Learning By Playing

There is a growing recognition in the learning sciences that video games can no longer be seen as impediments to education, but rather, they can be developed to enhance learning. Educational and developmental psychologists, education researchers, media psychologists, and cognitive psychologists are now joining game designers and developers in seeking out new ways to use video game play in the classroom. In Learning by Playing, a diverse group of contributors provide perspectives on the most current thinking concerning the ramifications of leisure video game play for academic classroom learning. The first section of the text provides foundational understanding of the cognitive skills and content knowledge that children and adolescents acquire and refine during video game play. The second section explores game features that captivate and promote skills development among game players. The subsequent sections discuss children and adolescents' learning in the context of different types of games and the factors that contribute to transfer of learning from video game play to the classroom. These chapters then form the basis for the concluding section of the text: a specification of the most appropriate research agenda to investigate the academic potential of video game play, particularly using those games that child and adolescent players find most compelling. Contributors include researchers in education, learning sciences, and cognitive and developmental psychology, as well as instructional design researchers.

The innovative and creative games in "Learning to Play, Playing to Learn foster social skills to help young people deal with conflict without resorting to violence. It guides parents and educators in helping children identify their own set of values and feelings while playing with others. It also discusses several ways to modify popular games to encourage fairness and trust in children and encourages the use of healthy play techniques to increase self-esteem, cooperation, personal responsibility, and emotional and physical health.

Describes play workshop experiences that give educators a deeper understanding of play-based learning and illustrate the power of play.

The Agency by Design guide to implementing maker-centered teaching and learning Maker-Centered Learning provides both a theoretical framework and practical resources for the educators, curriculum developers, librarians, administrators, and parents navigating this burgeoning field. Written by the expert team from the Agency by Design initiative at Harvard's Project Zero, this book Identifies a set of educational practices and ideas that define maker-centered learning, and introduces the focal concepts of maker empowerment and sensitivity to design. Shares cutting edge research that provides evidence of the benefits of maker-centered learning for students and education as a whole. Presents a clear Project Zero-based framework for maker-centered teaching and learning Includes valuable educator resources that can be applied in a variety of design and maker-centered learning environments Describes unique thinking routines that foster the primary maker capacities of looking closely, exploring complexity, and finding opportunity. A surge of voices from government, industry, and education have argued that, in order to equip the next generation for life and work in the decades ahead, it is vital to support maker-centered learning in various educational environments. Maker-Centered Learning provides insight into what that means, and offers tools and knowledge that can be applied anywhere that learning takes place.

Publisher description

By highlighting the learning potential with different play activities, this book shows how play can complement and enhance the social, emotional, perceptual motor and intellectual development of children in their early years.

Sandra Smidt sets out to explain what play is and why it is so important as one of the key ways of learning, particularly - but not solely - for young children. She argues that all play is purposeful, and can only truly considered to be play when the child has chosen what to do, where and how to do it. Using case studies drawn from all over the world, Smidt challenges some of the prevailing myths relating to play and pays close attention to what it is that early years professionals need to do to interpet the play, understand its purpose for the child and sometimes extend it. Attention is paid to the close links that play has with creativity, and the author also highlights the importance of being able to explain to colleagues, parents and even those in government, why play matters so much in terms of learning and development. This book will be of interest to anyone involved in early years’ education.

In this textbook the author takes as inspiration recent breakthroughs in game playing to explain how and why deep reinforcement learning works. In particular he shows why two-person games of tactics and strategy fascinate scientists, programmers, and game enthusiasts and unite them in a common goal: to create artificial intelligence (AI). After an introduction to the core concepts, environment, and communities of intelligence and games, the book is organized into chapters on reinforcement learning, heuristic planning, adaptive sampling, function approximation, and self-play. The author takes a hands-on approach throughout, with Python code examples and exercises that help the reader understand how AI learns to play. He also supports the main text with detailed pointers to online machine learning frameworks, technical details for AlphaGo, notes on how to play and program Go and chess, and a comprehensive bibliography. The content is class-tested and suitable for advanced undergraduate and graduate courses on artificial intelligence and games. It's also appropriate for self-study by professionals engaged with applications of machine learning and with games development. Finally it's valuable for any reader engaged with the philosophical implications of artificial and general intelligence, games represent a modern Turing test of the power and limitations of AI.