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Richard Clark’s observation that “…media are mere vehicles that deliver instruction but do not influence student achievement any more than the truck that delivers our groceries causes changes in our nutrition” is as misunderstood today as it was when first published in the Review of Educational Research in 1983. The convincing if little read scientific evidence presented by Clark has divided the field and caused considerable concern, especially among the providers of newer media for learning. A collection of writings about the “media effects debate,” as it has come to be called, was published in 2001. Edited by Clark, Learning From Media was the first volume in the series “Perspectives in Instructional Technology and Distance Education.” The series editors are convinced that the writings of Clark and those who take issue with his position are of critical importance to the field of instructional technology, Thus, a revised, second edition of Learning From Media is now being offered. The debate about the impact of media on learning remains a fundamental issue as new mediated approaches to teaching and learning are developed, and Clark’s work should be at the center of the discussion. The critical articles on both sides of this debate are contained in Learning From Media, 2nd Edition.
An evidence based, rigorous text reviewing 12 principles of experimental studies grounded in cognitive theory of multi-media learning.
Inside, readers will find a wealth of intelligently crafted, ready-to-use lesson plans and activities designed to help promote critical thinking skills for K-12 students, making this a perfect teaching resource for school and public librarians, educators, and literacy instructors.
As social media and Web 2.0 technologies continue to transform the learning trends and preferences of students, educators need to understand the applicability of these new tools in all types of learning environments. Best Practices for Teaching with Emerging Technologies will provide both new and experienced online, hybrid, and face-to-face instructors with: practical examples of how low-cost and free technologies can be used to support student learning best practices for integrating web-based tools into a course management system and managing student privacy in a Web 2.0 environment "Showcase" spotlights woven throughout the book, providing examples of how the tools described in the book are already being used effectively in educational settings an easy-to-reference format, organized with visual icons used to delineate each tool's visual, video, voice, and mobile features ideas for integrating mobile learning into your students' learning experiences. This practical, easy-to-use guide will serve the needs of educators seeking to refresh or transform their instruction. Readers will be rewarded with an ample yet manageable collection of proven emerging technologies that can be leveraged for generating content, enhancing communications with and between students, and cultivating participatory, student-centered learning activities.
Ten Steps to Complex Learning presents a path from a training problem to a training solution in a way that students, practitioners (both instructional designers and teachers), and researchers can understand and easily use. Practitioners can use this book as a reference guide to support their design of courses, materials, or environments for complex learning. Students in the field of instructional design can use this book to broaden their knowledge of the design of training programs for complex learning. Now fully revised to incorporate the most current research in the field, this second edition of Ten Steps to Complex Learning includes user-friendly examples and case studies, and demonstrates the application of the ten steps in relation to the design of serious games, learning networks, social media, and new developments in educational neuroscience.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets
A Co-Publication of Routledge and NAEYC Technology and Digital Media in the Early Years offers early childhood teacher educators, professional development providers, and early childhood educators in pre-service, in-service, and continuing education settings a thought-provoking guide to effective, appropriate, and intentional use of technology with young children. This book provides strategies, theoretical frameworks, links to research evidence, descriptions of best practice, and resources to develop essential digital literacy knowledge, skills and experiences for early childhood educators in the digital age. Technology and Digital Media in the Early Years puts educators right at the intersections of child development, early learning, developmentally appropriate practice, early childhood teaching practices, children’s media research, teacher education, and professional development practices. The book is based on current research, promising programs and practices, and a set of best practices for teaching with technology in early childhood education that are based on the NAEYC/FRC Position Statement on Technology and Interactive Media and the Fred Rogers Center Framework for Quality in Children’s Digital Media. Pedagogical principles, classroom practices, and teaching strategies are presented in a practical, straightforward way informed by child development theory, developmentally appropriate practice, and research on effective, appropriate, and intentional use of technology in early childhood settings. A companion website (http://teccenter.erikson.edu/tech-in-the-early-years/) provides additional resources and links to further illustrate principles and best practices for teaching and learning in the digital age.
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. 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 while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
"Clearly written and well organized, this book shows how to apply the principles of universal design for learning (UDL) across all subject areas and grade levels. The editors and contributors describe practical ways to develop classroom goals, assessments, materials, and methods that use UDL to meet the needs of all learners. Specific teaching ideas are presented for reading, writing, science, mathematics, history, and the arts, including detailed examples and troubleshooting tips. Particular attention is given to how UDL can inform effective, innovative uses of technology in the inclusive classroom. Subject Areas/Keywords: assessments, classrooms, content areas, curriculum design, digital media, educational technology, elementary, inclusion, instruction, learning disabilities, literacy, schools, secondary, special education, supports, teaching methods, UDL, universal design Audience: General and special educators in grades K-8, literacy specialists, school psychologists, administrators, teacher educators, and graduate students"--