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76 results for “Online Learning”
Expert consensus on core topics of sustainable development online learning module for family physicians: A Delphi study
<p><strong>Objectives</strong></p> <p>Medical institutions must provide learning experiences that enhance the knowledge and perspectives of Sustainable Development (SD) to prepare trainees of family medicine to become competent global citizens. The aim of this study was to develop an SD online learning module for trainees of family medicine.</p> <p><strong>Design</strong></p> <p>This mixed-methods study was conducted from January 2020 to May 2021, beginning with a literature review concerning Sustainable Development Goals (SDG) and Education for Sustainable Development (ESD) in medicine. In-depth interviews were held to assess the relevant needs of family medic<span>ine</span> training, followed by a two-round Delphi survey with experienced educators (n = 21) in family medicine to refine and achieve consensus on the appropriate SDG topics for family physicians.</p> <p><strong>Setting</strong></p> <p>All residency training programmes in Thailand.</p> <p><strong>Participants</strong></p> <p>Members of the Residency Training Committee, Royal College of Family Physicians of Thailand.</p> <p><strong>Results</strong></p> <p>The literature review and in-depth interviews identified 12 topics of SD that were required for family physicians. The first round of the Delphi survey was concluded by identifying 7 core topics with additional suggestions. In the second round, a consensus was obtained among the experienced educators regarding 7 core topics: 1) A definition of SD, 2) Principles of SD, 3) SDG, 4) A new concept of SD, 5) SD in the context of Thailand, 6) SD and principles of family medicine and 7) SD and family practice. These core topics were grouped within three main objectives and three sub-modules.</p> <p><strong>Conclusions</strong></p> <p>An online <span>learning module of SD for family physicians was developed using a modified Delphi method. This included three sub-modules: 1) Concept and principles of SD, 2) SDG and 3) SD and its integration with family practice. This online learning module will provide additional resources for trainees of family medicine and Thai family physicians to expand their knowledge and perspectives of sustainable development.</span></p>
Appendix for "How Programmers Find Online Learning Resources"
<p>This artifact contains the the documents needed to replicate our user study as well as the collected data to validate our observations presented in the paper "How Programmers Find Online Learning Resources" by Deeksha M. Arya, Jin L.C. Guo, and Martin P. Robillard.</p>
Expert consensus on core topics of sustainable development online learning module for family physicians: A Delphi study
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Data from: From trial to implementation, bringing team-based learning online – Duke-NUS Medical School's response to the COVID-19 pandemic
<p>The restrictions imposed by the COVID-19 pandemic resulted in Duke-NUS Medical School moving all their lessons online. Duke-NUS employs a team-based learning (TBL) pedagogy, which depends heavily on student discussion. In 2015, our university had implemented an eLearning week where lessons were conducted online. Using the already present online assessment processes, the data, insights and student feedback allowed for swift implementation of an online TBL module for home-based learning in response to the pandemic in 2020. These protocols were modified over the weeks, guided by feedback from students and faculty. An analysis of this online TBL module is presented herein.</p>
Data from: Evolutionary online behaviour learning and adaptation in real robots
Online evolution of behavioural control on real robots is an open-ended approach to autonomous learning and adaptation: robots have the potential to automatically learn new tasks and to adapt to changes in environmental conditions, or to failures in sensors and/or actuators. However, studies have so far almost exclusively been carried out in simulation because evolution in real hardware has required several days or weeks to produce capable robots. In this article, we successfully evolve neural network-based controllers in real robotic hardware to solve two single-robot tasks and one collective robotics task. Controllers are evolved either from random solutions or from solutions pre-evolved in simulation. In all cases, capable solutions are found in a timely manner (1 h or less). Results show that more accurate simulations may lead to higher-performing controllers, and that completing the optimization process in real robots is meaningful, even if solutions found in simulation differ from solutions in reality. We furthermore demonstrate for the first time the adaptive capabilities of online evolution in real robotic hardware, including robots able to overcome faults injected in the motors of multiple units simultaneously, and to modify their behaviour in response to changes in the task requirements. We conclude by assessing the contribution of each algorithmic component on the performance of the underlying evolutionary algorithm.
An Online Paleoclimate Data Assimilation with a Deep Learning-based Network
<p>OnlinePDA_zenodo.rar (files compressed with RAR compression) includes:</p> <p>1. Directory 'Code' contains the <em>LIM</em>.<em>m</em> and <em>NET</em>.<em>m</em> for the examination of predictive skills of the surrogate models; the <em>Exp_LIM</em>.<em>m</em> and <em>Exp_NET</em>.<em>m</em> for the reconstruction of SAT during the preindustrial period (851-1850 CE) and the instrumental period(1880-2000CE), for both pseudoproxy experiments and real proxy experiments; </p> <p>2. Directory 'Data' contains the necessary data in the <strong>model</strong>, <strong>prior</strong>, <strong>proxy</strong>, and <strong>obs</strong> directory used to perform the resconstruction. e.g. surrogate models, prior samples, pseudoproxy and real proxy, and instrumental data; <em>Figure</em>*.<em>m and</em> <em>CERMSE</em>*.<em>mat</em> provide the necessary code and data for plotting Figures 3-10</p>
A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave
<p><strong>Please cite the following paper when using this dataset:</strong></p> <p>N. Thakur, “A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave,” Journal of Data, vol. 7, no. 8, p. 109, Aug. 2022, doi: 10.3390/data7080109</p> <p><strong>Abstract</strong></p> <p>The COVID-19 Omicron variant, reported to be the most immune evasive variant of COVID-19, is resulting in a surge of COVID-19 cases globally. This has caused schools, colleges, and universities in different parts of the world to transition to online learning. As a result, social media platforms such as Twitter are seeing an increase in conversations, centered around information seeking and sharing, related to online learning. Mining such conversations, such as Tweets, to develop a dataset can serve as a data resource for interdisciplinary research related to the analysis of interest, views, opinions, perspectives, attitudes, and feedback towards online learning during the current surge of COVID-19 cases caused by the Omicron variant. Therefore this work presents a large-scale public Twitter dataset of conversations about online learning since the first detected case of the COVID-19 Omicron variant in November 2021. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter and the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.</p> <p><strong>Data Description</strong></p> <p>The dataset comprises a total of 52,984 Tweet IDs (that correspond to the same number of Tweets) about online learning that were posted on Twitter from 9th November 2021 to 13th July 2022. The earliest date was selected as 9th November 2021, as the Omicron variant was detected for the first time in a sample that was collected on this date. 13th July 2022 was the most recent date as per the time of data collection and publication of this dataset.</p> <p>The dataset consists of 9 .txt files. An overview of these dataset files along with the number of Tweet IDs and the date range of the associated tweets is as follows. Table 1 shows the list of all the synonyms or terms that were used for the dataset development. </p> <ul> <li>Filename: TweetIDs_November_2021.txt (No. of Tweet IDs: 1283, Date Range of the associated Tweet IDs: November 1, 2021 to November 30, 2021)</li> <li>Filename: TweetIDs_December_2021.txt (No. of Tweet IDs: 10545, Date Range of the associated Tweet IDs: December 1, 2021 to December 31, 2021)</li> <li>Filename: TweetIDs_January_2022.txt (No. of Tweet IDs: 23078, Date Range of the associated Tweet IDs: January 1, 2022 to January 31, 2022)</li> <li>Filename: TweetIDs_February_2022.txt (No. of Tweet IDs: 4751, Date Range of the associated Tweet IDs: February 1, 2022 to February 28, 2022)</li> <li>Filename: TweetIDs_March_2022.txt (No. of Tweet IDs: 3434, Date Range of the associated Tweet IDs: March 1, 2022 to March 31, 2022)</li> <li>Filename: TweetIDs_April_2022.txt (No. of Tweet IDs: 3355, Date Range of the associated Tweet IDs: April 1, 2022 to April 30, 2022)</li> <li>Filename: TweetIDs_May_2022.txt (No. of Tweet IDs: 3120, Date Range of the associated Tweet IDs: May 1, 2022 to May 31, 2022)</li> <li>Filename: TweetIDs_June_2022.txt (No. of Tweet IDs: 2361, Date Range of the associated Tweet IDs: June 1, 2022 to June 30, 2022)</li> <li>Filename: TweetIDs_July_2022.txt (No. of Tweet IDs: 1057, Date Range of the associated Tweet IDs: July 1, 2022 to July 13, 2022)</li> </ul> <p>The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used. For hydrating this dataset the Hydrator application (<a href="https://github.com/DocNow/hydrator/releases">link to download</a> and a <a href="https://towardsdatascience.com/learn-how-to-easily-hydrate-tweets-a0f393ed340e#:~:text=Hydrating%20Tweets">step-by-step tutorial</a> on how to use Hydrator) may be used.</p> <p><strong>Table 1</strong>. List of commonly used synonyms, terms, and phrases for online learning and COVID-19 that were used for the dataset development</p> <table> <tbody> <tr> <td> <p>Terminology</p> </td> <td> <p>List of synonyms and terms</p> </td> </tr> <tr> <td> <p>COVID-19</p> </td> <td> <p>Omicron, COVID, COVID19, coronavirus, coronaviruspandemic, COVID-19, corona, coronaoutbreak, omicron variant, SARS CoV-2, corona virus</p> </td> </tr> <tr> <td> <p>online learning</p> </td> <td> <p>online education, online learning, remote education, remote learning, e-learning, elearning, distance learning, distance education, virtual learning, virtual education, online teaching, remote teaching, virtual teaching, online class, online classes, remote class, remote classes, distance class, distance classes, virtual class, virtual classes, online course, online courses, remote course, remote courses, distance course, distance courses, virtual course, virtual courses, online school, virtual school, remote school, online college, online university, virtual college, virtual university, remote college, remote university, online lecture, virtual lecture, remote lecture, online lectures, virtual lectures, remote lectures</p> </td> </tr> </tbody> </table> <p> </p>
Impact of Learning Satisfaction on Achievement Perception in Adult Online Education
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Data for "Online Learning of Entrainment Closures in a Hybrid Machine Learning Parameterization"
<p>Calibration data and visualization tools for "Online Learning of Entrainment Closures in a Hybrid Machine Learning Parameterization".</p>
Transformation from Blended to Online Learning: A Four-year Longitudinal Cross-sectional Interprofessional Study
<p>Transformation from Blended to Online Learning: A Four-year Longitudinal Cross-sectional Interprofessional Study </p>
Adaptation and evolution of teaching method for university programming subject to the online learning environment - Commit Data
<p>This dataset contains commit UNIX timestamps for Copymaster assignment git repositories of students studying Operating Systems class at Technical University of Košice in the span of years 2017/2018 - 2020/2021.</p>
Adaptation and evolution of teaching method for university programming subject to the online learning environment - Student surveys dataset
<p>This dataset contains anonymized survey data of students studying Operating Systems class at the Technical University of Košice in the span of school years 2017/2018 to 2020/2021.</p>
Adaptation and evolution of teaching method for university programming subject to the online learning environment - Student point gain dataset
<p>This dataset contains anonymized study results of students studying Operating Systems class at the Technical University of Košice in the span of school years 2015/2016 to 2020/2021.</p>
Reducing Eye Strain and Anxiety Using a Digital Intervention During Online Learning Class Recess Among Children at Home: A Randomized Controlled Trial
ClinicalTrials.gov study NCT04309097. IPD Sharing: NO. Countries: 1. Publications: 5.
Learning Diagnostic Skills Online (German: Diagnostik Skills Online Lernen)
ClinicalTrials.gov study NCT05294094. IPD Sharing: YES. Countries: 1. Publications: 1.
Online Learning Module to Advance Research Related to People With Disabilities
ClinicalTrials.gov study NCT07220837. IPD Sharing: YES. Countries: 1. Publications: 11.
The Use of an Online Learning and Consent Platform in Infertility Treatment
ClinicalTrials.gov study NCT03962257. IPD Sharing: NO. Countries: 1. Publications: 1.
Information Support Using an Online Learning Platform for Malaysian Pediatric Leukemia and Lymphoma Parents
ClinicalTrials.gov study NCT05455268. IPD Sharing: NO. Countries: 1. Publications: 2.
Learning From Online Video Education for Controlled Ovarian Stimulation
ClinicalTrials.gov study NCT02979990. IPD Sharing: NO. Countries: 1. Publications: 7.
iLookOut for Child Abuse -Online Learning Module for Early Childcare Providers
ClinicalTrials.gov study NCT02225301. IPD Sharing: NO. Countries: 1. Publications: 3.
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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.