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.

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.

Artificial Intelligence (AI) has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards SDG 4. However, these rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks. This publication offers guidance for policy-makers on how best to leverage the opportunities and address the risks, presented by the growing connection between AI and education. It starts with the essentials of AI: definitions, techniques and technologies. It continues with a detailed analysis of the emerging trends and implications of AI for teaching and learning, including how we can ensure the ethical, inclusive and equitable use of AI in education, how education can prepare humans to live and work with AI, and how AI can be applied to enhance education. It finally introduces the challenges of harnessing AI to achieve SDG 4 and offers concrete actionable recommendations for policy-makers to plan policies and programmes for local contexts. [Publisher summary, ed]

In a context where schools are held more and more accountable for the education they provide, data-based decision making has become increasingly important. This book brings together scholars from several countries to examine data-based decision making. Data-based decision making in this book refers to making decisions based on a broad range of evidence, such as scores on students’ assessments, classroom observations etc. This book supports policy-makers, people working with schools, researchers and school leaders and teachers in the use of data, by bringing together the current research conducted on data use across multiple countries into a single volume. Some of these studies are ‘best practice’ studies, where effective data use has led to improvements in student learning. Others provide insight into challenges in both policy and practice environments. Each of them draws on research and literature in the field.

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.

The Ethics of Artificial Intelligence in Education identifies and confronts key ethical issues generated over years of AI research, development, and deployment in learning contexts. Adaptive, automated, and data-driven education systems are increasingly being implemented in universities, schools, and corporate training worldwide, but the ethical consequences of engaging with these technologies remain unexplored. Featuring expert perspectives from inside and outside the AIED scholarly community, this book provides AI researchers, learning scientists, educational technologists, and others with questions, frameworks, guidelines, policies, and regulations to ensure the positive impact of artificial intelligence in learning.

This is an open access book. The 2022 3rd International Conference on Artificial Intelligence and Education(ICAIE 2022) will be held in Chengdu, China during June 24-26, 2022. The meeting focused on the new trends in the development of "artificial intelligence" and "education" under the new situation, and jointly discussed how to empower and promote the high-quality development of "artificial intelligence" and "education". An ideal platform to share views and experiences with industry experts. The conference invites experts and scholars in the field to conduct wonderful exchanges based on their own research results based on the development of the times. The themes are around artificial intelligence technology and applications; intelligent and knowledge-based systems; information-based education; intelligent learning; advanced information theory and neural network technology ; software computing and algorithms; intelligent algorithms and computing and many other topics.