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152 results for “Higher education”
South African Higher Education Performance Data 2009 to 2016
<p>Student, staff, research and financial data for South African universities for the period 2009 to 2016.</p>
South African Open Data in Higher Education: Sources, resources and providers
<p>Spreadsheet of data sourced on South African sources, resources and providers of higher education open data. Composed through desk review as principle component of the situational analysis conducted for the 'Use of open data in the governance of South African higher education' research project, in the IDRC/WWWF 'Exploring Emerging Impacts of Open Data in the South' initiative.</p>
Interview data on provision and use of open data in South African higher education
<p>Data from interviews conducted with university planners, higher education studies researchers and the South African Department of Higher Education and Training (DHET). Data sourced for the 'Use of open data in the governance of South African higher education' research project, in the IDRC/WWWF 'Exploring Emerging Impacts of Open Data in the South' initiative.</p>
Survey: Open Science in Higher Education
<p>Open Science in (Higher) Education – data of the February 2017 survey</p> <p>This data set contains: </p> <ul> <li>Full raw (anonymised) data set (completed responses) of Open Science in (Higher) Education February 2017 survey. Data are in xlsx and sav format.</li> <li>Survey questionnaires with variables and settings (German original and English translation) in pdf. The English questionnaire was not used in the February 2017 survey, but only serves as translation.</li> <li>Readme file (txt)</li> </ul> <p>Survey structure</p> <p>The survey includes 24 questions and its structure can be separated in five major themes: material used in courses (5), OER awareness, usage and development (6), collaborative tools used in courses (2), assessment and participation options (5), demographics (4). The last two questions include an open text questions about general issues on the topics and singular open education experiences, and a request on forwarding the respondent’s e-mail address for further questionings. The online survey was created with Limesurvey[1]. Several questions include filters, i.e. these questions were only shown if a participants did choose a specific answer beforehand ([n/a] in Excel file, [.] In SPSS). </p> <p>Demographic questions</p> <p>Demographic questions asked about the current position, the discipline, birth year and gender. The classification of research disciplines was adapted to general disciplines at German higher education institutions. As we wanted to have a broad classification, we summarised several disciplines and came up with the following list, including the option “other” for respondents who do not feel confident with the proposed classification:</p> <ul> <li>Natural Sciences</li> <li>Arts and Humanities or Social Sciences</li> <li>Economics</li> <li>Law</li> <li>Medicine</li> <li>Computer Sciences, Engineering, Technics</li> <li>Other</li> </ul> <p>The current job position classification was also chosen according to common positions in Germany, including positions with a teaching responsibility at higher education institutions. Here, we also included the option “other” for respondents who do not feel confident with the proposed classification:</p> <ul> <li>Professor</li> <li>Special education teacher</li> <li>Academic/scientific assistant or research fellow (research and teaching)</li> <li>Academic staff (teaching)</li> <li>Student assistant</li> <li>Other</li> </ul> <p>We chose to have a free text (numerical) for asking about a respondent’s year of birth because we did not want to pre-classify respondents’ age intervals. It leaves us options to have different analysis on answers and possible correlations to the respondents’ age. Asking about the country was left out as the survey was designed for academics in Germany.</p> <p>Remark on OER question</p> <p>Data from earlier surveys revealed that academics suffer confusion about the proper definition of OER<sup><sup>[2]</sup></sup>. Some seem to understand OER as free resources, or only refer to open source software (Allen & Seaman, 2016, p. 11). Allen and Seaman (2016) decided to give a broad explanation of OER, avoiding details to not tempt the participant to claim “aware”. Thus, there is a danger of having a bias when giving an explanation. We decided not to give an explanation, but keep this question simple. We assume that either someone knows about OER or not. If they had not heard of the term before, they do not probably use OER (at least not consciously) or create them. </p> <p>Data collection</p> <p>The target group of the survey was academics at German institutions of higher education, mainly universities and universities of applied sciences. To reach them we sent the survey to diverse institutional-intern and extern mailing lists and via personal contacts. Included lists were discipline-based lists, lists deriving from higher education and higher education didactic communities as well as lists from open science and OER communities. Additionally, personal e-mails were sent to presidents and contact persons from those communities, and Twitter was used to spread the survey.</p> <p>The survey was online from Feb 6<sup>th</sup> to March 3<sup>rd </sup>2017, e-mails were mainly sent at the beginning and around mid-term. </p> <p>Data clearance</p> <p>We got 360 responses, whereof Limesurvey counted 208 completes and 152 incompletes. Two responses were marked as incomplete, but after checking them turned out to be complete, and we added them to the complete responses dataset. Thus, this data set includes 210 complete responses. From those 150 incomplete responses, 58 respondents did not answer 1st question, 40 respondents discontinued after 1st question. Data shows a constant decline in response answers, we did not detect any striking survey question with a high dropout rate. We deleted incomplete responses and they are not in this data set.</p> <p>Due to data privacy reasons, we deleted seven variables automatically assigned by Limesurvey: submitdate, lastpage, startlanguage, startdate, datestamp, ipaddr, refurl. We also deleted answers to question N<sup>o</sup> 24 (email address).</p> <p>References</p> <p>Allen, E., & Seaman, J. (2016). <em>Opening the Textbook: Educational Resources in U.S. Higher Education, 2015-16.</em></p> <p> </p> <p>First results of the survey are presented in the poster:</p> <p>Heck, Tamara, Blümel, Ina, Heller, Lambert, Mazarakis, Athanasios, Peters, Isabella, Scherp, Ansgar, & Weisel, Luzian. (2017). Survey: Open Science in Higher Education. Zenodo. http://doi.org/10.5281/zenodo.400561</p> <p>Contact:</p> <p>Open Science in (Higher) Education working group, see http://www.leibniz-science20.de/forschung/projekte/laufende-projekte/open-science-in-higher-education/.</p> <p> </p> <p>[1] https://www.limesurvey.org</p> <p>[2] The survey question about the awareness of OER gave a broad explanation, avoiding details to not tempt the participant to claim “aware”. </p>
Artificial Intelligence and Educational Quality in Higher Education: A Systematic Review
<p><span><span lang="EN-US">This study examines the impact of artificial intelligence (AI) on educational quality in higher education. The objective was to evaluate how AI optimizes teaching-learning processes and to identify challenges and opportunities for its implementation. A systematic review was conducted using the PRISMA protocol, selecting 22 documents from databases such as Scopus, Web of Science, EBSCO, and ProQuest (2002–2024). Results indicate that AI enhances education through personalized learning, administrative automation, and automated assessment. However, challenges like technological infrastructure needs, data privacy concerns, and resistance to change remain. AI’s transformative potential depends on strategic implementation that considers ethical and social factors.</span></span></p>
Integrating sustainability into higher education: Saudi Vision 2030
<p>It is a sustainability integration into higher education work.</p>
Data on Abusive Supervision, Organizational Cynicism and Playing Dumb in Higher Education Institutions
<p>This data set include the responses from the faculty and staff of Higher Education Institutions in Pakistan </p>
Resources for BMF CP72: The effectiveness of knowledge management systems in motivation and satisfaction in Vietnamese higher education institutions
<p>Code and data for reproducing the results in "BMF CP72: The effectiveness of knowledge management systems in motivation and satisfaction in Vietnamese higher education institutions" are available here.</p>
Times Higher Education Ranking Dataset
<p>Dataset containing university rankings. See source: <a href="https://www.timeshighereducation.com/world-university-rankings">https://www.timeshighereducation.com/world-university-rankings</a></p> <p>Originally used in:</p> <p>Anahideh, Hadis, and Nasrin Mohabbati-Kalejahi. "Local explanations of global rankings: insights for competitive rankings." IEEE Access 10 (2022): 30676-30693. <a href="https://ieeexplore.ieee.org/abstract/document/9733934" rel="nofollow">https://ieeexplore.ieee.org/abstract/document/9733934</a></p>
UU Webinar 2: Sam Sellar on time and value in digitalised higher education
<p>Title: The investment of time: Divergent theses on the value of higher education</p> <p>Abstract: This webinar will focus on what is valuable in and about higher education. It will then discuss this in relation to Edtech and deliberate where Edtech can and cannot contribute to supporting the sector.</p> <p>Speaker: Sam Sellar</p> <p>Bio: Sam Sellar is Dean of Research (Education Futures) and Professor of Education Policy at the University of South Australia. Sam’s research focuses on education policy, large-scale assessments and the datafication of education. Sam also works closely with teacher organisations around the world to understand the impact of digitalisation on teacher professional autonomy. His most recent book is titled Algorithms of education: How datafication and artificial intelligence shape policy (University of Minnesota Press), co-authored with Kalervo N. Gulson and P. Taylor Webb.</p> <p>Link to Sam’s website: https://people.unisa.edu.au/Sam.Sellar </p> <p>Date of event: 22 June 2023</p>
UU Webinar 1: Kean Birch on assets and rents in digitalised higher education
<p>Title: Rentiership in EdTech: Data as asset, data as rent?</p> <p>Abstract: This webinar will present the concepts of assetisation and rentiership. It will discuss different types of assets in higher education, how they are made valuable, and the consequences. The webinar is relevant for academics, practitioners, and policymakers.</p> <p>Speaker: Kean Birch</p> <p>Bio: Kean Birch is a Professor at York University, Canada. He is particularly interested in understanding technoscientific capitalism and draws on a range of perspectives from science & technology studies, economic geography, and economic sociology to study it. More specifically, his research focuses on the restructuring and transformation of the economy & financial knowledges and technoscience & technoscientific innovation. Currently, he is researching how different things (e.g. knowledge, personality, loyalty, etc.) are turned into ‘assets’ & how economic rents are then captured from those assets - basically, in processes of assetisation and rentiership.</p> <p>Link to Kean’s website: https://euc.yorku.ca/faculty/kean-birch/ </p> <p>Date of event: 22 June 2023</p>
Universities and Unicorns – New Forms of Value in Digital Higher Education (SRHE webinar)
<p>SRHE's Digital University Network organised an event on 21 September 2023. The research team from the ESRC-funded research project Universities and Unicorns (UU) project presented initial findings after the project finished. The project introduced fundamentally new ways to think about and examine the digitalising of the higher education sector. It investigated new forms of value creation and suggested that value in the sector increasingly lies in the creation of digital assets. Further, the project examined whether and how universities, companies and investors shift from forms of entrepreneurship to forms of rentiership. This has important consequences. Assetisation and rentiership imply a change from creating value via market exchange to capturing value via the ownership and control of assets, such as personal data.</p> <p><br>More about the webinar: https://srhe.ac.uk/civicrm/?civiwp=CiviCRM&q=civicrm%2Fevent%2Finfo&reset=1&id=641</p>
The Use of Open Educational Resources (OERs) in Teaching and Learning in Higher Education Distance Learning Programmes in Cameroon
<p>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.</p>
Framework sustainability for Higher Education
Open the record for dataset details and reuse information.
The Rising Influence of AI in Higher Education: Trends and Insights from a Bibliometric Analysis
<p>The purpose of this research was to examine the evolution, scope, and orientation of the scientific production on artificial intelligence applications in university students. The methodology, with a non-experimental design and qualitative approach, involved a search in Scopus, identifying 643 documents between 1975-2024, analyzed through VOSviewer and Bibliometrix. The results show an emerging field, but with rapid growth (4.59% per year), with notoriety of Kong, Abdulrahman and Chai. Research is predominantly in computer science (61%), social sciences (33%) and engineering (23%) from China, USA, Spain and Taiwan. Current applications focus on the use of AI in education, machine learning, support for academic decisions and student mental health. However, it is necessary to expand the approach towards ethical and regulatory aspects and the evaluation of multifaceted effects on different student profiles. In conclusion, although production is growing rapidly, more comprehensive perspectives are required to responsibly enhance the impact of these technologies on the university educational experience.</p>
Data for EUA Trends 2024 - European higher education institutions in times of transition
<p>Since 1999, the EUA Trends reports have consistently mapped developments in the European higher education landscape, by presenting comparative data from the perspective of higher education institutions. In the ninth edition of the European University Association’s long-running series, the Trends 2024 report provides an overview of how European higher education institutions have experienced changes over the past five years, due to higher education reforms, and in the wider context of societal, political, economic and technological changes, marked among others by the implications of Covid-19 pandemic and Russia’s war against Ukraine.</p> <p>Trends 2024 is based on survey data collected in April to July 2023.</p> <p>Responses were gathered from 489 higher education institutions in 46 European higher education systems. The survey was open to all higher education institutions in the European Higher Education Area (EHEA) that provide study programmes in at least one of the three degree cycles (bachelor’s, master’s, doctoral). One response per institution was collected.</p> <p>The survey addressed the higher education institutions’ perspectives and strategies regarding:</p> <p>· The institution and its context</p> <p>· The student life cycle and experience</p> <p>· Learning, teaching and teachers </p> <p>· Inclusion, equity and diversity</p> <p>· Engagement and outreach with society and community </p> <p>· Internationalisation</p> <p>Results of the survey are published in “<a href="https://www.eua.eu/publications/reports/trends-2024.html"><strong>Trends 2024 - European higher education institutions in times of transition</strong></a>”.</p> <p>The following files are available:</p> <ul> <li>Codebook including original questionnaire</li> <li>Dataset</li> </ul>
COVID-19 Impact on European Higher Education Survey Data
<p>This dataset consists of the results of the first European-Union-wide survey on the potential long-term impacts of COVID-19 on higher education (Huth, M. & Cominola, A., 2022 forthcoming), evaluating over 800 responses from students and faculty members of higher education institutions located in 17 different European countries. The data consists of variables related to the actual pre-pandemic, pandemic and intended future frequency of use of various educational tools and formats, as well as students and faculty members' attitude toward retaining digital teaching formats and media post-pandemic. The data is shared in SPSS file format (.sav) and as a .CSV file. A detailed item bank label overview is attached in excel format. The attitude related items consists of Technology Acceptance Model (TAM) construct variables following items used by Rizun et al. (2021) and Vladova et al. (2021). Following guidelines (Hair et al. 2019; Henseler, Ringle, and Sarstedt 2015) to establish discriminant validity before analysing the TAM variables in a structural equation model, the objective had to be disregarded. The data might still yield interesting descriptive insights nonetheless, which is why they are published here along with the variables used in the forthcoming publication by Huth and Cominola (2022). </p>
The Anthropocene and the Sustainable Development Goals: Key elements in geography higher education? Study Dataset.
<p>The document contains the Dataset of a study "The Anthropocene and the Sustainable Development Goals: Key elements in geography higher education? "</p>
Database for A Review of Collaboration Through Intercultural Competencies in Higher Education
<p><strong>Database used for the study: "A Review of Collaboration Through Intercultural Competencies in Higher Education"</strong></p>
SRL data feedback in higher education and validation of microlearning proposal
<p>This dataset corresponds to those used for the SRL in the category Feedback. It also presents the data collected from a process of expert validation of a training proposal based on microlearning for the development of digital teacher competence related to feedback. </p>
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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)
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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.