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5,141 results for “Educators”
Database for A Review of Collaboration Through Intercultural Competencies in Higher Education
<p><strong>Database used for the study: "A Review of Collaboration Through Intercultural Competencies in Higher Education"</strong></p>
SRL data feedback in higher education and validation of microlearning proposal
<p>This dataset corresponds to those used for the SRL in the category Feedback. It also presents the data collected from a process of expert validation of a training proposal based on microlearning for the development of digital teacher competence related to feedback. </p>
Data for study "Digital tools for innovative higher education teaching - a Scoping Review of Empirical Studies".
<p>The goal of this research is to point out, by a scoping review of the papers published between 2020 and 2023, the digital tools used in the teaching process at the higher education level. The dataset was collected using Web of Science and Scopus databases.</p> <p>The dataset contains the following elements:</p> <ol> <li>paper details (Authors, Publication Year, Article Title, DOI),</li> <li>OECD classification,</li> <li>teaching tool assigned to paper.</li> </ol>
Thoughts Dr.B.R Ambedkar on Women's Education
<p><strong>National webinar</strong> On <strong>Thoughts Dr.B.R Ambedkar on Women's Education</strong> on 16 April 2021 by, Shikshn Shastra Mahila Mahavidyalya Akkalkuwa Distt. Nandurbar (M.S.)</p>
Maturity models for education 4.0 towards Society 5.0: Systematic Literature Review
<p>DataSet</p>
Associated data underlying the article "OPEN GOVERNMENT DATA (OGD) IN EUROPEAN EDUCATIONAL PROGRAMS CURRICULUM CURRENT STATE AND PROSPECTS"
<p>Associated data underlying the article</p> <p>Rizun, Nina; Ciesielska, Magdalena; Alexopoulos, Charalampos; Saxena,Stuti; Papageorgiou, Georgios International Conference on Open Data (ICOD 2022): Book of abstracts, Faculty of Law, University of Zagreb, Zagreb, Croatia, pp. 26-29 (978-953-270-167-8).</p> <pre><a href="https://doi.org/10.5281/zenodo.8069910">https://doi.org/10.5281/zenodo.8069910</a></pre> <p>It is everybody’s knowledge now that Open Government Data (OGD) pertains to the availability of datasets pertaining to government operations and functioning via license-free formats (Afful-Dadzie and Afful-Dadzie, 2017) and these datasets are linked with different themes contingent upon the area of administration like energy, education, climate, tourism, environment, infrastructure, etc. (Ubaldi, 2013). Governments have been benchmarking their OGD initiatives across standard indices like ODIN, OKFN, Open Data Barometer, etc. (Lnenicka et al., 2022; Lnenicka and Nikiforova, 2021) and ample research exists on assessing the quality of OGD portals from the demand (i.e. the governments’ efforts at maintaining the quality of datasets) and supply (i.e. the perceptions of users regarding the quality of datasets) side (Crusoe et al., 2019; de Souza et al., 2022; Islam et al., 2021; Kaasenbrood et al., 2015; Khurshid et al., 2022; Lnenicka et al., 2022; Purwanto et al., 2020; Saxena and Janssen, 2017; Shehata and Elgllab, 2021; Talukder et al., 2019; Weerakkody et al., 2017; Wirtz et al., 2016; Wirtz et al., 2018; Wirtz et al., 2019; Zuiderwijk et al., 2015). Given the magnitude of academic interest on OGD- especially in the last 10 years- it remains to be assessed as to how far has the domain progressed in the academic environs and surprisingly, no research has been conducted to elucidate the infusion of this very significant domain- that is relatable to the extent to which the governments are forwarding their claims regarding the furtherance of transparent and corruption-free administration apart from bolstering citizen participation, collaboration and trust (Gil-Garcia et al., 2020; Hellberg and Hedstrom, 2015) besides serving as a means for value creation and innovation by a range of stakeholders (Jetzek et al., 2012; Jetzek et al., 2014) - in the diverse platforms that are meant for furthering academic dialogue and discussion.</p>
Dataset for the paper "How to Work on Equality and Inclusion when Introducing Computational Thinking and Educational Robotics in Early Childhood Education: A Systematic Review"
<p>Resources for the Systematic Literature Review (SLR) about Computational Thinking and Educational Robotics in Early Childhood Education for fostering equality and inclusion. The SLR is related to the project "COEDUIN-Alfabetización digital y STEAM en edades tempranas: propuesta co-educativa inclusiva" funded by Fundación Caja Canarias and Fundación La Caixa (ref. 2020EDU08).</p> <p>The SLR covers papers in WoS and Scopus from 2011 to 2022.</p>
Accelerating medical education with ChatGPT: An implementation guide
<p>This dataset is a collection of prompt examples of how ChatGPT has been used to accelerate medical education. </p>
Photovoice and case based learning activities in higher education (University of Zaragoza - Social Work degree)
<p>The collected variables are: students data (gender, age, university admittance marks from 0 to 14), academic perforrnace of photovoice (PV) activity (from 0 to 10), academic performance of Case based learning (CBL) activity (form 0 to 10), order of the exercises, satisfaction with the photovoice activity, satisfaction with the CBL activity, items and overall self-efficacy scale score</p>
Predicting educators' curiosity towards VR technologies
<p>Dataset about the curiosity of learning more about VR technology usage in education..</p>
Cognitive development among children in a low-income setting: cost-effectiveness analysis of a maternal nutrition education intervention in rural Uganda
<p><span>Inadequate nutrition and insufficient stimulation in early childhood can lead to long-term deficits in cognitive and social development. Evidence for policy and decision-making regarding the cost of delivering nutrition education is lacking in low and middle-income countries (LMIC). In rural Uganda, we conducted a cluster-randomized controlled trial (RCT) examining the effect of a maternal nutrition education intervention on developmental outcomes among children aged 6–8 months. This intervention led to significantly improved cognitive scores when the children reached the age of 20–24 months. When considering the potential for this intervention's future implementation, the desired effects should be weighed against the increased costs.</span><span> This study therefore aimed to assess the cost-effectiveness of this education intervention compared with current practice.</span><span> Health outcome data were based on the RCT. Cost data were collected via interviews with researchers involved in processing the intervention. This study considered a healthcare provider perspective for an 18-month time horizon.</span><span> The control group was considered as the current practice for the future large-scale implementation of this intervention. </span><span>A cost-effectiveness analysis was performed, including calculations of incremental cost-effectiveness ratios (ICERs). In addition, uncertainty in the results was characterized using one-way and probabilistic sensitivity analyses. The ICER for the education intervention compared with current practice was USD ($) 16.50 per cognitive composite score gained, with an incremental cost of </span><span>$265.79 and an incremental cognitive composite score of 16.11. The sensitivity analyses indicated the robustness of these results. The ICER was sensitive to changes in cognitive composite score and the cost of personnel, but the education intervention can be considered cost-effective compared with the current practice. The outcome of this study, including the cost analysis, health outcome, cost-effectiveness, and sensitivity analysis, can be useful to inform policymakers and stakeholders about effective resource allocation processes in Uganda and possibly other LMIC.</span></p>
DISCO step 1 - teacher education
<p>Data was collected among pre-service teachers in the fall of 2017 in a sample of 7 teacher education institutions within a Flemish province. Respondents filled out an online survey on their professional beliefs and self-efficacy with regards to diversity in the classroom.</p>
Education in the Anthropocene: Assessing planetary health science standards in the US
The environmental crises defining the Anthropocene demand ubiquitous mitigation efforts, met with collective support. Yet, disengagement and disbelief surrounding planetary health threats are pervasive, especially in the United States (US). This skepticism may be influenced by inadequate education addressing the scope and urgency of the planetary health crisis. We analyzed current K-12 science standards related to planetary health throughout the US, assessing their quality and potential predictors of variation. While planetary health education varies widely across the US with respect to the presence and depth of terms, most science standards neglected to convey these concepts with a sense of urgency. Furthermore, state/territory political affiliation and primary GDP contributor were each predictive of the quality of planetary health education. We propose that a nation-wide science standard could fully address the urgency of the planetary health crisis and prevent political bias from influencing the breadth and depth of concepts covered.
Supplemental; Materials - Current state of genomics in nursing: A scoping review of healthcare provider oriented (clinical and educational) outcomes (2012-2022)
<p>Supplemental Table S1 "Scoping Review Data Extraction Table"</p>
The Use of VR/AR End-User Development Tools in Education: Lessons Learned and Future Challenges
<p>Dataset of the paper "The Use of VR/AR End-User Development Tools in Education: Lessons Learned and Future Challenges"</p>
ChatGPT impacts in programming education: A short list of input questions and output answers from ChatGTP
<p><strong>Five questions (in red) were addressed to ChatGTP ver3.5 between the period July 24<sup>th</sup> – July 28<sup>th</sup> 2023, in order to identify the opinion of ChatGTP in the field of computer science programming and programmers training. It is chosen to pose questions related to the main applications, advantages, disadvantages, future implications, and ethical considerations.</strong></p>
CLARA Knowledge Graph of licensed educational resources (using RDF-star, Standard reification, Singleton properties, or Named graphs)
<p><strong>CLARA</strong><br>This deposit is part of the <a href="https://project.inria.fr/clara/">CLARA project</a>. The CLARA project aims to empower teachers in the task of creating new educational resources. And in particular with the task of handling the licenses of reused educational resources.</p> <p>The present deposit contains the RDF files created using an RDF mapping (<a href="https://rml.io/">RML</a>) and a mapper (<a href="https://github.com/morph-kgc/morph-kgc">Morph-KGC</a>). It also contains the files JSON used as input. The corresponding pipeline can be found on <a href="https://gitlab.univ-nantes.fr/clara/pipeline">Gitlab</a>. The data used in that pipeline originate from <a href="https://www.x5gon.org/">X5GON</a>, a European project aiming to generate and gather open educational resources.</p> <p><strong>Knowledge graph content</strong><br>The present Knowledge Graph contains information about 45K Educational Resources (ERs) and 135K subjects (extracted from DBpedia).<br>That information contains </p> <ul> <li>the author,</li> <li>its title and description</li> <li>the license,</li> <li>a URL to the resource itself,</li> <li>the language of the ER,</li> <li>its mimetype,</li> <li>and finally which subject it talks about, and to what extent.</li> </ul> <p><br>That extent is given by two scores: a PageRank score and a Cosinus score.</p> <p>A particularity of the knowledge graph is its heavy use of RDF reification, across large multi-valued properties.<br>Thus four versions of the knowledge graph exist, using Standard reification, Singleton property, Named graphs, and RDF-star.</p> <p>The Knowledge Graph also contains <a href="https://databus.dbpedia.org/dbpedia/generic/categories">categories</a> originating from DBpedia. They help precise the subjects that are also extracted from DBpedia.</p> <p>The KG.zip files contain five types of files:</p> <ul> <li><strong>Authors_[</strong>X<strong>].nt</strong> - Those contain the authors' nodes, their type, and name.</li> <li><strong>ER_[</strong>X<strong>].nt/nq/ttl</strong> - Those contain the ERs and their information using the respective RDF reification model.</li> <li><strong>categories_skos_[</strong>X<strong>].ttl</strong> - Those contain the hierarchy of DBpedia categories.</li> <li><strong>categories_labels.ttl </strong>- This file contains additional information about the categories.</li> <li><strong>categories_article.ttl</strong> - This file contains the RDF triples that link the DBpedia subjects to the DBpedia categories.</li> </ul> <p> </p> <p><strong>JSON content</strong></p> <p>The original dataset was cut into multiple JSON files in order to make its processing easier. DBpedia categories were extracted as RDF and aren't present in the JSON files.<br><br>There are two types of files in the input-json.zip file:</p> <ul> <li><strong>authors_[</strong>X<strong>].json</strong> - Which lists the authors names</li> <li><strong>ER_[</strong>X<strong>].json</strong> - Which lists the ERs and their related information.<br>That information contains: <ul> <li>their <em>title.</em></li> <li>their <em>description.</em></li> <li>their <em>language</em> (and <em>language_detected</em>, only the first one is used in the pipeline here).</li> <li>their <em>license.</em></li> <li>their <em>mimetype.</em></li> <li>the <em>authors.</em></li> <li>the <em>date</em> of creation of the resource.</li> <li>a <em>url</em> linking to the resource itself.</li> <li>the subjects (named <em>concepts</em>) associated with the resource. With the corresponding scores.</li> </ul> </li> </ul> <p> </p> <p>If you do use this dataset, you can cite the corresponding paper:</p> <ul> <li>Kieffer, M., Fakih, G. & Serrano-Alvarado, P. (2023). Evaluating Reification with Multi-valued Properties in a Knowledge Graph of Licensed Educational Resources. Semantics, Leipzig, Germany.</li> </ul>
Pediatric standardized residency training faculty's perceptions of medical education research
<p><strong>Background:</strong> Rigorous medical education research (MER) conducted by faculty in pediatric standardized residency training (SRT) can contribute to the promotion of child health care. </p> <p><strong>Aim:</strong> This study aimed to assess the perceptions of pediatric SRT faculty regarding MER in Guangdong Province, China. </p> <p><strong>Methods:</strong> A questionnaire survey was conducted involving 40 pediatric SRT clinical teachers from 10 hospitals in Guangdong Province.</p> <p><strong>Results:</strong> Among the 40 teachers, 16 (40.00%) stated that they did not participate any MER activities. The main challenges they encountered in conducting MER were the lack of training (72.50%), limited time (80.00%), and lack of grants funding (60.00%). Only 10 (25.00%)teachers were reported to have received grants for MER projects. </p> <p><strong>Conclusion: </strong>The findings highlight the importance of providing support and training to pediatric SRT faculty in Guangdong Province, China, to enhance their engagement in MER. Addressing these challenges can lead to significant improvements in child heal care promotion through evidence-based educational practices. </p>
Indoor Air Temperature and Occupant Behavior in Classroom of higher education building in Mediterranean climate
<p>Data collection Include the measurement of indoor and outdoor environmental parameters (air temperature and relative humidity) and occupant interactions with building systems (window and door status: open/closed, blind state, and thermostat/air-conditioning adjustment).</p><p>The outdoor air temperature, relative humidity, and wind speed were collected as potential control variables to indicate different outdoor conditions.</p><p>The indoor air temperature and relative humidity in the classroom were monitored using wireless sensors. Six RHT sensors were placed at different locations: one in the center (F98), two on the ceiling next to grilles (FA1 et F9B), one on the carpentry of one of the windows (F9D), one near the writing board (F94), and one in the corridor outside the classroom (F96). Indoor parameters were recorded at ten minutes intervals.</p><p>The number of occupants was determined hourly (morning and afternoon) by counting and surveying (attendance sheets). The usage schedules of the classroom were 8:30–18:00. The number of occupants varied from 0 to 31.</p><p>The states of doors and windows (open or close) were monitored using magnetic sensor that detects the opening of doors and windows. The states of the door and windows were recorded at ten minute intervals.</p><p>The window-blind closing rate was determined by visual observation. The closing rates were 0%, 25%, 50%, 75%, and 100%. Observations were conducted throughout the day in the morning and afternoon at 1 h intervals.</p><p>The state of the heating/air-conditioning system was determined to be off or on every hour (in the morning and afternoon) using the HVAC control panel (HMI)</p>
Extracting Educational Code Scenarios from Python Textbooks
<p><strong>Dataset Overview:</strong> This dataset complements the research project titled "Extracting Learning Scenarios from Python Textbooks." It consists of 1,017 chapter titles collected from 76 Python textbooks.</p><p><strong>Research Findings:</strong> Our analysis revealed that learning scenarios (referred to as "Scenarios") are a prevalent theme, constituting approximately 39.5% of the total chapter titles in comparison to other content categories. We further categorized these scenarios into four types, including Application Programming Interfaces, Data and Processing, Graphical User Interfaces, and other scenarios. Additionally, we identified a list of 19 Python modules commonly used within these scenarios.</p><p><strong>Purpose:</strong> We envision that this work and its insights can serve as stepping stones and lay the groundwork for further extraction and the effective application of how Python can be utilized for its diverse audience.</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.