Find research datasets worth reusing

opencc-by-4.0Jul 2024View details →

Code and data set for data analysis published as manuscript "Bacttle: a microbiology educational board game for lay public and schools"

Code that processed raw data and plots the figures of the manuscript "Bacttle: a microbiology educational board game for lay public and schools" Below is a table with the original survey questions. The ID corresponds to the column displayed on the data set. When letters are followed by a number (1 or 2), it means that the question was answered before playing the game (1) and after playing the game (2). <table> <tbody> <tr> <td> ID1 </td> <td> Question text </td> <td> Possible answers2 </td> </tr> <tr> <td> A </td> <td> How old are you? </td> <td>   </td> </tr> <tr> <td> B </td> <td> Do you know what a bacterium is? </td> <td> y/n </td> </tr> <tr> <td> C </td> <td> Do you know what a bacterial capsule is? </td> <td> y/n </td> </tr> <tr> <td> D </td> <td> Do bacteria have tools to harm each other? </td> <td> y/n/idk </td> </tr> <tr> <td> E </td> <td> Do bacteria reproduce at the same pace? </td> <td> y/n/idk </td> </tr> <tr> <td> F </td> <td> What is sporulation? </td> <td> A resistant state that some bacteria can achieve under unfavorable conditions. </td> </tr> <tr> <td> The release of toxins by bacteria. </td> </tr> <tr> <td> idk </td> </tr> <tr> <td> G </td> <td> What are flagella used for? </td> <td> Sticking to surfaces. </td> </tr> <tr> <td> Motility in liquid environments. </td> </tr> <tr> <td> idk </td> </tr> <tr> <td> H </td> <td> What does it mean to be lithotrophic? </td> <td> A bacterium can get energy from minerals. </td> </tr> <tr> <td> A bacterium can get energy from the sunlight. </td> </tr> <tr> <td> idk </td> </tr> <tr> <td> I </td> <td> Can bacteria be infected by viruses? </td> <td> y/n/idk </td> </tr> <tr> <td> J </td> <td> Are all bacteria harmful for humans? </td> <td> y/n/idk </td> </tr> <tr> <td> K </td> <td> How many bacteria are in a coffee spoon of yoghurt? </td> <td> Millions </td> </tr> <tr> <td> Hundreds </td> </tr> <tr> <td> idk </td> </tr> <tr> <td> L </td> <td> How easy did you find the gameplay? </td> <td> VE/E/A/D/VD </td> </tr> <tr> <td> M </td> <td> Did you find the card content easy to understand? </td> <td> VE/E/A/D/VD </td> </tr> <tr> <td> N </td> <td> Did you like the setup of the game? </td> <td> y/n/idk </td> </tr> <tr> <td> O </td> <td> Would you like to play this game again? </td> <td> y/n/idk </td> </tr> <tr> <td> P </td> <td> What can we improve? </td> <td>   </td> </tr> </tbody> </table> 1) Question A categorizes the player’s age; B and C assess the initial level of knowledge in microbiology (none -both questions are answered negatively-, basic -player knows what a bacterium is but not a bacterial capsule-, or advanced -both answers are positive-); questions D-I score knowledge acquisition; J and K are control questions; L-O evaluate the appreciation of the game; and P is an optional free text-entry answer for additional feedback.  2) y= yes, n=no, idk=I don’t know, VE=very easy, E=easy, A=adequate, D=difficult, VD=very difficult.

opencc-by-4.0Jul 2024View details →

Japan-Educated Officials in China's Wartime Central Administration (1944)

This dataset is made up of two files: <ol> <li>"CNKI-20231014003254589" is the raw extraction of bibliographical references from CNKI on the Chinese students who stuided in Japan before 1945. It consists in the main academic outputs on the topic, including journal article,s M.A. thesis, and doctoral dissertations.</li> <li>"CNKIjp2" is the pre-processed and cleaned file that was used for statistical analysis and topic modeling.</li> </ol> The markdown script presents the core of the methodological framework for my study of Chinese historiography on the Japan-educated students in the late imperial and republican period. I developed this script as part of the paper titled "Japan-Educated Officials in China’s Wartime Central Administration (1944)". In this paper, I present a review of the literature on the Chinese students who went to Japan to study between 1896 and 1945. While the literature in English and Japanese is small and allwo for close reading, the literature in Chinese is massive. To explore this literature and identify research trends, I chose to apply topic modeling to the dataet of references extracted from CNKI (知網). The documentary basis consists in the abstracts of the academic outputs.

opencc-by-4.0Oct 2021View details →

PE-HRI-temporal: A Multimodal Temporal Dataset in a robot mediated Collaborative Educational Setting

Please note that this dataset corresponds to the training data used in "Social robots as skilled ignorant peers for supporting learning "[7]. This (second) version of the dataset additionally includes labels (PE score and cluster labels for each datapoint).    This data set consists of multi-modal temporal team behaviors as well as learning outcomes collected in the context of a robot mediated collaborative and constructivist learning activity called JUSThink [1,2]. The data set can be useful for those looking to explore evolution of log actions, speech behavior, affective states, and gaze patterns for students to model constructs such as engagement, motivation, collaboration, etc. in educational settings.  In this data set, team level data is collected from 34 teams of two (68 children) where the children are aged between 9 and 12. There are two files:   PE-HRI_learning_and_performance.csv: This file consists of the team level performance and learning metrics which are defined below:  <ul> <li> last_error: This is the error of the last submitted solution. Note that if a team has found an optimal solution (error = 0) the game stops, therefore making last error = 0. This is a metric for performance in the task.  </li> <li> T_LG_absolute: It is a team-level learning outcome that we calculate by taking the average of the two individual absolute learning gains of the team members. The individual absolute gain is the difference between a participant’s post-test and pre-test score, divided by the maximum score that can be achieved (10), which grasps how much the participant learned of all the knowledge available. </li> <li> T_LG_relative: It is a team-level learning outcome that we calculate by taking the average of the two individual relative learning gains of the team members. The individual relative gain is the difference between a participant’s post-test and pre-test score, divided by the difference between the maximum score that can be achieved and the pre-test score. This grasps how much the participant learned of the knowledge that he/she didn’t possess before the activity.  </li> <li> T_LG_joint_abs: It is a team-level learning outcome defined as the difference between the number of questions that both of the team members answer correctly in the post-test and in the pre-test, which grasps the amount of knowledge acquired together by the team members during the activity </li> </ul> PE-HRI_behavioral_timeseries_w_labels.csv: In this file, for each team, the interaction of around 20-25 minutes is organized in windows of 10 seconds; hence, we have a total of 5048 windows of 10 seconds each. We report team level log actions, speech behavior, affective states, and gaze patterns for each window. More specifically, within each window, 26 features are generated in two ways:  <ol> <li>non-incremental</li> <li>incremental</li> </ol> A non-incremental type would mean the value of a feature in that particular time window while an incremental type would mean the value of a feature until that particular time window. The incremental type is indicated by an "_inc" at the end of the feature name. Hence, in the end, within each window, we have 52 values:  <ul> <li> T_add/(_inc): The number of times a team added an edge on the map in that window/(until that window). </li> <li> T_remove/(_inc): The number of times a team removed an edge from the map in that window/(until that window). </li> <li> T_ratio_add_rem/(_inc): The ratio of addition of edges over deletion of edges by a team in that window/(until that window). </li> <li> T_action/(_inc): The total number of actions taken by a team (add, delete, submit, presses on the screen) in that window/(until that window). </li> <li> T_hist/(_inc): The number of times a team opened the sub-window with history of their previous solutions in that window/(until that window). </li> <li> T_help/(_inc): The number of times a team opened the instructions manual in that window/(until that window). Please note that the robot initially gives all the instructions before the game-play while a video is played for demonstration of the functionality of the game.  </li> <li> T1_T1_rem/(_inc): The number of times either of the two members in the team followed the pattern consecutively: I add an edge, I then delete it in that window/(until that window). </li> <li> T1_T1_add/(_inc): The number of times either of the two members in the team followed the pattern consecutively: I delete an edge, I add it back in that window/(until that window). </li> <li> T1_T2_rem/(_inc): The number of times the members of the team followed the pattern consecutively: I add an edge, you then delete it in that window/(until that window). </li> <li> T1_T2_add/(_inc): The number of times the members of the team followed the pattern consecutively: I delete an edge, you add it back in that window/(until that window). </li> <li> redundant_exist/(_inc): The number of times the team had redundant edges in their map in that window/(until that window). </li> <li> positive_valence/(_inc): The average value of positive valence for the team in that window/(until that window). </li> <li> negative_valence/(_inc): The average value of negative valence for the team in that window/(until that window). </li> <li> difference_in_valence/(_inc): The difference of the average value of positive and negative valence for the team in that window/(until that window). </li> <li> arousal/(_inc): The average value of arousal for the team in that window/(until that window). </li> <li> gaze_at_partner/(_inc): The average of the the two team member's gaze when looking at their partner in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> gaze_at_robot/(_inc): The average of the the two team member's gaze when looking at the robot in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> gaze_other/(_inc): The average of the the two team member's gaze when looking in the direction opposite to the robot in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> gaze_at_screen_left/(_inc): The average of the the two team member's gaze when looking at the left side of the screen in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> gaze_at_screen_right/(_inc): The average of the the two team member's gaze when looking at the right side of the screen in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> T_speech_activity/(_inc): The average of the two team member's speech activity in that window/(until that window). Each individual member's speech activity is calculated as a percentage of time that they are speaking in that window/(until that window).  </li> <li> T_silence/(_inc): The average of the two team member's silence in that window/(until that window). Each individual member's silence is calculated as a percentage of time in that window/(until that window).  </li> <li> T_short_pauses/(_inc): The average of the two team member's short pauses over their speech activity in that window/(until that window). Each individual member's short pause refers to a brief pause of 0.15 seconds and is calculated as a percentage of time in that window/(until that window).  </li> <li> T_long_pauses/(_inc): The average of the two team members long pauses over their speech activity in that window/(until that window). Each individual member's long pause refers to a pause of 1.5 seconds and is calculated as a percentage of time in that window/(until that window).  </li> <li> T_overlap/(_inc): The average percentage of time the speech of the team members overlaps in that window/(until that window). </li> <li> T_overlap_to_speech_ratio/(_inc): The ratio of the speech overlap over the speech activity of the team in that window/(until that window). </li> </ul> Apart from these 52 values, within each window, we also indicate:  <ul> <li>team: The team to which the window belongs to.</li> <li>time_in_secs: Time in seconds until that window.</li> <li>window: The window number.</li> <li>normalized_time: The time when this window occurred with respect to the total duration of the task for a particular team. </li> <li>cluster_labels: The cluster number associated with each time window in reference to the productive and non-productive clusters found in [3]</li> <li>PE_score: The Productive Engagement score in each window</li> </ul> Lastly, we briefly elaborate on how the features are operationalised. We extract log behaviors from the recorded rosbags while the behaviors related to both gaze and affective states are computed through the open source library OpenFace [6] that returns both facial actions units (AUs) as well as gaze angles. For voice activity detection (VAD), that classifies if a piece of audio is voiced or unvoiced, we made use of the python wrapper for the open source Google WebRTC VAD. The literature that inspired our log, audio and video features as well as the tools used to extract them are described in more detail in [3,4]. However, in those papers, we make use of only the aggregate version of this data [5]. Please note that this dataset corresponds to the training data used in [7]. This (second) version of the dataset additionally includes labels (PE score and cluster labels for each datapoint).

opencc-by-4.0Oct 2023View details →

Dataset for algorithmic thinking skills assessment: Results from the virtual CAT pilot study in Swiss compulsory education

Overview This dataset was collected during a pilot study that evaluated the virtual Cross Array Task (CAT) platform as an assessment tool for algorithmic thinking (AT) skills among K-12 students in Swiss compulsory education. As algorithmic thinking becomes increasingly vital in our digital age, this study bridges the gap between traditional assessments and the needs of today's learners by introducing a digital platform. The virtual CAT, a digital adaptation of an unplugged assessment activity, offers scalable, automated assessments with reduced human intervention.Study Context, Location and Participants To demonstrate the virtual CAT's effectiveness, we conducted a pilot study in March 2023. The study was conducted in Switzerland, specifically within the Ticino canton. The sample consisted of 31 students (21 girls and 10 boys) from a preschool class (ages 4-6) and two low secondary classes (1st grade, ages 11-12). Data Collection Data collection was integrated into a validation module of the app.  Sessions required manual input for details like date, canton, and school information.  Students' details, anonymised for privacy, encompassed their gender and date of birth.  Each interaction within the platform was meticulously logged, capturing operations like task confirmations, command updates, mode changes, and more.Data Features The dataset comprises the following files:<ul><li>ALGORITHMS.csv</li><li>CANTONS.csv</li><li>DF.csv</li><li>LOGS.csv</li><li>RESULTS.csv</li><li>SCHOOLS.csv</li><li>SESSIONS.csv</li><li>STUDENTS_SESSIONS.csv</li></ul>These files collectively provide insights into the algorithmic actions of the students, demographic details, session logs, results, and more.Usage & Ethics In the spirit of open science, this dataset is made available to the public after meticulous anonymisation to ensure all participants' privacy and ethical treatment.  Initial authorisations were secured from school administrators, teachers, and parents.  Detailed communication regarding the study's nature, data handling, and objectives was transparently shared with all stakeholders. REFERENCES[1] A. Piatti, G. Adorni, L. El-Hamamsy, L. Negrini, D. Assaf, L. Gambardella & F. Mondada. (2022). The CT-cube: A framework for the design and the assessment of computational thinking activities. Computers in Human Behavior Reports, 5, 100166. <a href="https://doi.org/10.1016/j.chbr.2021.100166">https://doi.org/10.1016/j.chbr.2021.100166</a>[2] Adorni, G., & Piatti, S., & Karpenko, V. (2023). virtual CAT: An app for algorithmic thinking assessment within Swiss compulsory education. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10027851">https://doi.org/10.5281/zenodo.10027851</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/</a>[3] Adorni, G., & Karpenko, V. (2023). virtual CAT programming language interpreter. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10016535">https://doi.org/10.5281/zenodo.10016535</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/</a>[4] Adorni, G., & Karpenko, V. (2023). virtual CAT data infrastructure. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10015011">https://doi.org/10.5281/zenodo.10015011</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure</a>

opencc-by-4.0Jan 2021View details →

Supplementary Materials: A primer on gathering and analysing multi-level quantitative evidence for differential student outcomes in higher education

Example data sets, syntax files and macros for the tutorials in: Balloo, K., & Winstone, N. E. (2021). A primer on gathering and analysing multi-level quantitative evidence for differential student outcomes in higher education. Frontline Learning Research. <a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.14786%2Fflr.v9i2.675&data=04%7C01%7Ck.balloo%40surrey.ac.uk%7C50bb47bb433744dc8da208d8c2116202%7C6b902693107440aa9e21d89446a2ebb5%7C0%7C0%7C637472728228002863%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=fyA0y2hUkHESUJ7sVJ3s42Re4Yqa5XbgwW7AvEyGDdk%3D&reserved=0">https://doi.org/10.14786/flr.v9i2</a><a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.14786%2Fflr.v9i2.675&data=04%7C01%7Ck.balloo%40surrey.ac.uk%7C50bb47bb433744dc8da208d8c2116202%7C6b902693107440aa9e21d89446a2ebb5%7C0%7C0%7C637472728228002863%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=fyA0y2hUkHESUJ7sVJ3s42Re4Yqa5XbgwW7AvEyGDdk%3D&reserved=0">.675</a>  The data for all examples are fictional, and have only been designed to simulate the possible behaviour of institutional data for the purposes of demonstrating the analytical approaches in the primer. No inferences or conclusions should be drawn from the findings of these examples, because the results are not real. We anticipate that readers can use the example data sets as templates and substitute in their own data.

opencc-by-4.0Oct 2023View details →

Adaptive PE-HRI: Data for research on Social Educational Robots driven by a Productive Engagement Framework

This dataset corresponds to our work on developing autonomous social educational robots (namely Harry and Hermione) driven by a productive engagement framework in open ended collaborative learning environments. The data is collected in the context of a robot mediated collaborative and constructivist learning activity called JUSThink where each team interacts with the activity for around 1 hour consisting of a 30 minute collaborative play.  In this data set, team level multi-modal behavioral data is collected from 52 teams of two (104 children) where the children are aged between 9 and 12. The definitions are given below:  <ul> <li>condition: This column indicates which condition do the teams belong in. 0 and 1 for teams interacting with Harry and Hermione, respectively.</li> <li>Error: This is the error of the last submitted solution. Note that if a team has found an optimal solution (error = 0) the game stops, therefore making last error = 0. This is a metric for performance in the task. </li> <li>Learning Gain: It is a team-level learning outcome defined as the difference between the number of questions that both of the team members answer correctly in the post-test and in the pre-test, which grasps the amount of knowledge acquired together by the team members during the activity.</li> <li>Usefulness Score: The score quantifies the team's subjective evaluation of a robot intervention in terms of it's usefulness as perceived by each team member individually. The score can assume values of 1, 0, 0.5 if both found the suggestion useful, not useful, or if they differed in their evaluation, respectively</li> <li>PE Score: It is a quantification of the Productive Engagement state of the team, computed on the basis of quantifiable observable behaviors found conducive to learning in training phase</li> <li>Right_Suggestions: This metric captures the team's subjective evaluation of the robot's competence on a five-points likert scale to the statement "I think the robot was giving us the right suggestions". It is an average of the team member's individual answers. </li> <li>Right_Time: This metric captures the team's subjective evaluation of the robot's competence on a five-points likert scale to the statement "I think the robot gave us suggestions at the right time". It is an average of the team member's individual answers.</li> <li>Exploration: This variable represents how many interventions of Exploration type were received by a particular team normalized with respect to the entire data set. </li> <li>Reflection: This variable represents how many interventions of Reflection type were received by a particular team normalized with respect to the entire data set. </li> <li>Communication: This variable represents how many interventions of Communication type were received by a particular team normalized with respect to the entire data set. </li> <li>LG_status: This column indicates if a team belongs to a high learning or low learning group based on a mean split on the entire data set. </li> </ul> This dataset corresponds to the publication "Social robots as skilled ignorant peers for supporting learning": <a href="https://doi.org/10.3389/frobt.2024.1385780">https://doi.org/10.3389/frobt.2024.1385780</a>

opencc-by-4.0Jan 2021View details →

Sustainability in Chemical Education - A Global Young Chemists Survey

In 2020, young chemists of the German Young Chemists' Network (GDCh-JCF) designed a survey to get a snapshot of how their peers perceive the importance of sustainability in chemical education. With the help of the International Younger Chemists Network (IYCN) and the European Young Chemists' Network (EYCN) they were able to reach around 500 young chemists from 46 countries. Roughly half of the responses came from people that represented the German education system. Although the results need to be taken with a grain of salt as stated in the disclaimer (see .pdf document), they paint a picture of a desperate need for more sustainability topics to be covered by chemical education globally, particularly in Germany. More than 90% of young chemists globally call for more detailed coverage of sustainability topics while the current adequacy is only rated as good or better by a quarter of all respondents. A significant portion (>50% inside of Germany and >30% outside of Germany) does not feel prepared to contribute to the sustainability strategy of a company despite >80% rating the sustainability startegy of a company as an important factor for their career choice.

opencc-by-4.0Jan 2024View details →

Results of Survey on Playertypes by Gamification User Types Hexad Framework in Higher Education

Survey on playertypes via the validated quesitonaire published in Krath, J., von Korflesch, H.F.O. (2021). Player Types and Game Element Preferences: Investigating the Relationship with the Gamification User Types HEXAD Scale. In: Fang, X. (eds) HCI in Games: Experience Design and Game Mechanics. HCII 2021. Lecture Notes in Computer Science(), vol 12789. Springer, Cham. https://doi.org/10.1007/978-3-030-77277-2_18 Between 25.01.23 and 08.02.23 students of the University of Lübeck, Germany were invited to fill out an online questionnaire. The acquisition was done by sending an email to the students. No incentive was offered for participation, except to find out at one's own expression at the end of the survey. In addition to the validated questions, this also included questions about gender and study area. All participants agreed to anonymous data collection and publication. Participants were also asked to confirm that they were completing the survey for the first time, otherwise the return was removed from the result set.  The result set is formatted as CSV. Questions and identifiers of the data are shown in the first line.

opencc-by-4.0Jun 2024View details →

Survey questions and raw data for the study in the paper "Educational Technology for Tutors – What are Useful Tools and Information?"

The data include the questions data set, the answers dataset and the codebook for the questions conducted with soscisurvey (https://www.soscisurvey.de/de/index). The survey itself can be imported in soscisurvey (via the XML data) and reused. The answers are unedited.

opencc-by-4.0Aug 2024View details →

Dataset of Survey Results on the Integration of Industry 4.0 in University Education (Baja California, 2024)

This dataset contains the results of a survey conducted in 2024 on the integration of Industry 4.0 concepts and technologies in university education in Baja California. The survey was designed to assess the current state of adoption, challenges, and opportunities related to Industry 4.0 within academic institutions. The data includes responses from engineering students at the Autonomous University of Baja California (UABC) and the Polytechnic University of Baja California (UPBC). The insights gathered aim to inform future strategies for enhancing the implementation of Industry 4.0 in higher education curricula.

opencc-by-4.0Nov 2022View details →

Stories on Open Educational Practices in German Higher Education

Stories on Open Educational Practices in German Higher Education by Sigrid Fahrer, <a href="#_oewao57q7xt8">Tamara Heck</a>, <a href="#_fronuh2cauex">Ronny Röwert</a>, <a href="#_oshco7u6xx6k">Naomi Truan</a> citation suggestion: Fahrer, S., Heck, T., Röwert, R., Truan, N. (2022). Stories on Open Educational Practices in German Higher Education. Data set on autoethnographic reflections. <a href="https://doi.org/10.5281/zenodo.7326390">https://doi.org/10.5281/zenodo.7326390</a> The stories are part of the autoethnographic reflections of the four practitioners. They are based on the following research papers: <ul> <li>Cronin, C. (2017). Openness and Praxis: Exploring the Use of Open Educational Practices in Higher Education. The International Review of Research in Open and Distributed Learning, 18(5). <a href="https://doi.org/10.19173/irrodl.v18i5.3096">https://doi.org/10.19173/irrodl.v18i5.3096</a></li> <li>Hegarty, B. (2015). Attributes of Open Pedagogy: A Model for Using Open Educational Resources. Educational Technology, 55(4), 3–13. <a href="https://upload.wikimedia.org/wikipedia/commons/c/ca/Ed_Tech_Hegarty_2015_article_attributes_of_open_pedagogy.pdf">https://upload.wikimedia.org/wikipedia/commons/c/ca/Ed_Tech_Hegarty_2015_article_attributes_of_open_pedagogy.pdf</a></li> <li>Mayrberger, K. (2020). Open Educational Practices (OEP) in Higher Education. In M. A. Peters (Ed.), Springer eBook Collection. Encyclopedia of Educational Philosophy and Theory (pp. 1–7). Springer. <a href="https://doi.org/10.1007/978-981-287-532-7_710-1">https://doi.org/10.1007/978-981-287-532-7_710-1</a>.</li> <li>Wiley, D., & Hilton III, J. L. (2018). Defining OER-Enabled Pedagogy. The International Review of Research in Open and Distributed Learning, 19(4). <a href="https://doi.org/10.19173/irrodl.v19i4.3601">https://doi.org/10.19173/irrodl.v19i4.3601</a></li> </ul>

opencc-by-4.0Nov 2022View details →

Dataset and R script for the analysis in the article "Food waste between environmental education, peers, and family influence. Insights from primary school students in Northern Italy", Journal of Cleaner Production

We hereby publish the dataset (with metadata) and the R script (R Core team, 2018) used for implementing the analysis presented in the paper "Food waste between environmental education, peers, and family influence. Insights from primary school students in Northern Italy", Journal of Cleaner Production (Piras et al., 2023). The dataset is provided in csv format with semicolons as separators and "NA" for missing data. The dataset includes all the variables used in at least one of the models presented in the paper, either in the main text or in the Supplementary Material. Other variables gathered by means of the questionnaires included as Supplementary Material of the paper have been removed. The dataset includes inputted values for missing data on independent variables. These were inputted using two approaches: last observation carried forward (LOCF) - preferred when possible - and last observation carried backward (LOCB). The metadata are presented as a PDF file.

opencc-by-4.0Dec 2021View details →

Dataset to Model the Sustainability of a Primary School Digital Education Curricular Reform and Professional Development Program

This dataset contains the quantitative teacher data used to analyse the sustainability of an in-service teacher training program for Digital Education that took place from September 2019 to March 2020 in the Canton Vaud in Switzerland. As such, the study follows up on the 350 teachers over a year after the end of their professional development program had ended in order to model the sustainability of the reform,  understand to what extent sustainability had been reached, thus validating the curricular reform model and helping draw recommendations for researchers and practitioners involved in Digital Education curricular reforms. As such, approximately 290 teachers from grades 1-4 in primary school (ages 5-9) responded to two sustainability surveys using web-based questionnaire to provide information relating to their perception of the training sessions and adoption of the computer science activities. The study is accepted for publication in Education and Information Technologies.  A README is included and provides additional information regarding : - the requirements for re-use.  - the specific content of the 2 csv files

opencc-by-4.0Dec 2020View details →

Education Data in the Biographical Dictionary of Republican China

This dataset contains records of education of the 589 historical figures in the Biographical Dictionary of Republican China.

opencc-by-4.0Nov 2023View details →

How can higher education stakeholders support Edtech?

This video outlines policy recommendations for cross-sector digital transformation in higher education. It highlights the importance of collaboration between policy-makers, stakeholders, EdTech companies, and universities to advance digital technologies in higher education. The findings derive from the ESRC-funded project 'Universities and Unicorns: building digital assets in the higher education industry'.

opencc-by-4.0Nov 2023View details →

What are the key tensions in educational technology (Edtech)?

This video outlines the key challenges and tensions that have arisen in the higher education sector as it increasingly operates using digital technology, and how these can be used to direct future improvements of digital processes in higher education. The findings come from the ESRC-funded project 'Universities and Unicorns: building digital assets in the higher education industry'.

opencc-by-4.0Mar 2024View details →

A novel educational approach for safe endodontic syringe irrigation: a randomized controlled study

(1) Educational video emphasizing the fundamentals of safe irrigation practices, incorporating evidence-based guidelines on appropriate plunger forces and the required time for safe irrigant delivery.  (2) Dataset comprising the measurement data collected and processed during this study.