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2,611 results for “students”
Treatment Condition Student Data: D.A.R.E./keepin' it REAL Elementary Curriculum
<p>Raw, de-identified treatment condition data for waves 1, 2, and 3 associated with an evaluation of D.A.R.E. officers' delivery of the "keepin' it REAL" prevention program. These data were compared to control cases which were generated using the "Virtual Controls" algorithm developed by Hansen, et al., 2018 (Hansen, W.B. Saldana, S., Chen, S.H., & Ip, E.H. (2018). An algorithm for creating virtual controls using integrated and harmonized longitudinal data. Evaluation and the Health Professions. 41(2), 183-215. doi: 10.1177/0163278718772882). </p>
FEDORA. Excerpts from essays, transcript of interviews and group discussions on students' future perception. Part 3: Interviews, Finland.
<p>Finnish-language dataset. Related to a research article that is awaiting acceptance for publication: <em>Future, technology and agency: Students’ experiences from a course on futures thinking and quantum computing</em>.</p> <p>As per ethical concerns and participants' consent, the dataset is given in a fully anonymised form. Instead of students' interviews (the context of which is given in the article). In a nutshell, 21 upper-secondary school students were interviewed in 2018 regarding their experiences on taking an experimental science course that combined ideas from futures thinking and quantum computing. The present dataset contains all 245 transcribed passages from 21 student interviews that were initially marked as relevant to the research goals (i.e. how students saw their conceptions change over the course). Additionally, for each passage the final coding that was used in the analysis for the research paper is shown. The "number-letter codes" were used as shorthands; the full names of the codes correspond closely with the final, English-language codes in the paper.</p> <p>To preserve full anonymity, students are not identified by any marker or pseudonym here; rather, the passages are given alphabetically. The start and end of passages has not been checked for additional or missing first and last characters. Please also note that the character > marks change of speaker. Identifying the interviewer and interviewee should be straighforward based on the context.</p> <p>Please contact the corresponding author for more information.</p> <p> </p> <p>--</p> <p> </p> <p><a href="https://zenodo.org/communities/futuresthinking?page=1&size=20">FEDORA Project</a> README:</p> <p> </p> <p><strong>README</strong></p> <p><strong>Data Set Title:</strong> “FEDORA. Excerpts from essays, transcript of interviews and group discussions on students’ future perception. Finland"</p> <p><strong>Data Set Author/s:</strong> Antti Laherto, Tapio Rasa, Elina Palmgren (University of Helsinki)</p> <p><strong>Data Set Contact Person/s</strong>: Tapio Rasa<strong> </strong>(University of Helsinki), ORCID 0000-0003-1315-5207, tapio.rasa@helsinki.fi;</p> <p><strong>Data Set License</strong>: this data set is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.</p> <p><strong>Publication Year</strong>: 2021</p> <p><strong>Project Info</strong>: FEDORA<strong> </strong>(Future-oriented Science EDucation to enhance Responsibility and engagement in the society of Acceleration and uncertainty<strong> , </strong>funded by European Union, Horizon 2020 Programme. Grant Agreement num.<strong> </strong>872841,<br> www.fedora-project.eu)</p> <p> </p> <p><strong>Data set Contents</strong></p> <p>The data set consists of:</p> <p>One spreadsheet file, provided in two alternative formats (CSV and XLSX).</p> <p>Future_technology_agency_DATA_CSV.csv</p> <p>Future_technology_agency_DATA_XLSX.xlsx</p> <p> </p> <p><strong>Data set Documentation</strong></p> <p><em>Given above this README, on the ZENODO repository. https://zenodo.org/record/4734161</em></p>
Drop Project Student Plugin for IntelliJ IDEA - Evaluation Survey
<p>Contains a CSV file with students replies to the survey used to evaluate the Drop Project Student plugin for IntelliJ IDEA.</p> <p>To support international readers, the question names (CSV headers) were translated to English and/or match the numbering that appear in the paper. However, the student's textual replies to the open ended questions were left in their original language, Portuguese.</p>
PECEM student satisfaction paper dataset
<p>This dataset corresponds to student satisfaction questionnaire applied to students from the first to the eighth generations enroled in the Program of Combined Studies in Medicine (PECEM), the first and unique MD/PhD prgram in Mexico. It is a first approach for measurig their satisfaction.</p>
Practical reflection model for healthcare professional students
<p>This series of videos examines my method of reflective thinking to aid healthcare professional students in developing their personal and professional beleifs, attitudes and behaviours.</p>
Hackathons as a Pedagogical Strategy to Engage Students to Learn and to Adopt Software Engineering Practices
<p>Teaching Software Engineering is not a trivial duty since several pedagogical strategies can be used and sometimes the impact of these on students is uncertain. Hackathons are similar to marathons, however used to produce solutions to solve a specific problem in a short period of time and based on intense collaboration. Educational hackathons aim to promote learning in such an environment. The Undergraduate computing programs of PUCRS decided to use a hackathon as a pedagogical strategy aiming to motivate the students to practice the adoption of software development practices and to work in groups as a means to practice the development of social skills. Therefore, we conducted a case study to investigate: 1) The motivations to students to attend or not attend an educational hackathon, 2) The students perceptions about this hackathon, 3) The Software Engineering practices adopted by students. In this study, we identified factors that may affect students motivation to participate (e.g., improve the teamwork skills), some students expectations about the hackathon (e.g., work in teams), and the practices adopted by the students (e.g., pair programming). Some of our findings include that students enjoy participating in an informal educational environment (e.g., hackathons) to improve their technical skills and to build network with some colleagues. This study can provide insights to teachers that wants to organize some activity than traditional teaching and the students perspective about this kind of strategy.</p>
SQL Databases for Students and Educators
<p>Publicly accessible databases often impose query limits or require registration. Even when I maintain public and limit-free APIs, I never wanted to host a public database because I tend to think that the connection strings are a problem for the user.</p> <p>See https://databases.pacha.dev</p>
BDRC Returned Students Network Data
<p>These two files correspond to the edge list and the node list used to build the network of returned students in the <em>Biographical Dictionary of Republican China</em> (BDRC). </p>
Data and material for the manuscript "Mutation testing and self/peer assessment: analyzing their effect on students in a software testing course"
<p><strong>This repository is composed of two different parts: </strong></p> <ul> <li><a href="https://zenodo.org/record/4464300/files/Assessment%20data%20and%20Mutation%20Scores.xlsx?download=1">Assessment data and Mutation Scores</a> file contains the student-generated data used in the experience.</li> <li><a href="https://zenodo.org/record/4464300/files/experience-material.zip?download=1">Experience-material</a> file contains the files to be able to reproduce the experience.</li> </ul> <p> </p> <p><strong>The </strong><strong> <a href="https://zenodo.org/record/4464300/files/experience-material.zip?download=1">Experience-material</a> file for the lab is used in two sessions:</strong></p> <p>Session 1: Development and assessment of test suites</p> <p>In this session, the student has to develop a test suite for a program under test. At the end of the session, the test suite will be evaluated against a set of assessment criteria regarding the quality of the developed test suite.</p> <p>Files for this session:</p> <ul> <li>VVS-Lab6-S1 pdf file , with the description of this session.</li> <li>Material-S1 zip file, with the files required to complete this session.</li> </ul> <p>Session 2: Evaluation applying mutation testing with MuCPP</p> <p>In this session, the test cases designed in the first part of this lab will be evaluated based on the mutation adequacy criterion. This will be done by using the <a href="https://ucase.uca.es/mucpp/">MuCPP mutation tool</a>.</p> <p>Files for this session:</p> <ul> <li>VVS-Lab6-S2 pdf file, with the description of this session.</li> <li>Material-S2 zip file, with the files required to complete this session.</li> </ul> <p><em>The source code files family.[cpp|hpp] have been adapted from a listing in [1]. Note that, while considered to be fault free in this lab, these source files are used in other sessions where students are expected to detect some defects in them.</em></p> <p>[1] S. Wiener and L. J. Pinson, The C++ Workbook. USA: Addison-Wesley Longman Publishing Co., Inc., 1990.</p>
Individual work of Bachelor students monitored using a dynamic assessment dashboard
<p>This document explain how data were generated and how to interpret them. </p> <p><strong>LICENSE: CC0</strong><br> But if you want to combine data with other datasets, feel free to use them as if they were published under CC0 license. <br> Data were published in February 2017. At that time, Zenodo only provided CC BY, CC BY-SA, CC BY-NC, CC BY-ND and CC BY-NC-ND. No CC0 option was available.</p> <p> </p> <p><strong>HOW DATA WERE COLLECTED</strong><br> Data provided in this dataset were collected in Fall 2016 as part of the Bachelor course 'Formation des usagers en bibliothèques' taught at the University of applied sciences Geneva (Information Sciences Department). <br> Data were generated by the interaction of the students with DAD, the Dynamic assessment dashboard, software to be released in 2017 (https://github.com/grolimur/DAD). Only data related to individual activities are published in this set. They were then cleaned and anonymised.</p> <p>The activities.csv file is provided for information purpose.</p> <p>Data are provided in 2 formats:</p> <p>* Comma-spearated values (submissions.csv)<br> * SQLite (submissions.sqlite)</p> <p>CSV is not correctly read by Excel, it's recommended to convert it into an .xslx file before using it. <br> SQLite is provided in order to apply different sorting and filters to the data. It can be read using SQLite manager for Firefox (https://addons.mozilla.org/en-US/firefox/addon/sqlite-manager/).</p> <p> </p> <p><strong>CODEBOOK</strong><br> Here is the name, the meaning and the possible values of the columns (name - description [possible values]).</p> <p>ID - database row ID [(database unique row ID)]<br> ActivityID - unique activity ID [1, 2, 3, 4, 10, 14, 16, 21, 22, 23, 41]<br> StudentID - unique anonymous student ID [#50 to #72]<br> date - recorded by the database [timestamp]<br> year - year extracted from date [2016, 2017]<br> month - month extracted from date [01, 11, 12]<br> day - day extracted from date [1 to 31]<br> weekday - weekday calculated from date [1, 2, 3, 4, 5, 6, 7]<br> hour - hour extracted from date - 00 to 23]<br> minute - minute extracted from date [00 to 59]<br> second - second extracted from date [00 to 59]<br> gr - group activity or not [0]<br> validated - was the submission validated or not [-1 or 1]<br> week - week number [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, H1, H2]</p> <p><em>ActivityID meaning</em><br> Activities are described in activities.csv. This file conatins only individual activities description. <br> The 2 activities iwth the bonus particularity are not present in the data because students didn't need to apply for them. A bonus was automatically added when activity 1 AND 2 AND 3 AND 4 (bonus = activity 5) as well as when activities 21 AND 22 AND 23 (activity 24) were completed.</p> <p><em>Date format</em><br> The date is formatted like YYYY-MM-DD hh:mm:ss.</p> <p><em>Weekday meaning</em><br> 1 stands for Monday, 2 for Tuesday and so on until 7 for Sunday.</p> <p><em>Group meaning</em><br> 0 stands for 'individual activity' and 1 would stand for group activity'. <br> Only entries with gr="0" appears in this dataset.</p> <p><em>Validated meaning</em><br> -1 stands for 'rejected' and 1 for 'validated'.</p> <p><em>Week meaning</em><br> The course lasts 10 weeks. 1 stands for the 1st week and so on until 10 that stands for the 10th week. <br> H1 and H2 stand for the Christmas holiday weeks that took place between week 8 and 9. <br> No submission were sent in weeks 1, 2 and 10.</p> <p> </p>
What students answer when discussing about citation practices
<p>This document explain how data were generated and how to interpret them. </p> <p> </p> <p>LICENSE: CC0<br> But if you want to combine data with other datasets, feel free to use them as if they were published under CC0 license. <br> Data were published in February 2017. At that time, Zenodo only provided CC BY, CC BY-SA, CC BY-NC, CC BY-ND and CC BY-NC-ND. No CC0 option was available.</p> <p> </p> <p>HOW DATA WERE COLLECTED<br> The 21 recorded sessions took place between February 2013 and December 2016. <br> Data were collected using Turning Technologies' remote controls (called *clickers*) and TurningPoint software.</p> <p>The 4 versions of the quiz used during these 4 years are provided in the 'quizzes' folder for information purpose (in PDF and Powerpoint formats).</p> <p>Turning Technologies records data in a closed format (.tpzx) that can be exported and converted them into 3 formats provided here (these 3 files contain the same data):</p> <p>* Excel (.xslx)<br> * Comma-spearated values (.csv)<br> * SQLite (.sqlite)</p> <p>The first one was directly exported from TurningPoint and is provided for Excel users who can't read CSV correctly. <br> CSV was converted from Excel and is provided for non-Excel users. <br> Finally, SQLite is provided in order to apply different sorting and filters to the data. It can be read using SQLite manager for Firefox ([https://addons.mozilla.org/en-US/firefox/addon/sqlite-manager/](https://addons.mozilla.org/en-US/firefox/addon/sqlite-manager/)).</p> <p> </p> <p>CODEBOOK<br> Here is the name, the meaning and the possible values of the columns (name - meaning [possible values]). If students didn't answer the question, the value is '-'. </p> <p>Session - session number (chronological) [1 to 21]<br> AcademicYear - academic year [12-13, 13-14, 14-15, 15-16, 16-17]<br> Year - calendar year [2013, 2014, 2015, 2016]<br> Month - month (number) [1 to 12]<br> Day - day (number) [1 to 31]<br> Section - section abbreviation [CH, ESC, GM, IF, SIE, SV]<br> Level - students' level [BA2, BA3, MA]<br> Language - course's language [FR or EN]<br> DeviceID - clicker's ID [(unique ID within a session)]<br> Q1 - answers to question 1 [A, B, C, D, E]<br> Q2 - answers to question 2 [A, B, C, D]<br> Q3 - answers to question 3 [A or B]<br> Q4 - answers to question 4 [A or B]<br> Q5 - answers to question 5 [A or B]<br> Q6 - answers to question 6 [A or B]<br> Q7 - answers to question 7 [A or B]<br> Q8 - answers to question 8 [A or B]<br> Q9 - answers to question 9 [A or B]<br> Q8-9 - answers to the question 8-9 (merge) [A or B]<br> Q10 - answers to question 10 [1, 2]<br> Q11 - answers to question 11 [A or B]<br> Q12 - answers to question 12 [A, B]</p> <p>Section abbreviation meaning<br> * CH: chemistry<br> * ESC: school of criminal justice (Unil)<br> * GM: mechanical engineering<br> * IF: financial engineering<br> * SIE: environmental engineering<br> * SV: life sciences</p> <p>Level meaning <br> * BA2: 2nd year of Bachelor<br> * BA3: 3rd year of Bachelor<br> * MA: Master level</p> <p>Question types<br> For some questions, multiple answers were allowed: Q1, Q2, Q10 & Q12. <br> Half of the questions have only one correct answer, true or false: Q3, Q5, Q6, Q7, Q8, Q9 & Q8-9. <br> Finally, for 2 questions only one answer was accepted, but there is not only one correct answer: Q4 & Q11.</p> <p> </p> <p>INFORMATION ABOUT THE SESSIONS<br> Except otherwise stated below, all sessions were conducted like the original one: Q1 to Q12 (no Q8-9).<br> The original French version of the quiz has been translated into English for a few sessions with Master students.<br> For sessions 14 and 20, Q5 was removed and Q8 & Q9 were merged in Q8-9. <br> Session 18 was a short one with only 7 sevens questions: Q1, Q2, Q3, Q4, Q6, Q7 & Q9. </p> <p> </p> <p>CONTACT INFORMATION<br> If you have any question about these data, contact formations.bib@epfl.ch.</p> <p> </p>
Presence and proportion of plants in biodiversity inventories conducted by undergraduate students enrolled in animal-related courses
<p>Dataset on biodiversity inventories conducted by 110 undergraduate students enrolled in animal-related courses using the iNaturalist platform.</p>
GenderInEnergy Student Survey 2023
<p>Today, the education and training landscape worldwide is confronted with an urgent need to prepare students for key roles in what is known as the energy transition. This global shift involves moving away from traditional, non-renewable energy sources, such as fossil fuels, towards a cleaner and more sustainable energy system primarily based on renewable sources. Every country must effectively manage this transition while ensuring social sustainability, requiring individuals with new or combined skill sets at all levels, from manual labour positions to top executives.</p><p>In light of this context, we conducted a survey among students in universities and other tertiary education institutions worldwide to explore their perceptions of the energy transition and assess their interest in pursuing a career in the energy sector or other positions at the forefront of this transformation. It was conducted in summer 2023. The questionnaires were implemented on the web, namely on EUSurvey and Netigate. The survey made use of a structured questionnaire with a range of closed questions and the option to add open text responses where appropriate. The survey was distributed with the help of the broad network of organisations and experts in the studied countries. In addition, a student panel provided by Netigate was used to reach the target number of responses. The survey was aimed an any person in the target population, irrespective of gender and field of education and training. The survey took 5 to 10 minutes to answer. All answers were anonymous, and the respondents were not asked any identifiable information, such as e-mail address, to avoid bias. The questionnaire was available in English as well as French, German, Italian, Polish and Spanish. No weighting was applied to the dataset.</p>
Data analysis Protocol for a Joint Study into the Impacts of AI on professional Competencies of IT Professionals and Implications for Computing Students. ITiCSE 2024 Working Group 02.
<h1><a name="_Toc169648661"></a><span>Overview</span></h1> <p><strong><span> </span></strong></p> <p><span>The purpose of this protocol is to help us define a common protocol for sharing and analysing data for the ITiCSE 2024 working group: “<em>WG02: A Multi-Institutional-Multi-National Study into the Impacts of AI on Work Practices of IT Professionals and Implications for Computing Students</em>”. <span> </span>Excerpts from the working group plan to place the protocol in context (Clear et al., 2024) are given below.</span></p> <p><strong><em><span> </span></em></strong></p> <p><strong><em><span>Background and Related Work</span></em></strong></p> <p><em><span>As Artificial Intelligence (AI) continues to make its presence felt in transforming workplaces around the world [1,10], and the Information Technology industry in particular, it is essential to understand its impact on the work practices of IT professionals, and the implications for computing students and curricula. This research project builds on work initiated jointly, in Sweden, New Zealand and Scotland, investigating concerns about the increasing impacts of Artificial Intelligence in IT Sector workplaces for employee work engagement [11,13,1] and the implications for tertiary study, assessment and curricula in computing [4, 8, 10, 9].<span> </span></span></em></p> <p><em><span>“Work engagement”, has been defined as the positive inner state where employees are fully present and engaged in their work, and is closely linked to motivation, learning, productivity, and accountability [11, 13]. Within the context of (Generative) AI at work, IT professionals have been noted as early adopters of AI [10, 1]. Their involvement in implementing and utilising AI technologies can provide valuable insights into the interplay between AI and work engagement.<span> </span>The implications for students are significant as future IT professionals, who must acquire and enhance competencies to adapt and thrive in digital workplaces. </span></em></p> <p><em><span> </span></em></p> <p><strong><em><span>2</span></em></strong><em><span><span> </span><strong>Goals of the Working Group</strong></span></em></p> <p><em><span>By exploring the relationship between work engagement and learning, this study aims to shed light on the dynamics that drive employee engagement and its connection to the professional development of competencies. The previous study has interviewed IT professionals with the following research questions (RQ):</span></em></p> <p><em><span> </span></em></p> <p><em><span>RQ1: How does AI influence work engagement for IT professionals?</span></em></p> <p><em><span>RQ2: How does AI affect the socio-technical work dynamics for IT professionals?</span></em></p> <p><em><span>RQ3: What are the implications of integrating AI on the acquisition and enhancement of professional competencies and the learning processes of IT professionals?</span></em></p> <p><em><span> </span></em></p> <p><strong><em><span>3</span></em></strong><em><span><span> </span><strong>Methodology</strong></span></em></p> <p><em><span>This working group aims to analyse the corpus of interview data collected from multiple countries to better understand the implications for computing students, tertiary computing education curricula and assessment of the new professional competencies emerging from this work. This study informed by the literature on work engagement, automation and motivation for IT professionals [11, 13], will use a combination of multi-vocal literature review [7] and qualitative research methods [2, 5], including thematic analysis of the interviews, to investigate the state of the practice in and challenges IT Professionals face within their local/global work contexts. The literature on professional competencies in computing [4, 3, 6] will be drawn upon to characterise the new needs identified in this analysis.<span> </span>Further implications for computing curricula design and assessment will be developed from this analysis. </span></em></p> <p><span>REFERENCES</span></p> <p><span>[1]<span> </span>ACM Technology Policy Council. 2023. Principles for the development, deployment, and use of generative AI technologies, ACM New York.</span></p> <p><span>[2]<span> </span>Braun, V. and Clarke, V. 2021. One size fits all? What counts as quality practice in (reflexive) thematic analysis? <em>Qualitative research in psychology</em>, <em>18</em> (3). 328-352.</span></p> <p><span>[3]<span> </span>Clear, A., Clear, T., Vichare, A., Charles, T., Frezza, S., Gutica, M., Lunt, B., Maiorana, F., Pears, A. and Pitt, F. 2020. Designing Computer Science<span> </span>Competency Statements: A Process and Curriculum Model for the 21st Century in <em>Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education</em>, ACM, New York.</span></p> <p><span>[4]<span> </span>Clear, A., Parrish, A. and CC2020 Task Force. 2020. Computing Curricula 2020 - CC2020 - Paradigms for Future Computing Curricula ACM and IEEE-CS eds. <em>A Computing Curricula Series Report </em>ACM, New York.</span></p> <p><span>[5]<span> </span>Cruzes, D.S. and Dyba, T. 2011. Recommended steps for thematic synthesis in software engineering. in <em>2011 international symposium on empirical software engineering and measurement</em>, IEEE, 2011, 275-284.</span></p> <p><span>[6]<span> </span>Frezza, S., Clear, T. and Clear, A. 2020. Unpacking Dispositions in the CC2020 Computing Curriculum Overview Report in <em>2020 IEEE Frontiers in Education Conference (FIE)</em>, IEEE, Uppsala, Sweden. </span></p> <p><span>[7]<span> </span>Garousi, V., Felderer, M., & Mäntylä, M. V. 2019. Guidelines for including grey literature and conducting multivocal literature reviews in software engineering. <em>Information and Software Technology</em>, <em>106.</em> 101-121</span></p> <p><span>[8]<span> </span>Jacques, L. 2023. Teaching CS-101 at the Dawn of ChatGPT. <em>ACM Inroads</em>, <em>14</em> (2). 40-46.</span></p> <p><span>[9]<span> </span>Liffiton, M., Sheese, B., Savelka, J. and Denny, P. 2023. CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes. <em>arXiv preprint arXiv:2308.06921</em>.</span></p> <p><span>[10]<span> </span>Prather, J., Denny, P., Leinonen, J., Becker, B.A., Albluwi, I., Craig, M., Keuning, H., Kiesler, N., Kohn, T. and Luxton-Reilly, A. 2023. The robots are here: Navigating the generative ai revolution in computing education. <em>arXiv preprint arXiv:2310.00658</em>.</span></p> <p><span>[11]<span> </span>Roto, V., Palanque, P. and Karvonen, H., 2019. Engaging automation at work–a literature review. in <em>Human Work Interaction Design. Designing Engaging Automation: 5th IFIP WG 13.6 Working Conference, HWID 2018, Espoo, Finland, August 20-21, 2018, Revised Selected Papers 5</em>, Springer, 158-172.</span></p> <p><span>[12]<span> </span>SFIA Foundation. 2023. SFIA skills aligned to EU ICT Profiles, SFIA Institute, London.</span></p> <p><span>[13]<span> </span>Sharp, H., Baddoo, N., Beecham, S., Hall, T. and Robinson, H. 2009. Models of motivation in software engineering. <em>Information and software technology</em>, <em>51</em> (1). 219-233.</span></p> <p><em><span> </span></em></p>
Microbial Load on Dental Students' Hands: A Gender and Academic Stage Analysis
<p>This dataset supports a study investigating microbial contamination on the hands of dental students at Al-Hadi University College. The data were collected using impression sampling of the index and thumb fingers of 35 students (17 males, 18 females) from the third, fourth, and fifth academic years. Sampling occurred at the end of academic or clinical activities, and bacterial load was measured using colony-forming units (CFUs) cultured on MacConkey agar. Variables in the dataset include gender, academic stage, handedness, and CFU counts.</p>
Students' perceptions of an environmental education program.
<p>This database contains 185 records. It was developed using a questionnaire to evaluate students' perceptions of an environmental educommunication program. The variables measured are:</p> <p>1 Gender: 1 male, 2 female, 3 non-binary</p> <p>2 Age: 12 to 17</p> <p>3 Type of school. 1: private, 2: public, 3: state-funded private school</p> <p>4 -11: measured using the following scale: </p> <p> 5 totally agree</p> <p> 4 agree</p> <p> 3 neither agree nor disagree</p> <p> 2 disagree</p> <p> 1 strongly disagree</p>
Dataset: Evaluating Two Approaches to Assessing Student Progress in Cybersecurity Exercises
<p>This repository contains supplementary materials for the following conference paper:</p> <p>V. Švábenský, R. Weiss, J. Cook, J. Vykopal, P. Čeleda, J. Mache, R. Chudovský, A. Chattopadhyay.<br> <em>Evaluating Two Approaches to Assessing Student Progress in Cybersecurity Exercises.</em><br> In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education (SIGCSE 2022).<br> <a href="https://doi.org/10.1145/3478431.3499414">https://doi.org/10.1145/3478431.3499414</a></p> <p>Preprint available at: <a href="https://arxiv.org/abs/2112.02053">https://arxiv.org/abs/2112.02053</a></p> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).</p> <pre><code>@inproceedings{Svabensky2022evaluating, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Weiss, Richard and Cook, Jack and Vykopal, Jan and \v{C}eleda, Pavel and Mache, Jens and Chudovský, Radoslav and Chattopadhyay, Ankur}, title = {{Evaluating Two Approaches to Assessing Student Progress in Cybersecurity Exercises}}, booktitle = {Proceedings of the 53rd ACM Technical Symposium on Computer Science Education}, series = {SIGCSE '22}, location = {Providence, RI, USA}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, month = {03}, year = {2022}, pages = {787--793}, numpages = {7}, isbn = {978-1-4503-9070-5}, url = {https://doi.org/10.1145/3478431.3499414}, doi = {10.1145/3478431.3499414}, }</code></pre> <p><strong>Attached content</strong></p> <p>The materials include the research dataset, source code, and graphs. See the README.md file inside the attached ZIP file for more details.</p>
Perceived academic stress, hair and saliva cortisol concentrations, and their relationship with anthropometric measures associated with obesity in first-year medical students.
<p>Cortisol plays an important role between stress, weight gain, and the development of obesity. Therefore, we investigated the association between stress, eating behavior, cortisol, and anthropometric measures related to obesity in a sample of medical students. We determined cortisol concentrations by ELISA and related it to self-reported stress, eating behavior, and anthropometric measurements throughout the academic period. We report an increase in hair cortisol, higher self-reported stress scores, and BMI mainly in females. Finally, we found evidence of positive associations between capillary cortisol and BMI. Also, eating behavior is affected by perceived stress mainly in females.</p> <p>In this database, we provide weight variables, BMI, psychometric tests, and hormonal determinations.</p> <p><br> You can see two sheets in the Excel file. Sheet 1 includes the raw data and sheet 2 includes a description of each variable, as well as the bibliography (article or book) where each survey or protocol was obtained.</p> <p><br> In addition, the data specify the units of the hormonal variables, as well as the units of the anthropometric variables. If you have questions about the interpretation of psychometric test scores, you can contact our team for further details.</p> <p> </p>
Summary of the comparative analyses between the examination models as perceived by teachers and students.
<p>Comparisons were made between model A: traditional face-to-face examination, model B: open-book examination with proctoring, model C: open book examination without proctoring. The results show that for teachers and students open-book exams with or without proctoring had no significant differences and are more in line with an authentic assessment than face-to-face exams.</p>
Using a Hybrid Kano-Importance Questionnaire in the Acquisition of Data Related to Students' Expectations from Online Educational Platforms
<p>This dataset contains the data collected for the assessment of the quality attributes of a new online educational platform. The questionnaire used for data collection the Kano methodology and was designed as a hybrid Kano-importance questionnaire. The purpose of this data collection consists of the analysis of the students’ expectations regarding the features proposed for a new online educational platform. This analysis facilitates the identification of student needs during times of COVID-19 pandemic and post-pandemic times, while a transition to an online educational system was used throughout the world. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.