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195 results for “Educational Data”
Weather Data from Env. Education Center at Lancer Park 2018-08-27 t0 2018-09-14
<p># General Metadata for Lancer Park Environmental Education Center Atmospheric Sampling Station</p> <p>## Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <p> LP_weather_YYYY-MM-DD_metadata.txt<br> <br> Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>## Metadata File Created</p> <p> * 2019-10-27 by KF<br> <br> ## File Modified</p> <p>## Description</p> <p>These data are from a weather station installed at the Longwood University Environmental Education Center at Lancer Park (37.308189, -78.402768) as part of the Longwood Environmental Observatory.</p> <p>All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/</p> <p>## Station Specifics</p> <p> The specific at each site are:</p> <p> * Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor<br> * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300<br> * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300<br> * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket<br> * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor<br> * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor<br> * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor<br> * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor<br> * Data Collection - Campbell Scientific CR200 Data Logger<br> * The sensors are sampled every 15 minutes<br> <br> ## Measurement Parameters, units, and Variable Names</p> <p> * DATE - the date that the record was collected (YYYY-MM-DD)<br> * TIMESTAMP - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS)<br> * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. <br> * BattV_Avg - The average battery voltage (Volts)<br> * BattV - The battery voltage at the time of the sampling (Volts)<br> * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg)<br> * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg)<br> * BP_mmHg - The barometric pressure at the time of the sampling (mmHg)<br> * Rain_mm_Tot - The total rainfall during the sampling interval (mm)<br> * AirTC_Avg - The average air temperature during the sampling interval (dC) <br> * AirTC_Std - The standard deviation of the average air temperature (dC) <br> * AirTC - the air temperature at the time of the sampling (dC)<br> * RH - the relative humidity at the time of the sampling (%)<br> * RH_Min - the minimum relative humidity recorded (%)<br> * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) <br> * RH_Max - the maximum relative humidity recorded (%)<br> * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS)<br> * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data.<br> * BattV_Min - the minimum battery voltage (Volts)<br> * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) <br> * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2)<br> * SlrkW - the light flux density at the time of the sampling (kW/m^2)<br> * SlrMJ_Tot - the total light flux (MJ/m^2) <br> * WS_ms_Avg - the average wind speed during the sampling interval (m/s) <br> * WS_ms_Std - the standard deviation of the average wind speed (m/s) <br> * WS_ms - the wind speed at the time of the sampling event (m/s)<br> * WindDir - the wind direction at the time of the sampling event (degrees from true N)<br> * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s)<br> * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N)<br> * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N)<br> </p>
Extended data integration of motivational aspects in gamification and game-based learning educational designs
<p>This is the extended data for a systematic review article about the integration of motivational aspects in gamification and game-based learning educational designs related to teacher´s training and teacher´s professional development.</p>
Educational data collected from parents - regarding the analysis of online activities in schools in Romania (during the Covid-19 pandemic, March 2020 - April 2020)
<p>The responses of the 784 parents were collected through the questionnaire available at: <a href="https://forms.gle/Km8WE5QamrYYgXJi7" target="_new" rel="noopener"><strong>https://forms.gle/Km8WE5QamrYYgXJi7</strong></a></p> <p>It was designed with various types of responses, including binomial (yes/no), polynomial (multiple options), and open-ended responses, to capture a comprehensive range of data. This combined approach allows for both quantitative analysis of fixed-response questions and qualitative insights from open-ended questions. Patterns, correlations, and differences between various demographic groups and their experiences and attitudes toward online education can be identified.</p> <p>To protect the identity of the respondents and to obtain accurate responses, all data collected from teachers was anonymous. We did not collect any personal information whatsoever. This aspect was made clear to the respondents in the description of the questionnaire.</p>
Data sets - The attitude of computer science teachers to inclusive education, Motivation to teach, Perception of the possible impact of computer science on students with mental disabilities
<p>Data sets </p> <p>The attitude of computer science teachers to inclusive education, Motivation to teach, Perception of the possible impact of computer science on students with mental disabilities. <br>In the period from February to October 2024, a survey of 112 computer science teachers in Kazakhstan (Pavlodar region) was conducted to determine attitudes to inclusive education, motivation to teach, and perception of the possible impact of computer science on students with mental disabilities.</p> <p>Questionnaire <br>https://docs.google.com/document/d/1LzukKSqW_mHMZXbMtN0ecmmU4cKJiwgf0laTWBHQSng/edit?usp=sharing</p> <p><strong>This research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP14872400).</strong></p>
Times Higher Education Latin America Ranking's data, 2016-2019
<p>Top fifty latin american universities's data from Times Higher Education Latin America Ranking, 2016-2019</p>
Dataset for the exploration of data practices in Higher Education
<p>The present dataset contains the results of a survey distributed to all the professoriate of two universities, between January and March 2020.</p> <p>The several variables present in the dataset relate to the professional areas of knowledge and activity; the ongoing data practices in relation to six scales (as published in Raffaghelli. (2019) and theoretically explored in Raffaghelli (2019b); professional learning ecologies relating data practices in teaching and research.</p> <p>The script 1 covers some analysis relating specific scales.</p> <p>Other scripts might be added /welcomed as far as successive analysis are done over the shared data.</p> <p>========</p> <p>Raffaghelli, J. (2019) Delphi Study to validate the survey exploring academic awareness and engagement with data-driven practices. Zenodo. https://doi.org/10.5281/zenodo.3581290</p> <p>Raffaghelli, J. E. (2019b). Developing a framework for educators’ data literacy in the European context: Proposal, Implications and debate. In <em>International Conference on Education and New Learning Technologies EDULEARN</em> (pp. 10520–10530). Palma de Mallorca: IATED. https://doi.org/10.21125/edulearn.2019.2655</p>
Weather data from Environmental Education Center at Lancer Park from 2021-06-05 to 2021-07-19
<p>General Metadata for Lancer Park Environmental Education Center Atmospheric Sampling Station</p> <p>Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <pre><code>LP_weather_YYYY-MM-DD_metadata.txt</code></pre> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>Metadata File Created</p> <ul> <li>2019-10-27 by KF</li> </ul> <p>File Modified</p> <p>Description</p> <p>These data are from a weather station installed at the Longwood University Environmental Education Center at Lancer Park (37.308189, -78.402768) as part of the Longwood Environmental Observatory.</p> <p>All data are CC-BY and should be cited using the DOI available at <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p>Station Specifics</p> <p>The specific at each site are:</p> <pre><code>* Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300 * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300 * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor * Data Collection - Campbell Scientific CR200 Data Logger * The sensors are sampled every 15 minutes</code></pre> <p>Measurement Parameters, units, and Variable Names</p> <pre><code>* DATE - the date that the record was collected (YYYY-MM-DD) * TIMESTAMP - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS) * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Avg - The average battery voltage (Volts) * BattV - The battery voltage at the time of the sampling (Volts) * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg) * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg) * BP_mmHg - The barometric pressure at the time of the sampling (mmHg) * Rain_mm_Tot - The total rainfall during the sampling interval (mm) * AirTC_Avg - The average air temperature during the sampling interval (dC) * AirTC_Std - The standard deviation of the average air temperature (dC) * AirTC - the air temperature at the time of the sampling (dC) * RH - the relative humidity at the time of the sampling (%) * RH_Min - the minimum relative humidity recorded (%) * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RH_Max - the maximum relative humidity recorded (%) * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Min - the minimum battery voltage (Volts) * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2) * SlrkW - the light flux density at the time of the sampling (kW/m^2) * SlrMJ_Tot - the total light flux (MJ/m^2) * WS_ms_Avg - the average wind speed during the sampling interval (m/s) * WS_ms_Std - the standard deviation of the average wind speed (m/s) * WS_ms - the wind speed at the time of the sampling event (m/s) * WindDir - the wind direction at the time of the sampling event (degrees from true N) * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s) * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N) * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N)</code></pre>
Weather data from Environmental Education Center at Lancer Park from 2021-07-19 to 2021-08-17
<p>General Metadata for Lancer Park Environmental Education Center Atmospheric Sampling Station</p> <p>Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <pre><code>LP_weather_YYYY-MM-DD_metadata.txt</code></pre> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>Metadata File Created</p> <ul> <li>2019-10-27 by KF</li> </ul> <p>File Modified</p> <p>Description</p> <p>These data are from a weather station installed at the Longwood University Environmental Education Center at Lancer Park (37.308189, -78.402768) as part of the Longwood Environmental Observatory.</p> <p>All data are CC-BY and should be cited using the DOI available at <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p>Station Specifics</p> <p>The specific at each site are:</p> <pre><code>* Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300 * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300 * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor * Data Collection - Campbell Scientific CR200 Data Logger * The sensors are sampled every 15 minutes</code></pre> <p>Measurement Parameters, units, and Variable Names</p> <pre><code>* DATE - the date that the record was collected (YYYY-MM-DD) * TIMESTAMP - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS) * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Avg - The average battery voltage (Volts) * BattV - The battery voltage at the time of the sampling (Volts) * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg) * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg) * BP_mmHg - The barometric pressure at the time of the sampling (mmHg) * Rain_mm_Tot - The total rainfall during the sampling interval (mm) * AirTC_Avg - The average air temperature during the sampling interval (dC) * AirTC_Std - The standard deviation of the average air temperature (dC) * AirTC - the air temperature at the time of the sampling (dC) * RH - the relative humidity at the time of the sampling (%) * RH_Min - the minimum relative humidity recorded (%) * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RH_Max - the maximum relative humidity recorded (%) * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Min - the minimum battery voltage (Volts) * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2) * SlrkW - the light flux density at the time of the sampling (kW/m^2) * SlrMJ_Tot - the total light flux (MJ/m^2) * WS_ms_Avg - the average wind speed during the sampling interval (m/s) * WS_ms_Std - the standard deviation of the average wind speed (m/s) * WS_ms - the wind speed at the time of the sampling event (m/s) * WindDir - the wind direction at the time of the sampling event (degrees from true N) * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s) * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N) * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N)</code></pre>
Data from: Transforming medical education in Liberia through an international community of inquiry (2017 dataset)
<p>A critical component of building capacity in Liberia's physician workforce involves strengthening the country's only medical school, A.M. Dogliotti College of Medicine. Beginning in 2015, senior health sector stakeholders in Liberia invited faculty and staff from U.S. academic institutions and non-governmental organizations to join a partnership focused on improving undergraduate medical education in Liberia. Over the subsequent six years, the members of this partnership came together through an iterative, mutual-learning process and created what William Torbert et al describe as a "community of inquiry," in which practitioners and researchers pair action and inquiry toward evidence-informed practice and organizational transformation. This community of inquiry developed around a few key institutional and interpersonal relationships but expanded over time. Incorporating faculty, practitioners, and students from Liberia and the U.S., the community of inquiry consistently focused on following the vision, goals, and priorities of leadership in Liberia, irrespective of funding source or institutional affiliation. The work of the community of inquiry has incorporated multiple mixed methods assessments, stakeholder discussions, strategic planning, and collaborative self-reflection, resulting in transformation of M.D. education in Liberia. We suggest that the community of inquiry approach reported here can serve as a model for others seeking to form sustainable, international global health partnerships focused on organizational transformation.</p>
Data from: Transforming medical education in Liberia through an international community of inquiry (2016 dataset)
<p class="MsoNormal">A critical component of building capacity in Liberia's physician workforce involves strengthening the country's only medical school, A.M. Dogliotti College of Medicine. Beginning in 2015, senior health sector stakeholders in Liberia invited faculty and staff from U.S. academic institutions and non-governmental organizations to join a partnership focused on improving undergraduate medical education in Liberia. Over the subsequent six years, the members of this partnership came together through an iterative, mutual-learning process and created what William Torbert et al describe as a "community of inquiry," in which practitioners and researchers pair action and inquiry toward evidence-informed practice and organizational transformation. This community of inquiry developed around a few key institutional and interpersonal relationships but expanded over time. Incorporating faculty, practitioners, and students from Liberia and the U.S., the community of inquiry consistently focused on following the vision, goals, and priorities of leadership in Liberia, irrespective of funding source or institutional affiliation. The work of the community of inquiry has incorporated multiple mixed methods assessments, stakeholder discussions, strategic planning, and collaborative self-reflection, resulting in transformation of M.D. education in Liberia. We suggest that the community of inquiry approach reported here can serve as a model for others seeking to form sustainable, international global health partnerships focused on organizational transformation.</p>
Data from: Transforming medical education in Liberia through an international community of inquiry (2018 dataset)
<p>A critical component of building capacity in Liberia's physician workforce involves strengthening the country's only medical school, A.M. Dogliotti College of Medicine. Beginning in 2015, senior health sector stakeholders in Liberia invited faculty and staff from U.S. academic institutions and non-governmental organizations to join a partnership focused on improving undergraduate medical education in Liberia. Over the subsequent six years, the members of this partnership came together through an iterative, mutual-learning process and created what William Torbert et al describe as a "community of inquiry," in which practitioners and researchers pair action and inquiry toward evidence-informed practice and organizational transformation. This community of inquiry developed around a few key institutional and interpersonal relationships but expanded over time. Incorporating faculty, practitioners, and students from Liberia and the U.S., the community of inquiry consistently focused on following the vision, goals, and priorities of leadership in Liberia, irrespective of funding source or institutional affiliation. The work of the community of inquiry has incorporated multiple mixed methods assessments, stakeholder discussions, strategic planning, and collaborative self-reflection, resulting in transformation of M.D. education in Liberia. We suggest that the community of inquiry approach reported here can serve as a model for others seeking to form sustainable, international global health partnerships focused on organizational transformation.</p>
Data from: Work-Life Conflict Among Higher Education Institution Workers' During COVID-19: A Demands-Resources Approach
<p>Dataset from: Work-Life Conflict Among Higher Education Institution Workers' During COVID-19: A Demands-Resources Approach</p>
Additional Data: Mapping the Evolution of Computational Thinking in Education: A Bibliometrics Analysis of Scopus Database from 1987 to 2023
<p>The following is a selection of figures and tables from a bibliometric study that will be released later. The title of this study is Mapping the Evolution of Computational Thinking in Education: A Bibliometrics Analysis of Scopus Database from 1987 to 2023.</p> <p>In the online listing of the appendix, we will find three figures (Figure 5, Figure 6, and Figure 12) and three tables (Table 3, Table 4, and Table 4), also several references related to this research. It was important to us that the core of the study that is now being carried out not be diminished in any way, which is why we chose the photos and tables we did. This study was conceived and supported by the Indonesia Endowment Fund for Education (LPDP), which the Ministry of Finance administers in the Republic of Indonesia, to evaluate current trends and research problems in computational thinking for education. The Scopus database was used, and its range of coverage was from 1987 to 2023.</p> <p> </p>
The use of lexicographic resources in Croatian primary and secondary education - Survey Data
<p>The dataset contains the data collected in the survey on the use of dictionaries and other lexicographic resources in Croatian primary and secondary education, which was conducted from 1 February to 17 February 2023.</p>
Data from: Transforming medical education in Liberia through an international community of inquiry (2016 dataset)
Open the record for dataset details and reuse information.
Data from: Transforming medical education in Liberia through an international community of inquiry (2017 dataset)
Open the record for dataset details and reuse information.
Data from: Transforming medical education in Liberia through an international community of inquiry (2018 dataset)
Open the record for dataset details and reuse information.
Open Data as driver of critical data literacies in Higher Education: Which Data, Which Openness, Which Care?
<p>This Zenodo item contains the Report and subsidiary resources and dataset on the results of the workshop undertaken by the authors: "Open Data as driver of critical data literacies in Higher Education".</p> <p>Workshop Details:</p> <ul> <li>A workshop session offered at the OER20 [online] conference</li> <li><a href="https://oer20.oerconf.org/sessions/o-060/">https://oer20.oerconf.org/sessions/o-060/</a></li> <li>Wed, Apr 1 2020</li> <li>Theme: <a href="https://oer20.oerconf.org/tracks/open-education-for-civic-engagement-and-democracy/">Open education for civic engagement and democracy</a></li> <li>Access to the <a href="https://eu.bbcollab.com/collab/ui/session/playback/load/0983399114454947ba11426abbb3e17e">Recorded Session</a></li> </ul> <p>The workshop explored the educational potential of Open Data as a driver of interdisciplinary dialogue in learning design and pedagogical practices. It offered instruments for designing educational interventions in two simple phases:</p> <ol> <li>A conceptual (but dialogical!) introduction</li> <li>A “hands on” exercise</li> </ol> <p>The virtual environment used was Blackboard Collaborate as organized and supported by ALT and the OER20 Committee. In these conditions, 73 participants (conference attendees and external participants) engaged in the activity. The participants that expressed their geographical positions (when introducing themselves via the chat in the virtual conference environment) showed diversity, though most participants came from the UK, elsewhere in Europe, and some from Latin America.</p>
Why should we care about datafication? Critical data literacies in higher education
<p>This Zenodo item contains the Report and subsidiary resources and dataset on the results of the workshop undertaken by the authors: "Why should we care about datafication? Critical data literacies in higher education".</p> <p>Workshop Details:</p> <ul> <li>A workshop session offered at the OER20 [online] conference</li> <li><a href="https://oer20.oerconf.org/sessions/o-023/">https://oer20.oerconf.org/sessions/o-023/</a></li> <li>Wed, Apr 1 2020</li> <li>Theme: <a href="https://oer20.oerconf.org/tracks/openness-in-the-age-of-surveillance/">Openness in the age of surveillance</a></li> <li>Access to the <a href="https://eu.bbcollab.com/collab/ui/session/playback/load/7cadbebcf3cf499d8616529c880eacde">Recorded Session</a></li> </ul> <p>Through a hands-on Open Space conversation, facilitated via Mentimeter and Blackboard Collaborate we explored the challenges datafication poses for educators in our contemporary information ecosystem, and why all of us should care. The session tried to scaffold frameworks that offer participants a critical lens to analyse their own data literacies and explore pathways to data literacy and data activism in institutions and networks.</p> <p>The workshop was technically implemented with success, and all the interactions were undertaken without any imprevist, due to the great support given by the OER20 committee. The OER20 community got engaged with our proposal: 94 participants were visible in the chat side and actively contributing.</p>
South African Higher Education Performance Data 2009 to 2016
<p>Student, staff, research and financial data for South African universities for the period 2009 to 2016.</p>
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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
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.