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| deep learning in education: Deeper Learning Monica R. Martinez, Dennis McGrath, 2018-06-05 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. |
| deep learning in education: Deep Learning Michael Fullan, Joanne Quinn, Joanne McEachen, 2017-11-06 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. |
| deep learning in education: In Search of Deeper Learning Jal Mehta, Sarah Fine, 2019-04-22 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. |
| deep learning in education: Teaching for Deeper Learning Jay McTighe, Harvey F. Silver, 2020-01-22 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. |
| deep learning in education: Dive Into Deep Learning Joanne Quinn, Joanne McEachen, Michael Fullan, Mag Gardner, Max Drummy, 2019-07-15 The leading experts in system change and learning, with their school-based partners around the world, have created this essential companion to their runaway best-seller, Deep Learning: Engage the World Change the World. This hands-on guide provides a roadmap for building capacity in teachers, schools, districts, and systems to design deep learning, measure progress, and assess conditions needed to activate and sustain innovation. Dive Into Deep Learning: Tools for Engagement is rich with resources educators need to construct and drive meaningful deep learning experiences in order to develop the kind of mindset and know-how that is crucial to becoming a problem-solving change agent in our global society. Designed in full color, this easy-to-use guide is loaded with tools, tips, protocols, and real-world examples. It includes: • A framework for deep learning that provides a pathway to develop the six global competencies needed to flourish in a complex world — character, citizenship, collaboration, communication, creativity, and critical thinking. • Learning progressions to help educators analyze student work and measure progress. • Learning design rubrics, templates and examples for incorporating the four elements of learning design: learning partnerships, pedagogical practices, learning environments, and leveraging digital. • Conditions rubrics, teacher self-assessment tools, and planning guides to help educators build, mobilize, and sustain deep learning in schools and districts. Learn about, improve, and expand your world of learning. Put the joy back into learning for students and adults alike. Dive into deep learning to create learning experiences that give purpose, unleash student potential, and transform not only learning, but life itself. |
| deep learning in education: Preparing Teachers for Deeper Learning Linda Darling-Hammond, Jeannie Oakes, 2021-02-08 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. |
| deep learning in education: Implementing Deeper Learning and 21st Century Education Reforms Fernando M. Reimers, 2020-11-04 This open access book is a comparative analysis of recent large scale education reforms that broadened curriculum goals to better prepare students for the 21st century. The book examines what governments actually do when they broaden curriculum goals, with attention to the details of implementation. To this end, the book examines system level reforms in six countries at various levels of development. The study includes system level reforms in jurisdictions where students achieve high levels in international assessments of basic literacies, such as Singapore and Ontario, Canada, as well as in nations where students achieve much lower levels, such as Kenya, Mexico, Punjab-Pakistan and Zimbabwe. The chapters examine system-level reforms that focus on strengthening the capacity to teach the basics, as in Ontario and Pakistan, as well as reforms that aim at building the capacity to teach a much broader set of competencies and skills, such as Kenya, Mexico, Singapore and Zimbabwe. The volume includes systems at very different levels of spending per student and reforms at various points in the cycle of policy implementation, some just starting, some struggling to survive a governmental transition, and others that have been in place for an extended period of time. From the comparative study of these reforms, we aim to provide an understanding of how to build the capacity of education systems to teach 21st century skills at scale in diverse settings. |
| deep learning in education: Teaching for Deeper Learning Jay McTighe, Harvey F. Silver, 2020 Jay McTighe and Harvey Silver offer a practical guide to teaching seven essential thinking skills that will equip students for success in school and beyond-- |
| deep learning in education: Understanding by Design Grant P. Wiggins, Jay McTighe, 2005 What is understanding and how does it differ from knowledge? How can we determine the big ideas worth understanding? Why is understanding an important teaching goal, and how do we know when students have attained it? How can we create a rigorous and engaging curriculum that focuses on understanding and leads to improved student performance in today's high-stakes, standards-based environment? Authors Grant Wiggins and Jay McTighe answer these and many other questions in this second edition of Understanding by Design. Drawing on feedback from thousands of educators around the world who have used the UbD framework since its introduction in 1998, the authors have greatly revised and expanded their original work to guide educators across the K-16 spectrum in the design of curriculum, assessment, and instruction. With an improved UbD Template at its core, the book explains the rationale of backward design and explores in greater depth the meaning of such key ideas as essential questions and transfer tasks. Readers will learn why the familiar coverage- and activity-based approaches to curriculum design fall short, and how a focus on the six facets of understanding can enrich student learning. With an expanded array of practical strategies, tools, and examples from all subject areas, the book demonstrates how the research-based principles of Understanding by Design apply to district frameworks as well as to individual units of curriculum. Combining provocative ideas, thoughtful analysis, and tested approaches, this new edition of Understanding by Design offers teacher-designers a clear path to the creation of curriculum that ensures better learning and a more stimulating experience for students and teachers alike. |
| deep learning in education: What the Best College Students Do Ken Bain, 2012-08-27 The author of the best-selling What the Best College Teachers Do is back with more humane, doable, and inspiring help, this time for students who want to get the most out of college—and every other educational enterprise, too. The first thing they should do? Think beyond the transcript. The creative, successful people profiled in this book—college graduates who went on to change the world we live in—aimed higher than straight A’s. They used their four years to cultivate habits of thought that would enable them to grow and adapt throughout their lives. Combining academic research on learning and motivation with insights drawn from interviews with people who have won Nobel Prizes, Emmys, fame, or the admiration of people in their field, Ken Bain identifies the key attitudes that distinguished the best college students from their peers. These individuals started out with the belief that intelligence and ability are expandable, not fixed. This led them to make connections across disciplines, to develop a “meta-cognitive” understanding of their own ways of thinking, and to find ways to negotiate ill-structured problems rather than simply looking for right answers. Intrinsically motivated by their own sense of purpose, they were not demoralized by failure nor overly impressed with conventional notions of success. These movers and shakers didn’t achieve success by making success their goal. For them, it was a byproduct of following their intellectual curiosity, solving useful problems, and taking risks in order to learn and grow. |
| deep learning in education: Tools for Teaching Conceptual Understanding, Secondary Julie Stern, Krista Ferraro, Juliet Mohnkern, 2017-02-02 Students become experts and innovators through Concept-Based teaching Innovators don’t invent without a deep understanding of how the world works. With this foundation, they apply conceptual understanding to solve new problems. We want our students to not only retain ideas, but relate them to other things they encounter, using each new situation to add nuance and sophistication to their thinking. To do this, they need conceptual understanding. This book serves as a road map for Concept-Based teaching. Discover how to help students uncover conceptual relationships and transfer them to new situations. Specifically, teachers will learn: Strategies for introducing conceptual learning to students Four lesson frameworks to help students uncover conceptual relationships How to assess conceptual understanding, and How to differentiate concept-based instruction Look no further. For deep learning and innovative thinking, this book is the place to start. The authors tear down the false dichotomies of traditional vs innovative education and provide a practical toolkit for developing creativity and applying knowledge through Concept-Based learning. Every practitioner needs this book to juxtapose what worked well in the 20th Century with what is essential in the 21st Century and beyond. Michael McDowell, Superintendent Ross School District, Ross, CA While most good educators recognise the incredible value of teaching conceptually, it is challenging. The authors have created accessible, practical baby steps for every teacher to use. Dr. Vincent Chan, principal Fairview International School, Kuala Lumpur, Malaysia |
| deep learning in education: From Curiosity to Deep Learning Julie Coiro, Elizabeth Dobler, Karen Pelekis, 2019 In an era where personalized learning has often come to be associated with isolated one-to-one device technology, we thirst for this personal, constructivist, collaborative approach to digital inquiry. --Stephanie Harvey From Curiosity to Deep Learning: Personal Digital Inquiry in Grades K-5 reveals the powerful learning that results when you integrate purposeful technology into a classroom culture that values curiosity and deep learning. The centerpiece of this practical guide is Personal Digital Inquiry (PDI), a framework developed by Julie Coiro and implemented in classrooms by her co-authors, Elizabeth Dobler and Karen Pelekis. Clear, detailed examples offer ideas for K-5 teachers and school librarians to support their teaching. Personal emphasizes the significance of the personal relationship between teachers and students, and the role that students have in the learning process. Digital reflects the important role that digital texts and tools have come to play in both learning and teaching with inquiry. Inquiry lies at the core of PDI, because learners grow and change with opportunities to identify problems, generate personal wonderings, and engage in collaborative dialogue, making learning relevant and lasting. From Curiosity to Deep Learning: Personal Digital Inquiry in Grades K-5 shows you how to integrate inquiry with a range of digital tools and resources that will create a dynamic classroom for both you and your students. |
| deep learning in education: Deep Active Learning Kayo Matsushita, 2017-09-12 This is the first book to connect the concepts of active learning and deep learning, and to delineate theory and practice through collaboration between scholars in higher education from three countries (Japan, the United States, and Sweden) as well as different subject areas (education, psychology, learning science, teacher training, dentistry, and business).It is only since the beginning of the twenty-first century that active learning has become key to the shift from teaching to learning in Japanese higher education. However, “active learning” in Japan, as in many other countries, is just an umbrella term for teaching methods that promote students’ active participation, such as group work, discussions, presentations, and so on.What is needed for students is not just active learning but deep active learning. Deep learning focuses on content and quality of learning whereas active learning, especially in Japan, focuses on methods of learning. Deep active learning is placed at the intersection of active learning and deep learning, referring to learning that engages students with the world as an object of learning while interacting with others, and helps the students connect what they are learning with their previous knowledge and experiences as well as their future lives.What curricula, pedagogies, assessments and learning environments facilitate such deep active learning? This book attempts to respond to that question by linking theory with practice. |
| deep learning in education: Responsive Teaching Harry Fletcher-Wood, 2018-05-30 This essential guide helps teachers refine their approach to fundamental challenges in the classroom. Based on research from cognitive science and formative assessment, it ensures teachers can offer all students the support and challenge they need – and can do so sustainably. Written by an experienced teacher and teacher educator, the book balances evidence-informed principles and practical suggestions. It contains: A detailed exploration of six core problems that all teachers face in planning lessons, assessing learning and responding to students Effective practical strategies to address each of these problems across a range of subjects Useful examples of each strategy in practice and accounts from teachers already using these approaches Checklists to apply each principle successfully and advice tailored to teachers with specific responsibilities. This innovative book is a valuable resource for new and experienced teachers alike who wish to become more responsive teachers. It offers the evidence, practical strategies and supportive advice needed to make sustainable, worthwhile changes. |
| deep learning in education: Deeper Learning James A. Bellanca, 2014-11-14 Education authorities from around the globe explore deeper learning, a process that promotes higher-order thinking, reasoning, and problem solving to better educate students and prepare them for college and careers. Relying on research as well as their own experience, the authors show how to use intensive curriculum, instruction, assessment, and leadership practices to meet the needs of 21st century learners. |
| deep learning in education: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| deep learning in education: Deeper Learning, Dialogic Learning, and Critical Thinking Emmanuel Manalo, 2019-09-30 Deeper learning, dialogic learning, and critical thinking are essential capabilities in the 21st-century environments we now operate. Apart from being important in themselves, they are also crucial in enabling the acquisition of many other 21st-century skills/capabilities such as problem solving, collaborative learning, innovation, information and media literacy, and so on. However, the majority of teachers in schools and instructors in higher education are inadequately prepared for the task of promoting deeper learning, dialogic learning, and critical thinking in their students. This is despite the fact that there are educational researchers who are developing and evaluating strategies for such promotion. The problem is bridging the gap between the educational researchers’ work and what gets conveyed to teachers and instructors as evidence-based, usable strategies. This book addresses that gap: in it, leading scholars from around the world describe strategies they have developed for successfully cultivating students’ capabilities for deeper learning and transfer of what they learn, dialogic learning and effective communication, and critical thought. They explore connections in the promotion of these capabilities, and they provide, in accessible form, research evidence demonstrating the efficacy of the strategies. They also discuss answers to the questions of how and why the strategies work. A seminal resource, this book creates tangible links between innovative educational research and classroom teaching practices to address the all-important question of how we can realize our ideals for education in the 21st century. It is a must read for pre-service and in-service teachers, teacher educators and professional developers, and educational researchers who truly care that we deliver education that will prepare and serve students for life. |
| deep learning in education: Getting Smart Tom Vander Ark, 2011-09-20 A comprehensive look at the promise and potential of online learning In our digital age, students have dramatically new learning needs and must be prepared for the idea economy of the future. In Getting Smart, well-known global education expert Tom Vander Ark examines the facets of educational innovation in the United States and abroad. Vander Ark makes a convincing case for a blend of online and onsite learning, shares inspiring stories of schools and programs that effectively offer personal digital learning opportunities, and discusses what we need to do to remake our schools into smart schools. Examines the innovation-driven world, discusses how to combine online and onsite learning, and reviews smart tools for learning Investigates the lives of learning professionals, outlines the new employment bargain, examines online universities and smart schools Makes the case for smart capital, advocates for policies that create better learning, studies smart cultures |
| deep learning in education: Education for Life and Work National Research Council, Division of Behavioral and Social Sciences and Education, Board on Science Education, Board on Testing and Assessment, Committee on Defining Deeper Learning and 21st Century Skills, 2013-01-18 Americans have long recognized that investments in public education contribute to the common good, enhancing national prosperity and supporting stable families, neighborhoods, and communities. Education is even more critical today, in the face of economic, environmental, and social challenges. Today's children can meet future challenges if their schooling and informal learning activities prepare them for adult roles as citizens, employees, managers, parents, volunteers, and entrepreneurs. To achieve their full potential as adults, young people need to develop a range of skills and knowledge that facilitate mastery and application of English, mathematics, and other school subjects. At the same time, business and political leaders are increasingly asking schools to develop skills such as problem solving, critical thinking, communication, collaboration, and self-management - often referred to as 21st century skills. Education for Life and Work: Developing Transferable Knowledge and Skills in the 21st Century describes this important set of key skills that increase deeper learning, college and career readiness, student-centered learning, and higher order thinking. These labels include both cognitive and non-cognitive skills- such as critical thinking, problem solving, collaboration, effective communication, motivation, persistence, and learning to learn. 21st century skills also include creativity, innovation, and ethics that are important to later success and may be developed in formal or informal learning environments. This report also describes how these skills relate to each other and to more traditional academic skills and content in the key disciplines of reading, mathematics, and science. Education for Life and Work: Developing Transferable Knowledge and Skills in the 21st Century summarizes the findings of the research that investigates the importance of such skills to success in education, work, and other areas of adult responsibility and that demonstrates the importance of developing these skills in K-16 education. In this report, features related to learning these skills are identified, which include teacher professional development, curriculum, assessment, after-school and out-of-school programs, and informal learning centers such as exhibits and museums. |
| deep learning in education: Learning That Lasts Ron Berger, Libby Woodfin, Anne Vilen, 2016-04-05 A practical guide to deeper instruction—a framework for challenging, engaging, and empowering students of all ages For schools to meet ambitious new standards and prepare all students for college, careers, and life, research has shown unequivocally that nothing is more important that the quality of daily instruction. Learning That Lasts presents a new vision for classroom instruction that sharpens and deepens the quality of lessons in all subject areas. It is the opposite of a 'teacher-proof' solution. Instead, it is predicated on a model of instruction that honors teachers as creative and expert planners of learning experiences for their students and who wish to continuously grow in their instructional and content knowledge. It is not a theoretical vision. It is a model of instruction refined in some of the nation's most successful public schools—schools that are beating the odds to create remarkable achievement—sited primarily in urban and rural low-income communities. Using case studies and examples of powerful learning at all grade levels and in all disciplines, Learning That Lasts is a guide to creating classrooms that promote deeper understanding, higher order thinking, and student independence. Through text and companion videos, readers will enter inspiring classrooms where students go beyond basics to become innovators, collaborators, and creators. Learning That Lasts embraces a three-dimensional view of student achievement that includes mastery of knowledge and skills, character, and high-quality work. It is a guide for teachers who wish to make learning more meaningful, memorable, and connected to life, and inspire students to do more than they think possible. |
| deep learning in education: Harnessing Technology for Deeper Learning Scott McLeod, Julie Graber, 2018-09-21 By embracing technology in the classroom instead of ignorning or banning it, every educator can promote deeper learning across all subjects and grade levels. Using the 4 Shifts Protocol, 'Harnessing Technology for Deeper Learning' imparts valuable strategies for avoiding missteps, overcoming implemention challenges, and (re)designing instruction that is both meaningful and engaging. |
| deep learning in education: Machine Learning and Deep Learning in Real-Time Applications Mahrishi, Mehul, Hiran, Kamal Kant, Meena, Gaurav, Sharma, Paawan, 2020-04-24 Artificial intelligence and its various components are rapidly engulfing almost every professional industry. Specific features of AI that have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock higher levels of performance and efficiency, creating a wide span of industrial applications. However, there is a lack of research on the specific uses of machine/deep learning in the professional realm. Machine Learning and Deep Learning in Real-Time Applications provides emerging research exploring the theoretical and practical aspects of machine learning and deep learning and their implementations as well as their ability to solve real-world problems within several professional disciplines including healthcare, business, and computer science. Featuring coverage on a broad range of topics such as image processing, medical improvements, and smart grids, this book is ideally designed for researchers, academicians, scientists, industry experts, scholars, IT professionals, engineers, and students seeking current research on the multifaceted uses and implementations of machine learning and deep learning across the globe. |
| deep learning in education: Assessing Deeper Learning Douglas G. Wren, 2019-08-01 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. |
| deep learning in education: Applications of Machine Learning and Artificial Intelligence in Education Seda Khadimally, 2021 Focuses on the parameters of remote learning, machine learning, deep learning, and artificial intelligence under 21st-century learning and instructional contexts. Topics covered include data coding and social networking technology. |
| deep learning in education: Machine Learning for Kids Dale Lane, 2021-01-19 A hands-on, application-based introduction to machine learning and artificial intelligence (AI) that guides young readers through creating compelling AI-powered games and applications using the Scratch programming language. Machine learning (also known as ML) is one of the building blocks of AI, or artificial intelligence. AI is based on the idea that computers can learn on their own, with your help. Machine Learning for Kids will introduce you to machine learning, painlessly. With this book and its free, Scratch-based, award-winning companion website, you'll see how easy it is to add machine learning to your own projects. You don't even need to know how to code! As you work through the book you'll discover how machine learning systems can be taught to recognize text, images, numbers, and sounds, and how to train your models to improve their accuracy. You'll turn your models into fun computer games and apps, and see what happens when they get confused by bad data. You'll build 13 projects step-by-step from the ground up, including: • Rock, Paper, Scissors game that recognizes your hand shapes • An app that recommends movies based on other movies that you like • A computer character that reacts to insults and compliments • An interactive virtual assistant (like Siri or Alexa) that obeys commands • An AI version of Pac-Man, with a smart character that knows how to avoid ghosts NOTE: This book includes a Scratch tutorial for beginners, and step-by-step instructions for every project. Ages 12+ |
| deep learning in education: Measuring Human Return Joanne McEachen, Matthew Kane, 2018-09-20 Measure what matters for deeper learning Getting at the heart of what matters for students is key to deeper learning that connects with their lives, but what good is knowing what matters without also understanding how to bring it to life? What does it really take to know who students are, what they are truly learning, and why? Measuring Human Return solves this dilemma with a comprehensive, systematic process for measuring deeper learning outcomes. Educators will learn to assess students’ self-understanding, knowledge, competencies, and connections through vignettes, case studies, learning experiences and tools. The book helps readers: Develop key system capabilities to build the foundation for sustainable engagement, measurement, and change Discover five comprehensive frames for measuring deeper learning Engage in the process of collaborative inquiry Commit to the central, active role of learners by engaging them as partners in every aspect of their learning Discover how to take an authentic, formative, and inquiry-driven approach to measuring the outcomes that drive deeper learning. The book really hits the mark. The best thing about it is the in-depth discussion of systems. It is with great pleasure that I read and re-read this book. It delivers a good combination of big vision with specific strategies and techniques. Jeff Beaudry, Professor, Educational Leadership; University of Southern Maine; Portland, ME This is just what we need in our district. This engaging book will help Change Teams support their systems to effectively measure deeper learning. Readers will be drawn in by great examples from around the globe of educators putting students first. This energizing book calls us to take action for all of our students today and for our future. Charisse Berner, Director of Teaching and Learning, Curriculum; Bellingham Public Schools; Bellingham, WA |
| deep learning in education: Deep Learning Michael Fullan, Joanne Quinn, Joanne McEachen, 2017-11-06 Engage the World Change the World Deep Learning has claimed the attention of educators and policymakers around the world. This book not only defines what deep learning is, but takes up the question of how to mobilize complex, whole-system change and transform learning for all students. Deep Learning is a global partnership that works to: transform the role of teachers to that of activators who design experiences that build global competencies using real-life problem solving; and supports schools, districts, and systems to shift practice and how to measure learning in authentic ways. This comprehensive strategy incorporates practical tools and processes to engage students, educators, and families in new partnerships and drive deep learning. |
| deep learning in education: Educational Dialogues Karen Littleton, Christine Howe, 2010-01-21 Presents an illustrated case for the importance of dialogue and its role in developing non-passive interactive learning. |
| deep learning in education: Leadership for Deeper Learning Jayson W. Richardson, Justin Bathon, Scott McLeod, 2021-07-27 This exciting book explores how leaders have implemented, sustained, and pushed innovative, deeper learning opportunities in their school settings. Across the United States and around the world, the concept of a school is growing more action-oriented, performance-focused, digitally relevant, and democratically infused. In this book, you’ll hear from real schools and leaders about practices that are changing schools and leading to deeper learning experiences across seven categories of innovative practice—including vision, agency in learning, trust in teachers, openness to new ideas, over-communicating change, equity mindedness, and courage to live outside norms. Leadership for Deeper Learning looks at how school leaders change the status quo and create different learning environments for students and teachers. Rich in stories and strategies, this book will provide you with the ideas and tools to rethink and reignite learning for the future. |
| deep learning in education: Approaches and Applications of Deep Learning in Virtual Medical Care Zaman, Noor, Gaur, Loveleen, Humayun, Mamoona, 2022-02-25 The recent advancements in the machine learning paradigm have various applications, specifically in the field of medical data analysis. Research has proven the high accuracy of deep learning algorithms, and they have become a standard choice for analyzing medical data, especially medical images, video, and electronic health records. Deep learning methods applied to electronic health records are contributing to understanding the evolution of chronic diseases and predicting the risk of developing those diseases. Approaches and Applications of Deep Learning in Virtual Medical Care considers the applications of deep learning in virtual medical care and delves into complex deep learning algorithms, calibrates models, and improves the predictions of the trained model on medical imaging. Covering topics such as big data and medical sensors, this critical reference source is ideal for researchers, academicians, practitioners, industry professionals, hospital workers, scholars, instructors, and students. |
| deep learning in education: Visible Learning for Science, Grades K-12 John Almarode, Douglas Fisher, Nancy Frey, John Hattie, 2018-02-15 In the best science classrooms, teachers see learning through the eyes of their students, and students view themselves as explorers. But with so many instructional approaches to choose from—inquiry, laboratory, project-based learning, discovery learning—which is most effective for student success? In Visible Learning for Science, the authors reveal that it’s not which strategy, but when, and plot a vital K-12 framework for choosing the right approach at the right time, depending on where students are within the three phases of learning: surface, deep, and transfer. Synthesizing state-of-the-art science instruction and assessment with over fifteen years of John Hattie’s cornerstone educational research, this framework for maximum learning spans the range of topics in the life and physical sciences. Employing classroom examples from all grade levels, the authors empower teachers to plan, develop, and implement high-impact instruction for each phase of the learning cycle: Surface learning: when, through precise approaches, students explore science concepts and skills that give way to a deeper exploration of scientific inquiry. Deep learning: when students engage with data and evidence to uncover relationships between concepts—students think metacognitively, and use knowledge to plan, investigate, and articulate generalizations about scientific connections. Transfer learning: when students apply knowledge of scientific principles, processes, and relationships to novel contexts, and are able to discern and innovate to solve complex problems. Visible Learning for Science opens the door to maximum-impact science teaching, so that students demonstrate more than a year’s worth of learning for a year spent in school. |
| deep learning in education: Deep Learning Illustrated Jon Krohn, Grant Beyleveld, Aglaé Bassens, 2019-08-05 The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come. – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| deep learning in education: Teacher Education in the 21st Century Maria Jose Hernández-Serrano, 2021-06-16 In the face of unprecedented disruption from the COVID-19 pandemic and the rapid acceleration of digital technologies, it is necessary to rethink the competences required by teachers for meeting new and flexible learning demands. Teacher training is an area constantly evolving along with emerging social challenges that are transforming educational institutions and agents. This book provides teachers with skills, innovative solutions, cutting-edge studies, and methodologies to meet education and training system demands. In our changing world, preparing teachers worldwide for the challenges and shifts of this era involves the opportunity to exchange theories, practices, and experiences such as those contained in this book. |
| deep learning in education: Eight Myths of Student Disengagement Jennifer A. Fredricks, 2014-03-18 Many teachers report that student disengagement is one of the biggest challenges they encounter in the classroom, and research shows that there is a steady decline in students' engagement that begins as early as kindergarten and persists through the transitions to middle and high school. Young children are naturally curious and want to learn and explore, but unfortunately this all too often gives way to a lack of participation and effort, acting out and disrupting class, disaffection and withdrawal, and failure to deeply invest in academic content. Jennifer Fredricks's book goes beyond the idea that classroom management techniques and on-task behaviour ensure student engagement, to consider the emotional and cognitive dimensions that are critical for deeper learning and student achievement. Fredricks presents compelling strategies based on the research to demonstrate how instructional tasks, teacher-student relations, and peer dynamics all play a pivotal role in cultivating lasting student engagement. |
| deep learning in education: Visible Learning John Hattie, 2008-11-19 This unique and ground-breaking book is the result of 15 years research and synthesises over 800 meta-analyses on the influences on achievement in school-aged students. It builds a story about the power of teachers, feedback, and a model of learning and understanding. The research involves many millions of students and represents the largest ever evidence based research into what actually works in schools to improve learning. Areas covered include the influence of the student, home, school, curricula, teacher, and teaching strategies. A model of teaching and learning is developed based on the notion of visible teaching and visible learning. A major message is that what works best for students is similar to what works best for teachers – an attention to setting challenging learning intentions, being clear about what success means, and an attention to learning strategies for developing conceptual understanding about what teachers and students know and understand. Although the current evidence based fad has turned into a debate about test scores, this book is about using evidence to build and defend a model of teaching and learning. A major contribution is a fascinating benchmark/dashboard for comparing many innovations in teaching and schools. |
| deep learning in education: Deep Learning Applications in Medical Imaging Saxena, Sanjay, Paul, Sudip, 2020-10-16 Before the modern age of medicine, the chance of surviving a terminal disease such as cancer was minimal at best. After embracing the age of computer-aided medical analysis technologies, however, detecting and preventing individuals from contracting a variety of life-threatening diseases has led to a greater survival percentage and increased the development of algorithmic technologies in healthcare. Deep Learning Applications in Medical Imaging is a pivotal reference source that provides vital research on the application of generating pictorial depictions of the interior of a body for medical intervention and clinical analysis. While highlighting topics such as artificial neural networks, disease prediction, and healthcare analysis, this publication explores image acquisition and pattern recognition as well as the methods of treatment and care. This book is ideally designed for diagnosticians, medical imaging specialists, healthcare professionals, physicians, medical researchers, academicians, and students. |
| deep learning in education: Deep Learning Applications and Intelligent Decision Making in Engineering Senthilnathan, Karthikrajan, Shanmugam, Balamurugan, Goyal, Dinesh, Annapoorani, Iyswarya, Samikannu, Ravi, 2020-10-23 Deep learning includes a subset of machine learning for processing the unsupervised data with artificial neural network functions. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. When applied to engineering, deep learning can have a great impact on the decision-making process. Deep Learning Applications and Intelligent Decision Making in Engineering is a pivotal reference source that provides practical applications of deep learning to improve decision-making methods and construct smart environments. Highlighting topics such as smart transportation, e-commerce, and cyber physical systems, this book is ideally designed for engineers, computer scientists, programmers, software engineers, research scholars, IT professionals, academicians, and postgraduate students seeking current research on the implementation of automation and deep learning in various engineering disciplines. |
| deep learning in education: Cultivating Curiosity in K-12 Classrooms Wendy L. Ostroff, 2016 This book describes how teachers can create a structured, student-centered environment that allows for openness and surprise, and where inquiry guides authentic learning. Strategies for fostering student curiosity through exploration, novelty, and play; questioning and critical thinking; and experimenting and problem solving are also provided. |
| deep learning in education: Visible Learning for Literacy, Grades K-12 Douglas Fisher, Nancy Frey, John Hattie, 2016-03-22 Every student deserves a great teacher, not by chance, but by design — Douglas Fisher, Nancy Frey, & John Hattie What if someone slipped you a piece of paper listing the literacy practices that ensure students demonstrate more than a year’s worth of learning for a year spent in school? Would you keep the paper or throw it away? We think you’d keep it. And that’s precisely why acclaimed educators Douglas Fisher, Nancy Frey, and John Hattie wrote Visible Learning for Literacy. They know teachers will want to apply Hattie’s head-turning synthesis of more than 15 years of research involving millions of students, which he used to identify the instructional routines that have the biggest impact on student learning. These practices are visible for teachers and students to see, because their purpose has been made clear, they are implemented at the right moment in a student’s learning, and their effect is tangible. Yes, the aha moments made visible by design. With their trademark clarity and command of the research, and dozens of classroom scenarios to make it all replicable, these authors apply Hattie’s research, and show you: How to use the right approach at the right time, so that you can more intentionally design classroom experiences that hit the surface, deep, and transfer phases of learning, and more expertly see when a student is ready to dive from surface to deep. Which routines are most effective at specific phases of learning, including word sorts, concept mapping, close reading, annotating, discussion, formative assessment, feedback, collaborative learning, reciprocal teaching, and many more. Why the 8 mind frames for teachers apply so well to curriculum planning and can inspire you to be a change agent in students’ lives—and part of a faculty that embraces the idea that visible teaching is a continual evaluation of one’s impact on student’s learning. Teachers, it’s time we embrace the evidence, update our classrooms, and impact student learning in wildly positive ways, say Doug, Nancy, and John. So let’s see Visible Learning for Literacy for what it is: the book that renews our teaching and reminds us of our influence, just in time. |
| deep learning in education: An Introduction to Statistical Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor, 2023-08-01 An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users. |
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DeepSeek | 深度求索
深度求索（DeepSeek），成立于2023年，专注于研究世界领先的通用人工智能底层模型与技术，挑战人工智能前沿性难题。 基于自研训练框架、自建智算集群和万卡算力等资源，深度求索团队仅用半年 …
DEEP Definition & Meaning - Merriam-Webster
The meaning of DEEP is extending far from some surface or area. How to use deep in a sentence. Synonym Discussion of Deep.
DEEP definition and meaning | Collins English Dictionary
If you describe someone as deep, you mean that they are quiet and reserved in a way that makes you think that they have good qualities such as intelligence or determination.
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Deep - definition of deep by The Free Dictionary
Coming from or penetrating to a depth: a deep sigh. g. Sports Located or taking place near the outer boundaries of the area of play: deep left field. 2. Extending a specific distance in a given …
What does DEEP mean? - Definitions.net
Profound, having great meaning or import, but possibly obscure or not obvious. That is a deep thought! To a significant, not superficial, extent. In extent in a direction away from the observer. …
DeepL Translator - Wikipedia
DeepL Translator is a neural machine translation service that was launched in August 2017 and is owned by Cologne -based DeepL SE. The translating system was first developed within Linguee …
DEEP | definition in the Cambridge English Dictionary
DEEP meaning: 1. going or being a long way down from the top or surface, or being of a particular distance from…. Learn more.
DEEP Definition & Meaning | Dictionary.com
in difficult or serious circumstances; in trouble.in a situation beyond the range of one's capability or skill:You're a good student, but you'll be in deep water in medical school.
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Translate texts & full document files instantly. Accurate translations for individuals and Teams. Millions translate with DeepL every day.
DeepSeek | 深度求索
深度求索（DeepSeek），成立于2023年，专注于研究世界领先的通用人工智能底层模型与技术，挑战人工智能前沿性难题。 基于自研训练框架、自建智算集群和万卡算力等资源，深度求 …
DEEP Definition & Meaning - Merriam-Webster
The meaning of DEEP is extending far from some surface or area. How to use deep in a sentence. Synonym Discussion of Deep.
DEEP definition and meaning | Collins English Dictionary
If you describe someone as deep, you mean that they are quiet and reserved in a way that makes you think that they have good qualities such as intelligence or determination.
DeepL features to help elevate your language
Whether crafting an email, translating a document, or re-writing a text, clear and effective communication is paramount. DeepL is your go-to solution to all language-centric …
Deep - definition of deep by The Free Dictionary
Coming from or penetrating to a depth: a deep sigh. g. Sports Located or taking place near the outer boundaries of the area of play: deep left field. 2. Extending a specific distance in a given …
What does DEEP mean? - Definitions.net
Profound, having great meaning or import, but possibly obscure or not obvious. That is a deep thought! To a significant, not superficial, extent. In extent in a direction away from the …
DeepL Translator - Wikipedia
DeepL Translator is a neural machine translation service that was launched in August 2017 and is owned by Cologne -based DeepL SE. The translating system was first developed within …
DEEP | definition in the Cambridge English Dictionary
DEEP meaning: 1. going or being a long way down from the top or surface, or being of a particular distance from…. Learn more.
DEEP Definition & Meaning | Dictionary.com
in difficult or serious circumstances; in trouble.in a situation beyond the range of one's capability or skill:You're a good student, but you'll be in deep water in medical school.
