Full On Learning

The start point is your end-point: the learner. What kind of learner do you want to develop? What are the characteristics of an effective learner and how can we teach to support the development of these characteristics? If future employers are looking for people who can solve problems, think creatively and be innovative, what can we do, as part of our current curriculum provision to enable students to 'deliberately' practise this skill? If being intelligent is not, in fact, measured by your IQ score, and has far more to do with the ability to apply higher order thinking to unfamiliar contexts and create new solutions to existing problems, then what learning challenges can we design for Year 9 on a sunny Wednesday afternoon that will allow them to develop the emotional and intellectual resilience required to be able to do this? Full On Learning offers a range of tried & tested practical suggestions and ideas to construct the ideal conditions for the characteristics of effective learners to flourish. Shortlisted for the Education Resources Awards 2013, Secondary Resource - non ICT category and Educational Book Award category.

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.

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 Handbook of Academic Learning provides a comprehensive resource for educational and cognitive psychologists, as well as educators themselves, on the mechanisms and processes of academic learning. Beginning with general themes that cross subject and age level, the book discusses what motivates students to learn and how knowledge can be made personal for better learning and remembering. Individual chapters identify proven effective teaching methods for the specific domains of math, reading, writing, science, and critical problem solving, how students learn within those domains, and how learning can be accurately assessed for given domains and age levels. The Handbook takes a constructivist perspective to academic learning, emphasizing the construction of personal knowledge of an academic nature. Constructivism within the context of learning theory is viewed as involving an active learner that constructs an academic knowledge base through the development of cognitive strategies and metacognition. The book discusses the development of basic literacy skills that provide the foundation for higher order thinking and problem solving. Constructivism recognizes the social dimension of classroom learning and emphasizes the motivational elements of self-regulation and volition as essential learner characteristics. Written by authors who have first-hand experience with both theory development and the development of authentic classroom instructional techniques, the Handbook empowers educators to develop, implement, and field-test authentic instructional practices at their school site. The book provides a review of the literature, theory, research, and skill techniques for effective teaching and learning. Key Features * Identifies effective teaching with specific techniques * Covers elementary school through high school * Discusses teaching methods for all main subject areas: reading, writing, math, science, and critical thinking * Identifies how students learn to learn * Reviews theory, research, techniques, and assessment * Contains field tested examples for the educational professional at the school site * Provides a resource for staff development

Ever wondered what would happen if you stopped teaching in your lessons? You might be surprised. If you want your students to learn more and you to work less, then this book provides you with all the arguments and evidence you need to become a lazy, but outstanding teacher. Gathered over 10 years in the classroom, this handbook of tried-and-tested techniques shifts the emphasis away from the teaching and onto the learning, and makes your life so much easier in the process. Fed up missing out on those sunny Sundays? Maybe your marking could be done by the local community instead? Sick of planning lessons? Get the students to plan them for you. (After all, personalised learning can't involve 30 lesson plans!). This powerful book is packed full of easy-to-apply and highly effective strategies (which Ofsted have rated as 'outstanding' ). What's more, they all have the seal of approval of real students in real classrooms. In fact, many of them have been thought up by the students themselves, but that's why Jim Smith is called the Lazy Teacher. So, next time someone says to you to get a life, this book will make it possible.

Assessment has provided educational institutions with information about student learning outcomes and the quality of education for many decades. But has it informed practice and been fully incorporated into the learning cycle? Conrad and Openo argue that the potential inherent in many of the new learning environments being explored by educators and students has not been fully realized. In this investigation of a variety of assessment methods and learning approaches, the authors aim to discover the tools that engage learners and authentically evaluate education. They insist that moving to new learning environments, specifically those online and at a distance, afford opportunities for educators to adopt only the best practices of traditional face-to-face assessment while exploring evaluation tools made available by a digital learning environment in the hopes of arriving at methods that capture the widest set of learner skills and attributes.

Does your online instruction program sometimes feel like a constant scramble to keep pace with requests and deadlines? Modular design is the answer. Approaching projects, whether large and small, with an eye towards future uses will put you on the path to accomplishing broader, organizational goals. And by intentionally building documentation and structure into your process, you will create content that can easily be scaled, modified, adapted, and transformed to meet different learner needs. Hess, experienced in online instruction in both K-12 and academic libraries, shows you how, using project examples of various sizes to illustrate each chapter’s concepts. Her resource guides you through such topics as the eight components of modular online learning design; key considerations for choosing the design model that best fits your organization and project; techniques for connecting your online learning goals with institutional strategy; using the IDEA process to align OER content with your instructional needs; documenting your planning with checklists, scaffolds, and templates; ensuring equity of access with all content formats using the Accessibility Inventory Index; principles for scaling up, down, or laterally; three models for more meaningful and functional collaboration with internal or external partners; and formative testing as a foundation for ongoing evaluation and assessment.

In this book, scholars from around the world develop viable answers to the question of how it may be possible to promote students’ spontaneity in the use of learning and reasoning strategies. They combine their expertise to put forward new theories and models for understanding the underlying mechanisms; provide details of new research to address pertinent questions and problems; and describe classroom practices that have proven successful in promoting spontaneous strategy use. This book is a must for educators and researchers who truly care that schooling should cultivate learning and reasoning strategies in students that would prepare and serve them for life. A seminal resource, this book will address the basic problem that many educators are well acquainted with: that students can learn how to effectively use learning and reasoning strategies but not use them of their own volition or in settings other than the one in which they learned the strategies.