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The Future of Nursing explores how nurses' roles, responsibilities, and education should change significantly to meet the increased demand for care that will be created by health care reform and to advance improvements in America's increasingly complex health system. At more than 3 million in number, nurses make up the single largest segment of the health care work force. They also spend the greatest amount of time in delivering patient care as a profession. Nurses therefore have valuable insights and unique abilities to contribute as partners with other health care professionals in improving the quality and safety of care as envisioned in the Affordable Care Act (ACA) enacted this year. Nurses should be fully engaged with other health professionals and assume leadership roles in redesigning care in the United States. To ensure its members are well-prepared, the profession should institute residency training for nurses, increase the percentage of nurses who attain a bachelor's degree to 80 percent by 2020, and double the number who pursue doctorates. Furthermore, regulatory and institutional obstacles-including limits on nurses' scope of practice-should be removed so that the health system can reap the full benefit of nurses' training, skills, and knowledge in patient care. In this book, the Institute of Medicine makes recommendations for an action-oriented blueprint for the future of nursing.
This volume reports on the Case Study Project, an initiative that evaluates the experiences of math and science teachers across the United States and includes levels of schooling and achievement levels. The report focuses on typical public schools in the United States at three levels: elementary, junior high, and senior high with the intent of creating a composite profile of typical schools. The volume is divided into five chapters: "Rationale for the Study,""The Development and Implementation of Education Standards in the United States,""Individual Differences and the United States Education System,""The Role of School in United States Adolescents' Lives," and "Teachers and the Teaching Profession in the United States." Results show that the typical school had adopted curriculum standards or guidelines proposed by state departments of education, rather than by a national body. One exception to this was the influential set of national guidelines developed by the National Council of Teachers of Mathematics. Despite the general interest in standards, the choices made by parents, teachers, and students reflected many values, of which the desire for independence was one of the strongest. Students who were surveyed exhibited high optimism, especially those students from middle- class families. (RJM)
This book reviews one hundred years of educational reforms worldwide. Characterized by a tension between governing public and professional forces, the waves of educational reform reflect myriad efforts to define and fulfill professional and public expectations for the world of education. The first wave of reform, based on “progressive” ideals, spread across the globe after World War I, striving to place the student at the center of the education process and respond to the diverse needs of children and youth in a world that included massive population shifts. The second wave nearly obliterated the ideals of the progressive movement that had prevailed for sixty years. Drawing its principles from the business world, the second wave imposed competition, uniform standards, and measurable outputs on students, teachers, and schools, even at the cost of harming at-risk populations and encouraging the infiltration of private sector values into public education systems.The third wave was launched at the turn of the twenty-first century. Seeking to adjust instructional methods to modern reality, this reform rejected standardized curricula in favor of developing skills such as independent thinking, curiosity, innovation, collaboration among learners, and the ability to mine and process information. Book I reviews the three waves of reform in the United States, England, Canada, Japan, Singapore, Hong Kong, Australia, and Finland. Book II focuses on Israel’s education system — past, present, and future.
The vast disparities in college attendance and graduation rates between students from different class backgrounds is a growing social concern. Economic Inequality and Higher Education investigates the connection between income inequality and unequal access to higher education, and proposes solutions that the state and federal governments and schools themselves can undertake to make college accessible to students from all backgrounds. Economic Inequality and Higher Education convenes experts from the fields of education, economics, and public policy to assess the barriers that prevent low-income students from completing college. For many students from disadvantaged socioeconomic backgrounds, the challenge isn't getting into college, but getting out with a degree. Helping this group will require improving the quality of education in the community colleges and lower-tier public universities they are most likely to attend. Documenting the extensive disjuncture between the content of state-mandated high school testing and college placement exams, Michael Kirst calls for greater alignment between K-12 and college education. Amanda Pallais and Sarah Turner examine barriers to access at elite universities for low-income students—including tuition costs, lack of information, and poor high school records—as well as recent initiatives to increase socioeconomic diversity at private and public universities. Top private universities have increased the level and transparency of financial aid, while elite public universities have focused on outreach, mentoring, and counseling, and both sets of reforms show signs of success. Ron Ehrenberg notes that financial aid policies in both public and private universities have recently shifted towards merit-based aid, away from the need-based aid that is most helpful to low-income students. Ehrenberg calls on government policy makers to create incentives for colleges to increase their representation of low-income students. Higher education is often vaunted as the primary engine of upward mobility. Instead, as inequality in America rises, colleges may be reproducing income disparities from one generation to the next. Economic Inequality and Higher Education illuminates this worrisome trend and suggests reforms that educational institutions and the government must implement to make the dream of a college degree a reality for all motivated students.
A vital guide to building the platforms and systems that bring deep learning models to production. In Designing Deep Learning Systems you will learn how to: Transfer your software development skills to deep learning systems Recognize and solve common engineering challenges for deep learning systems Understand the deep learning development cycle Automate training for models in TensorFlow and PyTorch Optimize dataset management, training, model serving and hyperparameter tuning Pick the right open-source project for your platform Deep learning systems are the components and infrastructure essential to supporting a deep learning model in a production environment. Written especially for software engineers with minimal knowledge of deep learning’s design requirements, Designing Deep Learning Systems is full of hands-on examples that will help you transfer your software development skills to creating these deep learning platforms. You’ll learn how to build automated and scalable services for core tasks like dataset management, model training/serving, and hyperparameter tuning. This book is the perfect way to step into an exciting—and lucrative—career as a deep learning engineer. About the technology To be practically usable, a deep learning model must be built into a software platform. As a software engineer, you need a deep understanding of deep learning to create such a system. Th is book gives you that depth. About the book Designing Deep Learning Systems: A software engineer's guide teaches you everything you need to design and implement a production-ready deep learning platform. First, it presents the big picture of a deep learning system from the developer’s perspective, including its major components and how they are connected. Then, it carefully guides you through the engineering methods you’ll need to build your own maintainable, efficient, and scalable deep learning platforms. What's inside The deep learning development cycle Automate training in TensorFlow and PyTorch Dataset management, model serving, and hyperparameter tuning A hands-on deep learning lab About the reader For software developers and engineering-minded data scientists. Examples in Java and Python. About the author Chi Wang is a principal software developer in the Salesforce Einstein group. Donald Szeto was the co-founder and CTO of PredictionIO. Table of Contents 1 An introduction to deep learning systems 2 Dataset management service 3 Model training service 4 Distributed training 5 Hyperparameter optimization service 6 Model serving design 7 Model serving in practice 8 Metadata and artifact store 9 Workflow orchestration 10 Path to production
'Shirley zips into her skin-tight school uniform, which on the outside looks something like a ski suit. The lining of the suit in fact contains cabling that makes the suit a communication system and there are pressure pads where the suit touches skin that give a sense of touch. Next, she sits astride something that is a bit like a motorbike, except that it has no wheels and is attached firmly to the floor. Her feet fit on to something similar to a brake and accelerator and her gloved hands hold onto handlebars. She shouts, "I'm off to school, Dad". Her father, who is taking time out from his teleworking, begins to remind her that the family are going teleshopping in the virtual city later in the day, but it is too late, his daughter has already donned her school helmet. She is no longer in the real world of her real home, she is in the virtual world of her virtual school.' Is this the shape of the future of education? This book presents a vision of what will happen to education and training as information technology develops. The argument is simple. To prepare people for life in an information society they need to be taught with the technology of an information society. But what shape will that take? Can the classroom as we know it - a communications system which has been in place for four thousand years - be replaced? The authors argue that through the development of telecommunications for telelearning a genuine revolution in education is in the making. The book describes how, through the convergence of a cluster of new technologies including virtual reality, artificial intelligence, nanotechnology and the superhighways of telecommunications, a new educational paradigm will emerge in the form of a virtual class. Teachers, trainers and educators who worry about how best to prepare students for life in an ever-changing world will find much inspiration in this engagingly written and jargon-free book