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zenodo56/100

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

Overview This dataset was collected during a main 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 comprehensively investigate algorithmic competencies within compulsory education, exploring their variations and determining the factors influencing them, in Spring 2023 we conducted an experimental study with the virtual CAT's. The sample comprises 129 students (65 girls and 64 boys), selected from nine classes across five public schools in Ticino and Solothurn cantons. Data Collection During the data collection process, session and participant details were manually recorded by the administrator. Each session has been assigned a unique identifier, and specific details, such as the date, canton, school name and type, and the students’ HarmoS grade (HG) level, have been recorded.  Student information are limited to sex and date of birth, with birth dates used to calculate ages, a significant factor in our demographic analysis. To protect student privacy, unique identifiers have been assigned to each participant, keeping the data anonymous and secure. The assessment tool automatically tracked all user interaction within the platform. All data collected have been pseudonymised, aligning with prevailing open science practices in Switzerland (SNSF, 2021).  Data collection was integrated into a validation module of the app.  Data Features The dataset comprises the following files: <ul> <li>STUDENTS_SESSIONS.csv</li> <li>RESULTS.csv</li> <li>LOGS.csv</li> <li>CANTONS.csv</li> <li>ALGORITHMS.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-4.0Apr 2024View details →

zenodo52/100

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 2021View details →

zenodo52/100

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-4.0Oct 2023View details →

zenodo48/100

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-4.0Aug 2024View details →

zenodo48/100

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-4.0Nov 2022View details →

zenodo48/100

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 2021View details →

zenodo44/100

Open Education in European Libraries of Higher Education 2023 Dataset

This is the dataset that appends the 2023 edition of the SPARC Europe Open Education Survey amongst Higher Education institutions in Europe, in consultation with the European Network of Open Education Librarians (ENOEL). The report is for policymakers and practitioners who support or intend to support OE and OER in higher education institutions and academic libraries.

opencc-by-4.0Mar 2024View details →

zenodo44/100

Dataset for Paper "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort

# Dataset for Paper "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort" - Rev #1 This is the dataset for the paper titled "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort". In case of questions, feel free to contact the authors, *anonymised*, ORCID: https://orcid.org/*anonymised*, current affiliation and email: *anonymised* ## Survey 2019 ## The raw survey data for the initial 2019 survey is available in the file *survey2019_anon.csv*. Note that the data is anonymised as free-text comments have been removed. Explanations on the variables and their levels are given in the files *variables_survey2019.csv* and *values_survey2019.csv*. The questionnaire for the 2019 survey is contained in *survey2019_instrument.pdf*. ## Survey 2020 ## The raw survey data for the 2020 survey is available in the file *rdata_anon_survey2020.csv*. Additional scripts are supplied to reproduce the exploratory factor analysis. The main entry is the file *EFA.R*, which imports the data. The file contains some comments on the process. The questionnaire for the 2020 survey is contained in *survey2020_instrument.pdf*. ## Interviews ## The interview guide used for the five interviews is available in the file *interview_instrument.pdf*.

opencc-by-4.0May 2021View details →

zenodo44/100

Educational transformation and network learning dataset – qualitative data from an international collaborative EU-project

We are releasing our dataset of workshop outcomes acquired from the annual consortium conferences organized by the international “NextFood” consortium. The purpose of this project is to develop new ways of educating the future sustainability leaders of the agrifood sector, making sure that the professionals (farmers, advisers, businesses, students) have the right set of skills and competences needed to tackle the sustainability challenges we face ahead. Data gathering started from May 2018 yielding considerable amount of data on achievements, challenges and action plans related to educational transformation. This dataset will be updated by the time of project finalization. This work was funded by the European Union, through the Horizon 2020 project “NextFood”, Grant agreement No. 771738.

opencc-by-4.0Dec 2021View details →

zenodo44/100

How do native and non-native speakers recognize emotions in the instructor's voice in educational videos? Exploring the first step of the cognitive-affective model of e-learning for international learners [dataset]

Dataset for the journal article How do native and non-native speakers recognize emotions in the instructor’s voice in educational videos? Exploring the first step of the cognitive-affective model of e-learning for international learners.

opencc-by-4.0Jun 2022View details →

zenodo44/100

Dataset: Economical Accommodations for Neurodivergent Students in Software Engineering Education: Experiences from an Intervention in Four Undergraduate Courses

This dataset contains anonymised raw data and examples of accommodations made for neurodiverse students in four undergraduate courses in Computer Science and Software Engineering programmes. The dataset is published as a part of a book chapter in which we report the accommodations. Overall guidelines we followed, including their sources, are contained in guidelines.md. The raw data for the two surveys is contained in the two Excel files survey1.xlsx and survey2.xlsx. Free-text answers have been aggregated by neurodiverse and neurotypical students and anonymised, and are available in the files survey1_freetext_neurodiverse.txt, survey1_freetext_neurotypical.txt, survey2_freetext_neurodiverse.txt, and survey2_freetext_neurotypical.txt. The remaining files are examples of the adapted lecture slides and assignment texts. Here, files starting with WEBcourse are from a mandatory undergraduate course on web development, while files starting with SEcourse are from a mandatory undergraduate course giving an overview of Software Engineering.

opencc-by-4.0Oct 2022View details →

zenodo44/100

Investigating secondary students' stance on IoT driven educational activities - Dataset

This data set supports the research and the results that are presented in Glaroudis, D., Iossifides, A., Spyropoulou, N., Zaharakis, I. D., “Investigating Secondary Students' Stance on IoT Driven Educational Activities”. In Kameas A, and Stathis K. (Eds) Ambient Intelligence, LNCS 11249, 2018, pp. 188-203. Springer Nature Switzerland AG. DOI: https://doi.org/10.1007/978-3-030-03062-9_15.

opencc-by-4.0Jun 2019View details →

zenodo44/100

Chinese Educational Mission Dataset (1872-1881)

This series of 11 datasets is drawn from Rhoads, Edward J. M. Stepping Forth into the World: The Chinese Educational Mission to the United States, 1872-81. Hong Kong University Press, 2011. They document the 120 young Chinese who participated in the pioneering Chinese Educational Mission (CEM) in the United States (1872-1881). The first 8 files are drawn directly from the tables in Rhoads:  <ol> <li>Table 2.1 CEM students, by detachment (p.14-17)</li> <li>Table 5.1. Initial host family assignments (p.51-54)</li> <li>Table 7.1. CEM students in middle schools (by state and locality) (p. 90-94)</li> <li>Table 7.2 CEM students in public high schools (by state and locality) (p.96-99)</li> <li>Table 7.3 CEM students in private academies (by state and locality) (p.99-100)</li> <li>Table 8.1 CEM students in colleges (by academic year of enrollment) (p.116-118)</li> <li>Table 9.1 Deaths, dismissals, and withdrawals from the CEM (by date) (p.136)</li> <li>Table 9.2 CEM students in the June 1880 census (p.138-142)</li> </ol> Based on these tables, I created three synthetic datasets which can be used for statistical and network analyses:   <ol> <li>cem_attributes: students' vital attributes, including their multiple names and transliteration, date and place of birth, and other attribute data (one row for each individual). </li> <li>cem_host: students' host families in the United States</li> <li>cem_education: students' educational curricula </li> </ol> Each file contains two tabs, one for the data (data), one for the description of variables (key). Grey columns refer to the unstructured information given in the original source.

opencc-by-4.0Jan 2023View details →

zenodo44/100

Dataset for the evaluation of the scalability of a primary school Digital Education curricular reform

Dataset for the evaluation of the scalability of a primary school Digital Education curricular reform ======================================================= • If you publish material based on this dataset, please cite the following :             • The Zenodo repository : Laila El-Hamamsy, Barbara Bruno, Jessica Dehler Zufferey, & Francesco Mondada (2023). Dataset for the evaluation of the scalability of a primary school Digital Education curricular reform [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7912941             • The corresponding article : El-Hamamsy, L.*, Monnier, E.-C. *, Chessel-Lazzarotto F., Liégeois G., Bruno, B., Dehler Zufferey, J., and Mondada, F. (2023). An Adapted Cascade Model to Scale Primary School Digital Education Curricular Reforms and Teacher Professional Development Programs. arXiv. https://doi.org/10.48550/arXiv.2306.02751 • License: This work is licensed under a Creative Commons Attribution 4.0 International license (CC-BY-4.0) • Creator: El-Hamamsy, L., Bruno, B., Dehler Zufferey, J., and Mondada, F. • Date: May 9th 2023 • Subject: Educational change, Scalability,  Professional Development,  Digital Education, Curricular Reform,  Primary School • Dataset format: CSV • Dataset collection: September 2018 to September 2022 • Dataset size : < 100 kB • Dataset content : one excel file with detailed description below. Please note that the spreadsheet may contain missing values due to teachers either choosing not to respond to the questions or the questions not being presented at each of the training sessions.  To have access to the specific survey questions please refer to the associated publication [a]. • Abbreviations :   - DE : Digital Education   - PD : Professional Development • Funding : This work was funded by the the NCCR Robotics, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (grant number 51NF40_185543) # References [a] El-Hamamsy, L.*, Monnier, E.-C. *, Chessel-Lazzarotto F., Liégeois G., Bruno, B., Dehler Zufferey, J., and Mondada, F. (2023). An Adapted Cascade Model to Scale Primary School Digital Education Curricular Reforms and Teacher Professional Development Programs. arXiv. https://doi.org/10.48550/arXiv.2306.02751

opencc-by-4.0May 2023View details →

zenodo44/100

Dataset Worldwide Survey on the Impact of AI Chatbots and Large Language Models in Dental Education: Insights from Dental Educators

This dataset contains responses from participants regarding their awareness, knowledge, and perceptions of AI-powered tools in dental education. The data was collected during May-June 2023 to investigate the potential enhancement that AI can bring to dental education. The dataset includes variables related to participants' demographics, experiences, perceptions, and opinions. Details in the published protocol by Uribe, S. E., & Maldupa, I. (2023, June 2). Chatbots In Dental Education - Research Protocol. https://doi.org/10.17605/OSF.IO/3BSG2

opencc-by-4.0Jun 2023View details →

zenodo44/100

Higher Education Institutions in Poland Dataset

Higher Education Institutions in Poland Dataset This repository contains a dataset of higher education institutions in Poland. The dataset comprises 131 public higher education institutions and 216 private higher education institutions in Poland. The data was collected on 24/11/2022.  This dataset was compiled in response to a cybersecurity investigation of Poland's higher education institutions' websites [1]. The data is being made publicly available to promote open science principles [2]. Data The data includes the following fields for each institution: <ul> <li>Id: A unique identifier assigned to each institution.</li> <li>Region: The federal state in which the institution is located.</li> <li>Name: The original name of the institution in Polish.</li> <li>Name_EN: The international name of the institution in English.</li> <li>Category: Indicates whether the institution is public or private.</li> <li>Url: The website of the institution.</li> </ul> Methodology The dataset was compiled using data from two primary sources: <ul> <li>Public Higher Education Institutions: Data was sourced from the official website of the Ministry of Education and Science of Poland [3].</li> <li>Private Higher Education Institutions: Data was obtained from the RAD-on system, which is part of the Integrated Information Network on Science and Higher Education [4].</li> </ul> For the international names in English, the following methodology was employed: Both Polish and English names were retained for each institution. This decision was based on the fact that some universities do not have their English versions available in official sources. English names were primarily sourced from: <ul> <li>The Polish National Agency for Academic Exchange's official document [5].</li> <li>The website Studies in English [6].</li> <li>Official websites of the respective Higher Education Institutions.</li> </ul> In instances where English names were not readily available from the aforementioned sources, the GPT-3.5 model was employed to propose suitable names. These proposed names are distinctly marked in blue within the dataset file (hei_poland_en.xls). Usage This data is available under the Creative Commons Zero (CC0) license and can be used for academic research purposes. We encourage the sharing of knowledge and the advancement of research in this field by adhering to open science principles [2]. If you use this data in your research, please cite the source and include a link to this repository. To properly attribute this data, please use the following DOI: 10.5281/zenodo.8333573 Contribution If you have any updates or corrections to the data, please feel free to open a pull request or contact us directly. Let's work together to keep this data accurate and up-to-date. Acknowledgment We would like to express our gratitude to the Ministry of Education and Science of Poland and the RAD-on system for providing the information used in this dataset. We would like to acknowledge the support of the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), within the project "Cybers SeC IP" (NORTE-01-0145-FEDER-000044). This study was also developed as part of the Master in Cybersecurity Program at the Polytechnic University of Viana do Castelo, Portugal. References <ol> <li>Pending.</li> <li>S. Bezjak, A. Clyburne-Sherin, P. Conzett, P. Fernandes, E. Görögh, K. Helbig, B. Kramer, I. Labastida, K. Niemeyer, F. Psomopoulos, T. Ross-Hellauer, R. Schneider, J. Tennant, E. Verbakel, H. Brinken, and L. Heller, Open Science Training Handbook. Zenodo, Apr. 2018. [Online]. Available: [<a href="https://doi.org/10.5281/zenodo.1212496">https://doi.org/10.5281/zenodo.1212496</a>]</li> <li>Ministry of Education and Science of Poland. "Wykaz uczelni publicznych nadzorowanych przez Ministra właściwego ds. szkolnictwa wyższego - publiczne uczelnie akademickie." Nov 2022. [Online]. Available: <a href="https://www.gov.pl/web/edukacja-i-nauka/wykaz-uczelni-publicznych-nadzorowanych-przez-ministra-wlasciwego-ds-szkolnictwa-wyzszego-publiczne-uczelnie-akademickie">https://www.gov.pl/web/edukacja-i-nauka/wykaz-uczelni-publicznych-nadzorowanych-przez-ministra-wlasciwego-ds-szkolnictwa-wyzszego-publiczne-uczelnie-akademickie</a></li> <li>RAD-on System. "Dane instytucji systemu szkolnictwa wyższego i nauki." Nov 2022. [Online]. Available: <a href="https://radon.nauka.gov.pl/dane/instytucje-systemu-szkolnictwa-wyzszego-i-nauki">https://radon.nauka.gov.pl/dane/instytucje-systemu-szkolnictwa-wyzszego-i-nauki</a></li> <li>Polish National Agency for Academic Exchange. "List of the university-type HEIs." 2023. [Online]. Available: <a href="https://nawa.gov.pl/images/Aktualnosci/2023/Att.-2.-List-of-the-university-type-HEIs.pdf">https://nawa.gov.pl/images/Aktualnosci/2023/Att.-2.-List-of-the-university-type-HEIs.pdf</a></li> <li>Studies in English. [Online]. Available: <a href="http://www.studies-in-english.pl/">www.studies-in-english.pl</a></li> </ol>

opencc-bySep 2023View details →

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Dataset for Review and Agenda of Digital Forensics Education and Training

This repository has four .bib files encompassing 49 primary study entries, and one .CSV file with data extracted from such studies. The authors gratefully acknowledge the support of the Technology in Forensic Sciences project (Instituto Nacional de Ciência e Tecnologia em Ciências Forenses - \textbf{INCT Forense}, Grant \#465450/2014-8) for funding this work.  We also thank the \textbf{Araucária Funding Agency of Paraná} for their financial and institutional support, as well as \textbf{NAPI - Public Security and Forensic Science} for research funding (Grant \#22.632.926-9).   This work is also supported by CAPES Pro-Defesa (Grant \# V3084362P). Avelino Zorzo thanks \textbf{CNPq/Brazil} Grant \#306250/2021-7.  Edson OliveiraJr thanks \textbf{CNPq/Brazil} Grant \#311503/2022-5.

opencc-by-4.0Jul 2024View details →

zenodo40/100

EduQuick: A Dataset for Assessing Summarization of Informal Educational Content for Social Media

The presented dataset is a curated collection of model-generated text for educational TikTok content, abbreviated as EduQuick. This dataset is the result of evaluating and selecting high-quality content generated by the GPT-4 model following an empirical study. It aims to provide engaging and informative summaries suitable for TikTok's educational audience.

opencc-by-4.0Nov 2023View details →

zenodo40/100

Dataset used in the publication "Using of Transformers Models for Text Classification to Mobile Educational Applications"

Dataset used in the publication "Using of Transformers Models for Text Classification to Mobile Educational Applications". More info about the dataset can be found in the published article.

opencc-by-4.0Mar 2024View details →

zenodo40/100

Dataset for an article "Developing best practices for conducting STEM engineering projects in higher education"

These articles are part of the dataset used in the article.  These articles are anonymized manuscript drafts that were not published in the journal with other articles. Read more from LUMAT-B: <a href="https://urn.fi/urn:nbn:fi:hulib:editori:lumatb.v9i17"> https://urn.fi/urn:nbn:fi:hulib:editori:lumatb.v9i17 </a>

opencc-by-4.0Nov 2024View details →

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