Efficiency in Learning offers a road map of the most effective ways to use the three fundamental communication of training: visuals, written text, and audio. Regardless of how you are delivering your training materials—in the classroom, in print, by synchronous or asynchronous media—the book’s methods are easily applied to your lesson presentations, handouts, reference guides, or e-learning screens. Designed to be a down-to-earth resource for all instructional professionals, Efficiency in Learning’s guidelines are clearly illustrated with real-world examples.

Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions. Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms. Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning.

This book is a critical assessment of the knowledge base on educational effectiveness, covering a period of five decades of research. It formulates a “lean” theory of good schooling, and identifies and explains instances of “ineffectiveness”, such as low effect sizes of malleable conditions, for which expectations are highly strung. The book presents a systemic outlook on educational effectiveness and improvement, as it starts out from an integrated multi-level model that comprises system level, school level and instructional conditions. It offers a classification of school improvement strategies and scenarios for system level educational improvement. Above all, the analysis is very systematic, comprehensive and strongly grounded in theory. The book includes a case study analysis of various strands of improvement-oriented educational policy in the Netherlands as an illustration of some of the arguments used.

In contexts of instructed second language acquisition there is a need for teaching methods that are optimally efficient, i.e. teaching interventions that generate a maximal return on learners' and teachers' investment of time and effort. In the past couple of decades, many researchers have argued that insights from Cognitive Linguistics (CL) - when suitably translated for pedagogical purposes - can make a major contribution to fostering such language teaching efficiency. This collective volume assesses and supplements those CL proposals. The first part of the book positions CL-inspired language pedagogy vis-à-vis recent trends in mainstream applied linguistics and illustrates through several case studies that language-focused instruction (including CL-inspired instruction) is a useful - if not indispensable - complement to learner-autonomous, incidental acquisition. The second part demonstrates how CL research can help pedagogues identify hitherto neglected language elements that merit explicit targeting in second language instruction. The third part consists of contributions that put the pedagogical efficiency of several CL-inspired interventions to the test in classroom experiments. Additions to the currently available armoury of teaching methods are proposed. The kinds of target language items under examination in the book range from single words over multiword units to grammar patterns. Throughout, the volume illustrates how much pedagogy-oriented Cognitive Linguistics has matured in recent years.

To most of us, learning something "the hard way" implies wasted time and effort. Good teaching, we believe, should be creatively tailored to the different learning styles of students and should use strategies that make learning easier. Make It Stick turns fashionable ideas like these on their head. Drawing on recent discoveries in cognitive psychology and other disciplines, the authors offer concrete techniques for becoming more productive learners. Memory plays a central role in our ability to carry out complex cognitive tasks, such as applying knowledge to problems never before encountered and drawing inferences from facts already known. New insights into how memory is encoded, consolidated, and later retrieved have led to a better understanding of how we learn. Grappling with the impediments that make learning challenging leads both to more complex mastery and better retention of what was learned. Many common study habits and practice routines turn out to be counterproductive. Underlining and highlighting, rereading, cramming, and single-minded repetition of new skills create the illusion of mastery, but gains fade quickly. More complex and durable learning come from self-testing, introducing certain difficulties in practice, waiting to re-study new material until a little forgetting has set in, and interleaving the practice of one skill or topic with another. Speaking most urgently to students, teachers, trainers, and athletes, Make It Stick will appeal to all those interested in the challenge of lifelong learning and self-improvement.

Industrial policy, once relegated to resource allocation, technological improvements, and the modernization of industries, should be treated as a serious component of sustainability and developmental economics. A rich set of complimentary institutions, shared behavioral norms, and public policies have sustained economic growth from Britain's industrial revolution onwards. This volume revisits the role of industrial policy in the success of these strategies and what it can offer developed and developing economies today. Featuring essays from experts invested in the expansion of industrial policies, topics discussed include the most effective use of industrial policies in learning economies, development finance, and promoting investment in regional and global contexts. Also included are in-depth case studies of Japan and India's experience with industrial policy in the banking and private sector. One essay revisits the theoretical and conceptual foundations of industrial policy from a structural economics perspective and another describes the models, packages, and transformation cycles that constitute a variety of approaches to implementation. The collection concludes with industrial strategies for facilitating quality growth, realizing more sustainable manufacturing development, and encouraging countries to industrialize around their natural resources.

Evidence-Based Educational Methods answers the challenge of the "No Child Left Behind Act" of 2001 by promoting evidence-based educational methods designed to improve student learning. Behavioral scientists have been refining these instructional methods for decades before the current call for evidence-based education. Precision Teaching, Direct Instruction, Computerized Teaching, Personalized System of Instruction, and other unique applications of behavior analysis are all informed by the scientific principles of learning, have been tested in the laboratory, and are often shown to have significant success in field applications. This book details each of these approaches to education based on the principles of behavior analysis. Individuals and agencies responsible for instruction that leaves no child behind will find this compendium an important resource for meeting that challenge, and young educators will greatly benefit from this text, as they will see a blueprint of the evidence-based education systems being planned for the future. * The education literature is replete with fly-by-night ideas and unresearched opinions about how to teach children. This book has none of that. The reader is given researched educational methods. In fact, some methods draw on 3 or 4 decades of experimental data. The whole book is cohesive, not just a patchwork of different educators' opinions. All of the chapters are built on basic scientific principles of behavior, and all of the methods can be used with one another * This is a book by scientist-practitioners, but not for scientists only. A parent can read many of these chapters, see the merit in the methods, and convey the need and the process for each of the methods * No book stands alone, but is connected to a greater literature base. The reader is shown where other information can be found about these methods. * The only thing better than scientific data is scientific data supported by consumer testimonial

A surprisingly simple way for students to master any subject--based on one of the world's most popular online courses and the bestselling book A Mind for Numbers A Mind for Numbers and its wildly popular online companion course "Learning How to Learn" have empowered more than two million learners of all ages from around the world to master subjects that they once struggled with. Fans often wish they'd discovered these learning strategies earlier and ask how they can help their kids master these skills as well. Now in this new book for kids and teens, the authors reveal how to make the most of time spent studying. We all have the tools to learn what might not seem to come naturally to us at first--the secret is to understand how the brain works so we can unlock its power. This book explains: Why sometimes letting your mind wander is an important part of the learning process How to avoid "rut think" in order to think outside the box Why having a poor memory can be a good thing The value of metaphors in developing understanding A simple, yet powerful, way to stop procrastinating Filled with illustrations, application questions, and exercises, this book makes learning easy and fun.

Computational modeling and simulation has developed and expanded into a diverse range of fields such as digital signal processing, image processing, robotics, systems biology, and many more; enhancing the need for a diversifying problem solving applications in this area. Efficiency and Scalability Methods for Computational Intellect presents various theories and methods for approaching the problem of modeling and simulating intellect in order to target computation efficiency and scalability of proposed methods. Researchers, instructors, and graduate students will benefit from this current research and will in turn be able to apply the knowledge in an effective manner to gain an understanding of how to improve this field.