The acclaimed exploration of how public education can cultivate innovators—with a foreword by Russlynn Ali, a leading advocate for remaking schools Dime-a-dozen ideas for reforming education seem to be everywhere these days but few actually transform the everyday experience of the 50-million-plus students who are regularly subjected to traditional lecturing, note-taking, and rote learning—often with dismal results. Enter Deeper Learning, "a fast read [that] will interest educators who want to produce self-motivated, passionate learners" (Library Journal). Offering "uplifting" (Kirkus Reviews) anecdotes in what Tom Carroll of the National Commission on Teaching and America's Future calls a "rare blend of inspiration and practical action," Deeper Learning provides a blueprint for creating flexible environments that put students at the helm of their own collaborative learning experience. This paperback edition includes a new foreword by renowned education advocate Russlynn Ali and will empower and inspire educators everywhere to address the need for schools to be genuinely innovative.

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

"The best book on high school dynamics I have ever read."--Jay Mathews, Washington Post An award-winning professor and an accomplished educator take us beyond the hype of reform and inside some of America's most innovative classrooms to show what is working--and what isn't--in our schools. What would it take to transform industrial-era schools into modern organizations capable of supporting deep learning for all? Jal Mehta and Sarah Fine's quest to answer this question took them inside some of America's most innovative schools and classrooms--places where educators are rethinking both what and how students should learn. The story they tell is alternately discouraging and hopeful. Drawing on hundreds of hours of observations and interviews at thirty different schools, Mehta and Fine reveal that deeper learning is more often the exception than the rule. And yet they find pockets of powerful learning at almost every school, often in electives and extracurriculars as well as in a few mold-breaking academic courses. These spaces achieve depth, the authors argue, because they emphasize purpose and choice, cultivate community, and draw on powerful traditions of apprenticeship. These outliers suggest that it is difficult but possible for schools and classrooms to achieve the integrations that support deep learning: rigor with joy, precision with play, mastery with identity and creativity. This boldly humanistic book offers a rich account of what education can be. The first panoramic study of American public high schools since the 1980s, In Search of Deeper Learning lays out a new vision for American education--one that will set the agenda for schools of the future.

New Pedagogies for Deep Learning (NDPL) provides a comprehensive strategy for systemwide transformation. Using the 6 competencies of NDPL and a wealth of vivid examples, Fullan re-defines and re-examines what deep learning is and identifies the practical strategies for revolutionizing learning and leadership.

Far too often, our students attain only a superficial level of knowledge that fails to prepare them for deeper challenges in school and beyond. In Teaching for Deeper Learning, renowned educators and best-selling authors Jay McTighe and Harvey F. Silver propose a solution: teaching students to make meaning for themselves. Contending that the ability to "earn" understanding will equip students to thrive in school, at work, and in life, the authors highlight seven higher-order thinking skills that facilitate students' acquisition of information for greater retention, retrieval, and transfer. These skills, which cut across content areas and grade levels and are deeply embedded in current academic standards, separate high achievers from their low-performing peers. Drawing on their deep well of research and experience, the authors - Explore what kind of content is worth having students make meaning about. - Provide practical tools and strategies to help teachers target each of the seven thinking skills in the classroom. - Explain how teachers can incorporate the thinking skills and tools into lesson and unit design. - Show how teachers can build students' capacity to use the strategies independently. If our goal is to prepare students to meet the rigorous demands of school, college, and career, then we must foster their ability to respond to such challenges. This comprehensive, practical guide will enable teachers to engage students in the kind of learning that yields enduring understanding and valuable skills that they can use throughout their lives.

Preparing Teachers for Deeper Learning answers an urgent call for teachers who educate children from diverse backgrounds to meet the demands of a changing world. In today’s knowledge economy, teachers must prioritize problem-solving ability, adaptability, critical thinking, and the development of interpersonal and collaborative skills over rote memorization and the passive transmission of knowledge. Authors Linda Darling-Hammond and Jeannie Oakes and their colleagues examine what this means for teacher preparation and showcase the work of programs that are educating for deeper learning, equity, and social justice. Guided by the growing knowledge base in the science of learning and development, the book examines teacher preparation programs at Alverno College, Bank Street College of Education, High Tech High’s Intern Program, Montclair State University, San Francisco Teacher Residency, Trinity University, and University of Colorado Denver. These seven programs share a common understanding of how people learn that shape similar innovative practices. With vivid examples of teaching for deeper learning in coursework and classrooms; interviews with faculty, school partners, and novice teachers; surveys of teacher candidates and graduates; and analyses of curriculum and practices, Preparing Teachers for Deeper Learning depicts transformative forms of teaching and teacher preparation that honor and expand all students’ abilities, knowledges, and experiences, and reaffirm the promise of educating for a better world.

Deeper learning has been defined as “the skills and knowledge that students must possess to succeed in 21st century jobs and civic life” (William and Flora Hewlett Foundation, 2013). Assessing Deeper Learning: Developing, Implementing, and Scoring Performance Tasks examines the role of performance assessment to facilitate student attainment of the core competencies of deeper learning. The book details a journey that a large school district undertook to create a system of performance tasks designed to assess students’ proficiency in critical thinking, problem solving, and effective communication. Chapters devoted to the development and implementation of the district’s high-quality performance tasks and rubrics highlight successes and lessons learned during the journey. Additional chapters focus on such topics as types of performance assessments, instructional methods that promote student engagement and deeper learning, policy, and how teacher leaders can drive this innovation to serve the teaching, learning, assessment, and accountability needs of schools. Assessing Deeper Learning: Developing, Implementing, and Scoring Performance Tasks was written for teachers, administrators, superintendents, and policy makers to better understand the challenges and opportunities afforded by using performance assessment to promote deeper learning.