Learning the City: Translocal Assemblage and Urban Politics critically examines the relationship between knowledge, learning, and urban politics, arguing both for the centrality of learning for political strategies and developing a progressive international urbanism. Presents a distinct approach to conceptualising the city through the lens of urban learning Integrates fieldwork conducted in Mumbai's informal settlements with debates on urban policy, political economy, and development Considers how knowledge and learning are conceived and created in cities Addresses the way knowledge travels and opportunities for learning about urbanism between North and South
With a new introduction and afterword, this revised second edition is a practical, engaging exploration of mentoring and its power to transform learning. Filled with inspiring vignettes, Mentor shows how anyone who teaches can become a successful mentor to students. Topics covered include adult learning and development; the search for meaning as a motive for learning; education as a transformational journey; how adults change and develop; how learning changes the learner; barriers and incentives to learning and growth; and guiding adults through difficult transitions.
Reinforcement and Systemic Machine Learning for Decision Making There are always difficulties in making machines that learn from experience. Complete information is not always available—or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the impact of decisions and actions on a system over that period of time. This book takes a holistic approach to addressing that need and presents a new paradigm—creating new learning applications and, ultimately, more intelligent machines. The first book of its kind in this new and growing field, Reinforcement and Systemic Machine Learning for Decision Making focuses on the specialized research area of machine learning and systemic machine learning. It addresses reinforcement learning and its applications, incremental machine learning, repetitive failure-correction mechanisms, and multiperspective decision making. Chapters include: Introduction to Reinforcement and Systemic Machine Learning Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning Systemic Machine Learning and Model Inference and Information Integration Adaptive Learning Incremental Learning and Knowledge Representation Knowledge Augmentation: A Machine Learning Perspective Building a Learning System With the potential of this paradigm to become one of the more utilized in its field, professionals in the area of machine and systemic learning will find this book to be a valuable resource.
A real-world action plan for educators to create personalized learning experiences Learning Personalized: The Evolution of the Contemporary Classroom provides teachers, administrators, and educational leaders with a clear and practical guide to personalized learning. Written by respected teachers and leading educational consultants Allison Zmuda, Greg Curtis, and Diane Ullman, this comprehensive resource explores what personalized learning looks like, how it changes the roles and responsibilities of every stakeholder, and why it inspires innovation. The authors explain that, in order to create highly effective personalized learning experiences, a new instructional design is required that is based loosely on the traditional model of apprenticeship: learning by doing. Learning Personalized challenges educators to rethink the fundamental principles of schooling that honors students' natural willingness to play, problem solve, fail, re-imagine, and share. This groundbreaking resource: Explores the elements of personalized learning and offers a framework to achieve it Provides a roadmap for enrolling relevant stakeholders to create a personalized learning vision and reimagine new roles and responsibilities Addresses needs and provides guidance specific to the job descriptions of various types of educators, administrators, and other staff This invaluable educational resource explores a simple framework for personalized learning: co-creation, feedback, sharing, and learning that is as powerful for a teacher to re-examine classroom practice as it is for a curriculum director to reexamine the structure of courses.
This book shows how schools can–and must–develop expertise in «learning variation» (understanding how different kinds of minds learn) and apply this knowledge to classroom instruction in order to address the chronic learning challenges and achievement gap faced by millions of students. Barringer shows how using what we know about learning variation with a focus on discovering learning strengths, not just deficits, can help schools create plans for success for those students who often find it elusive. The book specifically addresses how school leaders can incorporate this knowledge into instructional practice and school-level policy through various professional development strategies. Schools for All Kinds of Minds: Provides a readable synthesis of the latest research from neuroscience, cognitive science, and child and adolescent development as it relates to understanding learning and its many variations. Links this information to strategies for understanding struggling learners and adapting school practices to accommodate a wider array of learning differences in a classroom. Demonstrates how this understanding of learning variation can change the way teachers and others help students succeed in various academic and content areas and acquire necessary 21st century skills. Includes discussion questions and facilitator guidelines for staff developers and teacher education programs; downloadable forms that accompany exercises from within the book; an action plan for schools to implement the ideas found in the book; and more.