scholarly journals Collaborative Research and Innovation in Europe

2020 ◽  
Author(s):  
Hendro Wicaksono
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Data Management ◽  
Collaborative Research ◽  
Best Practice ◽  
Research Organization ◽  
Industrial Companies ◽  
Research And Innovation ◽  
Research Organizations ◽  
German Research

The presentation gives an overview of best practice collaborations of higher education and industry in Germany. It then describes different research organization and their roles in the German research landscape. Finally, the presentation shows some project examples in the area of artificial intelligence and data management which are funded by German and EU research agencies and involve collaboration between universities, research organizations, industrial companies, and municipalities.

scholarly journals Toward a Framework for Teaching Artificial Intelligence to a Higher Education Audience

2022 ◽  
Vol 22 (2) ◽  
pp. 1-29
Author(s):  
Becky Allen ◽  
Andrew Stephen McGough ◽  
Marie Devlin
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Machine Learning ◽  
Best Practices ◽  
Mathematics Anxiety ◽  
Teaching And Learning ◽  
Best Practice ◽  
Educational Background ◽  
Educational Institutions ◽  
Key Factors

Artificial Intelligence and its sub-disciplines are becoming increasingly relevant in numerous areas of academia as well as industry and can now be considered a core area of Computer Science [ 84 ]. The Higher Education sector are offering more courses in Machine Learning and Artificial Intelligence than ever before. However, there is a lack of research pertaining to best practices for teaching in this complex domain that heavily relies on both computing and mathematical knowledge. We conducted a literature review and qualitative study with students and Higher Education lecturers from a range of educational institutions, with an aim to determine what might constitute best practices in this area in Higher Education. We hypothesised that confidence, mathematics anxiety, and differences in student educational background were key factors here. We then investigated the issues surrounding these and whether they inhibit the acquisition of knowledge and skills pertaining to the theoretical basis of artificial intelligence and machine learning. This article shares the insights from both students and lecturers with experience in the field of AI and machine learning education, with the aim to inform prospective pedagogies and studies within this domain and move toward a framework for best practice in teaching and learning of these topics.

PROFESSIONAL TRAINING OF ELECTRONICS ENGINEERS: APPLYING US EXPERICENCE TO UKRAINAN HIGHER EDUCATION INSTITUTIONS

2020 ◽  
pp. 80-85
Author(s):  
Olha Pavlenko
Keyword(s):  
Higher Education ◽  
Education Research ◽  
Best Practices ◽  
Cost Reduction ◽  
Professional Training ◽  
Higher Education Institutions ◽  
Academic Staff ◽  
Research And Innovation ◽  
Current State

The article discusses the current state of professional training of engineers, in particular, electronics engineers in Ukrainian higher education institutions (HEIs) and explores best practices from US HEIs. The research outlines the features of professional training of electronics engineers and recent changes in Ukrainian HEIs. Such challenges for Ukrainian HEIs as lack of collaboration between higher education and science with industry, R&D cost reduction for HEIs, and downsizing the research and academic staff, the disparity between the available quality of human capital training and the demanded are addressed. The study attempts to identify successful practices of US HEIs professional training of engineers in order to suggest potential improvements in education, research, and innovation for training electronics engineers in Ukraine.

scholarly journals Boundaries Between Research Ethics and Ethical Research Use in Artificial Intelligence Health Research

2021 ◽  
pp. 155626462110027
Author(s):  
Gabrielle Samuel ◽  
Jenn Chubb ◽  
Gemma Derrick
Keyword(s):  
Artificial Intelligence ◽  
Health Research ◽  
Decision Makers ◽  
Societal Impact ◽  
The Past ◽  
Research And Innovation ◽  
The United Kingdom ◽  
Responsible Research ◽  
Past Half

The governance of ethically acceptable research in higher education institutions has been under scrutiny over the past half a century. Concomitantly, recently, decision makers have required researchers to acknowledge the societal impact of their research, as well as anticipate and respond to ethical dimensions of this societal impact through responsible research and innovation principles. Using artificial intelligence population health research in the United Kingdom and Canada as a case study, we combine a mapping study of journal publications with 18 interviews with researchers to explore how the ethical dimensions associated with this societal impact are incorporated into research agendas. Researchers separated the ethical responsibility of their research with its societal impact. We discuss the implications for both researchers and actors across the Ethics Ecosystem.

scholarly journals The on-premise data sharing infrastructure e!DAL: Foster FAIR data for faster data acquisition

GigaScience ◽  
2020 ◽  
Vol 9 (10) ◽  
Author(s):  
Daniel Arend ◽  
Patrick König ◽  
Astrid Junker ◽  
Uwe Scholz ◽  
Matthias Lange
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Best Practice ◽  
Research Process ◽  
Research Data ◽  
Primary Data ◽  
Data Repository ◽  
Plant Genomics ◽  
Quality Of Data

Abstract Background The FAIR data principle as a commitment to support long-term research data management is widely accepted in the scientific community. Although the ELIXIR Core Data Resources and other established infrastructures provide comprehensive and long-term stable services and platforms for FAIR data management, a large quantity of research data is still hidden or at risk of getting lost. Currently, high-throughput plant genomics and phenomics technologies are producing research data in abundance, the storage of which is not covered by established core databases. This concerns the data volume, e.g., time series of images or high-resolution hyper-spectral data; the quality of data formatting and annotation, e.g., with regard to structure and annotation specifications of core databases; uncovered data domains; or organizational constraints prohibiting primary data storage outside institional boundaries. Results To share these potentially dark data in a FAIR way and master these challenges the ELIXIR Germany/de.NBI service Plant Genomic and Phenomics Research Data Repository (PGP) implements a “bring the infrastructure to the data” approach, which allows research data to be kept in place and wrapped in a FAIR-aware software infrastructure. This article presents new features of the e!DAL infrastructure software and the PGP repository as a best practice on how to easily set up FAIR-compliant and intuitive research data services. Furthermore, the integration of the ELIXIR Authentication and Authorization Infrastructure (AAI) and data discovery services are introduced as means to lower technical barriers and to increase the visibility of research data. Conclusion The e!DAL software matured to a powerful and FAIR-compliant infrastructure, while keeping the focus on flexible setup and integration into existing infrastructures and into the daily research process.

Artificial intelligence for decision making in higher education

2021 ◽  
pp. 1-2
Author(s):  
Carlos Enrique Montenegro Marin ◽  
Paulo Alonso Gaona Garcia ◽  
Edward Rolando Nuñez Valdez
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Decision Making

Improved differentiation classification of variable precision artificial intelligence higher education management

2021 ◽  
pp. 1-10
Author(s):  
Chao Dong ◽  
Yan Guo
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Data Mining ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Attribute Selection ◽  
Higher Education Management ◽  
Education Management ◽  
Decision Tree Classification

The wide application of artificial intelligence technology in various fields has accelerated the pace of people exploring the hidden information behind large amounts of data. People hope to use data mining methods to conduct effective research on higher education management, and decision tree classification algorithm as a data analysis method in data mining technology, high-precision classification accuracy, intuitive decision results, and high generalization ability make it become a more ideal method of higher education management. Aiming at the sensitivity of data processing and decision tree classification to noisy data, this paper proposes corresponding improvements, and proposes a variable precision rough set attribute selection standard based on scale function, which considers both the weighted approximation accuracy and attribute value of the attribute. The number improves the anti-interference ability of noise data, reduces the bias in attribute selection, and improves the classification accuracy. At the same time, the suppression factor threshold, support and confidence are introduced in the tree pre-pruning process, which simplifies the tree structure. The comparative experiments on standard data sets show that the improved algorithm proposed in this paper is better than other decision tree algorithms and can effectively realize the differentiated classification of higher education management.

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