Find research datasets worth reusing

opencc-by-4.0May 2024View details →

Dataset on motivations and perceptions regarding teaching as a career among teacher education students in Japan

This dataset was generated using the adapted Japanese version of the FIT-Choice scale, a framework originally proposed by Watt and Richardson. The adaptation and validation of the Japanese version were carried out by Saito, who utilized this dataset for the process. The data provide insights into the characteristics of motivations and perceptions regarding teaching as a career among teacher education students in Japan.

Eye Tracking Data for Research on the Educational Use of Feynman Diagrams

These data were collected within an eye tracking study as part of the PhD Thesis "The Educational Use of Feynman Diagrams: Opportunities, Challenges, Practices". The data were collected on a learning environment published under <a href="https://doi.org/10.5281/zenodo.11486241">this link</a>. Files including "et-data" are raw eye tracking data, mostly used to create and analyse transition-based metrics, files including "metrics" are metrics calculated by the Tobii Pro Lab software mostly used to calculate gaze durations on certain areas of interest. Files containing "questionnaire" are data from different questionnaires. The file"le-questions-analysis.xlsx" contains an item analysis of the answers within the learning environment.  Analysis scripts are published <a href="../records/11519651">here</a>. The thesis with all relevant information is published <a href="http://dx.doi.org/10.53846/goediss-10576">here</a>.

Socialist Systems, Global Cultural Spheres and Female Participation in Education: Supplementary Materials

This repository includes the dataset and Appendix used in the chapter "Socialist systems, global cultural spheres and female participation in education" by Michael Windzio, Fabian Besche-Truthe and Helen Seitzer, for the collected work titled "The Generosity of Social Polcies in Socialist and Post-socialist States" edited by Andreas Heinrich, Monika Ewa Kaminska, Heiko Pleines and Tobias Ten Brink.  This chapter is a product of the research conducted in the Collaborative Research Center 1342 “Global Dynamics of Social Policy” at the University of Bremen. The center is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—project number 374666841—SFB 1342.

Supplementary Material - Women's Journey in STEM Education in Brazil: A Rapid Review on Engineering and Computer Science

Supplementary Material  for the Rapid Review - Women's Journey in STEM Education in Brazil in Engineering and Computer Science courses

AI as a driver of quality education in public universities. A bibliometric review

Artificial intelligence (AI) is transforming teaching in public universities, enabling improvements and aiming for quality education. The purpose of this bibliometric research was to analyse the impact of AI on educational quality in public universities. The methodology used was a qualitative approach at a descriptive level, which included terms related to artificial intelligence and quality education in public universities; analysing 488 Scopus documents between 1987 and 2024, through the Vosviewer and Bibliometrix tools; and a review of 43 authors cited between 2020 and 2024 in the introduction and literature. The results reveal a growing interest in AI to personalise learning, provide high-quality resources and promote sustainability. Its potential to differentiate teaching and address specific needs of diverse learners is highlighted. It is concluded that AI can improve educational quality and equity, but strategies for ethical and responsible implementation are required, addressing challenges such as teacher training and change management.

The Use of Open Educational Resources (OERs) in Teaching and Learning in Higher Education Distance Learning Programmes in Cameroon

A cross sectional descriptive analysis was adopted for this study. The goal was to was find out the use OERs in teaching and learning in higher education distance learning by taking a snapshot from a cross-section of the population. The institution under study is the University of Buea. The research targeted all 25 students at the master level of the Distance Education Program in the Faculty of Education, University of Buea. All the students were purposively sampled due to the small nature of the target population. A carefully designed questionnaire was used for data collection. The questionnaire had both closed and opened-ended questions which required respondents to select from a variety of responses to cover the research questions. Data was calculated and presented using frequencies tables and bar charts.  After getting description of student’s responses, an analysis was done to show the situation of the use of OERs and in teaching and learning.

Exploring the Emotional Impact of AI-Driven Simulations in University Education: A Gender Perspective

This research evaluates the impact of AI-driven simulations using ChatGPT on university students' emotions, integrating educational psychology and gender studies frameworks. The study investigates how emotions such as interest, irritability, enthusiasm, discomfort, and disgust are affected by AI-enhanced active learning environments. Additionally, it explores gender differences in emotional responses to AI applications in education. Various statistical tests compare emotion scores, including gender comparisons and pre- and post-intervention analyses. The findings aim to enhance understanding of the effectiveness of AI in simulations and its influence on students' emotional experiences, thereby contributing to the design of more effective AI-based educational systems

opencc-by-4.0Dec 2023View details →

Figure 3 in Educational use of FishBase by public aquariums

Figure 3. – The percentage of respondents using different information types provided by FishBase.

opencc-by-4.0Apr 2024View details →

Educational and Career Trajectories in Russia: Open Datasets

The datasets contain data retrieved from the “Jobs in Russia portal”. The datasets contain information about the education, work experience, desired salary and some demographic characteristics of candidates who posted their CVs on the portal between 2015 and 2023. The datasets cover 83 regions of Russia. Dataset 1 contains information on the education and career trajectories (N=6221439), while Dataset 2 contains the information about the general activity of unemployed and job seeking candidates (N=7662089). Each candidate has a unique ID and there are characteristics such as age, education type, region, working experience. The datasets are available as .csv files.  Dataset 1 is designed to study educational and career trajectories. It was obtained by aggregating all CVs for each candidate into a single record with a unique id_candidate, then normalizing text information about the candidates' work experience and education, and finally excluding records that do not contain required information.  Dataset 2 includes all records for the available period, but only covers general information and the activity metric. It might be useful to study the general portal dynamics and, therefore, the activity on the Russian labor market. For citation, please use the data paper: Valko, D., Vasilevskaia, M., Bunina, M., Kozlova, M., Filippova, A. M., & Rud, D. (2024). Educational and Career Trajectories in Russia: Introducing a New Source and Datasets with a High Granularity. Research Data Journal for the Humanities and Social Sciences. <a href="https://doi.org/10.1163/24523666-bja10046" rel="nofollow">https://doi.org/10.1163/24523666-bja10046</a>

Comparing LLM-Generated Tips and Expert-Created Tips in Quantum Computing Education

This dataset includes the anonymized data from two studies with the goal to evaluate if LLM-generated tips can be used instead of expert-created tips to help students answer quantum computing questions. For the main study (main_test_anonymous.csv) a between-subject design was used to quizz participants of the QUIKSTART 2024 summer school, giving them four multiple-choice quantum physics questions and one tip per question. Each participant was assigned one of four conditions represented by the combination of "creator" and "labeled_as" column in the csv file.  In addition we asked to rate quality, correctness and helpfulness for each tip and to rate the perceived difficulty of the question. We removed demographics for anonymities sake. Additionally, we conducted a study directly comparing the LLM-generated and expert-created tips (tip_eval_anonymous.csv). Where we let experts and students rate the tips helpfulness, correctness, if they gave away the answer and if they pointed to relevant concepts. Furthermore, participants had to decide for each question which tip they preferred and were able to leave a comment to give their reasoning. We removed demographics for anonymities sake. These datasets were evaluated in the paper "LLM-Generated Tips Rival Expert-Created Tips in Helping Students Answer Quantum-Computing Questions" by Lars Krupp, Jonas Bley, Isacco Gobbi, Alexander Geng, Sabine Müller, Sungho Suh, Ali Moghiseh, Arcesio Castaneda Medina, Valeria Bartsch, Artur Widera, Herwig Ott, Paul Lukowicz, Jakob Karolus, Maximilian Kiefer-Emmanouilidis

Metagenomics education in a modular CURE format positively affects students' scientific discovery perception and data analytical skills

The targeted metagenomics study performed by Pollock et al. (Pollock 2018) was part of the Global Coral Microbiome Project (Vega Thurber Lab 2014).  Since the original publication of Pollock et al. (Pollock 2018) was written for a highly specialized academic public, we provided Bsc. students with a short introduction of the paper and guided them through the metadata table stored in the gcmp16S_map_r25.txt file under the GCMP Australia sequence data, OTU tables, and metadata folder (<a href="https://doi.org/10.6084/m9.figshare.c.3855466.v2">https://doi.org/10.6084/m9.figshare.c.3855466.v2</a>). In order to process the fastq-files and obtain OTU tables, a custom-made pipeline on a Galaxy instance (Galaxy Community) was used. This pipeline contained data analytical tools obtained from the Naturalis Biodiversity Center GitHub environment (<a href="https://github.com/naturalis">https://github.com/naturalis</a>). In order to align with the newest Galaxy best practices, we updated the tools and links to the original as well as the updated GitHub and toolshed versions are included in the references list. The names of the original raw fastq-files contain many dots which can hamper file-type recognition during downstream analyses in Galaxy. Therefore, prior to distributing the fastq-files to the students, these names were renamed using the ManageZIP tool (The BLFS Development Team 1999-2023). Dots were replaced with underscores, with an exception made for the last 2 file type extension dots. For example, the file name E1.2.Tur.pelt.1.20140814.M_S71_L001_R1_001.fastq.gz was changed into E1_2_Tur_pelt_1_20140814_M_S71_L001_R1_001_1.fastq.gz. Similarly, the filenames in the metadata table were adjusted accordingly. The total collection of renamed file names is available through this zenodo repository as wel as the accompanying metadata file.

Gamification of Student Development Projects in Software Engineering Education - Master's Thesis Dataset

This dataset contains all questionnaire results of the Master's Thesis Gamification of Student Development Projects in Software Engineering Education. The thesis will be linked to this set as soon as it is completed and publically available. In total, five questionnaires were conducted. To analyze users and requirements, a questionnaire with students and project supervisors was conducted. The implemented tool was evaluated using a pre-, mid-, and post-study questionnaire. This dataset contains a folder for each questionnaire including results in CSV format, results in HTML format, which is more human readable, and the questionnaire in PDF format. Due to the used survey tool, lamapoll, some parts of the files are in German. However, the questions and responses are all in English. Additionally, the scripts folder contains the Python scripts that were used to produce the graphics of the thesis. The Python libraries <code>matplotlib</code>, <code>pandas</code>, <code>scipy</code>, <code>numpy</code>, and <code>plot_likert</code> are required to run the scripts. Some of them use the two provided files <code>evalutation_concat_raw_excel.csv</code>, which is a concatenation of the results of the three evaluation questionnaires, and <code>sprint_stats.csv</code>, which contains statistics of the sprints relevant to the user study.

openother-pdFeb 2018View details →

Open Educational Practice (OEP): collection of scenarios

This diagram visualizes an exemplary selection of applications of the paradigm of open educational practices. Beginning with the stage of preparing a teaching unit (seminar, school lesson, etc. ) with corresponding open material (e.g. OER) (light red), we then move to the stage of sharing this unit and its content with the seminar, class, etc. online (orange). The next stage comprises making active use of the introduced content/material in a collaborative learning context as part of this seminar or working group) (yellow). This then might include, or be followed up by, shared instances of revising and optimization of the materials used, based on the experiences made and feedback gained during the session (green). Further contemplating on the experiences made, the teacher and/or the learners involved share their thoughts on these practices and examples online (blue). The diagram lists the described stages' scenarios (text on the right) and corresponding open tools (ideally open source) (icons below the rainbow). What needs to be emphasized here is the possibility of an iterative process optimization. This points towards the possibility of not necessarily going through Stage 1 (light red) in a linear way up to the last stage (blue), but rather making use of continuous back- and cross-referencing of neighbouring stages (indicated in the graphic via fluid color transitions). *** The graphic is provided in various formats (ai, psd, png, jpg, eps, svg) and released as Creative Commons Zero (CC0), so as to be open for further remixing ;) *** To the extent possible under law, <a href="https://zenodo.org/record/1183806"> Tobias Steiner</a> has waived all copyright and related or neighboring rights to 'Open Educational Practice (OEP): collection of scenarios'. This work is published from: Germany.

opencc-by-4.0Jun 2018View details →

PROCEEDING OF THE INTERNATIONAL RESEARCH EDUCATION & TRAINING CENTRE (PIRETC)

AGRICULTURAL, ENVIRONMENTAL & NATURAL SCIENCES SOCIAL, PEDAGOGY SCIENCES & HUMANITIES MEDICAL SCIENCE ECONOMIC, MANAGEMENT & MARKETING SCIENCES LEGAL, LEGISLATION AND POLITICAL SCIENCES

opencc-by-4.0Jun 2018View details →

Charter School Websites: A Physical Education and Physical Activity Content Analysis

Excel file for data set associated with analysis of 520 California elementary charter school websites' mentioning of physical education and physical activity opportunities.

opencc-by-nc-4.0Dec 2017View details →

CATCH-EyoU: Representation of the EU and Youth Active Citizenship in Educational Contexts: Cross-national Textbook Analysis

The data set contains the results of the quantitative and qualitative content analysis of local textbooks from different subjects (e.g. ESL, EFL, History, Social Sciences, Civic Education) at ISCED 3 level (upper secondary schools as well as vocational schools). Data collection was carried out using a specifically designed analytical grid (Ribeiro; Ferreira & Menezes, 2016). The grid was tested and applied by national research teams (Portugal, Czechia, Estonia, Germany, Italy and Sweden) participating to Catch-EyoU project – each textbook analysis implied filling one grid. In total, 34 textbooks were analysed across the participating countries. All grids include excerpts of the textbooks (text or images, whenever legally possible). Quantitative analysis data and bibliographical metadata are also included. Data collection and analysis was aimed at getting an overall picture of school texts, including the analysis of number of paragraphs, pages and exercises about topics such as the EU, active citizenship, intercultural awareness, political involvement. Although textbook analysis is relatively common and there are other European projects that include it, the data analysis grid is original (and relatively innovative in the area) as it directly relates to the project topics. The potential users can be other researchers who might be interested not only in the grid but also in the analysis itself, either to use it with other textbooks or compare it with existing research or with similar data collected in different countries; teachers might also find it useful to explore the analysis as a basis for their practice. Also other stakeholders may be interested in reanalyzing our data for comparative aims.

opencc-by-4.0Oct 2018View details →

Proceeding of The International Research Education & Training Centre.

AGRICULTURAL, ENVIRONMENTAL & NATURAL SCIENCES SOCIAL, PEDAGOGY SCIENCES & HUMANITIES MEDICINE, VETERINARY MEDICINE, PHARMACY AND BIOLOGY SCIENCES TECHNICAL AND APPLIED SCIENCES REGIONAL DEVELOPMENT AND INFRASTRUCTURE ECONOMIC, MANAGEMENT & MARKETING SCIENCES LEGAL, LEGISLATION AND POLITICAL SCIENCES

opencc-by-4.0Oct 2018View details →

Proceeding of The International Research Education & Training Centre

AGRICULTURAL, ENVIRONMENTAL & NATURAL SCIENCES SOCIAL, PEDAGOGY SCIENCES & HUMANITIES MEDICINE AND BIOLOGY SCIENCES REGIONAL DEVELOPMENT AND INFRASTRUCTURE ECONOMIC, MANAGEMENT & MARKETING SCIENCES LEGAL, LEGISLATION AND POLITICAL SCIENCES

opencc-by-4.0Dec 2018View details →

Is there a social life in Open Data? Open datasets exploring practices in Educational Technology Research

In the landscape of Open Science, Open Data [OD] plays a crucial role as data are one of the most basic components of research despite their diverse formats across scientific disciplines. Opening up data is a recent concern for policy makers and researchers as the basis for good open science practices. The common factor underlying these new practices – the relevance of promoting open data circulation and reuse – is mostly a social form of knowledge sharing and construction. However, while data sharing is being strongly promoted by policy making and is becoming a frequent practice in some disciplinary fields, open data sharing is much less developed in social sciences and in educational research. The Open Data hereby introduced is composed of two integrated datasets which collect data relating practices of Open Data publication and sharing in the field of Educational Thecnologies. This data was collected to suport the aim of investigating open data sharing in a selection of open data repositories as well as in the academic social network site ResearchGate. The research questions addressing this study are: <ol> <li>Do researchers in the field of Educational Technology publish Open Datasets [ODs]?</li> <li>To which extent are ODs compliant with the FAIR data principles?</li> <li>What is the social life relating to the ODs in terms of the metrics provided by the OD portals? As a subsidiary question: 3.a- To what extent do open data portals allow researchers to cultivate social practices around OD?</li> </ol>   In order to investigate OD presence in ResearchGate, the following research aim guided the second part of the study: Analysis of the presence of the same selected OD in ResearchGate and of the type of social activity OD exhibited by OD according to ResearchGate mètrics. The Open Data here presented is composed by two datasets. <ol> <li>23 datasets extracted out from 82 randomly selected datasets, from an inital total of 633. The data set presents a Codebook explaining the several categories of analysis or variables and the values assigned to the same. Since the analysis was performed in two phases (first selection and screening, second selection and classification) the codebook is divided in two tables. Moreover, the data set contains 6 Worksheets, ordered taking the workflow as reference: Codebook, Dataset_Extraction, Analysis_Workflow, Selected_Analysis. This dataset has been produced and curated by J.E.Raffaghelli</li> <li>The analysis of correspondance for the 23 datasets in ResearchGate. The data set presents a Codebook, and is divided into two worksheets: Codebook, ODT&RG. The last worksheet introduces the comparison between the social mètrics within RG of the found datasets and the relating papers. This dataset has been produced and curated by S. Manca and J.E. Raffaghelli.</li> </ol>