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5,141 results for “Educators”
Figures 2 and 3 in HighRes for Springer book chapter "Online Infrastructures For Open Educational Resources"
<p>Figure 2 and Figure 3 in high resolution for the book chapter:</p> <p>Marín, V. I., & Villar-Onrubia, D. (in press, 2022). Online Infrastructures For Open Educational Resources. In I. Jung & O. Zawacki-Richter (Eds.), <em>Handbook of Open, Distance and Digital Education</em> (Global Perspectives and Internationalization). Springer. <a href="https://doi.org/10.1007/978-981-19-0351-9_18-1">https://doi.org/10.1007/978-981-19-0351-9_18-1</a></p> <p>----</p> <p>Details for the figures:</p> <p>Fig. 2 Examples of national and regional digital infrastructures in Europe. (Note: The original figure of the Europe map was created by Commons user Alexrk2, CC BY-SA 3.0, shared via Wikimedia Commons)</p> <p>Figure 3. Examples of national and regional digital infrastructures in South America. (Note: The original figure of the South America map was created by TUBS, CC BY-SA 3.0, shared via Wikimedia Commons)</p>
MyPack education
<p>The MyPack project multiplied the short communication through social media with key messages addressed to a large audience, including stakeholders. MyPack short communication complemented the information published by the project YPACK and GLOPACK. Since the MyPack project includes also elaborated packaging as Blow® microtechnology, the difference between the two other projects, which have only biodegradable and compostable packaging.<br> Through the Twitter account, the MyPack project announced the idea of developing education cards or videos in the form of questions/answers addressed to a wide audience to promote the project outputs through #MyPacknews.</p> <p>The first cards are addressed to the <strong>dissemination of Blow® microtechnology device</strong> patented by UNIBAS and NINTEK partners as breathable packaging with their advantages.</p> <p>The 2<sup>nd</sup> card is dedicated to <strong>food waste</strong> and its negative environmental impact. The card illustrates that by throwing away food with an example of 1Kg of tomatoes (tomatoes are worldwide consumed), the product and the resources (e.g. water, land & energy) to produce it are also wasted. One kilo of tomato waste is equivalent to one car’s CO<sub>2 </sub>emission on the road for two km. With the slogan “All together to reduce food waste and preserve our planet”.</p> <p>The 3<sup>rd</sup> MyPack education card is in video format. It illustrates the active contribution of packaging waste to climate change, intending to raise public awareness and show the trade-off between packaging and food waste with a new concept. 2B partners calculated the CO<sub>2</sub> impact to produce three types of packaging to pack a half kilo of tomatoes (paper bag, mixed packaging with paperboard tray and PE film; and PET tray) and that for the production of the packed tomatoes. The video exposes that using a single kind of packaging corresponds to the waste of a percentage of tomatoes.</p> <p> </p> <p> </p>
Data from: Building communities of teaching practice and data-driven open education resources with NEON faculty mentoring networks
<p>With the growing availability and accessibility of big data in ecology, we face an urgent need to train the next generation of scientists in data science practices and tools. One of the biggest barriers for implementing a data-driven curriculum in undergraduate classrooms is the lack of training and support for educators to develop their own skills and time to incorporate these principles into existing courses or develop new ones. Alongside the research goals of the National Ecological Observatory Network (NEON), providing education and training are key components for building a community of scientists and users equipped to utilize large-scale ecological and environmental data. To address this need, the NEON Data Education Fellows program formed as a collaborative Faculty Mentoring Network (FMN) between scientists from NEON and university faculty interested in using NEON data and resources in their ecology classrooms. Like other FMNs, this group has two main goals: 1) to provide tools, resources, and support for faculty interested in developing data-driven curriculum, and (2) to make teaching materials that have been implemented and tested in the classroom available as open educational resources for other educators. We hosted this program using an open education and collaboration platform from the Quantitative Undergraduate Biology Education and Synthesis (QUBES) project. Here, we share lessons learned from facilitating five FMN cohorts and emphasize the successes, pitfalls, and opportunities for developing open education resources through community-driven collaborations.</p>
Overview: GERMAN LANGUAGE OER FOR SOCIAL SCIENCE METHODS EDUCATION
<p>This document contains the corpus of identified OER as well as OER-like on social science research methods in the German language and the used codes to classify the identified resources. This document is provided for secondary use as well as expansion and revision.</p> <p>The collection and classification of the corresponding OER and OER-like was last updated in August 2021 and has to be discussed accordingly. OER and OER-like that were created later or were offline during data collection are not included.</p> <p>The codings provided are only covering content-based aspects and not feature e.g. like license framework or opportunity to comment and discuss.</p>
Student Creativity in Information Literacy Instruction - OER video for high school educators
<p>This video was produced in an information literacy course at the School of Information Studies (ÉSIS) at the University of Ottawa, Canada, in Spring 2022. It is shared as an open educational resource (OER) using CC-BY license to be re(used) to teach viewers about creativity in the context of information literacy.</p> <p>This video is meant to help educators teach information literacy to their high school students using creativity.</p> <p>The learning objective includes understanding methods to incorporate creativity in the instruction of information literacy through the form of content creation. The main goal is to engage the viewers and incentivize the target audience to implement creativity in their educational methods related to information literacy.</p> <p>This video was created in screencast-o-matic and edited using the same program and Lightworks.</p> <p>The slides used are from a presentation template from SlidesCarnival and illustrations by Sergei Tikhonov.</p> <p>The references used to create this project are displayed at the end of the video.</p>
Distance Education and related terms
<p>Distance Education a broader term than digital education and online learning.</p>
Bibliographic Metadata from Zurich University of Teacher Education (PHZH) 2021
<p>The data for this set were extracted from unstructured bibliographic metadata in the online personal portraits of PHZH staff and from the PHZH repository. The two sources were merged, cleaned, and supplemented with relevant parameters regarding a comprehensive Open Access monitoring. The set provides the PHZH data basis for the national Open Access monitoring (<a href="https://oam.oamonitor.ch/">https://oam.oamonitor.ch/</a>) by swissuniversitis/CSAL.</p>
Data on Abusive Supervision, Organizational Cynicism and Playing Dumb in Higher Education Institutions
<p>This data set include the responses from the faculty and staff of Higher Education Institutions in Pakistan </p>
Leveraging Diversity in Software Engineering Education through Community Engaged Learning and a Supportive Network
<p>This replication package contains the schedule for the first cohort of our program described in the paper titled "Leveraging Diversity in Software Engineering Education through Community Engaged Learning and a Supportive Network".</p>
Summary-level results from the AFGen, GWAS and educational attainment GWAS
<p>Merged summary-level results to calculate genetic correlations between various traits. The columns are as follows:</p> <ul> <li>SNP: rs identifier for the SNP</li> <li>AFGen.EUR.Z: z-score from the AFGen GWAS of atrial fibrillation performed in European-ancestry samples</li> <li>EduYrs.Z: z-score from the educational attainment GWAS (data available from LDHub)</li> <li>afib.broad.Z: z-scores from the SiGN GWAS of atrial fibrillation</li> <li>allstroke.Z: z-scores from the SiGN GWAS of all stroke </li> <li>CCScCEmajor.Z: z-scores from the SiGN GWAS of CE stroke (defined by CCSc)</li> <li>CCScCRYPTCE.Z: z-scores from the SiGN GWAS of cryptogenic/CE minor stroke (defined by CCSc)</li> <li>CCScINCUNC.Z: z-scores from the SiGN GWAS of incomplete/unclassified stroke (defined by CCSc)</li> <li>CCScLAA.Z: z-scores from the SiGN GWAS of LAA stroke (defined by CCSc)</li> <li>CCScSAO.Z: z-scores from the SiGN GWAS of SAO stroke (defined by CCSc)</li> <li>CCScUNDETER.Z: z-scores from the SiGN GWAS of undetermined stroke (defined by CCSc)</li> <li>CCSpCEmajincl.Z: z-scores from the SiGN GWAS of CE stroke (defined by CCSp)</li> <li>CCSpCryptoincl.Z: z-scores from the SiGN GWAS of cryptogenic stroke (defined by CCSp) </li> <li>CCSpLAAmajincl.Z: z-scores from the SiGN GWAS of LAA stroke (defined by CCSp)</li> <li>CCSpSAOmajincl.Z: z-scores from the SiGN GWAS of SAO stroke (defined by CCSp)</li> <li>toastCE.Z: z-scores from the SiGN GWAS of CE stroke (defined by TOAST)</li> <li>toastLAA.Z: z-scores from the SiGN GWAS of LAA stroke (defined by TOAST)</li> <li>toastSAO.Z: z-scores from the SiGN GWAS of SAO stroke (defined by TOAST)</li> <li>toastUNDETER.Z: z-scores from the SiGN GWAS of undetermined stroke (defined by TOAST)</li> </ul> <p>CCSc, CCS Causative subtyping system; CCSp, CCS Phenotypic subtyping system; TOAST, TOAST subtyping system; CE, cardioembolic stroke; LAA, large artery atherosclerosis; SAO, small artery occlusion.</p> <p> </p>
Summer dataset of an operating education modular building
<p>We propose a dataset concerning a modular education building (NR3) located on CESI Engineering School of Nanterre (Approximate GPS coordinates: 48.9033,2.1925). NR3 building is composed of 16 shipping containers offering an available surface of ≈220m² on two levels. It integrates 4 classrooms named Turing (38 m²), Tesla (51 m²), Nobel (38 m²) and Lumière (38 m²); a IT room named Bell (8.7 m²); an office named Nobel2 (8.7m²); a technical room named Local_tech and a hall distributed on the 2 levels. NR3 building is equiped with approximately 200 sensors and actuators allowing the measurement of the physical behavior of the building and the control of HVAC systems.</p> <p>The dataset is composed of 14 csv tables including physical indoor and outdoor parameters (temperature, humidity, radiation…), total energetic consumption of the building, state and operating data of all systems (heat pump, CMV, shutters, windows…). Data were recorded during summer 2023, i.e. between the 21 of june and the 22 of september. A redme.xlsx file and a map of the building helps to understand each table.</p> <p> </p>
Dataset of adaptive Children-Robot Interaction for Education based on Autonomous Multimodal Users' Readings
<p># Dataset of adaptive Children-Robot Interaction for Education based on Autonomous Multimodal Users’ Readings </p> <p>## Background</p> <p>This dataset is generated from multiple interactions between a Social Robot (NAO) and 5th grade students from a private school in São Paulo, Brazil. </p> <p>In the interaction, the robot approached the content that teachers were approaching at the time with the participants students about the wasting system in Brazil.</p> <p>The measures here are the readings that the R-CASTLE system did for each answer the students gave to the questions the robot asked. </p> <p>For more information about how these measures were collected, please refer to this thesis at: https://doi.org/10.11606/T.55.2020.tde-31082020-093935</p> <p>Since the goal of the R-CASTLE is to provide autonomous adaptation, we built a ground-truth dataset based on human feedback of an expert in education operating the robot in loco. The person was teleoperating the robot to change its behaviour (or not) according to observed values of the participants as Face Gaze, Facial emotion displayed, Number of spoken words, the correctness of the answer (based on pre-defined answers), and the time students took to answer. These measures are the 5th columns of this csv file. The evaluator could decide to increase (1), maintain (0), or decrease (-1) the level of difficulties of the following questions depending on the mentioned observed measures. This is the human true label, stored in the 6th column. </p> <p>## Description:<br>Each row of this file is a tuple of the autonomous reading the robot made in the 5 first columns, plus the true label in the 6th row (True Value) and the Final Crisp Value using fuzzy classification in the 7th row (Final Crisp Value).</p> <p><br>Deviations (integer): number of face deviations of the participant during the question answering identified by the system.</p> <p>EmotionCount (integer): a balance between "good" and "bad" emotions (good - bad) identified by the system.</p> <p>NumberWord (integer): number of words comprised in the sentence the participant gave.</p> <p>SucRate/Ans/RWa: (between 0 and 1, where 0 is completely wrong and 1 is completely right): The success rate of the participant’s answer to that question, based on the expected answer programmed by their teachers.</p> <p>Time2ans (float): The time spent to answer the question since the robot has finished the question until the end of the participant’s speech in seconds.</p> <p>True Value (-1, 0, 1): Ground-truth value. Value of adaptation chosen by the human observing the interaction if the system needed to decrease, maintain, or increase the level of difficulty of asked questions. </p> <p>Final Crisp Value (float): value of calculated fuzzy output based on the implementations in the paper: https://doi.org/10.1145/3395035.3425201</p> <p><br>## Creators <br>Daniel Tozadore: dtozadore@gmail.com<br>Roseli Romero: rafrance@icmc.usp.br</p> <p><br>## License: <br>[Creative Commons Licenses](https://creativecommons.org/share-your-work/cclicenses/)</p>
Metadata of the Top 40 Journals in Distance Education: A Bibliometric Analysis of Impact 2018-2022
<p>This file contains metadata from the 40 most impactful journals in the field of distance education, selected through a rigorous bibliometric analysis using the SCImago Journal Rank (SJR) indicator provided by SCImago. Two main criteria guided the selection: the first targeted journals ranked in Q1 and Q2 in the specific category of "e-learning" within the social sciences and education area, highlighting publications that demonstrate high impact and relevance in the academic community according to the selected indicator. The second criterion was based on keyword searches in the journal titles, selecting those that include terms like e-learning, online, Distance Education, Technology Learning, Communications in Information, and Information Education and their variants (e.g., plural), also positioned in Q1 or Q2.</p> <p>The metadata, extracted from the Scopus database, covers publications from the period 2018 to 2022 and includes vital information such as document type, authors' names, article title, journal name, publication year, pages, volume, and issue number. Additionally, each article is identified by a Digital Object Identifier (DOI) and URLs for direct access to the full text online, along with abstracts and keywords. These elements together provide a comprehensive and accessible view of the articles, facilitating bibliometric analyses and related academic research.</p> <p>This compilation serves as an essential resource for researchers and educators interested in understanding the dynamics and development of the field of distance education, offering a solid foundation for future investigations and the formulation of evidence-based educational policies.</p>
Resources for BMF CP72: The effectiveness of knowledge management systems in motivation and satisfaction in Vietnamese higher education institutions
<p>Code and data for reproducing the results in "BMF CP72: The effectiveness of knowledge management systems in motivation and satisfaction in Vietnamese higher education institutions" are available here.</p>
Times Higher Education Ranking Dataset
<p>Dataset containing university rankings. See source: <a href="https://www.timeshighereducation.com/world-university-rankings">https://www.timeshighereducation.com/world-university-rankings</a></p> <p>Originally used in:</p> <p>Anahideh, Hadis, and Nasrin Mohabbati-Kalejahi. "Local explanations of global rankings: insights for competitive rankings." IEEE Access 10 (2022): 30676-30693. <a href="https://ieeexplore.ieee.org/abstract/document/9733934" rel="nofollow">https://ieeexplore.ieee.org/abstract/document/9733934</a></p>
Agile Minds, Innovative Solutions, and Industry–Academia Collaboration: Lean R&D Meets Problem-based Learning in Software Engineering Education
<p>Supplementary materials of the paper "Agile Minds, Innovative Solutions, and Industry–Academia Collaboration: Lean R&D Meets Problem-based Learning in Software Engineering Education"</p>
UU Webinar 2: Sam Sellar on time and value in digitalised higher education
<p>Title: The investment of time: Divergent theses on the value of higher education</p> <p>Abstract: This webinar will focus on what is valuable in and about higher education. It will then discuss this in relation to Edtech and deliberate where Edtech can and cannot contribute to supporting the sector.</p> <p>Speaker: Sam Sellar</p> <p>Bio: Sam Sellar is Dean of Research (Education Futures) and Professor of Education Policy at the University of South Australia. Sam’s research focuses on education policy, large-scale assessments and the datafication of education. Sam also works closely with teacher organisations around the world to understand the impact of digitalisation on teacher professional autonomy. His most recent book is titled Algorithms of education: How datafication and artificial intelligence shape policy (University of Minnesota Press), co-authored with Kalervo N. Gulson and P. Taylor Webb.</p> <p>Link to Sam’s website: https://people.unisa.edu.au/Sam.Sellar </p> <p>Date of event: 22 June 2023</p>
UU Webinar 1: Kean Birch on assets and rents in digitalised higher education
<p>Title: Rentiership in EdTech: Data as asset, data as rent?</p> <p>Abstract: This webinar will present the concepts of assetisation and rentiership. It will discuss different types of assets in higher education, how they are made valuable, and the consequences. The webinar is relevant for academics, practitioners, and policymakers.</p> <p>Speaker: Kean Birch</p> <p>Bio: Kean Birch is a Professor at York University, Canada. He is particularly interested in understanding technoscientific capitalism and draws on a range of perspectives from science & technology studies, economic geography, and economic sociology to study it. More specifically, his research focuses on the restructuring and transformation of the economy & financial knowledges and technoscience & technoscientific innovation. Currently, he is researching how different things (e.g. knowledge, personality, loyalty, etc.) are turned into ‘assets’ & how economic rents are then captured from those assets - basically, in processes of assetisation and rentiership.</p> <p>Link to Kean’s website: https://euc.yorku.ca/faculty/kean-birch/ </p> <p>Date of event: 22 June 2023</p>
Universities and Unicorns – New Forms of Value in Digital Higher Education (SRHE webinar)
<p>SRHE's Digital University Network organised an event on 21 September 2023. The research team from the ESRC-funded research project Universities and Unicorns (UU) project presented initial findings after the project finished. The project introduced fundamentally new ways to think about and examine the digitalising of the higher education sector. It investigated new forms of value creation and suggested that value in the sector increasingly lies in the creation of digital assets. Further, the project examined whether and how universities, companies and investors shift from forms of entrepreneurship to forms of rentiership. This has important consequences. Assetisation and rentiership imply a change from creating value via market exchange to capturing value via the ownership and control of assets, such as personal data.</p> <p><br>More about the webinar: https://srhe.ac.uk/civicrm/?civiwp=CiviCRM&q=civicrm%2Fevent%2Finfo&reset=1&id=641</p>
Taste aversion training can educate free-ranging crocodiles against toxic invaders
<p>Apex predators play critical ecological roles, making their conservation a high priority. In tropical Australia, some populations of freshwater crocodiles (<em>Crocodylus johnstoni</em>) have plummeted by >70% due to lethal ingestion of toxic invasive cane toads (<em>Rhinella marina</em>). Laboratory-based research has identified conditioned taste aversion (CTA) as a way to discourage consumption of toads. To translate those ideas into landscape-scale management, we deployed 2,395 baits (toad carcasses with toxin removed and containing a nausea-inducing chemical) across four gorge systems in north-western Australia and monitored bait uptake with remote cameras. Crocodile abundance was quantified with surveys. Free-ranging crocodiles rapidly learned to avoid toad baits but continued to consume control (chicken) baits. Toad invasion at our sites was followed by high rates of crocodile mortality (especially for small individuals) at a control site but not at nearby treatment sites. In areas with high connectivity to other waterbodies, repeated baiting over successive years had continuing positive impacts on crocodile survival. In summary, we succeeded in buffering the often-catastrophic impact of invasive cane toads on apex predators.</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.