Explore how students are disrupting education by using digital resources to support self-direct learning Free Agent Learning: Leveraging Students' Self-Directed Learning to Transform K-12 Education explores an emerging cohort of students that are self-directing their learning around interest-driven topics, the tools they're using to scaffold these experiences, and their motivations for these out-of-school learning behaviors. Readers will find new insights and frameworks for effectively leveraging the lived experiences of their students and transforming their schools' cultures, norms and practices. In this book, readers will learn how education leaders can translate a newly emerged understanding about students' self-directed learning into actionable knowledge to improve teaching and learning Free Agent Learners also offers: Info dispelling the myth that real learning only happens in a classroom Discussions of how modern students are using digital tools, content, and resources for purposeful learning outside of teacher direction or sponsorship Actionable tips and accessible strategies for the use of the Free Agent Learner Ecosystem to support school improvement Perfect for K-12 school and district administrators and decision-makers, Free Agent Learners is an eye-opening read for anyone involved in the education of primary and secondary school students.

Every child in America deserves to know that a path to a successful life exists and that they have the power to follow it. But many never set foot on that path because they grow up hearing the message that systemic forces control their destinies, or that they are at fault for everything that has gone wrong in their lives. These children often come from difficult circumstances. Many are raised by young, single parents, live in disadvantaged neighborhoods, attend substandard schools, and lack the moral safeguards of religious and civic institutions. As a result, they can be dispirited into cycles of learned helplessness rather than inspired to pursue their own possibilities. Yet this phenomenon is not universal. Some children thrive where others do not. Why? Are there personal behaviors and institutional supports that have proven to make a difference in helping young people chart a course for their futures? Agency answers with a loud and clear “yes!” This book describes four pillars that can uplift every young person as they make the passage into adulthood: Family, Religion, Education, and Entrepreneurship. Together, these pillars embody the true meaning of freedom, wherein people are motivated to embrace the ennobling responsibilities of building healthy social structures and shaping the outcomes of their own lives. For that reason, Ian Rowe calls the four pillars the FREE framework. With this framework in place, children are empowered to develop agency, which Rowe defines as the force of one’s free will, guided by moral discernment. Developing agency is the alternative to the debilitating ‘blame-the-system’ and ‘blame-the-victim’ narratives. It transcends our political differences and beckons all who dare to envision lives unshackled by present realities. In addition to making the case for agency, Rowe shares his personal story of success coming from an immigrant family. He defends America as an ever-improving country worthy of our esteem. He corrects misguided calls for “anti-racism” and “equity,” and champions a game plan for creating new agents of agency, dedicated to promoting the aspirational spirit of America’s children, and showing them the path that will set them FREE.

"A good balance between theory and practice . . . it definitely fills a void in the [lack of] texts in the area and the change literature in general . . . a good fit for my graduate class on 'Managing Organizational Change.'" —Anthony F. Buono, McCallum Graduate School of Business, Bentley College "Like Gareth Morgan's Images of Organization, this book is a superb blend of theory and practicality. It demystifies chaos and paradox, and it encourages the understanding of organizational dynamics from multiple perspectives. It is refreshing to read a book that presents diverse theories and interventions so even-handedly." —Andrea Markowitz, Ph.D., President, OB&D, Inc. Learning to Change: A Guide for Organizational Change Agents provides a comprehensive overview of organizational change theories and practices developed by both U.S. and European change theorists. The authors compare and contrast five fundamentally different ways of thinking about change: yellow print thinking, blue print thinking, red print thinking, green print thinking and white print thinking. They also discuss in detail the steps change agents take, such as diagnosis, change strategy, the intervention plan, and interventions. In addition, they explore the attributes of a successful change agent and provide advice for career and professional development. The book includes case studies that describe multiple approaches to organizational change issues. This book will appeal to both the practitioner and academic audiences. It can be used as a text in graduate courses in change management and will also be a useful reference for consultants and managers. Features: Discusses the abilities, attitudes, and styles of successful change agents Describes five fundamentally different ways of thinking about change Presents a state-of-the-art overview of change management insights, methods, and instruments Summarizes an extensive amount of organizational change literature Supplies readers with useful insights and courses of action that will allow them to design and implement change professionally Learning to Change became a bestseller upon its initial publication in the Netherlands. The color-model on change is very popular among thousands of managers and change consultants and presents a new approach to change processes and a new language for change.

Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcement and enable a machine to learn by itself. Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learn numerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn't shy away from math and expects familiarity with ML. Learn what RL is and how the algorithms help solve problems Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning Dive deep into a range of value and policy gradient methods Apply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learning Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more Get practical examples through the accompanying website

The book begins with a chapter on traditional methods of supervised learning, covering recursive least squares learning, mean square error methods, and stochastic approximation. Chapter 2 covers single agent reinforcement learning. Topics include learning value functions, Markov games, and TD learning with eligibility traces. Chapter 3 discusses two player games including two player matrix games with both pure and mixed strategies. Numerous algorithms and examples are presented. Chapter 4 covers learning in multi-player games, stochastic games, and Markov games, focusing on learning multi-player grid games—two player grid games, Q-learning, and Nash Q-learning. Chapter 5 discusses differential games, including multi player differential games, actor critique structure, adaptive fuzzy control and fuzzy interference systems, the evader pursuit game, and the defending a territory games. Chapter 6 discusses new ideas on learning within robotic swarms and the innovative idea of the evolution of personality traits. • Framework for understanding a variety of methods and approaches in multi-agent machine learning. • Discusses methods of reinforcement learning such as a number of forms of multi-agent Q-learning • Applicable to research professors and graduate students studying electrical and computer engineering, computer science, and mechanical and aerospace engineering

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.