What is data intelligence? How can data intelligence influence education system systematically? The paradigm shift of scientific research implies a coming age of data-driven educational research and practice. This book presents research and practice of data intelligence in education from three levels: (i) educational governance, (ii) teaching practice, and (iii) student learning. Each chapter gives an analysis of fundamental knowledge, key themes, the state-of-the-art technologies and education application cases. This interdisciplinary book is essential reading for anyone interested in applying big data technology in education and for different stakeholders including education administrators, teachers, students, and researchers to broaden their minds to wisely use educational data to solve complex problems in the education field.

Professor Xihua Cao (1920-2005) was a leading scholar at East China Normal University (ECNU) and a famous algebraist in China. His contribution to the Chinese academic circle is particularly the formation of a world-renowned 'ECNU School' in algebra, covering research areas include algebraic groups, quantum groups, algebraic geometry, Lie algebra, algebraic number theory, representation theory and other hot fields. In January 2020, in order to commemorate Professor Xihua Cao's centenary birthday, East China Normal University held a three-day academic conference. Scholars at home and abroad gave dedications or delivered lectures in the conference. This volume originates from the memorial conference, collecting the dedications of scholars, reminiscences of family members, and 16 academic articles written based on the lectures in the conference, covering a wide range of research hot topics in algebra. The book shows not only scholars' respect and memory for Professor Xihua Cao, but also the research achievements of Chinese scholars at home and abroad.

Technology transfer studies are usually framed through Economics and Management Sciences, but this volume Geography of Technology Transfer in China seeks to reveal the mechanism of technology transfer from the geographical perspective. It not only depicts the spatial evolution laws of glocal technology transfer networks, but also uses regression models to uncover the two-way effects between the networks and innovative capacity. In addition, this book highlights the integration and interaction of networks on both the global and local scales. A theoretical framework on glocal networks of technology transfer is established based on a series of economic geography bases in order to depict the spatial differences and coupling mechanism among multi-scaled networks in China.This book consists of 5 parts and 10 chapters, which illustrate the background, theoretical basis, spatial evolution, dual-way influences, and policy implications of technology transfer in China, presenting a clear structure both theoretically and empirically. The book begins with the 'what', 'why', and 'how' questions behind geographical studies on technology transfer to clarify the purpose of the book and its differentiation from present technology transfer studies. Thereafter, it discusses the 'holy trinity' framework of glocal technology transfer networks consisting of cultural, territorial, and networked subsystems. To this end, the spatial evolution of the technology transfer is highlighted through soical network analysis, which aims at depicting the geographical rules of China's technology transfer networks at global, domestic, and regional scales. Based on these discoveries, the next part of the book further analyzes, through a series of regression models such as ERGM and NBRM, the kinds of determinants which have influenced the network size and how the network has in turn affected local innovation capacity . Lastly, the policy implications connect the findings of empirical studies with the operability of the national innovation system. On the whole, this book extensively covers the theoretical, empirical, and practical applications of the geography of technology transfer in China.

What is data intelligence? How can data intelligence influence education system systematically? The paradigm shift of scientific research implies a coming age of data-driven educational research and practice. This book presents research and practice of data intelligence in education from three levels: (i) educational governance, (ii) teaching practice, and (iii) student learning. Each chapter gives an analysis of fundamental knowledge, key themes, the state-of-the-art technologies and education application cases. This interdisciplinary book is essential reading for anyone interested in applying big data technology in education and for different stakeholders including education administrators, teachers, students, and researchers to broaden their minds to wisely use educational data to solve complex problems in the education field.

The transformative digital technologies developed for Industry 4.0 are proving to be disruptive change drivers in higher education. Industry 4.0 technologies are forming the basis of Education 4.0. Industry 4.0 Technologies for Education: Transformative Technologies and Applications examines state-of-the-art tools and technologies that comprise Education 4.0. Higher education professionals can turn to this book to guide curriculum development aimed at helping produce the workforce for Industry 4.0. The book discusses the tools and technologies required to make Education 4.0 a reality. It covers online content creation, learning management systems, and tools for teaching, learning, and evaluating. Also covered are disciplines that are being transformed by Industry 4.0 and form the core of Education 4.0 curricula. These disciplines include social work, finance, medicine, and healthcare. Mobile technologies are critical components of Industry 4.0 as well as Education 4.0. The book looks at the roles of the Internet of Things (IoT), 5G, and cloud applications in creating the Education 4.0 environment. Highlights of the book include: Technological innovations for virtual classrooms to empower students Emerging technological advancements for educational institutions Online content creation tools Moodle as a teaching, learning, and evaluation tool Gamification in higher education A design thinking approach to developing curriculum in Education 4.0 Industry 4.0 for Service 4.0 and Research 4.0 as a framework for higher education institutions Eye-tracking technology for Education 4.0 The challenges and issues of the Internet of Things (IoT) in teaching and learning

This book examines, from a comparative perspective, the impact of the movement from the so-called knowledge-based economy towards the Intelligent Economy, which is premised upon the application of knowledge. This volume links the advent of this new technological revolution to the world of governance and policy formulation in education.

This book explores Learning Analytics (LA) programmes and practices in Malaysia as well as looking at the underlying forces, dilemmas and policy challenges for quality assurance in higher education institutions (HEIs). This chapters provide a comprehensive discussion of trends in academic quality assurance in higher education. It articulates a combination of theoretical issues and empirical analysis and offers a comprehensive guide to stakeholders in Management and Faculty on LA implementation in HEIs where the model in this book can be used to pave the way for a successful LA initiative. Learning Analytics is an emerging multidisciplinary technological practice with the ultimate goal of producing effective learning to improve students’ achievement in the tertiary level. The Learning Analytics model of Quality Assurance in this book is an essential guide for any faculty or manager in higher education, or researchers in higher education and learning analytics.

A new challenge has become present in the field of generative artificial intelligence (AI). The fundamental nature of education, a vital element for advancing the United Nations' Sustainable Development Goals (SDGs), now grapples with the transformative impact of AI technologies. As we stand at this intersection of progress and pedagogy, critical questions surface about the future roles of educators and the integrity of assessment processes. AI’s rapid progression prompts an exploration of the competencies our education systems must cultivate in a world where human and machine intelligence are becoming increasingly interconnected. Against this backdrop of transformative uncertainty, Integrating Generative AI in Education to Achieve Sustainable Development Goals addresses profound challenges and offers promising solutions at the crossroads of AI and education. This book assembles distinguished academics, researchers, and practitioners, forming a collective voice on the intersection of Generative AI and education. The three-part structure dissects the technical aspects of AI-powered innovations in educational design, explores multidisciplinary applications enhancing educational content, and highlights AI-driven solutions to address equality and inclusion concerns within educational systems. The book also underscores the importance of ethical considerations of generative AI to ensure a future where technology serves the broader goals of sustainability and equitable education.

The book is a collection of peer-reviewed best selected research papers presented at the International Conference on Data Intelligence and Cognitive Informatics (ICDICI 2021), organized by SCAD College of Engineering and Technology, Tirunelveli, India, during July 16–17, 2021. This book discusses new cognitive informatics tools, algorithms, and methods that mimic the mechanisms of the human brain which leads to an impending revolution in understating a large amount of data generated by various smart applications. The book includes novel work in data intelligence domain which combines with the increasing efforts of artificial intelligence, machine learning, deep learning, and cognitive science to study and develop a deeper understanding of the information processing systems.