This edited volume is a state-of-the-art comparison of primary science education across six East-Asian regions; namely, the People's Republic of China, Republic of Korea, Republic of China, Hong Kong SAR, Japan, and Singapore. While news of educational policies, classroom teaching, assessment, and other educational innovations here often surface in the international media, this book brings together for the first time relevant information regarding educational systems and strategies in primary science in East Asia. Above all, it is a readable yet comprehensive survey-readers would have an accurate sense of what has been accomplished, what has not worked so well, and what remains to be done. Invited experts in comparative education research and/or science education also provide commentary by discussing common themes across the six regions. These types of critical synoptic reviews add much value by enabling readers to understand broad commonalities and help synthesize what must surely be a bewildering amount of very interesting albeit confusing body of facts, issues, and policies. Education in East Asia holds many lessons (both positive and negative) to offer to the rest of the world to which this volume is a timely contribution to the literature.

Education in east Asia varies widely, due to the cultural and political histories of each country. The communist governments of China, North Korea, and Vietnam mandate schooling differently from the limited democracy of Hong Kong and the parliamentary government of Japan. The history of the educational philosophies, systems, and curricula of seventeen East Asian countries are described here, with a timeline highlighting educational developments, and a special "day in the life" feature, a personal account of what it is like for a student to attend school in that country. -Brunei -Cambodia -China -Hong Kong -Indonesia -Japan -Laos -Malaysia -Mongolia -North Korea -Philippines -Singapore -South Korea -Taiwan -Thailand -Timor Leste -Vietnam -Philippines -Singapore -Taiwan -Vietnam

Globalization has effected tremendous change to the character and functions of education worldwide. This unique book focuses on its impact upon Hong Kong and Singapore, and how these two East Asian Tigers have responded to the strong global tide of marketization in shaping and developing their education policies. The authors discuss the way in which increasingly prominent tides of marketization, privatization, corporatization and decentralization have influenced the governance and management of education in these two Asian economies. They aim to identify and examine the crucial socio-historical, socio-economic and socio-political factors for education reforms initiated in the two societies in recent years. Ka-Ho Mok and Jason Tan examine the education policy developments of these two cities, to draw wider conclusions as to how nation-states and/or local governments react and respond to the growing impact of globalization. Globalization and Marketization in Education will draw an interested readership from education policy researchers, policymakers and administrators. Scholars of public policy, and Asian, development and education studies will also find the book of special interest and value.

This book presents an improved design for service provisioning and allocation models that are validated through running genome sequence assembly tasks in a hybrid cloud environment. It proposes approaches for addressing scheduling and performance issues in big data analytics and showcases new algorithms for hybrid cloud scheduling. Scientific sectors such as bioinformatics, astronomy, high-energy physics, and Earth science are generating a tremendous flow of data, commonly known as big data. In the context of growing demand for big data analytics, cloud computing offers an ideal platform for processing big data tasks due to its flexible scalability and adaptability. However, there are numerous problems associated with the current service provisioning and allocation models, such as inefficient scheduling algorithms, overloaded memory overheads, excessive node delays and improper error handling of tasks, all of which need to be addressed to enhance the performance of big data analytics.

This book presents a language integrated query framework for big data. The continuous, rapid growth of data information to volumes of up to terabytes (1,024 gigabytes) or petabytes (1,048,576 gigabytes) means that the need for a system to manage and query information from large scale data sources is becoming more urgent. Currently available frameworks and methodologies are limited in terms of efficiency and querying compatibility between data sources due to the differences in information storage structures. For this research, the authors designed and programmed a framework based on the fundamentals of language integrated query to query existing data sources without the process of data restructuring. A web portal for the framework was also built to enable users to query protein data from the Protein Data Bank (PDB) and implement it on Microsoft Azure, a cloud computing environment known for its reliability, vast computing resources and cost-effectiveness.

Over the last two decades, the range of curricular offerings in Singapore has diversified almost beyond the ability of teacher preparation systems to cope. Teacher training has evolved from informal to formal, and from multiple 'providers' to a single institution responsible for pre-service teacher education. Teacher Preparation in Singapore is a non-celebratory and non-institution-based account of teacher preparation written with a critical academic lens. Contributing to the historiography of Singapore, as well as to the general history of teacher education, this book discusses the history of teacher preparation in Singapore from the colonial era, when Singapore was the centre of British Malaya, to the present day. It includes the pre-professional era of an informal approach to teacher education before the establishment of formal teacher training, the role of the colonial state and post-colonial state in the provision of teacher education, and issues such as policy borrowing, diffusion of educational philosophies, and developments paralleling those in the United Kingdom and elsewhere. This is a relevant and important book for researchers of education history, comparative and international education, and teacher education in Singapore.

This edited volume is a state-of-the-art comparison of primary science education across six East-Asian regions; namely, the People's Republic of China, Republic of Korea, Republic of China, Hong Kong SAR, Japan, and Singapore. While news of educational policies, classroom teaching, assessment, and other educational innovations here often surface in the international media, this book brings together for the first time relevant information regarding educational systems and strategies in primary science in East Asia. Above all, it is a readable yet comprehensive survey-readers would have an accurate sense of what has been accomplished, what has not worked so well, and what remains to be done. Invited experts in comparative education research and/or science education also provide commentary by discussing common themes across the six regions. These types of critical synoptic reviews add much value by enabling readers to understand broad commonalities and help synthesize what must surely be a bewildering amount of very interesting albeit confusing body of facts, issues, and policies. Education in East Asia holds many lessons (both positive and negative) to offer to the rest of the world to which this volume is a timely contribution to the literature.

This book presents an improved design for service provisioning and allocation models that are validated through running genome sequence assembly tasks in a hybrid cloud environment. It proposes approaches for addressing scheduling and performance issues in big data analytics and showcases new algorithms for hybrid cloud scheduling. Scientific sectors such as bioinformatics, astronomy, high-energy physics, and Earth science are generating a tremendous flow of data, commonly known as big data. In the context of growing demand for big data analytics, cloud computing offers an ideal platform for processing big data tasks due to its flexible scalability and adaptability. However, there are numerous problems associated with the current service provisioning and allocation models, such as inefficient scheduling algorithms, overloaded memory overheads, excessive node delays and improper error handling of tasks, all of which need to be addressed to enhance the performance of big data analytics.