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395 results for “Teacher”
Teachers Leading the Front Lines - Adolescent
ClinicalTrials.gov study NCT06248203. IPD Sharing: YES. Countries: 1. Publications: 16.
Implementing blended learning for clinician teachers: Identifying their needs and its impact on faculty development initiatives
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Interview guide of study conducted on: Identification of the challenges teachers face in teaching small problem-based learning (PBL) groups in the College of Medicine, King Faisal University, Kingdom of Saudi Arabia
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Use of photo-elicitation to evoke and solve dilemmas that prompt changes primary school teachers' visions
<p>Annex 1 (Supplementary Material) from</p> <p>Bautista García-Vera, A., Rayón Rumayor, L., & de la Heras Cuenca, A. M. (2019). Use of photo-elicitation to evoke and solve dilemmas that prompt changes primary school teachers' visions. Journal of New Approaches in Educational Research, 9(1), 1-16. https://doi.org/10.7821/naer.2020.1.499</p>
Anonymised dataset about Finnish teachers' digital technology usage in teaching
<p>Data have been collected in Finland in 2017–2019 from basic education teachers.The dataset is fully anonymised to make it suitable for public opening to support the reported results submitted for publication.</p> <p>Dataset contains the following variables:</p> <ol> <li>Gender (0=female, 1 = male)</li> <li>Municipality (unique numeric identifier)</li> <li>School (unique numeric identifier)</li> <li>Administrative_district (unique numeric identifier)</li> <li>Usage_of_computers_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_tablets_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_smartphones_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_digital_learning_environments_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_online_learning_materials_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_games_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_internet_for_information_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_videa_sharing_services_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_blogs_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_networking_services_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_digital_assessment_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_mobileapps_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_email_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Usage_of_office_suite_in_teaching (0 = “never”, 1 = “sometimes”, 2 = “weekly”, 3 = “daily”, 4 = “several hours per day”)</li> <li>Total_scores_in_ICTskilltest (range 0-30)</li> <li>Age (years in numbers)</li> <li>Digital_self_efficacy (perceived level of competence on a scale of 0-100% in relation to one's own work)</li> <li>In_service_training (perceived adequacy level on a scale of 0 to 100% in relation to one's own work)</li> <li>STEM_teacher (in the case of a teacher of STEM subjects = 1, otherwise = 0)</li> <li>Humanities_social_science_teacher (in the case of a teacher of humanities or social science subjects = 1, otherwise = 0)</li> <li>Arts_skills_teacher (in the case of a teacher of arts and skills subjects = 1, otherwise = 0)</li> <li>Use_of_devices_for_teaching (the sum variable of the use of digital devices, i.e., the maximum use of any type of device)</li> <li>Versatility_of_usage (the sum variable for regular (= at least weekly) use of different applications)</li> <li>Teacher_type (0 = classroom teacher, 1 = subject teacher, missing value = other teaching staff)</li> <li>Classroom_teacher in the case of a classroom teacher = 1, otherwise = 0</li> </ol>
Modifiying Biggs (1999)'s diagram to include teacher's level of stimulation and creativity
<p>I upgraded Biggs (1999) diagram on teaching method, student orientation and level of engagement. I included teacher's level of stimulation and creativity. I proposed student's research proposal with breakout session as an optimal teaching method to reach the highest level of student's engagement.</p>
Teachers' responses per School for the research project "Perspectives on Learning Technologies"
<p>A Microsoft Excel dataset that includes teachers' responses for a research project entitled "Perspectives on learning Technologies" conducted in year 2014. Teachers from three Schools (Psychology, Electrical Engineering Electronics and Computer Science-EEE & CS and Law) of the University of Liverpool participated in it.</p>
The Teachers' Occupational Wellbeing Study
<p><em><span lang="EN-US">The Teachers’ Occupational Wellbeing Study</span></em><span lang="EN-US"> examines Finnish teachers’ occupational wellbeing, along with its predictors and outcomes, using a comprehensive survey questionnaire. Attention is also given to demographic aspects, such as regional differences and variation across educational levels and institutions. The aim is to use the findings to inform interventions, teacher training, and policy development.</span></p> <p><span lang="EN-US">The study began in spring 2020, at the onset of the COVID-19 pandemic and was started by Professor Katariina Salmela-Aro (1961-2025). Since then, data have been collected biannually every spring and autumn. In addition, the collection of longitudinal data began in spring 2024. This design enables the examination of both general trends (cross-sectional data) and individual-level changes over time (longitudinal study).</span></p> <p><span lang="EN-US">The survey data have been gathered in close collaboration with the <a href="https://www.oaj.fi/en/" target="_blank" rel="noopener">Trade Union of Education in Finland</a> (OAJ), consisting of responses from its members. The participants include teachers from across Finland and all educational levels.</span></p> <p><span lang="EN-US">The study is currently part of and funded by the EDUCA Flagship. More information about the study and its main results can be found on the <strong><a href="https://educaflagship.fi/en/research/study-of-teachers-work-related-well-being">EDUCA Flagship website </a></strong>and in </span>the supporting materials provided on this page. </p> <p>The project is currently led by University Lecturer Dr. Lauri Hietajärvi (<a href="mailto:lauri.hietajarvi@helsinki.fi" target="_self">lauri.hietajarvi@helsinki.fi</a>) from University of Helsinki, Finland, and managed by Dr. Olli-Pekka Heinimäki (<a href="mailto:lauri.hietajarvi@helsinki.fi" target="_self">olli-pekka.heinimaki@helsinki.fi</a>) from the same university. </p> <p>****</p> <p>Selected sections of datasets from 2020 to 2024 have been made openly available for anyone to use. Please read the relevant documentation carefully before working with the data. Should you have any questions, please contact the project management.</p> <p>The attached documents includes:</p> <ul> <li><strong>Teacher occupational wellbeing study data collection report – openly distributed version</strong>: Details about the project and data collection.</li> <li> <p><strong>Time Series 2020–2025</strong>: Key wellbeing trends across the full dataset (each time point).</p> </li> <li> <p><strong>Representativeness of the Data</strong>: Estimates of representativity at each measurement point.</p> </li> <li> <p><strong>Teacher_occupational_wellbeing_measures_overall</strong>: Summary of all measures included in the survey across timepoints.</p> </li> <li> <p><strong>README</strong>: Information about the open-access datasets and how they were generated from the original data.</p> </li> <li> <p><strong>2020_Spring.zip – 2024_Spring.zip</strong>: Nine open-access datasets collected at different time points (each provided in both .sav and .csv formats).</p> </li> <li> <p><strong>Scale documentation 2020–2024 open</strong>: Scale documents for each of the published nine datasets.</p> </li> </ul> <p> </p>
Best Practices and Strategies of Teachers in Virtual Learning Instructions Amidst Covid-19 Pandemic
<p><span>This study investigates the demographic profiles, educational backgrounds, and professional development of teachers, alongside their adoption of various teaching strategies in a virtual instructional context. Through analysis of multiple datasets concerning teachers’ age, gender, educational attainment, field of specialization, and the seminars and training they attended, the study evaluates the prevalence of specific teaching practices and their effectiveness. Additionally, using Chi-square tests, the study examines the potential relationships between teacher profiles and the extent of their practice implementations. Findings reveal a workforce characterized by a high level of experience and academic achievement, with a significant gender disparity. Crucially, no significant statistical correlations were found between teacher demographics or professional backgrounds and the teaching practices employed, suggesting a standardized adoption of educational strategies across various teacher profiles. The results emphasize the role of professional development in maintaining teaching efficacy in diverse educational settings, particularly in adapting to virtual platforms.</span></p>
Survey on teachers professional competences 2024 Spain
<p>782 teachers answer to a survey about how they plan, implement and assess their teaching at school.</p>
Dataset of use, acceptance, attitude and training needs in active method-ologies of Spanish teachers.
<p>Dataset collected for the purpose of analyzing various aspects related to the implementation of active methodologies (AM) in the educational field in Spain. This dataset includes data on: Use of active methodologies: information on the frequency and the way in which teachers use different active methodologies in their classes, such as cooperative learning, project-based learning, challenge-based learning, among others. Student acceptance: evaluation of how students perceive and respond to active methodologies, observing their willingness to participate and the benefits they consider derived from these methodologies. Teacher attitudes: Teachers' opinions and perceptions about active methodologies, including their effectiveness, advantages, and difficulties in implementing them in the classroom. Training needs: information on teachers' perceptions about the need for training in the use of active methodologies, particularly in emerging areas such as the use of digital technologies in the classroom, and the preparation to implement these strategies more effectively.</p>
Teacher Tapp Survey 3 - 3rd March 2021
<p>This survey was conducted with 6000+ UK teachers on 3rd March 2021 using 'Teacher Tapp' - see www.teachertapp.co.uk</p> <p>Questions asked were:</p> <p>1) When I was a child: In secondary school, I had some lessons where the science teacher and a teacher of another subject taught lessons together' - to what extent do you agree?</p> <p>2) 'When I was a child: In secondary school, I *never* had a lesson where teachers from two subjects taught a lesson together' - to what extent do you agree?</p> <p>3) 'Epistemic Insight means knowledge about knowledge (e.g. how disciplines interact). What are the biggest barriers to teaching this comprehensively in your school?' (including e.g. pressure of teaching to the test)</p> <p>4) 'Have you or are you expecting to talk to pupils about COVID-19 for any of the following reasons?' (including e.g. 'to sort truths from misinformation')</p>
Teacher Tapp Survey 2 - 15th August 2020
<p>This survey was conducted with 6000+ UK teachers on 15th August 2020 using 'Teacher Tapp' - see www.teachertapp.co.uk</p> <p>Questions asked were:</p> <p>1) Have you or are you expecting to talk to pupils about COVID-19 for any of the following reasons? (including e.g. 'to sort truths from misinformation'.</p> <p>2) 'My teaching includes ensuring students learn about the similarities and differences between (curriculum) disciplines' - to what extent do you agree?</p> <p>3) 'When we try to understand a global challenge (like COVID-19), students need to appreciate that different disciplines (e.g. science, philosophy, history, mathematics) can give different perspectives on how to answer' - to what extent do you agree?</p> <p>4) 'It’s important for students to know about the similarities and differences between (curriculum) disciplines?' - to what extent do you agree?</p>
Survey among English Language Teachers of Kazakhstan
<p>Survey among English Language Teachers of Kazakhstan on ELT.</p>
Qualitative research: Behavioral antecedents of crowdsourcing in science: academic teachers' perspective
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Focus research group: Behavioral antecedents of crowdsourcing in science: academic teachers' perspective
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NOTICE OF THE DELETION OF UNPUBLISHED ARTICLE: Competence, Leadership Skills, and Professional Commitment of Elementary Teachers in the National Capital Region
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Effects of Wages on the Well-being of University Teachers in China
<p>This dataset contains survey data on the job well-being of university teachers in China, including variables such as salary satisfaction, perceived workload, organizational culture, and student interaction. Data were collected in 2023 from 283 teachers through an online questionnaire and are stored in SAV format.</p>
Teachers as creative agents: How self-beliefs and self-regulation drive teachers' creative activity (Study 1, Dataset)
<p>Dataset for: Zielińska, A., Lebuda, I., Gop, A., & Karwowski, M. (2024, Study 1). Teachers as creative agents: How self-beliefs and self-regulation drive teachers’ creative activity. <em>Contemporary Educational Psychology, 77</em>, 102267. https://doi.org/10.1016/j.cedpsych.2024.102267</p>
HAND IN HAND: Empowering Teachers Across Europe to Deal with Social, Emotional and Diversity Related Career Challenges - International Dataset
<p><span>HAND IN HAND: Empowering teachers across Europe to deal with social, emotional and diversity related career challenges (HAND:ET) is a European (Erasmus K3) policy experimentation project that brings together 11 partners and 13 associated partners from seven countries (Austria, Croatia, Denmark, Germany, Portugal, Slovenia, and Sweden).</span></p> <p><span>The project focuses on teachers by supporting their development of social and emotional competencies as well as their diversity awareness (SEDA) to empower them for the complexity of everyday working life with increasingly diverse classrooms and enable them to deal flexibly with new challenges. It also puts the teachers' well-being at the centre by highlighting how developing SEDA competencies simultaneously fosters self-care for teachers, giving a central role to the voices of teachers.</span></p> <p><span>Information on the project can be accessed at: <a href="https://www.handinhand.si/en/" target="_blank" rel="noopener">https://www.handinhand.si/en/</a></span></p> <p><span>Further information on the project and the evaluation results can be found in the following publication: Kozina, A. (2024). Empowering Teachers Across Europe to Deal with Social, Emotional and Diversity Related Challenges, Volume 1: Experimentation Perspectives. Waxman.</span></p> <p><span>Information on the instruments used in the evaluation is provided in the attached document "HAND_ET_Scale_Documentation.pdf".</span></p> <p><span>Design: All countries implementing the experiment (Austria, Croatia, Portugal, Slovenia and Sweden) invited schools to participate. The schools participating in the experiment were randomly allocated to either the experimental or the control group. Central to the experiment’s design, schools had to agree to their participation in either condition (experimental or control). The intention was to test the effectiveness of the HAND:ET system in promoting SEDA competencies: to compare the changes in the same competencies from before implementing the HAND:ET system (pre-test) to after implementing the system (post-test) in a group of teachers (and other school staff) who took part in the experiment (experimental group) with a group of teachers (and other school staff) who did not participate (the control group). </span></p> <p><span> </span><span>Data collection period: T1 (pre-test) data was collected August to September 2022, T2 (post-test) data was collected Mai to July 2023.</span></p> <p><span> </span><span>Sample: The HAND_ET datafile includes primary and/or secondary teachers from Austria, Croatia, Portugal, Slovenia, and Sweden, who responded to the questionnaire either at first time point or second time point or both. In Austria, teachers teaching students in grades 1 to 4, in Croatia those teaching students in grades 1 to 8, in Portugal those teaching students in grades 1 to 12, in Slovenia those teaching students in grades 1 to 9, while in Sweden teachers teaching students in grades 4 to 9 participated.</span></p> <p><span> </span><span>The HAND_ET datafile is structured as follows: </span></p> <p><span>·</span><span> </span><span>Variables from 'IDS' to 'Gender' include general information about the participant (e.g group, participation in pre- or post-test, gender)</span></p> <p><span>·</span><span> </span><span>Variables from 'TC06exp' to 'TCN22_TCN2204' capture responses from questionnaire items administered during the pre-test.</span></p> <p><span>·</span><span> </span><span>Variables from 'TC02_2' to 'TCN30_TCN30Q04_TCN30G03_2' contain responses to questionnaire items used in the post-test data collection.</span></p> <p><span>·</span><span> </span><span>Variables from 'observe' to 'coopteach' include scale scores for the scales administered in pre-test.</span></p> <p><span>·</span><span> </span><span>Variables from 'observe_2' to 'coopteach_2' include scale scores for the scales administered in post-test.</span></p> <p><span>·</span><span> </span><span>Variables from 'observe_D' to 'CloseC_D' include scale scores calculated as differences between measurements at Time 1 (T1) and Time 2 (T2) for all questionnaire scales.</span></p> <p><span>·</span><span> </span><span>Variables from 'observe_D.1' to 'CloseS_D.5' contain scale score differences (T1-T2) with missing values imputed. As we did 5 imputations, there are five variables for each scale (as indicated at the end of the variable name).</span></p> <p><span> </span><span>The scale score for each participant at each point in time was computed as the arithmetic mean of responses to the items of a scale measuring a SEDA construct. A scale value was only computed if responses for at least half of the items of a scale were available. No overall scale score was computed for multidimensional constructs. Subscales were treated as separate scales in the analysis. The difference score was computed as the T2 minus T1 scale score. A positive value corresponds to an increase of the scale score and a negative value indicates a decrease in the scale score of T2 compared to T1.</span></p> <p><span> </span><span>The dataset includes variables with imputed scale scores. We only imputed the difference score for each of the outcome variables five times. The imputation was carried out separately for each of the scales based on the three variables: group, T1, and T2 scale score. We used predictive mean matching with the R package mice (Buuren & Groothuis-Oudshoorn, 2011) for imputation. The teachers that have a missing in the group variable have no imputed values.</span></p>
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)
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.