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29 results for “distance learning”
Students' perceived obstacles with Forced Online Distance Learning during the CoVID-19 outbreak and their preferences to continue with the introduced teaching methods after the reopening of the University of Maribor [Project documentation]
<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 first project with the working title: The Side Effects of Forced Online Distance Education (FODE).</p> <p>The aim of the second study, conducted during the first wave of the epidemic in March 2020, was to investigate the response of university students to the new situation. The project documentation provided for the Forced Online Distance Learning (FODL) consists of:</p> <ul> <li>abstract,</li> <li>instrument,</li> <li>copy of the descriptive statistics,</li> <li>and SPSS dataset.</li> </ul>
Data - Teaching and Learning of Introduction to Software Engineering Experimentation to Distance-Learning Students: a Quasi-Experiment
<p>Data from a quasi-experiment on "Teaching and Learning of Introduction to Software Engineering Experimentation to Distance-Learning Students: a Quasi-Experiment"</p> <p> </p> <p>Funded by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) 311503/2022-5</p>
BRAIN Journal-Developing Distance Learning Environments in the Context of Cross-Border Cooperation-Figure 5. Weekly session stats for 2016
<p>On following figures, statistic usage is given as Monthly and Weekly statistic for individual users and sessions. The number of the individual users and of the sessions is far better for the 2015 period, especially for the extent of time until the end of June. This is reasonable because it was during the project and the site is frequently put forward in promotional conferences, in press, in direct contacts with schools, and companies. After that period the site was not promoted additionally, and the only pointer to the site is a number of links found on our institutional websites as one of the services we are offering to the students. Considering all that, the figures for 2015 and even 2016, seems to be quite satisfactory. (Figure 1, Figure 4, and Figure 5).</p>
BRAIN Journal-Developing Distance Learning Environments in the Context of Cross-Border Cooperation-Figure 3. Monthly user/session stats for 2016
<p>It is also interesting to observe that in Figure 3, where the usage for 2016 is presented, that in the non-promotional period, the site is mostly used in January (before January exam term), in April, May, and June (before the June exam session and during colloquial exams) or in July and August (before the September exam session).</p>
BRAIN Journal-Developing Distance Learning Environments in the Context of Cross-Border Cooperation-Figure 2. Monthly user/session stats for 2015
<p>On following figures, statistic usage is given as Monthly and Weekly statistic for individual users and sessions. The number of the individual users and of the sessions is far better for the 2015 period, especially for the extent of time until the end of June. This is reasonable because it was during the project and the site is frequently put forward in promotional conferences, in press, in direct contacts with schools, and companies. After that period the site was not promoted additionally, and the only pointer to the site is a number of links found on our institutional websites as one of the services we are offering to the students. Considering all that, the figures for 2015 and even 2016, seems to be quite satisfactory. (Figure 1, Figure 4, and Figure 5). </p>
BRAIN Journal-Developing Distance Learning Environments in the Context of Cross-Border Cooperation-Figure 1. Components of EduWebCast System
<p>The aim of this partnership would be to implement an infrastructure for live and on-demand video streaming of learning material for the targeted groups and, to this purpose, to establish a long and fruitful cooperation between teachers, pupils, and students on both sides of the border. The joint creation and administration of the webcast project is the ground stone of the partnership between the two universities and will result in more common projects based on the materials obtained through the project, contests between pupils and students, possible periodic educational exchanges. </p>
Questionnaire and Data of Neck Pain and Distance learning among university Students During COVID-19
<p> The shift to online learning during COVID-19 led to increased musculoskeletal discomforts and impacted students' quality of life. Neck pain, once a minor issue, has become more prevalent due to prolonged electronic device use in new learning methods. This study aims to measure the prevalence of neck pain among University of Sharjah (UOS) students during the COVID-19 online learning period and to investigate the factors that provoked it.</p><p> This cross-sectional study used an online survey distributed to UOS students via social media from February 16 to March 12, 2021. Demographic data, Neck Disability Index (NDI) assessments, and pain management information were gathered and analyzed using SPSS 24 through univariate and bivariate methods.</p><p>The prevalence of neck pain during COVID-19, among 325 UOS students was found to be 62.7%, 64.41% of which had neck pain at the time of doing the survey. The mean NDI percentage point was 19.19%. The most common location of pain was the back of the neck 38.77%). Participants reported prolonged sitting (81.23%) while leaning forward as the most prevalent cause of their neck pain. Bivariate analysis showed that the most significant factors affecting the NDI were lifting, personal care, headaches, concentration, sleeping, recreational activities, reading, anxiousness, depression, and work. Moreover, subjects who did not seek medical attention treatment had significantly higher NDI scores as compared to those who did (83.89 vs 125.80, p=0.002).</p><p>By determining the prevalence of neck pain among UOS students, this research can attract attention to the importance of postural changes and time spent in e-learning on neck pain, and the gaps of previously published articles can be filled. Future studies about neck pain and additional preventative measures should be catalyzed in the UAE.</p>
The Use of Open Educational Resources (OERs) in Teaching and Learning in Higher Education Distance Learning Programmes in Cameroon
<p>A cross sectional descriptive analysis was adopted for this study. The goal was to was find out the use OERs in teaching and learning in higher education distance learning by taking a snapshot from a cross-section of the population. The institution under study is the University of Buea. The research targeted all 25 students at the master level of the Distance Education Program in the Faculty of Education, University of Buea. All the students were purposively sampled due to the small nature of the target population. A carefully designed questionnaire was used for data collection. The questionnaire had both closed and opened-ended questions which required respondents to select from a variety of responses to cover the research questions. Data was calculated and presented using frequencies tables and bar charts. After getting description of student’s responses, an analysis was done to show the situation of the use of OERs and in teaching and learning.</p>
A metadata-based approach for research discipline prediction using machine learning techniques and distance metrics
<p>The dataset is based on the paper: </p> <p>Hoang-Son Pham, Hanne Poelmans and Amr Ali-Eldin ‘’A metadata-based approach for research discipline prediction using machine learning techniques and distance metrics’’, IEEE Access (2023).</p> <p>The dataset includes: </p> <p>1. a list of project metadata extracted from FRIS portal</p> <p>2. a list of VODS disciplines</p> <p>3. a distance matrix</p> <p> </p> <p>* Kindly refer to our paper for more details on the dataset.</p> <p>https://ieeexplore.ieee.org/document/10156853</p>
Raw Data for: "Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances"
<p>This repository contains all the raw data to reproduce the manuscript:</p> <p>D. Schwalbe-Koda et al. "Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances". arXiv:2307.10935 (2023)</p> <p>The raw data should be used in combination with the code hosted on GitHub: <a href="https://github.com/dskoda/Zeolites-AMD">https://github.com/dskoda/Zeolites-AMD</a>.</p> <p><strong>Description of the data</strong></p> <p>The data in this link contains all necessary information to reproduce the manuscript. In combination with the code hosted on GitHub, it can be visualized and analyzed accordingly. The full description on the columns and results is available on the GitHub code.<br> The data files in this repository are:</p> <p>- `hparams_rnd_*.json`: results of the hyperparameter optimization of all classifiers studied in this work. The data was produced by randomly sampling the train-validation-test sets. In some cases, the data was normalized (`_norm_`), and the train set was kept `balanced` or `unbalanced`.<br> - `hyp_dm`: distance matrix of all hypothetical zeolites towards the known zeolites<br> - `hyp_predictions`: predictions of the synthesis conditions for all hypothetical zeolites<br> - `xgb_ensembles*`: pickle files containing the serialized ensemble models used in the evaluation of the data in this work. The models can be loaded with the `xgboost` Python package.</p> <p><strong>License</strong></p> <p>The data and all the content from this repository is distributed under the Creative Commons Attribution 4.0 (CC-BY 4.0)</p> <p>This work was produced under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.</p> <p>Dataset released as: LLNL-MI-854709.</p>
Distance learning and face-to-face learning in medical PBL course during COVID-19 pandemic: an investigation and teaching experience
<p>Our research provides an experience for the PBL class focusing on discussion and communication. In the post-pandemic era, whether face-to-face or distance learning, classes should be adjusted properly to let students conduct effective communication in time.</p>
The Effectiveness of Distance Yoga Learning in Improving Ma-ternal Pandemic-related Depression and Stress During Pregnancy
ClinicalTrials.gov study NCT06913582. IPD Sharing: NO. Countries: 1. Publications: 20.
Distance and Hybrid Learning for Dental Student During COVID-19 Pandemic
ClinicalTrials.gov study NCT04401371. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Distance Learning and Neck Problems During Pandemic Period
ClinicalTrials.gov study NCT04925999. IPD Sharing: NO. Countries: 1. Publications: 2.
Data from: Isolation by distance, resistance and/or clusters? Lessons learned from a forest-dwelling carnivore inhabiting a heterogeneous landscape
Open the record for dataset details and reuse information.
Data for EMO2023 Paper "Feature-based Benchmarking of Distance-based Multi/Many-objective Optimisation Problems: A Machine Learning Perspective"
<p><strong>Data for Paper "Feature-based Benchmarking of Distance-based Multi/Many-objective Optimisation Problems: A Machine Learning Perspective"</strong></p> <p><br> The file <strong>dbmopp_dataset_perf.csv</strong> contains results from the 945 x 30 instances, with the following columns:</p> <ul> <li><em>design_id</em>: problem identifier</li> <li><em>n_var</em>: number of variables {2, ..., 20}</li> <li><em>n_obj</em>: number of objectives {2, ..., 10}</li> <li><em>nonident_ps</em>: non-identical Pareto sets {0 (no), 1 (yes)}</li> <li><em>var_density</em>: varying density {0 (no), 1 (yes)}</li> <li><em>n_discon_ps</em>: number of disconnected Pareto sets {0, ..., 6}</li> <li><em>n_local_fronts</em>: number of local fronts {0, ..., 6}</li> <li><em>n_resist_regions</em>: number of dominance resistance regions {0, ..., 6}</li> <li><em>instance_id</em>: instance (fold) identifier {1, ..., 30}</li> <li><em>budget</em>: number of evaluations performed by the algorithm {5000, 10000, 30000, 50000}</li> <li><em>algo</em>: multi-objective evolutionary algorithm {NSGAII, IBEA, MOEAD, Random}</li> <li><em>hypervolume</em>: hypervolume reached by the algorithm [0.0, 1.0]</li> </ul> <p> </p> <p>The file <strong>dbmopp_dataset_perf_aggregated.csv</strong> contains average results from the 945 problems, with the following columns:</p> <ul> <li><em>design_id</em>: problem identifier</li> <li><em>n_var</em>: number of variables {2, ..., 20}</li> <li><em>n_obj</em>: number of objectives {2, ..., 10}</li> <li><em>nonident_ps</em>: non-identical Pareto sets {0 (no), 1 (yes)}</li> <li><em>var_density</em>: varying density {0 (no), 1 (yes)}</li> <li><em>n_discon_ps</em>: number of disconnected Pareto sets {0, ..., 6}</li> <li><em>n_local_fronts</em>: number of local fronts {0, ..., 6}</li> <li><em>n_resist_regions</em>: number of dominance resistance regions {0, ..., 6}</li> <li><em>budget</em>: number of evaluations performed by the algorithm {5000, 10000, 30000, 50000}</li> <li><em>algo</em>: multi-objective evolutionary algorithm {NSGAII, IBEA, MOEAD, Random}</li> <li><em>hypervolume_avg</em>: average hypervolume reached by the algorithm [0.0, 1.0]</li> <li><em>best</em>: 1 if the corresponding algorithm obtains the best average hypervolume, 0 otherwise</li> </ul> <p> </p>
Assessing the Effect of Distance Learning Vocational Rehabilitation on Employment Outcomes of Veterans With Psychiatric Illness and Histories of Legal Convictions
ClinicalTrials.gov study NCT03331354. IPD Sharing: NO. Countries: 1. Publications: 0.
Face-to-Face Versus Distance Learning of Basic Suturing Skills
ClinicalTrials.gov study NCT05275829. IPD Sharing: NO. Countries: 1. Publications: 0.
Online Distance Learning Outcomes Compared to Traditional Classroom Learning in Medicine
ClinicalTrials.gov study NCT03079349. IPD Sharing: NO. Countries: 0. Publications: 1.
Improving Asthma Outcomes Through Spirometry Distance Learning
ClinicalTrials.gov study NCT01168635. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.