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5,141 results for “Educators”
Additional data to the article "Educational Open Government Data in Germany"
<p>This dataset contains the raw data of the quantitative evaluation of German open government portals, conducted in March 2020 as well as the questions to the interviews we conducted with researchers from educational research.</p>
Theories and Principles of Bilingual Education
<p>This annotated bibliography discusses chapters from three books (<em>Foundations of Bilingual Education and Bilingualism, Immersion Education: International Perspectives, </em>and <em>Literacy and Bilingualism: A Handbook for All Teachers)</em> and an article entitled <em>Biliteracy, Empowerment, and Transformative Pedagogy.</em> The academic works discussed are mainly about theories and principles of bilingualism, bilingual education, and immersion education which relate to second/foreign language teaching in different contexts.</p>
Dataset for intercontinental assessment of the educational environmental project "IO-00118-17".
<p>Rams, S., Porcel de Peralta, M. & Odetti, H. (2020). <em>Dataset for intercontinental assessment of the educational environmental project "IO-00118-17".</em> DOI: 10.5281/zenodo.4295551</p>
General Education Teachers Use of Evidence-Based Practices in Autism Education- V1
<p>This dataset was developed to examine factors that impact teacher's use of EBPs with students with autism in mainstream schools in Ireland. </p>
Systematic Literature Review results: PRISMA Statement Phases for Education and Communication based on Circular Economy and Bioenergy Literature
<p>Results of each phase of the prisma statement of the flow Diagram.</p>
Analysis of the Causes of Workforce Shortages: Create Guidelines for Education and Workforce Development in Transportation to Generate Future Careers for the Navajo Nation
<p>Activities were dedicated to the collections, summarizations and categorizing more research articles related to the analysis of the sources of workforce deficiencies. The focus was on the technical and educational solution methods and strategies, guideline relevant to the workforce development. The times also were committed to the development of educational modules related to carrier’s opportunities in the transport industry into a couple of courses. Several NTU STEM students were interviewed, selected and introduced for the summer research internships in transportation areas to LSU Tran SET center. Outreach activities conducted with the community and some of the high school professionals related to the factors, which have the greatest impact on job choice decisions of students entering the workforce in transportation industries. In order to increase the community knowledge and awareness several intern students were hired and guided to do research about the impact of transport trades and jobs chances on communities in the Navajo Nation.</p>
SDG EDUCATION DATASET
<p>Dataset created in order to analyse the existence of documents around the Sustainable Development Goals in the scientific literature in Education produced since 2015</p>
Teacher Characteristics, Knowledge and Use of Evidence-Based Practices in Autism Education in Ireland_Dataset_V1
<p>Dataset to accompany the paper "<strong>Teacher Characteristics, Knowledge and Use of Evidence-Based Practices in Autism Education in Ireland". </strong>The purpose of this paper was to identify differences in teachers knowledge and use of evidence-based practices across different teacher characteristics. </p>
South African Open Data in Higher Education: Sources, resources and providers
<p>Spreadsheet of data sourced on South African sources, resources and providers of higher education open data. Composed through desk review as principle component of the situational analysis conducted for the 'Use of open data in the governance of South African higher education' research project, in the IDRC/WWWF 'Exploring Emerging Impacts of Open Data in the South' initiative.</p>
Interview data on provision and use of open data in South African higher education
<p>Data from interviews conducted with university planners, higher education studies researchers and the South African Department of Higher Education and Training (DHET). Data sourced for the 'Use of open data in the governance of South African higher education' research project, in the IDRC/WWWF 'Exploring Emerging Impacts of Open Data in the South' initiative.</p>
The Effects of Education on Students’ Perception of Modeling in Software Engineering
<p>The attached file accompanies the paper titled "The Effects of Education on Students’ Perception of Modeling in Software Engineering". This file contains both the raw data and summary data from the survey conducted at the three institutions, NAU, BGU, and Concordia.</p>
datasets bmc medical education
<p>Datasets of a research developed at Federal University of Viçosa, Brazil; it contains data from a survey on health-related quality of life, developed among medical students, at Federal University of Viçosa.</p> <p> </p>
Survey: Open Science in Higher Education
<p>Open Science in (Higher) Education – data of the February 2017 survey</p> <p>This data set contains: </p> <ul> <li>Full raw (anonymised) data set (completed responses) of Open Science in (Higher) Education February 2017 survey. Data are in xlsx and sav format.</li> <li>Survey questionnaires with variables and settings (German original and English translation) in pdf. The English questionnaire was not used in the February 2017 survey, but only serves as translation.</li> <li>Readme file (txt)</li> </ul> <p>Survey structure</p> <p>The survey includes 24 questions and its structure can be separated in five major themes: material used in courses (5), OER awareness, usage and development (6), collaborative tools used in courses (2), assessment and participation options (5), demographics (4). The last two questions include an open text questions about general issues on the topics and singular open education experiences, and a request on forwarding the respondent’s e-mail address for further questionings. The online survey was created with Limesurvey[1]. Several questions include filters, i.e. these questions were only shown if a participants did choose a specific answer beforehand ([n/a] in Excel file, [.] In SPSS). </p> <p>Demographic questions</p> <p>Demographic questions asked about the current position, the discipline, birth year and gender. The classification of research disciplines was adapted to general disciplines at German higher education institutions. As we wanted to have a broad classification, we summarised several disciplines and came up with the following list, including the option “other” for respondents who do not feel confident with the proposed classification:</p> <ul> <li>Natural Sciences</li> <li>Arts and Humanities or Social Sciences</li> <li>Economics</li> <li>Law</li> <li>Medicine</li> <li>Computer Sciences, Engineering, Technics</li> <li>Other</li> </ul> <p>The current job position classification was also chosen according to common positions in Germany, including positions with a teaching responsibility at higher education institutions. Here, we also included the option “other” for respondents who do not feel confident with the proposed classification:</p> <ul> <li>Professor</li> <li>Special education teacher</li> <li>Academic/scientific assistant or research fellow (research and teaching)</li> <li>Academic staff (teaching)</li> <li>Student assistant</li> <li>Other</li> </ul> <p>We chose to have a free text (numerical) for asking about a respondent’s year of birth because we did not want to pre-classify respondents’ age intervals. It leaves us options to have different analysis on answers and possible correlations to the respondents’ age. Asking about the country was left out as the survey was designed for academics in Germany.</p> <p>Remark on OER question</p> <p>Data from earlier surveys revealed that academics suffer confusion about the proper definition of OER<sup><sup>[2]</sup></sup>. Some seem to understand OER as free resources, or only refer to open source software (Allen & Seaman, 2016, p. 11). Allen and Seaman (2016) decided to give a broad explanation of OER, avoiding details to not tempt the participant to claim “aware”. Thus, there is a danger of having a bias when giving an explanation. We decided not to give an explanation, but keep this question simple. We assume that either someone knows about OER or not. If they had not heard of the term before, they do not probably use OER (at least not consciously) or create them. </p> <p>Data collection</p> <p>The target group of the survey was academics at German institutions of higher education, mainly universities and universities of applied sciences. To reach them we sent the survey to diverse institutional-intern and extern mailing lists and via personal contacts. Included lists were discipline-based lists, lists deriving from higher education and higher education didactic communities as well as lists from open science and OER communities. Additionally, personal e-mails were sent to presidents and contact persons from those communities, and Twitter was used to spread the survey.</p> <p>The survey was online from Feb 6<sup>th</sup> to March 3<sup>rd </sup>2017, e-mails were mainly sent at the beginning and around mid-term. </p> <p>Data clearance</p> <p>We got 360 responses, whereof Limesurvey counted 208 completes and 152 incompletes. Two responses were marked as incomplete, but after checking them turned out to be complete, and we added them to the complete responses dataset. Thus, this data set includes 210 complete responses. From those 150 incomplete responses, 58 respondents did not answer 1st question, 40 respondents discontinued after 1st question. Data shows a constant decline in response answers, we did not detect any striking survey question with a high dropout rate. We deleted incomplete responses and they are not in this data set.</p> <p>Due to data privacy reasons, we deleted seven variables automatically assigned by Limesurvey: submitdate, lastpage, startlanguage, startdate, datestamp, ipaddr, refurl. We also deleted answers to question N<sup>o</sup> 24 (email address).</p> <p>References</p> <p>Allen, E., & Seaman, J. (2016). <em>Opening the Textbook: Educational Resources in U.S. Higher Education, 2015-16.</em></p> <p> </p> <p>First results of the survey are presented in the poster:</p> <p>Heck, Tamara, Blümel, Ina, Heller, Lambert, Mazarakis, Athanasios, Peters, Isabella, Scherp, Ansgar, & Weisel, Luzian. (2017). Survey: Open Science in Higher Education. Zenodo. http://doi.org/10.5281/zenodo.400561</p> <p>Contact:</p> <p>Open Science in (Higher) Education working group, see http://www.leibniz-science20.de/forschung/projekte/laufende-projekte/open-science-in-higher-education/.</p> <p> </p> <p>[1] https://www.limesurvey.org</p> <p>[2] The survey question about the awareness of OER gave a broad explanation, avoiding details to not tempt the participant to claim “aware”. </p>
The Jalakeli Project: Women and Education, and the Role of the House of Narasingh
<p>An interview with Thoidingjam Lakshmipriya Devi and Huidrom Anuradha Devi. daughters of Maharaj Kumari Angousana Devi, of the Shree Shree Govinda Jiu Jalakeli Pala of Manipur.</p> <p>On getting their education, the establishment of Dhanamanjuri University in Manipur by their grandmother Maharani Dhanamanjuri Devi; their education and teachers at Tamphasana Girls High School founded by their aunt Maharaj Kumari Tamphasana Devi; and the founding of Tombisana Boys High School by their second aunt Maharaj Kumari Tombisana Devi.</p> <p>Interview by L. Somi Roy.<br> Recorded by Rajeev Rajkumar</p> <p>The Jalakeli Project</p> <p>Manipur Women's Oral Histories</p> <p>Imasi Foundation 2017</p>
Education- and age-specific fertility rates for 50 African and Latin American countries between 1970 and 2020
<p><strong>Version 2</strong></p> <p>Education- and age-specific fertility rates for 50 African and Latin American countries between 1970 and 2020 as published in<strong> <a href="https://www.demographic-research.org/articles/volume/49/31/">Durowaa-Boateng et. al (2023</a></strong><a href="10.4054/DemRes.2023.49.31">)</a>. </p> <p>The data and figures can be accessed and downloaded from <strong><a href="https://populationafrica.org/">https://populationafrica.org/</a>.</strong></p> <p> </p> <p><strong>Education- and age-specific fertility rates for 50 African and Latin American countries between 1970 and 2020</strong>.</p> <p>The fertility rates are consistent with the United Nation's World Population Prospects (UN WPP) 2022 fertility rates.</p> <p>The Bayesian model developed to reconstruct the fertility rates using Demographic and Health Surveys and the UN WPP is published in a <a href="https://pure.iiasa.ac.at/id/eprint/18890/">working paper</a>.</p> <p><strong>Abstract:</strong></p> <p>Consistent and reliable time series of education- and age-specific fertility rates for the past are difficult to obtain in developing countries, although they are needed to evaluate the impact of women’s education on fertility along periods and cohorts. In this paper, we propose a Bayesian framework to reconstruct age-specific fertility rates by level of education using prior information from the birth history module of the Demographic and Health Surveys (DHS) and the UN World Population Prospects. In our case study regions, we reconstruct age- and education-specific fertility rates which are consistent with the UN age specific fertility rates by four levels of education for 50 African and Latin American countries from 1970 to 2020 in five-year steps. Our results show that the Bayesian approach allows for estimating reliable education- and age-specific fertility rates using multiple rounds of the DHS surveys. The time series obtained confirm the main findings of the literature on fertility trends, and age and education specific differentials.</p> <p><strong>Funding:</strong></p> <p>These data sets are part of the <a href="https://www.oeaw.ac.at/vid/research/research-projects/bayesedu">BayesEdu Project </a>at Wittgenstein Centre for Demography and Global Human Capital (IIASA, OeAW, University of Vienna) funded from the “Innovation Fund Research, Science and Society” by the Austrian Academy of Sciences (ÖAW).</p> <p><strong>Variables:</strong></p> <p>Country: Country names</p> <p>Education: Four education levels, No Education, Primary Education, Secondary Education and Higher Education.</p> <p>Age group: Five-year age groups between 15-19 and 45-49. </p> <p>Year: Five-year periods between 1970 and 2020.</p> <p>Median: Median education and age-specific fertility rate estimate </p> <p>Upper_CI: 95% Upper Credible Interval</p> <p>Lower_CI: 95% Lower Credible Interval</p> <p> </p> <p><strong>List of countries:</strong></p> <table> <tbody> <tr> <td> <p>Angola</p> </td> </tr> <tr> <td> <p>Benin</p> </td> </tr> <tr> <td> <p>Brazil</p> </td> </tr> <tr> <td> <p>Burkina Faso</p> </td> </tr> <tr> <td> <p>Burundi</p> </td> </tr> <tr> <td> <p>Cameroon</p> </td> </tr> <tr> <td> <p>Central African Republic</p> </td> </tr> <tr> <td> <p>Chad</p> </td> </tr> <tr> <td> <p>Colombia</p> </td> </tr> <tr> <td> <p>Comoros</p> </td> </tr> <tr> <td> <p>Congo</p> </td> </tr> <tr> <td> <p>Côte D'Ivoire</p> </td> </tr> <tr> <td> <p>DR Congo</p> </td> </tr> <tr> <td> <p>Ecuador</p> </td> </tr> <tr> <td> <p>Egypt</p> </td> </tr> <tr> <td> <p>Eswatini</p> </td> </tr> <tr> <td> <p>Ethiopia</p> </td> </tr> <tr> <td> <p>Gabon</p> </td> </tr> <tr> <td> <p>Gambia</p> </td> </tr> <tr> <td> <p>Ghana</p> </td> </tr> <tr> <td> <p>Guatemala</p> </td> </tr> <tr> <td> <p>Guinea</p> </td> </tr> <tr> <td> <p>Honduras</p> </td> </tr> <tr> <td> <p>Kenya</p> </td> </tr> <tr> <td> <p>Lesotho</p> </td> </tr> <tr> <td> <p>Liberia</p> </td> </tr> <tr> <td> <p>Madagascar</p> </td> </tr> <tr> <td> <p>Malawi</p> </td> </tr> <tr> <td> <p>Mali</p> </td> </tr> <tr> <td> <p>Mexico</p> </td> </tr> <tr> <td> <p>Morocco</p> </td> </tr> <tr> <td> <p>Mozambique</p> </td> </tr> <tr> <td> <p>Namibia</p> </td> </tr> <tr> <td> <p>Nicaragua</p> </td> </tr> <tr> <td> <p>Niger</p> </td> </tr> <tr> <td> <p>Nigeria</p> </td> </tr> <tr> <td> <p>Paraguay</p> </td> </tr> <tr> <td> <p>Peru</p> </td> </tr> <tr> <td> <p>Rwanda</p> </td> </tr> <tr> <td> <p>Sao Tome and Principe</p> </td> </tr> <tr> <td> <p>Senegal</p> </td> </tr> <tr> <td> <p>Sierra Leone</p> </td> </tr> <tr> <td> <p>South Africa</p> </td> </tr> <tr> <td> <p>Sudan</p> </td> </tr> <tr> <td> <p>Tanzania</p> </td> </tr> <tr> <td> <p>Togo</p> </td> </tr> <tr> <td> <p>Tunisia</p> </td> </tr> <tr> <td> <p>Uganda</p> </td> </tr> <tr> <td> <p>Zambia</p> </td> </tr> <tr> <td> <p>Zimbabwe</p> </td> </tr> </tbody> </table>
Educator Perceptions of DevOps Teaching Recommendations and Their Alignment with Common Challenges
<p>DevOps education presents unique pedagogical challenges due to the diversity of tools, rapid technological change, and the multidisciplinary nature of the field. Although previous work has proposed recommendations to address these challenges, it is unclear how educators perceive these recommendations and whether they align with the challenges encountered in practice. In this paper, we present a mixed methods study involving 11 DevOps educators who interacted with Improve, a tool that presents a curated set of educational challenges and recommendations derived from previous literature. Educators indicated which recommendations they already use, which they intend to use, and which challenges they experience and are motivated to address. Our findings show that 22.6% of the recommendations were new to educators and considered potentially useful, while 59.2% were already in use. Additionally, 66.3% of the challenges were considered relevant, with most of them having linked recommendations that educators were already using or willing to adopt. This study provides empirical insights into the perceived usefulness of existing recommendations, identifies gaps in challenge-recommendation mappings, and supports future efforts to design and disseminate more targeted educational guidance for DevOps teaching.</p>
CONSORT flow diagram for The assessment of educational and supportive care to the infertile females undergoes In Vitro Fertilization procedure by clinical pharmacist: a randomized clinical trial
<p><strong>The assessment of educational and supportive care to the infertile females undergoes In Vitro Fertilization procedure by </strong>a <strong>clinical pharmacist: a randomized clinical trial</strong>.</p>
Youth, the international labour unions, water ethics and education. (Spanish)
<p>Presentation made at Panel 5, the Educational, Ethical, and Preservationist Water Question, The Youth and the Climatic Struggle, First International Forum of the Federal University of Pernambuco (UFPE), in Preparation for the 2025 UNCOP30, 21-24 November 2023 Recife, Pernambuco, Brazil.</p>
Youth, Science, education and struggles to preserve fragile ecosystems in South America
<p>Presentation made at Panel 5, the Educational, Ethical, and Preservationist Water Question, The Youth and the Climatic Struggle, First International Forum of the Federal University of Pernambuco (UFPE), in Preparation for the 2025 UNCOP30, 21-24 November 2023 Recife, Pernambuco, Brazil.</p>
Water ethics, education and preservation(in Spanish)
<p>Presentation made at Panel 5, the Educational, Ethical, and Preservationist Water Question, The Youth and the Climatic Struggle, First International Forum of the Federal University of Pernambuco (UFPE), in Preparation for the 2025 UNCOP30, 21-24 November 2023 Recife, Pernambuco, Brazil.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.