It builds a story about the power of teachers, feedback, and a model of learning and understanding. The research involves many millions of students and represents the largest ever evidence based research into what actually works in schools to improve learning. Areas covered include the influence of the student, home, school, curricula, teacher, and teaching strategies. A model of teaching and learning is developed based on the notion of visible teaching and visible learning.A major message is that what works best for students is similar to what works best for teachers - an attention to setting challenging learning intentions, being clear about what success means, and an attention to learning strategies for developing conceptual understanding about what teachers and students know and understand.Although the current evidence based fad has turned into a debate about test scores, this book is about using evidence to build and defend a model of teaching and learning. A major contribution is a fascinating benchmark/dashboard for comparing many innovations in teaching and schools.

    Longer training, ever more advanced technologies—neither seems to prevent grievous errors. But in a hopeful turn, acclaimed surgeon and writer Atul Gawande finds a remedy in the humblest and simplest of techniques: the checklist. First introduced decades ago by the U.S. Air Force, checklists have enabled pilots to fly aircraft of mind-boggling sophistication. Now innovative checklists are being adopted in hospitals around the world, helping doctors and nurses respond to everything from flu epidemics to avalanches. Even in the immensely complex world of surgery, a simple ninety-second variant has cut the rate of fatalities by more than a third.In riveting stories, Gawande takes us from Austria, where an emergency checklist saved a drowning victim who had spent half an hour underwater, to Michigan, where a cleanliness checklist in intensive care units virtually eliminated a type of deadly hospital infection. He explains how checklists actually work to prompt striking and immediate improvements. And he follows the checklist revolution into fields well beyond medicine, from disaster response to investment banking, skyscraper construction, and businesses of all kinds.An intellectual adventure in which lives are lost and saved and one simple idea makes a tremendous difference, The Checklist Manifesto is essential reading for anyone working to get things right.

    How does professional baseball evaluate hitters? Is a singles hitter like Wade Boggs more valuable than a power hitter like David Ortiz? Should NFL teams pass or run more often on first downs? Could professional basketball have used statistics to expose the crooked referee Tim Donaghy? Does money buy performance in professional sports?In Mathletics, Wayne Winston describes the mathematical methods that top coaches and managers use to evaluate players and improve team performance, and gives math enthusiasts the practical tools they need to enhance their understanding and enjoyment of their favorite sports--and maybe even gain the outside edge to winning bets. Mathletics blends fun math problems with sports stories of actual games, teams, and players, along with personal anecdotes from Winston's work as a sports consultant. Winston uses easy-to-read tables and illustrations to illuminate the techniques and ideas he presents, and all the necessary math concepts--such as arithmetic, basic statistics and probability, and Monte Carlo simulations--are fully explained in the examples.After reading Mathletics, you will understand why baseball teams should almost never bunt, why football overtime systems are unfair, why points, rebounds, and assists aren't enough to determine who's the NBA's best player--and much, much more.

    The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout. Author resource page: http: //www.stat.columbia.edu/ gelman/arm/

    Drawing heavily on the author's own real-world experiences, the book stresses design and analysis. Coverage is divided into two parts, the first being a general guide to techniques for the design and analysis of computer algorithms. The second is a reference section, which includes a catalog of the 75 most important algorithmic problems. By browsing this catalog, readers can quickly identify what the problem they have encountered is called, what is known about it, and how they should proceed if they need to solve it. This book is ideal for the working professional who uses algorithms on a daily basis and has need for a handy reference. This work can also readily be used in an upper-division course or as a student reference guide. THE ALGORITHM DESIGN MANUAL comes with a CD-ROM that contains: * a complete hypertext version of the full printed book. * the source code and URLs for all cited implementations. * over 30 hours of audio lectures on the design and analysis of algorithms are provided, all keyed to on-line lecture notes.

    Using examples from a wide range of disciplines, the authors give readers expert advice on the entire dissertation process: selecting a suitable topic; conducting a literature review; managing data overload; building an argument; presenting the material, data, and results; and working with faculty committees. The entire text has been updated and fresh examples have been added to it. This edition features an up-to-the-minute discussion of online research and the use of software packages. The authors have expanded their coverage of qualitative work, and added information about the use of mixed methods to the book. These updates and more make the Third Edition of Surviving Your Dissertation a must have resource for graduate students.Key Features of the Third Edition: Walks readers through the dissertation process as an ideal mentor would.Devotes more attention to qualitative work, and touches upon mixed methods.Discusses online library resources and completing one′s dissertation via the Internet.Features new material on the use of graphics.Includes information about informed consent forms.

    This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

    The author has a direct style. His book presents detailed and intensive explanations. Many diagrams and key examples are used to aid understanding, as well as the application of calculus to physics and engineering and economics. The text is well organized, and it covers single variable and multivariable calculus in depth. An instructor's manual and student guide are available online at http: //ocw.mit.edu/ans7870/resources/Strang/....

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    With cases studies found throughout, you will easily learn to apply principles to real life.

    Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

    A clear and effective guide to policy analysis addressing the psychology, as well as the logic, of the analytical process Full of helpful hints, such as warnings about language traps, strategies for economizing on data collection, and checklists for generating solutions, this book is widely used by students, practicing policy officials in government, and professionals in executive-level training programmes.

    It will also help graduate students finish in less time, for less money, and with less trouble.Based on interviews with career counselors, graduate students, and professors, Getting What You Came For is packed with real-life experiences. It has all the advice a student will need not only to survive but to thrive in graduate school, including: instructions on applying to school and for financial aid; how to excel on qualifying exams; how to manage academic politics--including hostile professors; and how to write and defend a top-notch thesis. Most important, it shows you how to land a job when you graduate.

    In Richard Halgin and Susan Krauss Whitbourne’s Abnormal Psychology: Clinical Perspectives on Psychological Disorders, students are shown the human side of Abnormal Psychology. Through the wide