A three-pronged approach to overcoming mediocrity, presented by one of the nation's top business theorist. Replete with case examples, this book details how employee reasoning, learning and action--properly developed--can counteract the self-defeating behavior affecting many organizations.
There are many reasons to be curious about the way people learn, and the past several decades have seen an explosion of research that has important implications for individual learning, schooling, workforce training, and policy. In 2000, How People Learn: Brain, Mind, Experience, and School: Expanded Edition was published and its influence has been wide and deep. The report summarized insights on the nature of learning in school-aged children; described principles for the design of effective learning environments; and provided examples of how that could be implemented in the classroom. Since then, researchers have continued to investigate the nature of learning and have generated new findings related to the neurological processes involved in learning, individual and cultural variability related to learning, and educational technologies. In addition to expanding scientific understanding of the mechanisms of learning and how the brain adapts throughout the lifespan, there have been important discoveries about influences on learning, particularly sociocultural factors and the structure of learning environments. How People Learn II: Learners, Contexts, and Cultures provides a much-needed update incorporating insights gained from this research over the past decade. The book expands on the foundation laid out in the 2000 report and takes an in-depth look at the constellation of influences that affect individual learning. How People Learn II will become an indispensable resource to understand learning throughout the lifespan for educators of students and adults.
Confronted with rising citizen discontent, the Reinventing Government movement, and new technological challenges, public organizations everywhere are seeking means of improving their performance. Their quest is not new, rather, the concern with improving the performance of government organizations has existed since the Scientific Management Movement. Public Sector Performance brings together in a single volume the classic, enduring principles and processes that have defined the field of public sector performance, as written in the words of leading practitioners and scholars. Taken as a whole, this volume provides a performance compass for today's public managers, helping them to reconstruct the public's confidence in, and support of, government.Defined here as managing public organizations for outcomes, performance is examined in all its varied dimensions: organizing work, managing workers, measuring performance, and overcoming resistance to performance-enhancing innovations. The selected articles are interesting, thought provoking, and instructive. They are classics in that they have been widely cited in the scholarly literature and have enduring value to public managers who seek to understand the many dimensions of performance. The book is organized into three sections: Performance Foundations, Performance Strategies, and Performance Measurement. Excerpts from additional selected articles feature special topics and wisdom from performance experts.
Knowledge representation and reasoning is the foundation of artificial intelligence, declarative programming, and the design of knowledge-intensive software systems capable of performing intelligent tasks. Using logical and probabilistic formalisms based on answer set programming (ASP) and action languages, this book shows how knowledge-intensive systems can be given knowledge about the world and how it can be used to solve non-trivial computational problems. The authors maintain a balance between mathematical analysis and practical design of intelligent agents. All the concepts, such as answering queries, planning, diagnostics, and probabilistic reasoning, are illustrated by programs of ASP. The text can be used for AI-related undergraduate and graduate classes and by researchers who would like to learn more about ASP and knowledge representation.
Offers research and practice insights into the emerging discipline and field of knowledge management and aims to accelerate a global adoption of knowledge management (KM) as a distinct and critical field of study for today's professionals. It is suitable for universities, research centres and organizations working on KM.
This book is open access under a CC BY 4.0 license. This volume describes and explains the educational method of Case-Based Clinical Reasoning (CBCR) used successfully in medical schools to prepare students to think like doctors before they enter the clinical arena and become engaged in patient care. Although this approach poses the paradoxical problem of a lack of clinical experience that is so essential for building proficiency in clinical reasoning, CBCR is built on the premise that solving clinical problems involves the ability to reason about disease processes. This requires knowledge of anatomy and the working and pathology of organ systems, as well as the ability to regard patient problems as patterns and compare them with instances of illness scripts of patients the clinician has seen in the past and stored in memory. CBCR stimulates the development of early, rudimentary illness scripts through elaboration and systematic discussion of the courses of action from the initial presentation of the patient to the final steps of clinical management. The book combines general backgrounds of clinical reasoning education and assessment with a detailed elaboration of the CBCR method for application in any medical curriculum, either as a mandatory or as an elective course. It consists of three parts: a general introduction to clinical reasoning education, application of the CBCR method, and cases that can used by educators to try out this method.
Reasoning about knowledge—particularly the knowledge of agents who reason about the world and each other's knowledge—was once the exclusive province of philosophers and puzzle solvers. More recently, this type of reasoning has been shown to play a key role in a surprising number of contexts, from understanding conversations to the analysis of distributed computer algorithms. Reasoning About Knowledge is the first book to provide a general discussion of approaches to reasoning about knowledge and its applications to distributed systems, artificial intelligence, and game theory. It brings eight years of work by the authors into a cohesive framework for understanding and analyzing reasoning about knowledge that is intuitive, mathematically well founded, useful in practice, and widely applicable. The book is almost completely self-contained and should be accessible to readers in a variety of disciplines, including computer science, artificial intelligence, linguistics, philosophy, cognitive science, and game theory. Each chapter includes exercises and bibliographic notes.