES uses the Integrated Postsecondary Education Data System (IPEDS) as the "primary source for information on U.S. colleges, universities, and technical and vocational institutions." According to NCES, this layer "contains directory information for every institution in the 2023-24 IPEDS universe. Includes name, address, city, state, zip code and various URL links to the institution's home page, admissions, financial aid offices and the net price calculator. Identifies institutions as currently active, and institutions that participate in Title IV federal financial aid programs for which IPEDS is mandatory." Gallaudet UniversityData currency: 2023Data source: IPEDS Complete Data FilesData modification: Removed fields with coded values and replaced with descriptionsFor more information: Integrated Postsecondary Education Data SystemSupport documentation: Data DictionaryFor feedback, please contact: ArcGIScomNationalMaps@esri.com U.S. Department of Education (ED) Per ED, "ED"s mission is to promote student achievement and preparation for global competitiveness by fostering educational excellence and ensuring equal access. ED was created in 1980 by combining offices from several federal agencies." ED"s employees and budget "are dedicated to:Establishing policies on federal financial aid for education, and distributing as well as monitoring those funds.Collecting data on America"s schools and disseminating research.Focusing national attention on key educational issues.Prohibiting discrimination and ensuring equal access to education."

  4. Student Growth

    • data.delaware.gov
    application/rdfxml +5
    Updated Feb 17, 2025
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    Department of Education (2025). Student Growth [Dataset]. https://data.delaware.gov/Education/Student-Growth/kqmb-6xbs
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    application/rdfxml, csv, application/rssxml, json, tsv, xmlAvailable download formats
    Dataset updated
    Feb 17, 2025
    Dataset provided by
    United States Department of Educationhttp://ed.gov/
    Authors
    Department of Education
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    This file contains the average percent of student growth achieved in English/Language Arts, Mathematics, and English Language Proficiency. The statistics reported are those used within Delaware Student Success Framework (DSSF) since 2018. Each student has a growth target to achieve in ELA (grades 4-8), Math (grades 4-8), and, if an English Learner, in English Language Proficiency (grades K-12).The average percent of student growth achieved statistic shows, on average, how close a group of students came to achieving their target.

  5. Educational attainment in the U.S. 1960-2022

    • statista.com
    Updated May 30, 2025
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    Statista (2025). Educational attainment in the U.S. 1960-2022 [Dataset]. https://www.statista.com/statistics/184260/educational-attainment-in-the-us/
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    Dataset updated
    May 30, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    In 2022, about 37.7 percent of the U.S. population who were aged 25 and above had graduated from college or another higher education institution, a slight decline from 37.9 the previous year. However, this is a significant increase from 1960, when only 7.7 percent of the U.S. population had graduated from college. Demographics Educational attainment varies by gender, location, race, and age throughout the United States. Asian-American and Pacific Islanders had the highest level of education, on average, while Massachusetts and the District of Colombia are areas home to the highest rates of residents with a bachelor’s degree or higher. However, education levels are correlated with wealth. While public education is free up until the 12th grade, the cost of university is out of reach for many Americans, making social mobility increasingly difficult. Earnings White Americans with a professional degree earned the most money on average, compared to other educational levels and races. However, regardless of educational attainment, males typically earned far more on average compared to females. Despite the decreasing wage gap over the years in the country, it remains an issue to this day. Not only is there a large wage gap between males and females, but there is also a large income gap linked to race as well.

  6. d

    2016-2017 Elem MS Quality Reports

    • catalog.data.gov
    • data.cityofnewyork.us
    Updated Nov 29, 2024
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    data.cityofnewyork.us (2024). 2016-2017 Elem MS Quality Reports [Dataset]. https://catalog.data.gov/dataset/2016-2017-elem-ms-quality-reports
    Explore at:
    Dataset updated
    Nov 29, 2024
    Dataset provided by
    data.cityofnewyork.us
    Description

    New York City Department of Education 2016 - 2017 Elementary , Middle School Quality Reports. The Quality Review is a process that evaluates how well schools are organized to support student learning and teacher practice. It was developed to assist New York City Department of Education (NYCDOE) schools in raising student achievement by looking behind a school’s performance statistics to ensure that the school is engaged in effective methods of accelerating student learning.

  7. d

    School Learning Modalities, 2020-2021

    • catalog.data.gov
    • data.virginia.gov
    • +3more
    Updated Mar 26, 2025
    + more versions
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    Centers for Disease Control and Prevention (2025). School Learning Modalities, 2020-2021 [Dataset]. https://catalog.data.gov/dataset/school-learning-modalities-2020-2021
    Explore at:
    Dataset updated
    Mar 26, 2025
    Dataset provided by
    Centers for Disease Control and Prevention
    Description

    The 2020-2021 School Learning Modalities dataset provides weekly estimates of school learning modality (including in-person, remote, or hybrid learning) for U.S. K-12 public and independent charter school districts for the 2020-2021 school year, from August 2020 – June 2021. These data were modeled using multiple sources of input data (see below) to infer the most likely learning modality of a school district for a given week. These data should be considered district-level estimates and may not always reflect true learning modality, particularly for districts in which data are unavailable. If a district reports multiple modality types within the same week, the modality offered for the majority of those days is reflected in the weekly estimate. All school district metadata are sourced from the National Center for Educational Statistics (NCES) for 2020-2021. School learning modality types are defined as follows: In-Person: All schools within the district offer face-to-face instruction 5 days per week to all students at all available grade levels. Remote: Schools within the district do not offer face-to-face instruction; all learning is conducted online/remotely to all students at all available grade levels. Hybrid: Schools within the district offer a combination of in-person and remote learning; face-to-face instruction is offered less than 5 days per week, or only to a subset of students. Data Information School learning modality data provided here are model estimates using combined input data and are not guaranteed to be 100% accurate. This learning modality dataset was generated by combining data from four different sources: Burbio [1], MCH Strategic Data [2], the AEI/Return to Learn Tracker [3], and state dashboards [4-20]. These data were combined using a Hidden Markov model which infers the sequence of learning modalities (In-Person, Hybrid, or Remote) for each district that is most likely to produce the modalities reported by these sources. This model was trained using data from the 2020-2021 school year. Metadata describing the location, number of schools and number of students in each district comes from NCES [21]. You can read more about the model in the CDC MMWR: COVID-19–Related School Closures and Learning Modality Changes — United States, August 1–September 17, 2021. The metrics listed for each school learning modality reflect totals by district and the number of enrolled students per district for which data are available. School districts represented here exclude private schools and include the following NCES subtypes: Public school district that is NOT a component of a supervisory union Public school district that is a component of a supervisory union Independent charter district “BI” in the state column refers to school districts funded by the Bureau of Indian Education. Technical Notes Data from September 1, 2020 to June 25, 2021 correspond to the 2020-2021 school year. During this timeframe, all four sources of data were available. Inferred modalities with a probability below 0.75 were deemed inconclusive and were omitted. Data for the month of July may show “In Person” status although most school districts are effectively closed during this time for summer break. Users may wish to exclude July data from use for this reason where applicable. Sources K-12 School Opening Tracker. Burbio 2021; https

  8. W

    2015-2016 School Quality Report - Elem, Middle & K-8 Schools

    • cloud.csiss.gmu.edu
    csv, json, rdf, xml
    Updated Jun 28, 2019
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    United States (2019). 2015-2016 School Quality Report - Elem, Middle & K-8 Schools [Dataset]. https://cloud.csiss.gmu.edu/uddi/dataset/2015-2016-school-quality-report-elem-middle-k-8-schools
    Explore at:
    xml, rdf, json, csvAvailable download formats
    Dataset updated
    Jun 28, 2019
    Dataset provided by
    United States
    Description

    The Quality Review is a process that evaluates how well schools are organized to support student learning and teacher practice. It was developed to assist New York City Department of Education (NYCDOE) schools in raising student achievement by looking behind a school’s performance statistics to ensure that the school is engaged in effective methods of accelerating student learning.

  9. W

    2015 - 2016 School Quality Report Results for YABC

    • cloud.csiss.gmu.edu
    csv, json, rdf, xml
    Updated Jun 6, 2019
    + more versions
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    United States (2019). 2015 - 2016 School Quality Report Results for YABC [Dataset]. https://cloud.csiss.gmu.edu/uddi/dataset/2015-2016-school-quality-report-results-for-yabc
    Explore at:
    rdf, json, xml, csvAvailable download formats
    Dataset updated
    Jun 6, 2019
    Dataset provided by
    United States
    Description

    New York City Department of Education 2015 - 2016 School Quality Report Results for YABC. The Quality Review is a process that evaluates how well schools are organized to support student learning and teacher practice. It was developed to assist New York City Department of Education (NYCDOE) schools in raising student achievement by looking behind a school’s performance statistics to ensure that the school is engaged in effective methods of accelerating student learning.

  10. U.S. students' beliefs on taking out loans for online higher education...

    • statista.com
    Updated Apr 23, 2025
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    Veera Korhonen (2025). U.S. students' beliefs on taking out loans for online higher education 2021-23 [Dataset]. https://www.statista.com/topics/3115/e-learning-and-digital-education/
    Explore at:
    Dataset updated
    Apr 23, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Veera Korhonen
    Description

    In 2023, seven percent of students strongly agreed that it was worthwhile for borrowers to take out loans for education after high school that is a predominantly online program in the United States. In comparison, 12 percent strongly disagreed with this belief.

  11. Colleges and Universities

    • gis-fema.hub.arcgis.com
    • sdgs.amerigeoss.org
    • +2more
    Updated Aug 26, 2020
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    Esri U.S. Federal Datasets (2020). Colleges and Universities [Dataset]. https://gis-fema.hub.arcgis.com/datasets/d257743c055e4206bd8a0f2d14af69fe
    Explore at:
    Dataset updated
    Aug 26, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri U.S. Federal Datasets
    Area covered
    Description

    Colleges and Universities This feature layer, utilizing data from the National Center for Education Statistics (NCES), displays colleges and universities in the U.S. and its territories. NCES uses the Integrated Postsecondary Education Data System (IPEDS) as the "primary source for information on U.S. colleges, universities, and technical and vocational institutions." According to NCES, this layer "contains directory information for every institution in the 2023-24 IPEDS universe. Includes name, address, city, state, zip code and various URL links to the institution's home page, admissions, financial aid offices and the net price calculator. Identifies institutions as currently active, and institutions that participate in Title IV federal financial aid programs for which IPEDS is mandatory." Gallaudet UniversityData currency: 2023Data source: IPEDS Complete Data FilesData modification: Removed fields with coded values and replaced with descriptionsFor more information: Integrated Postsecondary Education Data SystemSupport documentation: Data DictionaryFor feedback, please contact: ArcGIScomNationalMaps@esri.com U.S. Department of Education (ED) Per ED, "ED"s mission is to promote student achievement and preparation for global competitiveness by fostering educational excellence and ensuring equal access. ED was created in 1980 by combining offices from several federal agencies." ED"s employees and budget "are dedicated to:Establishing policies on federal financial aid for education, and distributing as well as monitoring those funds.Collecting data on America"s schools and disseminating research.Focusing national attention on key educational issues.Prohibiting discrimination and ensuring equal access to education."

  12. d

    2005 - 2017 Quality Review Ratings

    • catalog.data.gov
    • data.cityofnewyork.us
    • +3more
    Updated Nov 29, 2024
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    data.cityofnewyork.us (2024). 2005 - 2017 Quality Review Ratings [Dataset]. https://catalog.data.gov/dataset/2005-2017-quality-review-ratings
    Explore at:
    Dataset updated
    Nov 29, 2024
    Dataset provided by
    data.cityofnewyork.us
    Description

    The Quality Review is a process that evaluates how well schools are organized to support student learning and teacher practice. It was developed to assist New York City Department of Education (NYCDOE) schools in raising student achievement by looking behind a school’s performance statistics to ensure that the school is engaged in effective methods of accelerating student learning.

  13. School Learning Modalities, 2021-2022

    • healthdata.gov
    • datahub.hhs.gov
    • +4more
    application/rdfxml +5
    Updated Jan 6, 2023
    + more versions
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    Centers for Disease Control and Prevention (2023). School Learning Modalities, 2021-2022 [Dataset]. https://healthdata.gov/w/aitj-yx37/default?cur=TIEI0nKv4UG
    Explore at:
    xml, csv, tsv, application/rdfxml, application/rssxml, jsonAvailable download formats
    Dataset updated
    Jan 6, 2023
    Dataset authored and provided by
    Centers for Disease Control and Prevention
    License

    https://www.usa.gov/government-workshttps://www.usa.gov/government-works

    Description

    The 2021-2022 School Learning Modalities dataset provides weekly estimates of school learning modality (including in-person, remote, or hybrid learning) for U.S. K-12 public and independent charter school districts for the 2021-2022 school year and the Fall 2022 semester, from August 2021 – December 2022.

    These data were modeled using multiple sources of input data (see below) to infer the most likely learning modality of a school district for a given week. These data should be considered district-level estimates and may not always reflect true learning modality, particularly for districts in which data are unavailable. If a district reports multiple modality types within the same week, the modality offered for the majority of those days is reflected in the weekly estimate. All school district metadata are sourced from the https://nces.ed.gov/ccd/files.asp#Fiscal:2,LevelId:5,SchoolYearId:35,Page:1">National Center for Educational Statistics (NCES) for 2020-2021.

    School learning modality types are defined as follows:

      • In-Person: All schools within the district offer face-to-face instruction 5 days per week to all students at all available grade levels.
      • Remote: Schools within the district do not offer face-to-face instruction; all learning is conducted online/remotely to all students at all available grade levels.
      • Hybrid: Schools within the district offer a combination of in-person and remote learning; face-to-face instruction is offered less than 5 days per week, or only to a subset of students.
    Data Information
      • School learning modality data provided here are model estimates using combined input data and are not guaranteed to be 100% accurate. This learning modality dataset was generated by combining data from four different sources: Burbio [1], MCH Strategic Data [2], the AEI/Return to Learn Tracker [3], and state dashboards [4-20]. These data were combined using a Hidden Markov model which infers the sequence of learning modalities (In-Person, Hybrid, or Remote) for each district that is most likely to produce the modalities reported by these sources. This model was trained using data from the 2020-2021 school year. Metadata describing the location, number of schools and number of students in each district comes from NCES [21].
      • You can read more about the model in the CDC MMWR: https://www.cdc.gov/mmwr/volumes/70/wr/mm7039e2.htm" target="_blank">COVID-19–Related School Closures and Learning Modality Changes — United States, August 1–September 17, 2021.
      • The metrics listed for each school learning modality reflect totals by district and the number of enrolled students per district for which data are available. School districts represented here exclude private schools and include the following NCES subtypes:
        • Public school district that is NOT a component of a supervisory union
        • Public school district that is a component of a supervisory union
        • Independent charter district
      • “BI” in the state column refers to school districts funded by the Bureau of Indian Education.
    Technical Notes
      • Data from August 1, 2021 to June 24, 2022 correspond to the 2021-2022 school year. During this time frame, data from the AEI/Return to Learn Tracker and most state dashboards were not available. Inferred modalities with a probability below 0.6 were deemed inconclusive and were omitted. During the Fall 2022 semester, modalities for districts with a school closure reported by Burbio were updated to either “Remote”, if the closure spanned the entire week, or “Hybrid”, if the closure spanned 1-4 days of the week.
      • Data from August 1, 2022 to December 31, 2022 correspond to the 2022-2023 school year and were processed in a similar manner to data from the 2021-2022 school year.
      • Data for the month of July may show “In Person” status although most school districts are effectively closed during this time for summer break. Users may wish to exclude July data from use for this reason where applicable.
    Sources

  14. d

    2011-2012 Early Childhood Progress Report

    • catalog.data.gov
    • data.cityofnewyork.us
    Updated Nov 29, 2024
    + more versions
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    data.cityofnewyork.us (2024). 2011-2012 Early Childhood Progress Report [Dataset]. https://catalog.data.gov/dataset/2011-2012-early-childhood-progress-report
    Explore at:
    Dataset updated
    Nov 29, 2024
    Dataset provided by
    data.cityofnewyork.us
    Description

    The Quality Review is a process that evaluates how well schools are organized to support student learning and teacher practice. It was developed to assist New York City Department of Education (NYCDOE) schools in raising student achievement by looking behind a school’s performance statistics to ensure that the school is engaged in effective methods of accelerating student learning.

  15. N

    Students Receiving Recommended Special Education Programs by Program Type

    • data.cityofnewyork.us
    • catalog.data.gov
    application/rdfxml +5
    Updated Mar 3, 2022
    + more versions
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    Department of Education (DOE) (2022). Students Receiving Recommended Special Education Programs by Program Type [Dataset]. https://data.cityofnewyork.us/Education/Students-Receiving-Recommended-Special-Education-P/xjpe-rx7t
    Explore at:
    csv, application/rdfxml, tsv, application/rssxml, xml, jsonAvailable download formats
    Dataset updated
    Mar 3, 2022
    Dataset authored and provided by
    Department of Education (DOE)
    Description

    This data presents statistics for students fully, partially, not receiving special education services, related services, and transportation services. Data is categorized by district, superintendent, citywide counts, and percentages.

    PLEASE NOTE: The complete dataset is located under the "ATTACHMENT" section

  16. a

    Education

    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • hub.arcgis.com
    Updated Jun 26, 2017
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    Florida Department of Agriculture and Consumer Services (2017). Education [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/maps/FDACS::education/about
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    Dataset updated
    Jun 26, 2017
    Dataset authored and provided by
    Florida Department of Agriculture and Consumer Services
    Area covered
    Description

    The education data displayed in this theme of the Florida’s Roadmap to Living Healthy are measures utilized by the Florida Department of Education and the U.S. Department of Education to quantify educational opportunities and accountability in Florida. School grades provide an easily understandable way to measure the performance of a school. Parents and the general public can use the school grade and its components to understand how well each school is serving its students. In addition to school grades, this themed map includes Career/Technical Education Statistics, Graduation Rates, School District Grades, School Improvement Ratings, the 300 Lowest Performing Elementary Schools, Persistently Low Performing Schools, Head Start Centers, Title I Part A Program Recipients, and other education-related topics. This unique breakdown of education data can be used to better identify the specific educational needs of individual communities in Florida in relation to other social determinants that may be indicative or correlated to academic achievement and attainment.

  17. U.S. opinion on the impact of AI on student learning after high school 2024

    • statista.com
    Updated Jan 30, 2025
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    Statista (2025). U.S. opinion on the impact of AI on student learning after high school 2024 [Dataset]. https://www.statista.com/statistics/1551424/us-opinion-on-impact-of-ai-after-high-school/
    Explore at:
    Dataset updated
    Jan 30, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Mar 1, 2024 - Mar 25, 2024
    Area covered
    United States
    Description

    According to a survey conducted in 2024, over half of Americans believed that students' use of artificial intelligence (AI) in education after high school has a negative impact on their learning, at 53 percent. In contrast, only 27 percent felt that use of AI after high school has a positive impact on student learning.

  18. Higher Education Market Analysis, Size, and Forecast 2025-2029: North...

    • technavio.com
    Updated Apr 15, 2025
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    Technavio (2025). Higher Education Market Analysis, Size, and Forecast 2025-2029: North America (US and Canada), Europe (France, Germany, UK), APAC (China, India, Japan), South America (Brazil), and Middle East and Africa [Dataset]. https://www.technavio.com/report/higher-education-market-analysis-industry-analysis
    Explore at:
    Dataset updated
    Apr 15, 2025
    Dataset provided by
    TechNavio
    Authors
    Technavio
    Time period covered
    2021 - 2025
    Area covered
    Global
    Description

    Snapshot img

    Higher Education Market Size 2025-2029

    The higher education market size is forecast to increase by USD 117.9 billion, at a CAGR of 18.9% between 2024 and 2029.

    The market is experiencing significant shifts driven by advances in educational content delivery methods and the increasing prioritization of technology-integrated course offerings. This transformation is fueled by the growing expectation for flexible and accessible learning solutions, as well as the need to accommodate the rising cost of higher education. Institutions are increasingly adopting digital platforms and tools to enhance teaching and learning experiences, enabling students to access course materials and engage with instructors from anywhere, at any time. However, this transition poses challenges, such as ensuring data security and privacy, addressing the digital divide, and maintaining academic rigor in a technology-driven environment. To capitalize on these opportunities and navigate these challenges effectively, higher education institutions must remain agile and innovative, continuously adapting to the evolving needs of students and the market.

    What will be the Size of the Higher Education Market during the forecast period?

    Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
    Request Free SampleThe market continues to evolve, with dynamic market activities unfolding across various sectors. Student support services, such as continuing education and financial aid, remain crucial in ensuring student success. Curriculum development and research collaboration are key areas of focus for higher education institutions, driving the need for instructor training and campus infrastructure improvements. Digital literacy and educational technology are increasingly integrated into degree programs, from bachelor's degrees to doctoral degrees, with online courses and blended learning becoming more prevalent. Digital archives and online libraries provide essential resources for students and faculty, while research funding and tuition fees shape the financial landscape. Retention rates and graduation rates are essential metrics, with career services and alumni relations playing a crucial role in student engagement and post-graduation success. Personalized learning, gamified learning, and adaptive learning are innovative approaches to teaching and learning, while faculty development and e-learning platforms support the ongoing professional development of educators. K-12 education and international students contribute to the diversity of the higher education landscape, with joint degree programs, associate degrees, and certificate programs offering flexible educational paths. Intellectual property, learning analytics, and faculty recruitment are critical areas of focus for institutions seeking to stay competitive in the ever-changing educational landscape. In this dynamic market, higher education institutions must continually adapt to meet the evolving needs of students and the workforce. From curriculum development to faculty training, digital resources to student support services, the ongoing evolution of the market is shaping the future of education.

    How is this Higher Education Industry segmented?

    The higher education industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. Learning MethodOnlineOfflineHybridEnd-userPrivate collegesState universitiesCommunity collegesGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW)

    By Learning Method Insights

    The online segment is estimated to witness significant growth during the forecast period.The market is experiencing significant changes as institutions adapt to evolving student needs and technological advancements. Online courses are becoming more prevalent, with platforms transitioning from static content delivery to interactive, immersive environments. Digital tools, such as real-time collaboration features, virtual classroom experiences, and adaptive learning algorithms, are enhancing engagement and catering to the demands of diverse learner demographics, including working professionals and non-traditional students. Hybrid models, which blend online and in-person instruction, are gaining popularity as institutions recognize the complementary strengths of both modalities. Curriculum development is also undergoing transformation, with personalized learning and gamified approaches gaining traction. Research collaboration and study abroad programs continue to be essential components of higher education, while educational technology and faculty development are key areas of investment. Financial aid and student loans remain critic

  19. The number of students in elementary school after-school care services in...

    • data.gov.tw
    csv
    Updated Jun 1, 2025
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    Department of Education, Changhua County Government (2025). The number of students in elementary school after-school care services in Changhua County [Dataset]. https://data.gov.tw/en/datasets/47508
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    csvAvailable download formats
    Dataset updated
    Jun 1, 2025
    Dataset provided by
    United States Department of Educationhttp://ed.gov/
    Authors
    Department of Education, Changhua County Government
    License

    https://data.gov.tw/licensehttps://data.gov.tw/license

    Area covered
    Changhua County
    Description

    Changhua County Elementary School Children's Afterschool Care Service Program (low-income, disabled, indigenous, and general students)

  20. g

    Recent College Graduates Survey, 1976-1977: [United States] - Archival...

    • search.gesis.org
    Updated May 30, 2021
    + more versions
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    United States Department of Education. National Center for Education Statistics (2021). Recent College Graduates Survey, 1976-1977: [United States] - Archival Version [Dataset]. http://doi.org/10.3886/ICPSR06377
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    Dataset updated
    May 30, 2021
    Dataset provided by
    ICPSR - Interuniversity Consortium for Political and Social Research
    GESIS search
    Authors
    United States Department of Education. National Center for Education Statistics
    License

    https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de439897https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de439897

    Area covered
    United States
    Description

    Abstract (en): The Recent College Graduates (RCG) survey estimates the potential supply of newly qualified teachers in the United States and explores the immediate post-degree employment and education experiences of individuals obtaining bachelor's or master's degrees from American colleges and universities. The RCG survey, which focuses heavily, but not exclusively, on those graduates qualified to teach at the elementary and secondary levels, is designed to meet the following objectives: (1) to determine how many graduates become eligible or qualified to teach for the first time and how many are employed as teachers in the year following graduation, by teaching field, (2) to examine the relationships among courses taken, student achievement, and occupational outcomes, and (3) to monitor unemployment rates and average salaries of graduates by field of study. The RCG survey collects information on education and employment of all graduates (date of graduation, field of study, whether newly qualified to teach, further enrollment, financial aid, employment status, and teacher employment characteristics) as well as standard demographic characteristics such as earnings, age, marital status, sex, and race/ethnicity. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Students within one year of attaining a bachelor's or a master's degree from an American college or university. A two-stage stratified sampling approach was employed. The first stage consisted of drawing a sample of bachelor's and master's degree-granting institutions from Higher Education General Information Survey (HEGIS)/Integrated Postsecondary Education Data System (IPEDS) completions files. Institutions were stratified by control (public or private), by region, and by the proportion of degrees awarded in the field of education (over or under a specified number). Within each of these strata, institutions were selected according to size (size being measured by the sum of bachelor's and master's degrees awarded that year). The second stage consisted of the selection of a core sample of graduates (bachelor's and master's degree recipients) who received their degrees from the sampled institutions during the 1976-1977 academic year. Sampling rates of graduates differed by major field of study. The institution sample consisted of 300 institutions of which 30 were Historically Black Colleges (HBCs). The graduate sample was stratified by degree received and major field of study (vocational education, special education, other education, and noneducation). Data are representative at the national level. 2001-01-05 SAS and SPSS data definition statements have been created for this collection. Also, the codebook and data collection instrument were converted to a PDF file. The codebook and data collection instrument are provided by ICPSR as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided on the ICPSR Web site.

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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

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20 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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