Schools in all Pacific Island Countries and Territories have been included in their respective Education Management Information Systems in 2015 by the Statistics for Development Division of SPC. This data can be used for applications such as disaster mitigation and optimise emergency response and service delivery.

This dataset is the result of the initial dissemination of a survey submitted to STEAM practitioners, aiming at gaining insight on their on-going STEAM practices, which have informed the project's mapping of STEAM practices (Deliverable 4.2).

This dataset is for the article, 'Emergency remote teaching in higher education: Mapping the first global online semester'.

Abstract: Due to the Covid-19 pandemic that spread globally in 2020, higher education courses were subsequently offered in fully remote, online formats. A plethora of primary studies began investigating a range of topics exploring teaching and learning in higher education, particularly during the initial semester. In order to provide an overview and initial understanding of this emerging research field, a systematic mapping review was conducted that collates and describes the characteristics of 282 primary empirical studies. Findings reveal that research has been carried out mostly descriptively and cross-sectionally, focusing predominantly on undergraduate students and their perceptions of teaching and learning during the pandemic. Studies originate from a broad range of countries, are overwhelmingly published open access, and largely focused on the fields of Health & Welfare and Natural Sciences, Mathematics & Statistics. Educational technology used for emergency remote teaching are most often synchronous collaborative tools, used in combination with text-based tools. The findings are discussed against pre-pandemic research on educational technology use in higher education teaching and learning, and perspectives for further research are provided.

Bond, M., Bedenlier, S., Marín, V. I., & Händel, M. (2021, March 15). Emergency remote teaching in higher education: Mapping the first global online semester (Pre-print). https://doi.org/10.31219/osf.io/gsdu7

This map layer is a subset of the Columbus Points of Interest layer and shows education facilities in the City of Columbus. Daycares, elementary, middle, and high schools, colleges and universities, vocational schools, and other educational entities are included. This layer is maintained through a cooperative effort by multiple departments of the City of Columbus using first-hand knowledge of the area as well as a variety of authoritative data sources. While significant effort is made to ensure the data is as accurate and comprehensive as possible, some points of interest may be excluded and included points may not be immediately updated as change occurs.

ABSTRACT This paper maps the productions of the Mathematics Teachers Training generated by the Working Group on Mathematical Education in Higher Education, of the Brazilian Society of Mathematical Education. This investigation is aimed to analyze, among other aspects, the initial training of Mathematics teachers, the role of supervised internship practice and the development of the teaching professional. We sought to highlight the concerns that researchers, whose main field of interest is teaching in Higher Education, have about the initial and continued training of Mathematics teachers. We have identified, by means of Content Analysis, six pillars in which the topics presented in the body of the analysis are concentrated.

If you have a drone or are thinking abut getting one to fly with your students or for your research, there are several things that you should consider. In this article we look at what you need to do to ensure you are safe and comply with the UK regulations.

This project was centered on the apparent tension between keeping schools safe and keeping students attached to school. The project used comprehensive administrative data from the North Carolina public school system available through the North Carolina Education Research Data Center (NCERDC). This dataset, along with juvenile court record data and publicly-available data from the North Carolina adult criminal justice system, linked administrative information from the same individuals in both school disciplinary records and the juvenile and adult criminal justice systems. The ultimate goal of this project was to determine if different policy choices by schools causally decrease rates of in-school violence in the short run and/or increase rates of conviction and incarceration in the long term.

The interest in program- and colleges of education- level evaluation and alignment of student learning outcomes to course content has been increasing over the past several decades. Curriculum mapping establishes the links between content and expected student learning outcomes. Curriculum map is an overview of what is taking place in the classroom; and it includes evaluation tools and activities. Social Studies Department, Federal Capital Territory (FCT) College of Education Zuba, Abuja, recently completed an accreditation exercise by National Commission for Colleges of Education Abuja, Nigeria. The audit reported that there was no match between the student learning outcomes and Social Studies curricula. The purpose of this paper was to align the Nigeria Certificate in Education (NCE) (Social Studies) minimum standards with student learning outcomes to determine gaps and redundancies. The paper also looked at how virtual education enhances curriculum mapping during COVID-19 pandemic. Minimum standards learning outcomes were modified from existing learning outcomes to better align with college learning outcomes and the Social Studies Core and Elective Competencies. All NCE Social Studies courses were mapped to the Social Studies Core and Elective Competencies and assessed to determine the gaps and redundancies. The study used the documentary research method. The purposeful sampling strategy was used to select the research site. Potential gaps were defined as coverage for each competency in about ≤20% of the courses and potential redundancies was considered as coverage of ≥80% of the courses. The mapping exercise revealed gaps; and no redundancies in course content. The findings of the mapping exercises should be used to improve the content provided to NCE Social Studies students at FCT College of Education Zuba, with the overall objective of enhancing the quality of the education provided to those students and helping them to be better students that are prepared for a successful career in Social Studies.

This dataset accompanies the systematic mapping review on Extended Reality in Early Childhood Education. It provides the underlying data collected and analyzed during the review of 121 peer-reviewed empirical studies related to the application of Extended Reality (XR) technologies—Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR)—in early childhood education.

The dataset includes two main components:

Data Extraction Sheet:This file contains the full list of included articles, each identified by a study code. For each study, relevant metadata and thematic notes are provided, including XR modality, country of study, educational stage, developmental domain, curricular area, and thematic trends. Studies are categorized according to key dimensions used in the review, such as participant demographics, research design, and reported outcomes.

Quality Assessment Sheet (MMAT):This file documents the methodological quality appraisal conducted using an adapted version of the Mixed Methods Appraisal Tool (MMAT). For each study, the scoring across all MMAT categories is shown, along with the total score and the final inclusion decision. This provides transparency regarding the quality filtering process applied during study selection.

The dataset is intended to support research transparency, replication, and future meta-analyses. It may be of interest to researchers, educators, and policymakers working in the fields of educational technology, early childhood education, and systematic evidence synthesis.

This web map displays the California Department of Education's (CDE) core set of geographic data layers. This content represents the authoritative source for all statewide public school site locations and school district service areas boundaries for the 2018-19 academic year. The map also includes school and district layers enriched with student demographic and performance information from the California Department of Education's data collections. These data elements add meaningful statistical and descriptive information that can be visualized and analyzed on a map and used to advance education research or inform decision making.

This layer shows education level for adults 25+. Counts broken down by sex. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized by the count of total adults (25+) and the percentage of adults (25+) who were not high school graduates. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B15002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Digital Map Of Education
This dataset falls under the category Traffic Generating Parameters Workplaces.
It contains the following data: Location of Public Schools in the City of Rio de Janeiro
This dataset was scouted on 2022-02-15 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://arcg.is/fy5LC0See URL for data access and license information.

The map of the sectorisation of the public colleges of Hauts-de-Seine allows you to quickly know the sector college associated with a section of road.

In the Versailles Academy, the Yvelines Department and the Val d’Oise Department benefit from a comprehensive mapping solution for managing and consulting the school sectorisation of colleges. This solution, aimed at families and partners, makes it possible to disseminate very widely reliable and accessible information on the sectorisation of colleges, competence of the Departments.

For the Directorate of Education, Citizenship and Colleges (DECC), the challenge of this project was to propose a similar scheme on Hauts-de-Seine within the framework of the Unified Directorate and thus replace the current solution with a complete mapping application. A finer geographical data based on the wireline of roads/streets, a real management mesh of school sectorisation, has been created to visualise and manipulate the data as closely as possible.

The Department’s school sectorisation solution is based on two separate modules:

• A school sector management module for in-house DECC;
• A module to consult the school map of colleges for the general public.

On the basis of the official sectorisation data provided by the DECC (stopped) and the wireline of streets from the IGN topographic database (BD Topo), the SIG and Open data teams, have made the sections of streets reliable in order to assign to each of them its connecting college. They then set up a management web mapping application based on the Department’s GIS solutions. Through it, the DECC can reliable the data and refine the school map according to the evolution of the territory: new college, new neighborhood, evolution of the student population, etc.

The interactive map for the general public exploits all the data thus reliable and refined. This school map of the public colleges of Hauts-de-Seine allows families to quickly and simply know the college of attachment of their children.

Special observations

The map is provided as an indication and only the deliberation voted by the Conseil Départemental des Hauts-de-Seine is enforceable. The information presented relates to the school year 2022-2023. The school map is updated each year according to the evolution of the sectorisations of the colleges.

Related links

Link to the general public web application of the school map School map of public colleges

Related data

Link to College Dataset Public and Private Colleges

This feature layer provides the educational attainment levels in the City of Tempe by census tract. The feature layer was created by clipping the ACS Educational Attainment Variables - Boundaries 2014-18, downloaded from Esri's Living Atlas, to the City of Tempe boundary layer.https://tempegov.maps.arcgis.com/home/item.html?id=84e3022a376e41feb4dd8addf25835a3

The Massachusetts Educator License Mapping Tool links educator licenses to courses. The current version of the mapping tool reflects several important changes from the 2018-19 pilot version, including:

• Educators with a moderate disabilities and/or severe disabilities license are considered "in-field" for all courses within the grade span and content areas for the license. (e.g. a teacher with a PreK-8 Moderate Disabilities license will be "in-field" when teaching a grade 5 math course but is considered "out-of-field" if teaching grade 4 music).
• Legacy licenses are appropriately reflected in the crosswalk.
• Some specific courses were updated with additional licensure fields deemed appropriate. (e.g. a teacher with a Business license will be "in-field" when teaching a marketing course).
• Updates to the courses that an Early Childhood or Elementary teacher is "in-field" for teaching (e.g. an Elementary teacher is not "in-field" when teaching Music).

Financial overview and grant giving statistics of Map Education Inc.

Additional file 4.

The global Curriculum Mapping Software market is projected to reach USD XX million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). Growing demand for educational software to enhance teaching and learning, increasing need for curriculum alignment, and government initiatives to improve educational standards are the key factors driving market growth. Additionally, the cloud-based deployment model is gaining popularity due to its scalability, cost-effectiveness, and ease of access. The competitive landscape of the Curriculum Mapping Software market is characterized by a mix of established and emerging players. Top players in the market include Top Hat, Kiddom, PlanbookEdu, LearnZillion, Eduphoria!, OnCourse Systems for Education, Skyward, LessonWriter, Workday, School Software Group, Leepfrog Technologies, and currIQūnet. Companies are focusing on strategic partnerships, new product launches, and technological advancements to gain a competitive edge. The market is segmented based on application (higher education institutions, K-12 schools, and others), deployment type (cloud-based and on-premise), and region (North America, Europe, Asia Pacific, Middle East & Africa, and South America). North America holds the largest market share, followed by Europe.

It has been identified that the first-year experience is crucial to student motivation and throughput of study programs, therefore it is interesting to look at the state of the art of computer science study programs in Norway. This data is part of a PhD project and relates to Study 1. In this study we present a survey and study of the number of undergraduate computer science programs in Norway and map their characteristics in order to gather an up to date overview of the selection of programs. Through a systematic review of all Norwegian undergraduate programs using data from national databases we have found that there are 12 institutions offering 56 different programs in Norway in 2018. The study showed that the characteristics of these programs vary, that is, the amount of computer science courses during the first year, the number of students, admission requirements, student satisfaction and time commitment. This article presents these findings along with an analysis of what characteristics impact the students’ contentment and learning experience.