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5,141 results for “Educators”
MakeCode and CODAL: Intuitive and Efficient Embedded Systems Programming for Education (Artifact Evaluation)
<p>This artifact allows others to reproduce the results seen in this paper for MakeCode and CODAL, using the BBC micro:bit. The artifact contains an offline build environment for CODAL and MakeCode, allowing evaluators to test and build programs locally. In addition, we also provide espruino and micropython virtual machines to further increase repeatability of our results. Evaluators should download the virtual machine containing all pre-requisite tools, and use an oscilloscope to observe wave forms (used for timing) generated by the micro:bit, and a serial terminal to observe results reported from the micro:bit over serial.</p> <p>Full documentation is available at: https://lancaster-university.github.io/lctes-artefact-evaluation/</p>
Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 3. Education level of the studies
<p>Figure 3 presents the education levels of the mobile and ubiquitous learning practices. Most studies took place at the tertiary level (66.7%). Also, 25.5% of the studies were conducted at the primary school level and 7.8% at the secondary school level.</p>
Our Mythical Education Workshops, 2017
<p>A short movie from the workshops <em>Our Mythical Education</em> in the High School “Strumienie” at the outskirts of Warsaw, with the participation of the Mikołaj Rej XI High School in Warsaw, May 15, 2017, during the conference & workshops <em>Our Mythical Hope in Children’s and Young Adults’ Culture... The (In)efficacy of Ancient Myths in Overcoming the Hardships of Life</em>, within the ERC Consolidator Grant <em>Our Mythical Childhood... The Reception of Classical Antiquity in Children’s and Young Adults’ Culture in Response to Regional and Global Challenges</em> at the Centre for Studies on the Classical Tradition (OBTA), Faculty of “Artes Liberales”, University of Warsaw. For more see: <a href="http://omc.obta.al.uw.edu.pl/hope">http://www.omc.obta.al.uw.edu.pl/hope</a>.</p> <p>Barbara Strycharczyk ("Strumienie" High School) – supervision, Katarzyna Marciniak – co-operation, Elżbieta Olechowska – English proofreading, BG Multimedia – production.</p> <p>The movie is available also at: <a href="http://www.youtube.com/watch?v=sIFey_V6RPw&">https://www.youtube.com/watch?v=sIFey_V6RPw&</a>. </p>
ScintPi: A low-cost, easy-to-build GPS ionospheric scintillation monitor for DASI studies of space weather, education, and citizen science initiatives
<p>These data sets contain ionospheric scintillation (GPS L1) observations (S4 indices) collected by a ScintPi prototype during 2018 at a low magnetic latitude station (Presidente Prudente). ScintPi is a low-cost, easy-to-build GPS ionospheric scintillation monitor for DASI studies of space weather, education, and citizen science initiatives.</p> <p>The file named "ScintPi_PPR_S4_2018.mat" contains S4 values (s4mat) for 2018 as a function of universal time (utmat), day-of-year (doymat), GPS satellite identifier number (prnmat), GPS satellite elevation (elmat) and azimuth (azmat) angles.</p> <p>The file named "ScintPi_PPR_20180211.mat" contains example raw (10 Hz) measurements made by ScintPi on February 11, 2018. The file contains values of receiver's altitude, latitude and longitude (variables alt,lat, and lon), GPS satellite azimuth and elevation (variables el and az), GPS identifier number (prn), signal-to-noise ratio (snr), and day-of-year (doy).</p>
Views of the students and prospective teachers of the National and Kapodistrian University of Athens and School of Pedagogical and Technological Education, regarding the necessity of certified pedagogical competence.
<p>views of the students and prospective teachers of the National and Kapodistrian University of Athens and School of Pedagogical and Technological Education, regarding the necessity of certified pedagogical competence.</p>
PERCEIVE Education, Research Development and Innovation in the South-East Development Region A Case Study 20191006
<p>The data set include secondary data extracted from EUROSTAT database. The data set referring to the education and research infrastructure on Sud-Est NUTS II region from Romania for the period 2007-2013. </p>
Dataset: What Are Cybersecurity Education Papers About? A Systematic Literature Review of SIGCSE and ITiCSE Conferences
<p>This repository contains supplementary materials for the following conference paper:</p> <p>Valdemar Švábenský, Jan Vykopal, Pavel Čeleda.<br> <em>What Are Cybersecurity Education Papers About? A Systematic Literature Review of SIGCSE and ITiCSE Conferences.</em><br> In Proceedings of the 51st ACM Technical Symposium on Computer Science Education (SIGCSE 2020).<br> <a href="https://doi.org/10.1145/3328778.3366816">https://doi.org/10.1145/3328778.3366816</a></p> <p>Preprint available at: <a href="https://arxiv.org/abs/1911.11675">https://arxiv.org/abs/1911.11675</a></p> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).</p> <pre><code class="language-json">@inproceedings{Svabensky2020what, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and \v{C}eleda, Pavel}, title = {{What Are Cybersecurity Education Papers About? A Systematic Literature Review of SIGCSE and ITiCSE Conferences}}, booktitle = {Proceedings of the 51st ACM Technical Symposium on Computer Science Education}, series = {SIGCSE '20}, location = {Portland, OR, USA}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, month = {03}, year = {2020}, pages = {2--8}, numpages = {7}, isbn = {978-1-4503-6793-6}, url = {https://doi.org/10.1145/3328778.3366816}, doi = {10.1145/3328778.3366816}, }</code></pre> <p><strong>Attached content</strong></p> <p>The file "SIGCSE 2020 Literature Review.xlsx" is an Excel spreadsheet with three sheets corresponding to 1) all papers found by automated search, 2) manually excluded papers, and 3) papers included in the literature review. There are also three CSV files that correspond to the three individual sheets.</p>
Weather Data from Env. Education Center at Lancer Park 2018-10-30 to 2019-02-14
<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>
Weather Data from Env. Education Center at Lancer Park 2019-07-15 to 2019-08-28
<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>
Weather Data from Env. Education Center at Lancer Park 2019-08-28 to 2019-10-10
<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>
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>
Text-fig. 2 Associate Professor RNDr. Václav Ziegler CSc. in Faculty of Education of Charles University at Prague during Trends in didactics of biology, conference on the 20th anniversary of the restoration activities of the Department of Biology and Environmental Studies at the Faculty of Education of Charles University in Prague 2. October 2014. (photo: author 2014) in Václav Ziegler Septagenarian
Text-fig. 2 Associate Professor RNDr. Václav Ziegler CSc. in Faculty of Education of Charles University at Prague during Trends in didactics of biology, conference on the 20th anniversary of the restoration activities of the Department of Biology and Environmental Studies at the Faculty of Education of Charles University in Prague 2. October 2014. (photo: author 2014)
Text-fig. 1 Associate Professor RNDr. Václav Ziegler, CSc. is speaking and teaching during palaeontological excursion with students from Faculty of Education, Charles University - future teachers in Kutná Hora area. (photo: Marek, J.: 2006) in Václav Ziegler Septagenarian
Text-fig. 1 Associate Professor RNDr. Václav Ziegler, CSc. is speaking and teaching during palaeontological excursion with students from Faculty of Education, Charles University - future teachers in Kutná Hora area. (photo: Marek, J.: 2006)
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>
AI Platforms Overview for Higher Education
<p>This release includes a detailed table comparing various AI platforms. The table covers:</p> <ul> <li>Focus: Primary applications of each platform.</li> <li>Strengths: Key features and benefits.</li> <li>Uniqueness: Distinctive aspects.</li> <li>Limitations: Notable constraints.</li> </ul> <p>How to Use</p> <ol> <li>Download the attached file.</li> <li>Review the table for a comprehensive comparison.</li> </ol> <p>DOI<br>For more information, visit the DOI page: [10.5281/zenodo.13738758](https://doi.org/10.5281/zenodo.13738758)</p> <p>Contact<br>For any inquiries, contact [bsrinivasan@twu.edu](mailto:bsrinivasan@twu.edu).</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>
Enhancing Motivation in Software Engineering Education through Gamified Agile Project-based Learning
<p>Project-based learning (PBL), e.g., student software development projects, is an essential part of today's Software Engineering (SE) education. They allow students to work on real-world projects and gain practical experience as a team. However, several challenges arise in such projects, including learning new technologies and dealing with communication and coordination issues within the team. These factors can lead to a lack of motivation to contribute to the project and a decrease in productivity, potentially resulting in an insufficient project outcome. This paper aims to promote student motivation in PBL and increase team productivity by applying gamification. We conducted a user and requirements analysis to identify the needs of students and supervisors of such projects. Based on the insights, we designed and implemented DinoDev, a gamified project management tool that combines project management features with gamification elements. The DinoDev concept was evaluated in a student project, indicating increased motivation and team productivity. The findings are valuable for advancing research on using gamification in PBL and for lecturers to improve their students' motivation and team productivity in SE education.</p>
THE EVOLUTION OF EDUCATION FROM TRADITIONS TO FORMAL SYSTEMS UNDER THE RANA REGIME IN NEPAL
<p><span>This study examines the historical evolution of education in Nepal, particularly focusing on the transition from non-formal to formal education during the Rana period (1846–1951). The purpose of the study is to analyze how education, initially confined to family traditions and religious teachings, was systematically introduced by the Ranas, primarily for their own elite class, while public education was deliberately suppressed to maintain political control. Utilizing a qualitative research design, data was collected from secondary sources including books, articles, and online databases, and analyzed using descriptive, analytical, and historical methods. The findings highlight that while Junga Bahadur Rana initiated formal education following his visit to Britain, these efforts were limited to benefiting the Rana family. Subsequent rulers made minimal reforms under external pressure, yet the overall Rana regime left Nepal largely illiterate, with two generations deprived of educational opportunities. The study underscores the need for further exploration of the long-term impacts of this suppression on Nepal’s socio-economic development and suggests that future research could investigate the post-Rana educational reforms and their role in shaping modern Nepalese society.</span></p>
Current Applications and Future Prospects of Artificial Intelligence in Higher Medical Education: A Bibliometric Analysis
<p>This dataset supports the study titled <em>"Current Applications and Future Prospects of Artificial Intelligence in Higher Medical Education: A Bibliometric Analysis"</em>. It includes bibliometric data from Web of Science on the use of artificial intelligence in higher medical and pharmaceutical education. The dataset covers relevant literature, keywords, and citation analysis to provide insights into research trends, institutional contributions, and future directions in the field. This data can be valuable for researchers interested in the intersection of AI and medical education, as well as for those conducting further bibliometric analyses.</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>
ScienceDex guides
Understand access before you commit
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.