For Learning Theory/Cognition and Instruction, Advanced Educational Psychology, and Introductory Educational Psychology courses. An essential resource for understanding the main principles, concepts, and research findings of key learning theories –especially as they relate to education–this proven text blends theory, research, and applications throughout, providing its readers with a coherent and unified perspective on learning in educational settings. The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed.

Learning Theory and Online Technologies offers a powerful overview of the current state of elearning, a foundation of its historical roots and growth, and a framework for distinguishing among the major approaches to elearning. It effectively addresses pedagogy (how to design an effective online environment for learning), evaluation (how to know that students are learning), and history (how past research can guide successful online teaching and learning outcomes). An ideal textbook for undergraduate education and communication programs, and Educational Technology Masters, PhD, and Certificate programs, readers will find Learning Theory and Online Technologies provides a synthesis of the key advances in elearning theory, the key frameworks of research, and clearly links theory and research to successful learning practice.

Anne Meyer and David Rose, who first laid out the principles of UDL, provide an ambitious, engaging discussion of new research and best practices. This book gives the UDL field an essential and authoritative learning resource for the coming years. In the 1990s, Anne Meyer, David Rose, and their colleagues at CAST introduced Universal Design for Learning (UDL) as a framework to improve teaching and learning in the digital age, sparking an international reform movement. Now Meyer and Rose return with Universal Design for Learning: Theory and Practice, an up-to-date multimedia online book (with print and e-book options) that leverages more than a decade of research and implementation. This is the first significant new statement on UDL since 2002, an ambitious, engaging exploration of ideas and best practices that provides the growing UDL field with an essential and authoritative learning resource for the coming years. This new work includes contributions from CAST's research and implementation teams as well as from many of CAST's collaborators in schools, universities, and research settings. Readers are invited to contribute ideas, perspectives, and examples from their own practice in an online community of practice. --

Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs. The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.

This introduction to the concepts and techniques of formal learning theory is based on a number-theoretical approach to learning and uses the tools of recursive function theory to understand how learners come to an accurate view of reality.

This volume develops an effective theory approach to understanding deep neural networks of practical relevance.

Defines learning and shows how the learning process is studied. Clearly written and user-friendly, Introduction to the Theories of Learning places learning in its historical perspective and provides appreciation for the figures and theories that have shaped 100 years of learning theory research. The 9th edition has been updated with the most current research in the field. With Pearson's MySearchLab with interactive eText and Experiment's Tool, this program is more user-friendly than ever. Learning Goals Upon completing this book, readers should be able to: Define learning and show how the learning process is studied Place learning theory in historical perspective Present essential features of the major theories of learning with implications for educational practice Note: MySearchLab does not come automatically packaged with this text. To purchase MySearchLab, please visit: www.mysearchlab.com or you can purchase a ValuePack of the text + MySearchLab (at no additional cost).

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.

The goal of learning theory is to approximate a function from sample values. To attain this goal learning theory draws on a variety of diverse subjects, specifically statistics, approximation theory, and algorithmics. Ideas from all these areas blended to form a subject whose many successful applications have triggered a rapid growth during the last two decades. This is the first book to give a general overview of the theoretical foundations of the subject emphasizing the approximation theory, while still giving a balanced overview. It is based on courses taught by the authors, and is reasonably self-contained so will appeal to a broad spectrum of researchers in learning theory and adjacent fields. It will also serve as an introduction for graduate students and others entering the field, who wish to see how the problems raised in learning theory relate to other disciplines.