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5,141 results for “Educators”
Unfair Inequality in Education: A Benchmark for AI-Fairness Research (Aequitas WP7 Use Case S2)
<h1>Unfair Inequality in Education: A Benchmark for AI-Fairness Research</h1> <p>This dataset proposes a novel benchmark specifically designed for AI fairness research in education. It can be used for challenging tasks aimed at improving students' performance and reducing dropout rates which are also discussed in the paper to emphasize significant research directions. By prioritizing fairness, this benchmark aims to foster the development of bias-free AI solutions, promoting equal educational access and outcomes for all students.</p> <h2>Structure</h2> <p><code>benchmark</code> contains:</p> <ul> <li>the proposed dataset (<code>dataset.csv</code>), </li> <li>the mask for dealing with missing values (<code>missing_mask.csv</code>), and</li> <li> <div> <div>the meta-columns providing grouping criteria and sample weights for each student (<code>meta_cols.csv</code>).</div> </div> </li> </ul> <p><code>raw_data</code> includes:</p> <ul> <li>the original dataset (<code>original.csv</code>), and</li> <li>the intermediate stages of the pre-processing and validation pipelines (<code>split</code>, <code>pre_processed</code>, and <code>validation</code>).</li> </ul> <p><code>res</code> contains the documentation, including:</p> <ul> <li>the transformation mapping each column of the original dataset to the proposed one, along with the missingness category and original text (<code>meta_data_mapping.csv</code>),</li> <li>the value type and domains of each column of the proposed datasets (<code>meta_data_stats.json</code>), and</li> <li> <div> <div>the statistical indices of the validation pipeline (<code>bias_preservation_results.json</code>).</div> </div> </li> </ul> <p><code>src</code> contains the source code for running the pre-processing and corresponding analysis:</p> <ul> <li><code>pre_processing</code> and <code>stats</code>contain the code for the two corresponding tasks, and</li> <li><code>pre_processing.py</code> and <code>split.py</code> are two entry points.</li> </ul> <p>Finally, <code>Dockerfile</code> and <code>requirements.txt</code> set up the environment for running the applications across multiple platforms and with Python, respectively.</p>
Dataset for Review and Agenda of Digital Forensics Education and Training
<p>This repository has four .bib files encompassing 49 primary study entries, and one .CSV file with data extracted from such studies.</p> <p><strong>The authors gratefully acknowledge the support of the Technology in Forensic Sciences project (Instituto Nacional de Ciência e Tecnologia em Ciências Forenses - \textbf{INCT Forense}, Grant \#465450/2014-8) for funding this work. </strong></p> <p><strong>We also thank the \textbf{Araucária Funding Agency of Paraná} for their financial and institutional support, as well as \textbf{NAPI - Public Security and Forensic Science} for research funding (Grant \#22.632.926-9). </strong></p> <p><strong>This work is also supported by CAPES Pro-Defesa (Grant \# V3084362P).</strong></p> <p><strong>Avelino Zorzo thanks \textbf{CNPq/Brazil} Grant \#306250/2021-7. </strong></p> <p><strong>Edson OliveiraJr thanks \textbf{CNPq/Brazil} Grant \#311503/2022-5. </strong></p>
Transforming Higher Education Institutions through EDI Leadership: A Bibliometric Exploration
<p>Bibliometric data for article titled "Transforming Higher Education Institutions through EDI Leadership: A Bibliometric Exploration"</p>
EduQuick: A Dataset for Assessing Summarization of Informal Educational Content for Social Media
<p>The presented dataset is a curated collection of model-generated text for educational TikTok content, abbreviated as EduQuick. This dataset is the result of evaluating and selecting high-quality content generated by the GPT-4 model following an empirical study. It aims to provide engaging and informative summaries suitable for TikTok's educational audience.</p>
PROCRAFT Final Meeting - Educational activities on WWII aircraft at the Montreal Aviation Museum by Jean Desbiens – Montreal Museum
Open the record for dataset details and reuse information.
Unveiling Hurdles in Software Engineering Education: The Role of Learning Management Systems
<p>Learning management systems (LMSs) are established tools in higher education, especially in the field of software engineering (SE). The onset of the COVID-19 pandemic further amplified the utilization of these systems, which necessitated their integration into educational curricula for both lecturers and students. However, adopting LMSs within SE education has presented distinctive challenges impeding their seamless incorporation into the courses. This paper aims to scrutinize the challenges and requirements encountered by professors, lecturers, and students in the domain of SE education when using LMSs. We conducted an empirical study that included (i) a survey with 47 professors/lecturers and 133 students, (ii) an analysis of the ensuing data, and (iii) 18 additional interviews conducted with professors and lecturers to delve into nuanced variations in viewpoints. The findings derived from our study reveal that the challenges and requirements pertaining to LMSs are rather specific depending on the scope and size of the respective courses. Nevertheless, many participants have a consensus on numerous challenges and requirements for improving certain features of LMSs in order to improve their usage in SE education. The findings are valuable for advancing research and development in the field of LMSs and provide guidance for lecturers in SE education.</p>
Dataset used in the publication "Using of Transformers Models for Text Classification to Mobile Educational Applications"
<p>Dataset used in the publication "Using of Transformers Models for Text Classification to Mobile Educational Applications".</p> <p>More info about the dataset can be found in the published article.</p>
(Processed data) How do Spanish educational researchers use X's platform to promote the dissemination of scientific knowledge: a descriptive study: a descriptive study
<p>Processed data used in the article 'How do Spanish educational researchers use X's platform to promote the dissemination of scientific knowledge: a descriptive study', from the project Comscienciaeduspain (FCT-20-15761), executed with the collaboration of the Spanish Foundation for Science and Technology – Ministry of Science and Innovation.</p>
Dimensions of Education March, 2024
Open the record for dataset details and reuse information.
UK04 - Educational - Southampton (United Kingdom)
<p>Data files for building: UK04 - Educational - Southampton (United Kingdom)</p> <p>Languages: English</p> <p>These files are part of the public benchmark repository created as a part of the crossCert EU project. </p> <p>This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.</p> <p>The files are organised in the following folders (note that not all files are always provided):</p> <ol> <li>Main data and Results, with: <ol> <li>Neutral data inventory.</li> <li>Neutral results report.</li> <li>Original EPC certificate.</li> </ol> </li> <li>Energy Consumption Data, with: <ol> <li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li> </ol> </li> <li>Drawings <ol> <li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models.</li> </ol> </li> <li>Other Data <ol> <li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li> </ol> </li> <li>Dynamic Model <ol> <li>Data to run a dynamic model of the building, if available.</li> </ol> </li> </ol> <p>The files have been redacted to exclude confidential information. </p>
Students' perceptions of an environmental education program.
<p>This database contains 185 records. It was developed using a questionnaire to evaluate students' perceptions of an environmental educommunication program. The variables measured are:</p> <p>1 Gender: 1 male, 2 female, 3 non-binary</p> <p>2 Age: 12 to 17</p> <p>3 Type of school. 1: private, 2: public, 3: state-funded private school</p> <p>4 -11: measured using the following scale: </p> <p> 5 totally agree</p> <p> 4 agree</p> <p> 3 neither agree nor disagree</p> <p> 2 disagree</p> <p> 1 strongly disagree</p>
Replication Data for: The colonial legacy of education: Evidence from Tunisia
<p>This repository contains replication data for Ben Salah et al. (2024), "The colonial legacy of education: Evidence from Tunisia" (<em>Journal of Comparative Economics</em>, <a href="https://doi.org/10.1016/j.jce.2024.09.002" target="_blank" rel="noopener">https://doi.org/10.1016/j.jce.2024.09.002</a>). All non-confidential data inputs are included, as well as final data outputs, and a pdf file containing the final tables and figures for all main text and supplementary tables and figures.</p> <p>The manuscript and supplementary information are available at the JCE, <a href="https://doi.org/10.1016/j.jce.2024.09.002">here</a>.</p>
Dataset for an article "Developing best practices for conducting STEM engineering projects in higher education"
<p>These articles are part of the dataset used in the article. </p> <p>These articles are anonymized manuscript drafts that were not published in the journal with other articles.</p> <p>Read more from LUMAT-B:<span> <a href="https://urn.fi/urn:nbn:fi:hulib:editori:lumatb.v9i17"> https://urn.fi/urn:nbn:fi:hulib:editori:lumatb.v9i17 </a> </span></p>
How Creatively Are We Teaching and Assessing Creativity in Computing Education: A Systematic Literature Review
<p>Data from:</p> <p>Wouter Groeneveld, Brett A. Becker, and Joost Vennekens. 2022. How Creatively Are We Teaching and Assessing Creativity in Computing Education: A Systematic Literature Review. In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2022),March 3–5, 2022, Providence, RI, USA. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3478431.349936</p> <p><strong>When referring to this dataset, please cite the above article. That contains the DOI of this dataset. Please do not cite this dataset directly without citing the article.</strong></p>
Supporting data for the AI education publication statistics in "An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates"
<p>Supporting data for the AI education publication statistics presented in the paper "An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates" to be published at the Twelfth AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-22). The data was used to plot the figure showing the cumulative number of publications from 1976 to 2020 relating to AI education.</p>
Data set on the Higher-Education Pact in Germany, 2007 - 2020
\begin{tabular}{@{\extracolsep{5pt}} ll} \\[-1.8ex]\hline \hline \\[-1.8ex] Variable & Description \\ \hline \\[-1.8ex] HASC\_1 & State name \\ Nb & State number \\ ccluster & 1 = Western Territorial State, 2 = Eastern State, \\ & 3 = City State \\ Ltype & 1 = Western Territorial State, 2 = Eastern State. \\ ZstA.0717 & Additional entrants (sum 2007 – 2017) to 2005-value \\ zStA.U0717 & Additional entrants a traditional universities \\ zStA.F0717 & Additional entrants a UAS \\ zStA.Fratio0717 & Share of entrants at UAS to total entrants. \\ Ant.StA05 & Enrollment rate 2005 \\ Ant.StA18 & Enrollment rate 2018 \\ RelStVZA05 & Adivsing relationship 2005 \\ RelStVZA18 & Adivsing relationship 2018 \\ GW05 & Share of graduates in the humanities 2005 \\ GW18 & Share of graduates in the humanities 2018 \\ NaWi05 & share of graduates in the natural sciences 2005 \\ NaWi18 & share of graduates in the natural sciences 2018 \\ SRW05 & share of graduates in the social sciences 2005 \\ SRW18 & share of graduates in the social sciences 2018 \\ WandS05 & migration balance 2005 \\ WandS18 & migration balance 2017 \\ \hline \\[-1.8ex] BUMI0717 & federal funds received by the HSP-programme \\ & (sum 2007 – 2018) \\ BUMIzStA0717 & BUMI0717 per additional entrant \\ Einw18 & population size 2018 \\ BUMI.GWP16 & federal funds covering all federal grants-in-aid \\ & in the higher-education field \\ STUD18 & number of students in 2018 \\ zstaeinw & additional entrants per capita \\ deltaantsta & relative change in the enrollment rate \\ deltabetrrel & relative change in the adising relationship \\ deltagw & relative change in the humanities \\ deltanw & relative change in the natural sciences \\ deltasrw & relative change in the social sciences \\ bumieinw & Federal funds (HSP) per capita \\ bumigwp16einw & Overall federal funds per capita \\ bumistud & Federal funds (HSP) per student \\ Prof05 & Number of professors 2005 \\ Prof17 & Number of professors 2017 \\ \hline \\[-1.8ex] \end{tabular}
Dataset related to article "How Academics and the Public Experienced Immersive Virtual Reality for Geo-education"
<p>The dataset is associated with the paper entitled: How Academics and the Public Experienced Immersive Virtual Reality for Geo-education.</p> <p>It contains feedback regarding users’ experience with Immersive Virtual Reality for geological exploration, through a tailored approach developed by Tibaldi et al. (2020) where the Virtual Landscape is based on 3D photogrammetry-based high-resolution models.</p> <p>Such feedback has been acquired through anonymous questionnaires during nine dissemination events held in 2018 and 2019 in various locations (Vienna in Austria, Milan and Catania in Italy and Santorini in Greece), in the framework of the following projects: i) the MIUR project ACPR15T4_00098–Argo3D (http://argo3d.unimib.it/); ii) 3DTeLC Erasmus+Project 2017-1-UK01-KA203-036719 (<a href="http://www.3dtelc.com">http://www.3dtelc.com</a>); iii) EGU 2018 Public Engagement Grant (https://www.egu.eu/outreach/peg/) .</p> <p>In the dataset, feedback has been grouped into categories, based on users age and background:</p> <p>i) Middle and High School Students (Schools students, results in Sheet 1);</p> <p>ii) MSc Students in Earth Sciences (MSc, results in Sheet 2);</p> <p>iii) Academics/Researchers in Earth Sciences, that include PhD students and postdocs (Academics, results in Sheet 3);</p> <p>iv) Lay Public (i.e. participants that do not belong to the other groups, results in Sheet 4).</p> <p>It lists a total of 459 records; further details are available in the manuscript.</p> <p>If you use this dataset, please do cite the following papers:</p> <p>Bonali et al., How Academics and the Public Experienced Immersive Virtual Reality for Geo-education. Geosciences.</p> <p>Tibaldi, A.; Bonali, F.L.; Vitello, F.; Delage, E.; Nomikou, P.; Antoniou, V.; Becciani, U.; Van Wyk de Vries, B.; Krokos, M.; Whitworth, M. Real world–based immersive Virtual Reality for research, teaching and communication in volcanology. Bull. Volcanol. 2020, 82, 1–12.</p> <p> </p> <p> </p> <p> </p>
Final project report: The Side Effects of Forced Online Distance Education (FODE)
<p>The outbreak of COVID-19 forced most universities into distance education. Three didacticians and researchers from the University of Maribor, Slovenia: Kosta Dolenc, Mateja Ploj Virtič and Andrej Šorgo formed a self-initiated initiative project group during the COVID-19 epidemic and started the project with the working title: The Side Effects of Forced Online Distance Education (FODE). The aim of the project, was to investigate the response of university teachers and students to the new situation.</p>
Sexually antagonistic selection on educational attainment and body size in Estonian children
<p>Natural selection is a key mechanism of evolution, which results from the differential reproduction of phenotypes. We describe fecundity selection at different parity transitions on 15 anthropometric traits and educational attainment in Estonian children, who were born between 1938 and 1962 and measured at around 13 years of age (Juhan Aul’s database). The sample sizes reach up to 7000 in boys and 10 000 in girls. The direction of selection on educational attainment and bodily traits was sexually antagonistic, and it occurred via different parity transitions in boys and girls. Compared to boys with primary education, obtaining tertiary education was associated with 3.5 times and secondary education two times higher odds of becoming a father. Transition to motherhood was not related to educational attainment, while education above primary was associated with lower odds (OR = 0.5 – 0.7) to progression to parities above one and two. Selection on anthropometric traits occurred almost exclusively via childlessness in boys, while among the girls, most of the traits that were associated with becoming a mother were additionally associated with a transition from one child to higher parities. Male (but not female) fitness was thus primarily determined by traits related to mating success. Selection favoured stronger and larger boys and smaller girls. Selection on girls favoured some traits that associate with perceived femininity while other feminine traits were selected against.</p> <p>This record includes the data and R code to reproduce the statistical analyses the results are based on. </p> <p> </p>
Medical Education Journal Data and Supplemental Files (2000 - 2020)
<p>This is the supplemental data, figures, and tables for <em>The Voices of Medical Education Science: Describing the Landscape</em>. This also includes the author thesaurus and institution thesaurus with supporting read me files. </p> <p><strong>Abstract</strong> </p> <p>Introduction</p> <p>Medical education has been described as a dynamic and growing field, driven in part by its unique body of scholarship. The voices of authors who publish medical education literature have a powerful impact on the discourses of the community. While there have been numerous studies looking at aspects of this literature, there has been no comprehensive view of recent publications.</p> <p>Method</p> <p>The authors conducted a bibliometric analysis of all articles published in 24 medical education journals published between 2000-2020 to identify article characteristics, with an emphasis on author gender, geographic location, and institutional affiliation. This study replicates and greatly expands on two previous investigations by examining all articles published in these core medical education journals. </p> <p>Results </p> <p>The journals published 37,263 articles with the most articles published in 2020 (n=3,957, 10.7%) and least in 2000 (n=711, 1.9%) representing a 456.5% increase. The articles were authored by 139,325 authors of which 62,708 were unique. Men were more prevalent across all authorship positions (n=62,828; 55.7%) than women (n=49,975; 44.3%). Authors listed 154 country affiliations with the United States (n=42,236, 40.4%), United Kingdom (n=12,967, 12.4%), and Canada (n=10,481, 10.0%) most represented. Ninety-three countries (60.4%) were low- or middle-income countries accounting for 9,684 (9.3%) author positions. Few articles were written by multinational teams (n=3,765; 16.2%). Authors listed affiliations with 4,372 unique institutions. Across all author positions, 48,189 authors (46.1%) were affiliated with institutions ranked globally as Top 200 institutions by the Times Higher Education ranking. </p> <p>Discussion </p> <p>There is a relative imbalance of author voices in medical education. If the field values a diversity of perspectives, there is considerable opportunity for improvement.</p>
ScienceDex guides
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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)
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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.