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177 results for “student data”
Data for the article "Professionalism, emotional wellbeing, and dropout intention in health professions students during the pandemic"
<p>Dataset from a study of attitudes and perceptions of medicine and nursing students in Peru during the COVID-19 pandemic. Survey was applied from 2020-07-24 to 2021-04-16.</p> <p>This dataset is described in the article: </p> <p>Castagnetto, J.M., Hancco-Monrroy, D.E., Caballero-Apaza, L.M. <em>et al.</em> Professionalism, emotional wellbeing, and dropout intention in health professions students during the pandemic. <em>Sci Data</em> <strong>12</strong>, 1259 (2025). <a href="https://doi.org/10.1038/s41597-025-05508-5">https://doi.org/10.1038/s41597-025-05508-5</a> (<a href="https://www.nature.com/articles/s41597-025-05508-5">https://www.nature.com/articles/s41597-025-05508-5</a>)</p>
Student activity data
<p>This dataset includes anonymized records of student activities in a peer review activity performed through Synergy platform. This data comes from an educational technology course delivered in an European university. There were 30 participants from different departments (Computer science, Math, Physics, Preschool and Elementary Education) In this course, the main course assignment was a learning design project in which students worked in groups. The activity lasted 3 weeks and for each project 2-3 reviewers were assigned.</p>
Missouri reservoir water quality data from the Statewide Lake Assessment Program (SLAP), the Lakes of Missouri Volunteer Program (LMVP), and the Reservoir Observer Student Scientists (ROSS) program
This dataset of limnological water quality data continues from Jones et al., 2024, starting in 2017 until 2021. It is from 195 reservoirs, the majority of which are in the state of Missouri (MO) in the USA collected by the University of Missouri Limnology Lab. Water quality parameters analyzed in the MU Limnology Lab during this time frame include: areal pigment absorption coefficient, alkalinity, alpha (light utilization efficiency P-E parameter), ammonium (NH4), ammonium-debt, anatoxin, chlorophyll a (corrected and uncorrected for pheophytins), chloride, cylindrospermopsin, seston d13C, seston d15N, dissolved turbidity, dissolved organic carbon, Ek (light saturation P-E parameter), FVFM (maximum quantum yield of PSII for photochemistry), gross primary production, microcystin, nitrate & nitrite (NO3), nitrate-debt, particulate nitrogen, particulate phosphorus, phosphorus-debt, pheophytin, particulate carbon, particulate inorganic matter, particulate organic matter, phycocyanin (PHYCO), saxitoxin, Secchi disk depth, silica, soluble reactive phosphorus, total dissolved nitrogen (TDN), total dissolved phosphorus (TDP), total nitrogen (TN), total phosphorus (TP), total suspended solids (TSS), and urea. Most of the samples were collected during the summer months (May-September) when the reservoirs were thermally stratified, but a few were taken during the rest of the year (October-April). The majority of samples were taken at the deepest point in the reservoir directly up-reservoir of the dam. Sampling was conducted from a boat most of the time, but a few samples were taken from shorelines and drinking water treatment intake pipes. Most of the data come from the Statewide Lake Assessment Project (SLAP) and the Lakes of Missouri Volunteer Program (LMVP) funded by the Missouri Department of Natural Resources. This data represents duplicate or triplicate water samples collected from either the water surface, integrated over the depth of the epilimnion, or from discrete dep
Data from the "The Psychology of Professional and Student Actors: Creativity, Personality, and Motivation"
<p>Data associated with:</p> <p>Dumas, D., Doherty, M., Organisciak, P. (2020) "The Psychology of Professional and Student Actors: Creativity, Personality, and Motivation". PLOS ONE.</p> <p>Description of work associated with this data:</p> <blockquote> <p>As a profession, acting is marked by a high-level of economic and social riskiness concomitantly with the possibility for artistic satisfaction and/or public admiration. Current understanding of the psychological attributes that distinguish professional actors is incomplete. Here, we compare samples of professional actors (n = 104), undergraduate student actors (n = 100), and non-acting adults (n = 92) on 26 psychological dimensions and use machine-learning methods to classify participants based on these attributes. Nearly all of the attributes measured here displayed significant univariate mean differences across the three groups, with the strongest effect sizes being on Creative Activities, Openness, and Extraversion. A cross-validated Least Absolute Shrinkage and Selection Operator (LASSO) classification model was capable of identifying actors (either professional or student) from non-actors with a 92% accuracy and was able to sort professional from student actors with a 96% accuracy when age was included in the model, and a 68% accuracy with only psychological attributes included. In these LASSO models, actors in general were distinguished by high levels of Openness, Assertiveness, and Elaboration, but professional actors were specifically marked by high levels of Originality, Volatility, and Literary Activities.</p> </blockquote>
Dataset for Paper "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort
<p># 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</p> <p>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".</p> <p>In case of questions, feel free to contact the authors, *anonymised*, ORCID: https://orcid.org/*anonymised*, current affiliation and email: *anonymised*</p> <p>## Survey 2019 ##<br> 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*.<br> The questionnaire for the 2019 survey is contained in *survey2019_instrument.pdf*.</p> <p>## Survey 2020 ##<br> 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.<br> The questionnaire for the 2020 survey is contained in *survey2020_instrument.pdf*.</p> <p>## Interviews ##<br> The interview guide used for the five interviews is available in the file *interview_instrument.pdf*.</p>
Biological data science courses at UMONS, Belgium: student's activity for 2019-2020
<p>Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2019-2020.</p> <p>Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (<a href="mailto:philippe.grosjean@umons.ac.be">philippe.grosjean@umons.ac.be</a>) and Guyliann Engels (<a href="mailto:guyliann.engels@umons.ac.be">guyliann.engels@umons.ac.be</a>) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software. The courses were also taught at the Campus Charleroi by Raphaël Conotte (<a href="mailto:raphael.conotte@umons.ac.be">raphael.conotte@umons.ac.be</a>) that also contributed to a part of the learnr exercises and of the inline course.</p> <p><strong>How to use these data?</strong></p> <p>The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (<a href="https://frictionlessdata.io/">https://frictionlessdata.io</a>). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at <a href="https://create.frictionlessdata.io/">https://create.frictionlessdata.io</a>. There is a large set of libraries and tools for different programming languages available at <a href="https://frictionlessdata.io/tooling/libraries/">https://frictionlessdata.io/tooling/libraries/</a>. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (<a href="https://www.sciviews.org/learnitdown/">https://www.sciviews.org/learnitdown/</a>).</p> <p>For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.</p>
Biological data science courses at UMONS, Belgium: student's activity for 2020-2021
<p>Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2020-2021.</p> <p>Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (<a href="mailto:philippe.grosjean@umons.ac.be">philippe.grosjean@umons.ac.be</a>) and Guyliann Engels (<a href="mailto:guyliann.engels@umons.ac.be">guyliann.engels@umons.ac.be</a>) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software. The courses were also taught at the Campus Charleroi by Raphaël Conotte (<a href="mailto:raphael.conotte@umons.ac.be">raphael.conotte@umons.ac.be</a>) that also contributed to a part of the learnr exercises and of the inline course.</p> <p><strong>How to use these data?</strong></p> <p>The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (<a href="https://frictionlessdata.io">https://frictionlessdata.io</a>). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at <a href="https://create.frictionlessdata.io">https://create.frictionlessdata.io</a>. There is a large set of libraries and tools for different programming languages available at <a href="https://frictionlessdata.io/tooling/libraries/">https://frictionlessdata.io/tooling/libraries/</a>. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (<a href="https://www.sciviews.org/learnitdown/">https://www.sciviews.org/learnitdown/</a>).</p> <p>For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.</p>
Biological data science courses at UMONS, Belgium: student's activity for 2018-2019
<p>Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2018-2019.</p> <p>Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (<a href="mailto:philippe.grosjean@umons.ac.be">philippe.grosjean@umons.ac.be</a>) and Guyliann Engels (<a href="mailto:guyliann.engels@umons.ac.be">guyliann.engels@umons.ac.be</a>) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software.</p> <p><strong>How to use these data?</strong></p> <p>The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (<a href="https://frictionlessdata.io/">https://frictionlessdata.io</a>). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at <a href="https://create.frictionlessdata.io/">https://create.frictionlessdata.io</a>. There is a large set of libraries and tools for different programming languages available at <a href="https://frictionlessdata.io/tooling/libraries/">https://frictionlessdata.io/tooling/libraries/</a>. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (<a href="https://www.sciviews.org/learnitdown/">https://www.sciviews.org/learnitdown/</a>).</p> <p>For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.</p>
Doctoral Students' Educational Needs in Research Data Management: Quantitative Data of Perceived Importance and Current Competencies
<p>These data sets include numerically coded answers to Likert-like scale questions concerning the importance and perceived current research data management competencies of doctoral students. Interviewees were 35 doctoral students and faculty members. Interview forms are attached. The data is connected with the research article: https://doi.org/10.2218/ijdc.v16i1.684</p>
Who's Who of American Returned Students 遊美同學錄 (1917): Kinship Data
<p>This dataset is derived from the <em>Whoʻs Who of American Returned Students </em>遊美同學錄<em> </em>[<em>Youmei Tongxue Lu</em>] published in<em> </em>Peking [Beijing] in 1917, compiled by the Returned Students’ Information Bureau (Liumei xuesheng tongxunchu 留美學生通訊處) established at Tsinghua School in 1915. This book is crucial for documenting the early <em>liumei</em>'s experiences during the transitional period between the late Qing dynasty and the early years of the Republic (1911-). </p> <p>The dataset records information on kinship ties mentioned in the biographies of 192 students. The data has been extracted automatically through natural language processing, and then manually checked and validated by the author. The dataset records a total of 184 relatives and 216 ties (97 fathers, 80 siblings, 17 uncles, 16 spouses). It is structured as a four-column edge list: </p> <ul> <li>ego: name of the biographed individual</li> <li>relative: names of relatives mentioned in the biography</li> <li>relation: nature of relation (father, uncle, sibling, spouse)</li> <li>relation2: specification on the nature of relation (for siblings and spouses)</li> </ul> <p>In addition, the second tab provides further information on the parents' occupational backgrounds (when available). The occupations have been classified into four main categories: business (merchants, entrepreneurs), intellectual (scholars, teachers, headmasters), officials, other (professionals, renteers...). </p>
Who's Who of American Returned Students 遊美同學錄 (1917): Affiliation Data (Chinese)
<p>This dataset is derived from the <em>Whoʻs Who of American Returned Students </em>遊美同學錄<em> </em>[<em>Youmei Tongxue Lu</em>] published in<em> </em>Peking [Beijing] in 1917, compiled by the Returned Students’ Information Bureau (Liumei xuesheng tongxunchu 留美學生通訊處) established at Tsinghua School in 1915. This book is crucial for documenting the early <em>liumei</em>'s experiences during the transitional period between the late Qing dynasty and the early years of the Republic (1911-). </p> <p>The dataset records all the institutions to which the students were affiliated in the course of their lives, including the educational institutions in which they studied in China, the United States, and other countries; the public or private organizations in which they were employed; as well as their memberships in clubs and associations. The names of organizations were retrieved automatically from the Chinese biographies using <a href="https://bookdown.enpchina.eu/rpackage/HistTextRManual.html#4_Named_Entity_Recognition">named entity recognition</a> (SpaCy model), then manually cleaned, classified, and validated by the author. </p> <p>The attached file contains three tabs for (1) the list of affiliations (data); (2) the classification of organizations (class), and (3) the description of variables (key). The dataset records a total of 2,883 affiliations, linking 401 unique individuals to 1,344 unique institutions, distributed as followed: </p> <table> <tbody> <tr> <td><strong>category</strong></td> <td><strong>n</strong></td> </tr> <tr> <td>education</td> <td>565</td> </tr> <tr> <td>association</td> <td>271</td> </tr> <tr> <td>administration</td> <td>132</td> </tr> <tr> <td>business</td> <td>110</td> </tr> <tr> <td>facility</td> <td>92</td> </tr> <tr> <td>media</td> <td>66</td> </tr> <tr> <td>government</td> <td>49</td> </tr> <tr> <td>factory</td> <td>30</td> </tr> <tr> <td>other</td> <td>22</td> </tr> <tr> <td>military</td> <td>7</td> </tr> </tbody> </table>
Who's Who of American Returned Students 遊美同學錄 (1917): Affiliation Data (English)
<p>This dataset is derived from the <em>Whoʻs Who of American Returned Students </em>遊美同學錄<em> </em>[<em>Youmei Tongxue Lu</em>] published in<em> </em>Peking [Beijing] in 1917, compiled by the Returned Students’ Information Bureau (Liumei xuesheng tongxunchu 留美學生通訊處) established at Tsinghua School in 1915. This book is crucial for documenting the early <em>liumei</em>'s experiences during the transitional period between the late Qing dynasty and the early years of the Republic (1911-). </p> <p>The dataset records all the institutions to which the students were affiliated in the course of their lives, including the educational institutions in which they studied in China, the United States, and other countries; the public or private organizations in which they were employed; as well as their memberships in clubs and associations. The names of organizations were retrieved automatically using <a href="https://bookdown.enpchina.eu/rpackage/HistTextRManual.html#4_Named_Entity_Recognition">named entity recognition</a> (SpaCy model), then manually cleaned, classified, and validated by the author. </p> <p>The attached file contains three tabs for (1) the list of affiliations (data), (2) the classification of institutions (class); (3) the description of variables (key). The dataset records a total of 3,431 affiliations, linking 401 unique individuals to 1,305 unique institutions, distributed as followed: </p> <table> <tbody> <tr> <td><strong>category_main</strong></td> <td><strong>n</strong></td> </tr> <tr> <td>education</td> <td>535</td> </tr> <tr> <td>association</td> <td>255</td> </tr> <tr> <td>media</td> <td>108</td> </tr> <tr> <td>administration</td> <td>92</td> </tr> <tr> <td>business</td> <td>85</td> </tr> <tr> <td>facility</td> <td>47</td> </tr> <tr> <td>executive</td> <td>43</td> </tr> <tr> <td>railway</td> <td>39</td> </tr> <tr> <td>production</td> <td>33</td> </tr> <tr> <td>banking</td> <td>15</td> </tr> <tr> <td>other</td> <td>15</td> </tr> <tr> <td>event</td> <td>13</td> </tr> <tr> <td>military</td> <td>13</td> </tr> <tr> <td>judiciary</td> <td>8</td> </tr> <tr> <td>legislative</td> <td>4</td> </tr> </tbody> </table> <p> </p> <p> </p>
Who's Who of American Returned Students 遊美同學錄 (1917): Vital Data
<p>This dataset is derived from the <em>Whoʻs Who of American Returned Students </em>遊美同學錄<em> </em>[<em>Youmei Tongxue Lu</em>] published in<em> </em>Peking [Beijing] in 1917, compiled by the Returned Students’ Information Bureau (Liumei xuesheng tongxunchu 留美學生通訊處) established at Tsinghua School in 1915. This book is crucial for documenting the early <em>liumei</em>'s experiences during the transitional period between the late Qing dynasty and the early years of the Republic (1911-). </p> <p>The dataset provides biographical information on the 401 individuals recorded in the directory, including their multiple names, gender, date and place of birth, family situation, date of arrival in the United States, date of return, source of funding, field of study. The data has been first retrieved automatically using Natural Language Processing techniques , and then manually checked and validated by the author. The information is given in both Chinese and English. </p> <p>The Excel file contains two tabs, one for the data, one for the description of variables (key). </p> <p> </p>
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>
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>
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>
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>
Extracted and Anonymised Qualitative Data on Students' Acceptance of an Early Warning System
<p>The data published in this record was adopted in the following study: </p> <p><em><strong>Exploring Higher Education students' experience with AI-powered educational tools: The case of an Early Warning System </strong></em></p> <p>The study analyses the students' experience of an early warning system developed at a fully online university. The study is based on 21 semi-structured interviews that yielded a corpus of 21,761 words, for which a mixed inductive and deductive codification approach was applied after thematic analysis. We focused on 11 themes, 52 subthemes, and 396 coded segments to perform content analysis. Our findings revealed that the students, primarily senior workers with a high-level academic self-efficacy, had little experience with this type of system and low expectations about it. However, a usage experience triggered interest and meaningful reflections on the mentioned tool. Nevertheless, a comparative analysis between disciplines related to Computer Science and Economics showed higher confidence and expectation about the system and artificial intelligence overall by the first group. These results highlight the relevance of supporting students' further experiences and understanding of artificial intelligence systems in education to accept them and mainly to participate in iterative development processes of such tools to achieve quality, relevance, and fairness.</p> <p>The three records attached as part of the dataset include:</p> <p>1- The General CodeTree with exemplar coding excerpts in Spanish<br> 2- Extract of transcriptions in English<br> 3- Full Report in Spanish as extracted from NVIVO, including the extracted codes for the synthesis (1,2) in blue, and the comments made by the two researchers engaged in the interrater agreement.<br> 4- General Content Analysis (Spreadsheet ODS)</p> <p> </p>
Data for Academic Engagement among students
<p>SPSS Data set for an original study carried out in 2019-2020 in which academic engagement was measured using a modified work engagement scales, and looked at three domains: dedication, absorption, vigor. A number of individual, psychological and institutional level variables were explored with regard to academic engagement. </p>
ScienceDex guides
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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.