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Use the Cloud to Individualize Your Instruction and Watch Your Students Thrive! Cloud-based technology offers massive benefits to the classroom. But technology tools require conscientious implementation by educators. This book is the all-in-one resource you need to be sure your students reap the fullest rewards of how cloud-based tools can facilitate learning. In Deeper Learning Through Technology, teacher and top ed-blogger Ken Halla explains: How to implement new tech tools to create a self-paced, learner-centered classroom Strategies for leveraging cloud technology to ensure that students have access to an individualized, personalized education Real-life case studies and activities that will make applying the book’s strategies to your classroom enjoyable and achievable
"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".
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.
How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.
"In this book, Weigel shows how faculty can help students develop skills in research, problem solving, critical thinking, and knowledge management by using web-based collaboration tools. He outlines a blended, "bricks and clicks" approach to learning that emphasizes cognitive apprenticeship and communities of inquiry. Weigel's vision of "depth education" relies strongly on virtual teams and embedded assessment as a means to cultivate connection between students and educators.
How to utilize digital technology to engage learners in deep learning is an issue that warrants significant attention in 21st century education. Deep learning refers to learners engagement in critical and creative thinking, making inferences and transferring knowledge. Modern technologies like virtual reality, artificial intelligence, and 3D visualization provide the platform for deep learning in an educational setting more effectively. This book presents a collection of essays on the relationship between digital technologies and deep learning. The edited volume focuses on cognitive, metacognitive and affective processes in digital technology-based deep learning. A unique feature of the book is its emphasis on bridging the theories with practice where the practice of deep learning with digital technology is well-grounded in relevant theories and theoretical frameworks. Moreover, the book includes case studies to effectively promote the application of digital technology in deep learning. As such, the book is rightly poised to address current issues facing deep learning and digital technology in education. The audience will find this book a useful companion as they will soon discover that this book provides helpful information on both theoretical and practical aspects in deep learning with digital technology. It also serves as an excellent resource for researchers and individual professionals who seek to understand the relationship between deep learning and digital technology in education.
"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.
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
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.
An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, machine translation, AI games, driverless cars, and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution. Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning—major trends, possible developments, and significant challenges.