Technologies for Sustainable Global Higher Education

Digital technologies are influencing the way we learn, live, work, and exist in different contexts of society in the digital age. There are a variety of learning systems that support innovative digital approaches, and universities and organizations around the world are investing in building their own e-learning platforms. Digital technologies are enabling wider access to education and new markets for student recruitment, resulting in increased income prospects for global higher education institutions. Technology enables numerous data and information sources, which give greater access to information and data. It also enables highly virtual environments, which impact teaching and the classroom. The widespread use and application of digital technologies in the teaching and learning process provoke pedagogical inquiry and mediation. It is in this context that Technologies for Sustainable Global Higher Education focuses on analyzing the application of digital technologies in the teaching–learning process. The chapters in this edited collection seek to answer questions relevant to the context of higher education, such as: What is the concept of digital technologies? How is digital technology used to mediate the learning process? What technologies are used to qualify education in higher education? This book provides answers to these questions by focusing on issues central to improving education through digital technologies, digital learning, and pedagogical practices in digital education. It also provides case studies of higher education institutions. Cover Half Title Series Page Title Page Copyright Page Table of Contents Preface Editors Contributors Introduction: Is a Seamless Learning Experience Design Framework an Answer to Attaining Quality Digital Learning in Higher Education? 0.1 Introduction and Background 0.1.1 Possible Solutions 0.1.2 Proposing the SLED Framework 0.2 Conclusion Resources Chapter 1: A Comprehensive Review of the Literature on Digital Higher Education Pedagogies 1.1 Introduction 1.2 Digital Pedagogy 1.3 Methodological Approach 1.3.1 Methodological Procedures 1.3.2 Bibliometric Analysis 1.3.3 Bradford’s Law 1.3.3.1 Lotka’s Law 1.3.4 Zipf’s Law 1.3.5 Countries 1.3.6 Relevance of Publications by Author 1.3.7 Main Scientific Sources 1.3.8 Most Impactful Authors 1.3.9 Three-Field Plot 1.3.10 Word TreeMap 1.4 Digital Pedagogy in Higher Education 1.5 Final Considerations References Chapter 2: Digital Education Knowledge from Theory to Teaching Experiences in Three European Universities 2.1 Introduction 2.2 Theoretical Framework 2.2.1 Sustainable Digital Education at Instituto Universitário de Lisboa (ISCTE) 2.2.2 Collaborative Online International Learning (COIL) at Université Gustave Eiffel (UGE) 2.2.3 Online Learning and Offline Extended Classroom at Politecnico di Milano (POLIMI) 2.3 Education Pedagogy and Applied Technologies 2.3.1 Digital Learning Pedagogies and Technologies from Theory 2.3.2 Digital Learning Pedagogies and Technologies from UGE 2.3.3 Digital Learning Pedagogies and Technologies from POLIMI 2.4 Evaluation Process 2.4.1 Digital Education Assessment Techniques 2.4.2 Assessment Practice from COIL Project 2.4.3 Examination Typology of POLIMI 2.5 Final Considerations Acknowledgments References Chapter 3: Skills for Safety, Security, and Well-Being in the DigComp Framework Revision and Their Relevance for a Sustainable Global (Higher) Education 3.1 Contextualization 3.2 Theoretical Framework 3.2.1 The European Digital Framework and the United Nations Sustainable Goals (UNSDGs) 3.2.1.1 DigComp Framework and Sustainable Education 3.2.2 Concept of the Knowledge, Skills, and Attitudes (KSA) 3.2.3 Setting Knowledge Skills and Attitudes for Safety Area 3.2.3.1 Digital Safety and Security Underpinning Theory 3.2.3.2 Digital Health and Well-Being Underpinning Theory 3.3 Methodology 3.3.1 Research Problem and Design 3.3.1.1 Phases of the Research 3.3.1.2 Research Tools 3.3.1.3 Research Analysis 3.4 Results and Discussions 3.5 Conclusions Acknowledgments Notes References Chapter 4: Digital Technologies as a Key Driver of Sustainable Global Higher Education 4.1 Introduction 4.2 Sustainable Global Higher Education 4.2.1 United Nations’ Role in Sustainable Global Higher Education 4.3 Digital Technologies in Higher Education 4.3.1 Digital Technologies Used in Learning Management 4.3.2 Digital Technologies Used in Teaching 4.3.3 Digital Technologies Used in Labs 4.3.4 Digital Technologies Used in Assessments 4.3.5 Digital Technologies Used in Group Activities 4.3.6 Digital Technologies Used in Academic Advising 4.4 Conclusions References Chapter 5: Higher Education: Networks and Technology – The Complex World of Sustainability 5.1 The Role of Social Networks in the Learning Process 5.2 The Complex World of Dependencies 5.3 Higher Education Institutions and Sustainability 5.4 Final Thoughts References Chapter 6: Artificial Intelligence and Blockchain in Higher Education Institutions: A Bibliometric Review 6.1 Introduction 6.2 Methodology – Bibliometric Review 6.3 Methodological Path 6.4 Results of Bibliometrics 6.5 Final Conclusion References Chapter 7: Educational Strategies in Smart and Sustainable Cities for Education in the Post-Covid Era 7.1 Introduction 7.2 Smart and Sustainable Cities 7.3 Educational Strategy 7.4 Methodological Approach 7.5 Results 7.6 Final Considerations References Chapter 8: Accounting Education: New Pedagogies and Digital Approaches Based on the Research Agenda 8.1 Introduction 8.2 Accounting Education 8.2.1 Evolution 8.2.2 Trends 8.2.3 Digital Accounting 8.3 Materials and Methods 8.3.1 Data Collection and Research Strategy 8.3.2 Data Analysis and Visualization 8.4 Results 8.4.1 Conceptual Structure of Knowledge 8.4.2 Intellectual Structure of Knowledge 8.4.3 Social Structure of Knowledge 8.5 Discussion and Conclusions 8.6 Implications of the Research 8.6.1 Theoretical Implications 8.6.2 Practical Implications 8.6.3 Limitations References Chapter 9: International Mobility Challenges in Higher Education in the Digital Era 9.1 Introduction 9.1.1 Research Agenda for Digital International Mobility in Higher Education 9.1.2 The Emergent Importance of Digital International Mobility in Higher Education 9.2 The Internationalization of Universities and Student Mobility 9.3 The Case of Iscte in Europe 9.3.1 Giving Voice to Students 9.3.2 International Mobility and Digital Teaching 9.4 Conclusion Acknowledgments Notes References References of the Systematic Literature Review Chapter 10: Artificial Intelligence: Applicability of This Technology to Higher Education – A Scoping Review 10.1 Introduction 10.2 Artificial Intelligence 10.3 Methodology 10.4 Result 10.4.1 Bibliometric Analysis of the Selected Publications 10.4.2 Main Scientific Sources 10.5 Artificial Intelligence: The Applicability of This Technology to Higher Education 10.5.1 Artificial Intelligence in Higher Education 10.5.2 Applicability of AI to Higher Education 10.5.3 Synthesis 10.6 Final Conclusion References Chapter 11: Case Study of Two Higher Education Institutions in the Use of a National MOOC Platform Toward Sustainable Development 11.1 Introduction 11.2 Role of MOOC in the Portuguese Higher Education Context 11.2.1 NAU Platform 11.3 Methodology 11.4 The Case at the Polytechnic Institute of Tomar (IPT) 11.4.1 MOOC in Sustainable Tourism 11.4.2 MOOC in Introduction to Programming 11.5 MOOCs in Health: A Case Study Based on the Nursing School of Porto Experience 11.5.1 The Ecare-COPD Training Program 11.5.2 The Ecare-COVID19 Professional Update Program 11.5.3 Further Developments 11.6 Conclusions References Index Edge computational intelligence is an interface between Edge Computing and Artificial Intelligence (AI) technologies. This interfacing represents a paradigm shift in the world of work by enabling a broad application areas and customer-friendly solutions. Edge computational intelligence technologies are just in their infancy . Edge Computational Intelligence for AI-Enabled IoT Systems looks at the trends and advances in edge computing and edge AI, the services rendered by them, related security and privacy issues, training algorithms, architectures, and sustainable AI-enabled IoT systems. Together these technologies benefit from ultra-low latency, faster response times, lower bandwidth costs and resilience from network failure, and the book explains the advantages of systems and applications using intelligent IoT devices that are at the edge of a network and close to users. It explains how to make most of edge and cloud computing as complementary technologies or used in isolation for extensive and widespread applications. The advancement in IoT devices, networking facilities, parallel computation and 5G, and robust infrastructure for generalized machine learning have made it possible to employ edge computational intelligence in diverse areas and in diverse ways. The book begins with chapters that cover Edge AI services on offer as compared to conventional systems. These are followed by chapters that discuss security and privacy issues encountered during the implementation and execution of edge AI and computing services The book concludes with chapters looking at applications spread across different areas of edge AI and edge computing and also at the role of computational intelligence in AI-driven IoT systems. Deep learning can provide more accurate results compared to machine learning. It uses layered algorithmic architecture to analyze data. It produces more accurate results since learning from previous results enhances its ability. The multi-layered nature of deep learning systems has the potential to classify subtle abnormalities in medical images, clustering patients with similar characteristics into risk-based cohorts, or highlighting relationships between symptoms and outcomes within vast quantities of unstructured data.Exploring this potential, Deep Learning for Smart Healthcare: Trends, Challenges and Applications is a reference work for researchers and academicians who are seeking new ways to apply deep learning algorithms in healthcare, including medical imaging and healthcare data analytics. It covers how deep learning can analyze a patient's medical history efficiently to aid in recommending drugs and dosages. It discusses how deep learning can be applied to CT scans, MRI scans and ECGs to diagnose diseases. Other deep learning applications explored are extending the scope of patient record management, pain assessment, new drug design and managing the clinical trial process.Bringing together a wide range of research domains, this book can help to develop breakthrough applications for improving healthcare management and patient outcomes. "This book provides readers a comprehensive understanding of the application of machine Learning and deep Learning in proteomics, genomics, microarrays, text mining and related fields. The key objective is to provide machine learning applications to biological science problems, focusing on problems related to bioinformatics"-- Provided by publisher Unique selling point: Advanced AI solutions for problems in genetics, virology, and related areas of life science Core audience: Researchers in bioinformatics Place in the market: High-level reference book on advanced applied technology