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76 results for “Online Learning”
CSC OnDemand: An Innovative Online Learning Platform for Implementing Coordinated Specialty Care
ClinicalTrials.gov study NCT03465371. IPD Sharing: YES. Countries: 1. Publications: 0.
Online Flipped Learning in Nursing Student
ClinicalTrials.gov study NCT05967559. IPD Sharing: NO. Countries: 1. Publications: 0.
Online Learning of Veterinary Anatomy During COVID-19 Pandemic
ClinicalTrials.gov study NCT04418284. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Unplanned Shifting to Online Distance Learning: Nursing Students' Perception and Achievement
ClinicalTrials.gov study NCT04372693. IPD Sharing: NO. Countries: 1. Publications: 0.
Comparison of Hands-on Versus Online Learning About Ocular Ultrasound
ClinicalTrials.gov study NCT04834700. IPD Sharing: NO. Countries: 1. Publications: 0.
Online RPG Interactive Adventure Games Combined With an E-book Learning Obstetric Fetal Heart Rate Monitoring Technology
ClinicalTrials.gov study NCT06966960. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Machine Learning-based Classification of Symptom Clusters and Online CBT
ClinicalTrials.gov study NCT06350201. IPD Sharing: NO. Countries: 1. Publications: 0.
Online Learning Portal on Under Five Pneumonia
ClinicalTrials.gov study NCT04495361. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Impact of Synchronous Online Learning on the Theoretical Training of New Paediatric Nurses
ClinicalTrials.gov study NCT06650020. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Generated Learning Content for 200 Online Language Learning Units employing GPT3.5
<p>This dataset includes 200 units of learning content in German generated utilizing GPT3.5, employed in an evaluation study to examine correctness and appropriateness to be used in an online language learning course.</p> <p>The dataset contains:</p> <ul> <li>Nouns (A-H),</li> <li>Verbs (a-d),</li> <li>Sentences (Aa-Da, ABb, CDc, ACa, BDa)</li> <li>A text containing nouns, and verbs</li> </ul> <p>of random topics. Topics were selected using a random surfer model. <br>Hence, the language proficiency level varies.</p> <p>Here is an example:</p> <table> <tbody> <tr> <td> <p>ID</p> </td> <td> <p><strong>Food (“Essen”)</strong></p> </td> <td> <p><strong>Fruits (“Obst”)</strong></p> </td> </tr> <tr> <td> <p>A</p> </td> <td> <p>Brot</p> </td> <td> <p>Banane</p> </td> </tr> <tr> <td> <p>B</p> </td> <td> <p>Apfel</p> </td> <td> <p>Mango</p> </td> </tr> <tr> <td> <p>C</p> </td> <td> <p>Fleisch</p> </td> <td> <p>Zitrone</p> </td> </tr> <tr> <td> <p>D</p> </td> <td> <p>Wurst</p> </td> <td> <p>Grapefruit</p> </td> </tr> <tr> <td> <p>E</p> </td> <td> <p>Ei</p> </td> <td> <p>Wassermelone</p> </td> </tr> <tr> <td> <p>F</p> </td> <td> <p>Kartoffel</p> </td> <td> <p>Erdbeere</p> </td> </tr> <tr> <td> <p>G</p> </td> <td> <p>Toast</p> </td> <td> <p>Melone</p> </td> </tr> <tr> <td> <p>H</p> </td> <td> <p>Gurke</p> </td> <td> <p>Limette</p> </td> </tr> <tr> <td> <p>a</p> </td> <td> <p>schneiden</p> </td> <td> <p>schälen</p> </td> </tr> <tr> <td> <p>b</p> </td> <td> <p>backen</p> </td> <td> <p>einfrieren</p> </td> </tr> <tr> <td> <p>c</p> </td> <td> <p>grillen</p> </td> <td> <p>pressen</p> </td> </tr> <tr> <td> <p>d</p> </td> <td> <p>würzen</p> </td> <td> <p>mixen</p> </td> </tr> <tr> <td> <p>Aa</p> </td> <td> <p>Ich schneide das Brot.</p> </td> <td> <p>Ich schäle die Banane.</p> </td> </tr> <tr> <td> <p>Ba</p> </td> <td> <p>Ich schneide einen Apfel.</p> </td> <td> <p>Ich schäle eine Mango.</p> </td> </tr> <tr> <td> <p>Ca</p> </td> <td> <p>Ich schneide das Fleisch.</p> </td> <td> <p>Ich schäle die Zitrone.</p> </td> </tr> <tr> <td> <p>Da</p> </td> <td> <p>Die Wurst schneiden.</p> </td> <td> <p>Ich schäle die Grapefruit.</p> </td> </tr> <tr> <td> <p>ABb</p> </td> <td> <p>Ich backe Brot und esse dazu einen Apfel.</p> </td> <td> <p>Ich kann Banane und Mango einfrieren, wenn ich sie nicht gleich esse.</p> </td> </tr> <tr> <td> <p>CDc</p> </td> <td> <p>Ich will heute Abend Fleisch und Wurst auf dem Grill grillen.</p> </td> <td> <p>Ich presse die Zitrone und die Grapefruit für den Saft.</p> </td> </tr> <tr> <td> <p>ACa</p> </td> <td> <p>Ich schneide Brot und Fleisch für das Sandwich.</p> </td> <td> <p>Ich schäle Bananen und Zitronen zum Frühstück.</p> </td> </tr> <tr> <td> <p>BDa</p> </td> <td> <p>Ich schneide den Apfel und die Wurst.</p> </td> <td> <p>Ich muss die Mango und Grapefruit schälen.</p> </td> </tr> <tr> <td> <p>Text</p> </td> <td> <p>Ich backe gerne frisches Brot.</p> <p>Dazu schneide ich eine Gurke.</p> <p>Auf dem Grill brate ich Fleisch.</p> <p>Das Wurstbrot schmeckt auch lecker.</p> <p>Ein Ei gehört auf jedes Toast.</p> <p>Mit Kartoffeln mache ich Pommes.</p> <p>Ein Apfel rundet das Frühstück ab.</p> <p>Ich würze das Steak mit Salz.</p> <p>Eine Wurst kann man auch grillen.</p> </td> <td> <p>Ich liebe Obst.</p> <p>Heute kaufe ich Bananen, Mangos und Zitronen.</p> <p>Auch Grapefruits und Wassermelonen sind im Angebot.</p> <p>Ich schäle die Bananen und friere sie ein.</p> <p>Die Mango presse ich zu Saft.</p> <p>Die Zitronen verwende ich für Limonade.</p> <p>In den Mixer kommen Erdbeeren und Melone.</p> <p>Eine Limette verleiht dem Smoothie den letzten Kick.</p> </td> </tr> </tbody> </table> <p>The study conducted can be found here: Rüdian, Sylvio; Pinkwart, Niels (2023): Auto-generated language learning online courses using generative AI models like ChatGPT. 21. Fachtagung Bildungstechnologien (DELFI). DOI: 10.18420/delfi2023-14. Bonn: Gesellschaft für Informatik e.V.. PISSN: 1617-5468. ISBN: 978-3-88579-732-6. pp. 65-76. Best-Paper-Kandidaten. Aachen. 11.-13. September 2023</p>
Online Survey of Clinicians to Learn About Their Interpretation of Capnography Waveforms
ClinicalTrials.gov study NCT02822183. IPD Sharing: NO. Countries: 0. Publications: 0.
Dataset: Online Learning Module of the "Training Course on Underlying Cause-of-Death Coding – ICD-10'" of the Virtual Learning Environment of the Brazilian Health System
<p><strong>README</strong></p> <p> </p> <p><strong>Dataset name:</strong><em> avasus_dataset.csv </em></p> <p><strong>Version: </strong>1.0 </p> <p><strong>Dataset period:</strong> July 24, 2018 - February 22, 2024</p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>1533</p> <p><strong>Number of Attributes: 5</strong></p> <p><strong>Missing Values: </strong>Yes</p> <p><strong>Area(s): </strong>Health and education<strong> </strong></p> <p><strong>Sources: </strong></p> <ul> <li>Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2024a);</li> <li>Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2024d). </li> </ul> <p><strong>Description</strong>:<strong> </strong>The "avasus_dataset.csv" dataset (see Table 1) originates from participants of the "Training Course on Underlying Cause-of-Death Coding – ICD-10". The course was available on the Brazilian National Health System - AVASUS (Brasil, 2024a). This dataset provides elementary data to analyze the course's scope and participant profiles.</p> <p><br><strong>Note</strong>: The dataset's content is provided in Brazilian Portuguese (pt-br), originating from native speakers.</p> <p><strong>Table 1: </strong>Description of AVASUS dataset features. </p> <div> <table> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>Datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>certificate</strong></p> </td> <td> <p>The period in which the course participant obtained the right to a certificate.</p> </td> <td> <p>Datetime</p> </td> <td> <p>year-month-day hours, minutes, and seconds.</p> </td> </tr> <tr> <td> <p><strong>gender </strong></p> </td> <td> <p>Gender of the course participant. </p> </td> <td> <p>Categorical</p> </td> <td> <ul> <li> <p>Female;</p> </li> <li> <p>Male; or</p> </li> <li> <p>Not informed.</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>region</strong></p> </td> <td> <p>Brazilian region in which the participant resides.</p> </td> <td> <p>Categorical</p> </td> <td> <ul> <li> <p>North;</p> </li> <li> <p>Northeast;</p> </li> <li> <p>Central-West;</p> </li> <li> <p>Southeast;</p> </li> <li> <p>South;</p> </li> <li> <p>Abroad; or</p> </li> <li> <p>Not reported.</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>course_evaluation</strong></p> </td> <td> <p>A score given to the course by the participant. </p> </td> <td> <p>Numerical</p> </td> <td> <p>0, 1, 2, 3, 4, 5, or NaN.</p> </td> </tr> <tr> <td> <p><strong>evaluation_commentary</strong></p> </td> <td> <p>Comment made by the participant about the course.</p> </td> <td> <p>Categorical</p> </td> <td> <p>Free text or NaN.</p> </td> </tr> </tbody> </table> </div> <p> </p> <p><strong>Dataset name:</strong><em> cbo_dataset.csv </em></p> <p><strong>Version: </strong>1.0 </p> <p><strong>Dataset period:</strong> July 24, 2018 - February 22, 2024</p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>1135</p> <p><strong>Number of Attributes: 6</strong></p> <p><strong>Missing Values: </strong>Yes</p> <p><strong>Area(s): </strong>Health and education<strong> </strong></p> <p><strong>Sources: </strong></p> <ul> <li>Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2024a);</li> <li>Brazilian Occupational Classification (CBO) (Brasil, 2024b);</li> <li>National Registry of Health Establishments (CNES) (Brasil, 2024c); </li> <li>Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2024d). </li> </ul> <p><strong>Description</strong>:<strong> </strong>The "cbo_dataset.csv" dataset (see Table 2) originates from participants of the "Training Course on Underlying Cause-of-Death Coding – ICD-10". The course was available on the Brazilian National Health System - AVASUS (Brasil, 2024a). This dataset provides elementary data to analyze the course's scope and the participants' professional profiles.</p> <p><br><strong>Note</strong>: The dataset's content is provided in Brazilian Portuguese (pt-br), originating from native speakers.</p> <p><strong>Table 1: </strong>Description of AVASUS dataset features. </p> <div> <table> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>Datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>gender </strong></p> </td> <td> <p>Gender of the course participant. </p> </td> <td> <p>Categorical</p> </td> <td> <ul> <li> <p>Female;</p> </li> <li> <p>Male; or</p> </li> <li> <p>Not informed.</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>region</strong></p> </td> <td> <p>Brazilian region in which the participant resides.</p> </td> <td> <p>Categorical</p> </td> <td> <ul> <li> <p>North;</p> </li> <li> <p>Northeast;</p> </li> <li> <p>Central-West;</p> </li> <li> <p>Southeast;</p> </li> <li> <p>South;</p> </li> <li> <p>Abroad; or</p> </li> <li> <p>Not reported.</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>course_evaluation</strong></p> </td> <td> <p>A score given to the course by the participant. </p> </td> <td> <p>Numerical</p> </td> <td> <p>0, 1, 2, 3, 4, 5, or NaN.</p> </td> </tr> <tr> <td> <p><strong>evaluation_commentary</strong></p> </td> <td> <p>Comment made by the participant about the course.</p> </td> <td> <p>Categorical</p> </td> <td> <p>Free text or NaN.</p> </td> </tr> <tr> <td> <p><strong>CBO_Code</strong></p> </td> <td> <p>Participant's occupation code.</p> </td> <td> <p>Numerical</p> </td> <td> <p>Participant's professional occupation code.</p> </td> </tr> <tr> <td> <p><strong>CBO_Description</strong></p> </td> <td> <p>Textual description of the participant's professional occupation.</p> </td> <td> <p>Categorical</p> </td> <td> <p>Text coded according to the Brazilian Classification of Occupations.</p> </td> </tr> </tbody> </table> </div> <p> </p> <p><strong>REFERENCES</strong></p> <p>Brasil (2024a). AVASUS - Virtual Learning Environment of the Brazilian Health System. Available from: <a href="https://avasus.ufrn.br/local/avasplugin/dashboard/transparencia.php">https://avasus.ufrn.br/local/avasplugin/dashboard/transparencia.php</a>. Accessed Jul 8, 2024.</p> <p>Brasil (2024b). CBO - classificação brasileira de ocupações. Available from: <a href="https://cbo.mte.gov.br/cbosite/pages/home.jsf">https://cbo.mte.gov.br/cbosite/pages/home.jsf</a>. Accessed Oct 23, 2024.</p> <p>Brasil (2024c). CNES - cadastro nacional de estabelecimentos de saúde. Available from: <a href="https://cnes.datasus.gov.br/">https://cnes.datasus.gov.br/</a>. Accessed Oct 23, 2024.</p> <p>Brasil (2024d). IBGE - Instituto Brasileiro de Geografia e Estatística. Estimativas da População. Available from: <a href="https://agenciadenoticias.ibge.gov.br/agencia-noticias/2012-agencia-de-noticias/noticias/39525-censo-2022-informacoes-de-populacao-e-domicilios-por-setores-censitarios-auxiliam-gestao-publica">https://agenciadenoticias.ibge.gov.br/agencia-noticias/2012-agencia-de-noticias/noticias/39525-censo-2022-informacoes-de-populacao-e-domicilios-por-setores-censitarios-auxiliam-gestao-publica</a>. Accessed Jun 19, 2024.</p> <p> </p> <p><strong>ARTICLE:</strong></p> <p>Data Report: Online Learning Module of the ``Training Course on Underlying Cause-of-Death Coding – ICD-10'' of the Virtual Learning Environment of the Brazilian Health System<br> </p> <p><strong>AUTHORS:</strong></p> <p>Aldiney J. Doreto<sup>1,2</sup>, António M. Teixeira<sup>3</sup>, Janaina L. R. S. Valentim<sup>1,4</sup>, João P. Q. Santos<sup>1,4</sup>, Talita K. de B. Pinto<sup>1</sup>, Yluska M. M. B. Mendes<sup>5</sup>, Aline P. Dias<sup>1</sup>, Karla M. D. Coutinho<sup>1,6</sup>, Ednara N. Gonçalves<sup>1</sup>, Andréa S. Pinheiro<sup>1</sup>, Felipe Fernandes<sup>1</sup>, Natalia A. N. Batista<sup>1,7</sup>, Karilany D. Coutinho<sup>1,8</sup>, and Ricardo A. M. Valentim<sup>1,8</sup></p> <p> </p> <p><sup>1</sup>Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil </p> <p><sup>2</sup>University of Minho (UMinho)/Open University of Portugal (UAb), Lisbon, Portugal</p> <p><sup>3</sup>Department of Education and Distance Learning, Open University of Portugal (UAb), Lisbon, Portugal</p> <p><sup>4</sup>Advanced Nucleus for Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, RN, Brazil</p> <p><sup>5</sup>Secretariat of Health Surveillance and Environment of the Brazilian Ministry of Health, Brasilia, Federal District, Brazil</p> <p><sup>6</sup>Health Sciences Graduate Program, Federal University of Rio Grande do Norte, Natal, Brazil</p> <p><sup>7</sup>Social Sustainability and Development Graduate Program, Open University of Portugal, Lisbon, Portugal</p> <p><sup>8</sup>Department of Biomedical Engineering, Federal University of Rio Grande do Norte, Natal, RN, Brazil</p>
Data Set of an online controlled experiment to study adaptive learning
<p>Online-controlled experiment evaluation - Data Set</p> <p>Digital learning platforms are more and more used in blended classroom scenarios in Germany. However, as learning processes are different among students, adaptive learning platforms can offer personalized learning, e.g. by individual feedback and corrections, task sequencing, or recommendations. As digital learning platforms are already used in classroom settings, we propose the transformation of these plat-forms into adaptive learning environments. To measure the effectiveness and improvements achieved through the adaptions an online-controlled experiment design is created. In our experiment, we therefore investigate the effectiveness of different inter-ventions on a large user group in a four-month online-controlled experiment. For this purpose, the highly frequented German learning platform Orthografietrainer.net was transformed into an adaptive learning platform and users were randomly assigned to different interventions.</p> <p>The experimental design is published here: N. Rzepka, K. Simbeck, H.-G. Müller, and N. Pinkwart An Online Controlled Experiment Design to Support the Transformation of Digital Learning towards Adaptive Learning Platforms Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU,, SciTePress, 2022, ISBN 978-989-758-562-3 </p> <p>The architectural concept is published here: Rzepka, N., Simbeck, K., Müller, H.-G. & Pinkwart, N., (2022). Adaptive Learning as a Service – A concept to extend digital learning platforms?. In: Henning, P. A., Striewe, M.-0. 0. & Wölfel, M.-0. 0. (Hrsg.), 20. Fachtagung Bildungstechnologien (DELFI). Bonn: Gesellschaft für Informatik e.V.. (S. 237-238). DOI: 10.18420/delfi2022-049 </p> <p>The findings of this experiment are published here: tba</p> <p>The code to this evaluation can be found on Zenodo: <a href="https://doi.org/10.5281/zenodo.7755546">10.5281/zenodo.7755546</a></p>
Data Set: Solution Probability in Online Learning Environments
<pre>Solution Probability Model and Fairness Evaluation This in-session prediction model seeks to predict the users’ performance on the Orthografietrainer.net platform. The target variable is binary and predicts if the user will do the following sentence correctly or not. For fairness evaluations the best models (MLP and DTE), and the worst model (SVM) are considered. A random state is not set, thus, results might differ marginally. A detailed description of the solution probability model and the fairness evaluation can be found here: tba</pre>
Adaptation and evolution of teaching method for university programming subject to the online learning environment - Changelog Data
<p>This dataset contains raw data from changelogs of students studying Operating Systems class at the Technical University of Košice in the year 2020/2021.</p> <p>All of the data is anonymized and all names are replaced with the string *Anonymized name*. All of the content is in the Slovak language.</p>
Adaptation and evolution of teaching method for university programming subject to the online learning environment
<p>This is a full dataset for the article "Adaptation and evolution of teaching method for university programming subject to the online learning environment". Individual parts of this dataset can be found at the following URLs:</p> <p>- The GitLab commit dataset can be found at https://doi.org/10.5281/zenodo.7767673;<br> - student survey dataset can be found at https://doi.org/10.5281/zenodo.7785161;<br> - student point gain dataset can be found at https://doi.org/10.5281/zenodo.7785190;<br> - and changelog dataset can be found at https://doi.org/10.5281/zenodo.7767694.</p> <p>Since the changelog dataset is not publicly available due to the identifiable nature of the data and the risk of compromising participants’ privacy and confidentiality, this dataset is also not open. If you want to get access you must request it.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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