Collaborative Learning

An examination of renowned theorists Laura Mulvey and Peter Wollen's independent and collaborative films, and how they intersect with feminism, film theory and psychoanalysis.This book focuses on reno

Artists Heather and Ivan Morison and their studio have established an ambitious collaborative practice that transcends traditional divisions between art, architecture, theatre and activism. Their work

How do bureaucracies remember? The conventional view is that institutional memory is static and singular, the sum of recorded files and learned procedures. There is a growing body of scholarship that suggests contemporary bureaucracies are failing at this core task. This Element argues that this diagnosis misses that memories are essentially dynamic stories. They reside with people and are thus dispersed across the array of actors that make up the differentiated polity. Drawing on four policy examples from four sectors (housing, energy, family violence and justice) in three countries (the UK, Australia and New Zealand), this Element argues that treating the way institutions remember as storytelling is both empirically salient and normatively desirable. It is concluded that the current conceptualisation of institutional memory needs to be recalibrated to fit the types of policy learning practices required by modern collaborative governance.

This volume offers a historical and critical analysis of the emerging field of the learning sciences, which takes an interdisciplinary approach to understanding and improving how children and adults learn. It features a wide range of authors, including established scholars who founded and guided the learning sciences through the initial turbulence of forming a new line of academic inquiry, as well as newcomers who are continuing to shape the field. This diversity allows for a broad yet selective perspective on what the learning sciences are, why they came to be, and how contributors conduct their work. Reflections on the Learning Sciences serves both as a starting point for discussion among scholars familiar with the discipline and as an introduction for those interested in learning more. It will benefit graduate students and researchers in computer science, educational psychology, instructional technology, science, engineering, and mathematics.

A comprehensive and integrated introduction to the phenomena and theories of perceptual learning, focusing on the visual domain.Practice or training in perceptual tasks improves the quality of percept

In Data Sketches, Nadieh Bremer and Shirley Wu document the deeply collaborative, deeply creative process behind 24 unique data visualization projects, spanning 12 different topics. The book provides

In Data Sketches, Nadieh Bremer and Shirley Wu document the deeply collaborative, deeply creative process behind 24 unique data visualization projects, spanning 12 different topics. The book provides

Today, more people want to know how to make a meaningful difference to what they care about. But for that, traditional approaches to learning often fall short. In this book, we offer a theoretical and practical way forward. We introduce the concept of social learning spaces for developing both new capabilities and a sense of agency. We provide a rich framework for focusing on the value of social learning spaces: how to generate this value, monitor it, and learn iteratively through the process. The book is a useful extension and refinement of 'communities of practice' for those familiar with the theory. For those who are not, the chapters will lay out a new way to approach learning. This volume is written to serve the needs of readers across fields, including researchers, educators, and leaders in business, government, healthcare, and international development.

Today, more people want to know how to make a meaningful difference to what they care about. But for that, traditional approaches to learning often fall short. In this book, we offer a theoretical and practical way forward. We introduce the concept of social learning spaces for developing both new capabilities and a sense of agency. We provide a rich framework for focusing on the value of social learning spaces: how to generate this value, monitor it, and learn iteratively through the process. The book is a useful extension and refinement of 'communities of practice' for those familiar with the theory. For those who are not, the chapters will lay out a new way to approach learning. This volume is written to serve the needs of readers across fields, including researchers, educators, and leaders in business, government, healthcare, and international development.

This book presents a new model for supervision as a collaborative process, and explores how this process can benefit practitioners and help improve their practice. Louis offers practical support

This book presents a new model for supervision as a collaborative process, and explores how this process can benefit practitioners and help improve their practice. Louis offers practical support

Bringing together an international team of researchers, this volume explores practice in second language learning - activities which aim to develop skills in or knowledge of a second language. The book begins with two theoretical overviews of practice as applied to learning to speak in a second language and in cognitive accounts of second language acquisition. This theory underpins the volume, which is split into two main sections: receptive and productive practice. The studies look at practice in English, German and Spanish as a second language in various contexts including traditional classrooms, periods of study abroad and online language learning. The differing research designs used mean that the chapters contain clear implications for classroom pedagogy and further directions for research, teaching and learning in different contexts.

The book is a comprehensive guide for students and practitioners who want to take a collaborative approach in their design practice. Authors Marty Maxwell Lane and Rebecca Tegtmeyer introduce a range

The book is a comprehensive guide for students and practitioners who want to take a collaborative approach in their design practice. Authors Marty Maxwell Lane and Rebecca Tegtmeyer introduce a range

Informal Learning, Practitioner Inquiry and Occupational Education explores how practitioners in a variety of occupations perform their jobs and argues that working and learning are intricately connec

This book explores learning in the arts and highlights ways in which art and creativity can ignite learning in schools, informal learning spaces, and higher education. The focus is on learning in, wit

This book introduces Raspberry Pi, using real world applications in computer vision, machine learning, and deep learning. It provides a detailed, step-by-step, approach to application development for

Advances in computer graphic technologies have inspired new efforts to understand the potential of multimedia instruction as a means of promoting human learning. In Multimedia Learning, Third Edition, Richard E. Mayer takes an evidence-based approach to improving education using well-designed multimedia instruction. He reviews 15 principles of multimedia instructional design that are based on more than 200 experimental research studies and grounded in a cognitive theory of how people learn from words and graphics. The result is the latest instalment of what Mayer calls the Cognitive Theory of Multimedia Learning, a theory introduced in previous editions of Multimedia Learning and in The Cambridge Handbook of Multimedia Learning, Second Edition. This edition provides an up-to-date and systematic summary of research studies on multimedia learning, supplemented with complementary evidence from around the globe. It is well-suited to graduate and undergraduate courses in psychology, educati

Advances in computer graphic technologies have inspired new efforts to understand the potential of multimedia instruction as a means of promoting human learning. In Multimedia Learning, Third Edition, Richard E. Mayer takes an evidence-based approach to improving education using well-designed multimedia instruction. He reviews 15 principles of multimedia instructional design that are based on more than 200 experimental research studies and grounded in a cognitive theory of how people learn from words and graphics. The result is the latest instalment of what Mayer calls the Cognitive Theory of Multimedia Learning, a theory introduced in previous editions of Multimedia Learning and in The Cambridge Handbook of Multimedia Learning, Second Edition. This edition provides an up-to-date and systematic summary of research studies on multimedia learning, supplemented with complementary evidence from around the globe. It is well-suited to graduate and undergraduate courses in psychology, educati

This book will help readers understand fundamental and advanced statistical models and deep learning models for robust speaker recognition and domain adaptation. This useful toolkit enables readers to apply machine learning techniques to address practical issues, such as robustness under adverse acoustic environments and domain mismatch, when deploying speaker recognition systems. Presenting state-of-the-art machine learning techniques for speaker recognition and featuring a range of probabilistic models, learning algorithms, case studies, and new trends and directions for speaker recognition based on modern machine learning and deep learning, this is the perfect resource for graduates, researchers, practitioners and engineers in electrical engineering, computer science and applied mathematics.

100 Ideas for Primary Teachers: Outdoor Learning is packed with inspiring ideas to generate enthusiasm and engage pupils in learning through positive outdoor experiences. These can then be used as a s

Online learning is ubiquitous for millions of students worldwide, yet our understanding of student experiences in online learning settings is limited. The geographic distance that separates faculty fr

Implementing evidence-based and innovative teaching practices can feel like juggling: If you have standards-based learning in one hand and social-emotional learning in the other, what do you do with c

Around the world, children embark on learning to read in their home language or writing system. But does their specific language, and how it is written, make a difference to how they learn? How is learning to read English similar to or different from learning in other languages? Is reading alphabetic writing a different challenge from reading syllabic or logographic writing? Learning to Read across Languages and Writing Systems examines these questions across seventeen languages representing the world's different major writing systems. Each chapter highlights the key features of a specific language, exploring research on learning to read, spell, and comprehend it, and on implications for education. The editors' introduction describes the global spread of reading and provides a theoretical framework, including operating principles for learning to read. The editors' final chapter draws conclusions about cross-linguistic universal trends, and the challenges posed by specific languages and

Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.

Blended learning offers educators the opportunity to reimagine teaching and learning. It allows teachers to partner with their students to assess, track, and reflect on learning. This partnership give

Joseph Gordon-Levitt’s creative collaborative community HITRECORD looks at love from both sides in this ingenious flippable book.So, you just got dumped, huh? Or did you just dump someone? Doesn’t mat

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every cha

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every cha

Much has been written about the escalating intolerance of worldviews other than one's own. Reasoned arguments based on facts and data seem to have little impact in our increasingly post-truth culture dominated by social media, fake news, tribalism, and identity politics. Recent advances in the study of human cognition, however, offer insights on how to counter these troubling social trends. In this book, psychologist Jon F. Wergin calls upon recent research in learning theory, social psychology, politics, and the arts to show how a deep learning mindset can be developed in both oneself and others. Deep learning is an acceptance that our understanding of the world around us is only temporary and is subject to constant scrutiny. Someone who is committed to learning deeply does not simply react to experiences, but engages fully with that experience, knowing that the inevitable disquietude is what leads to efficacy in the world.

Much has been written about the escalating intolerance of worldviews other than one's own. Reasoned arguments based on facts and data seem to have little impact in our increasingly post-truth culture dominated by social media, fake news, tribalism, and identity politics. Recent advances in the study of human cognition, however, offer insights on how to counter these troubling social trends. In this book, psychologist Jon F. Wergin calls upon recent research in learning theory, social psychology, politics, and the arts to show how a deep learning mindset can be developed in both oneself and others. Deep learning is an acceptance that our understanding of the world around us is only temporary and is subject to constant scrutiny. Someone who is committed to learning deeply does not simply react to experiences, but engages fully with that experience, knowing that the inevitable disquietude is what leads to efficacy in the world.

According to Bayesian epistemology, rational learning from experience is consistent learning, that is learning should incorporate new information consistently into one's old system of beliefs. Simon M. Huttegger argues that this core idea can be transferred to situations where the learner's informational inputs are much more limited than Bayesianism assumes, thereby significantly expanding the reach of a Bayesian type of epistemology. What results from this is a unified account of probabilistic learning in the tradition of Richard Jeffrey's 'radical probabilism'. Along the way, Huttegger addresses a number of debates in epistemology and the philosophy of science, including the status of prior probabilities, whether Bayes' rule is the only legitimate form of learning from experience, and whether rational agents can have sustained disagreements. His book will be of interest to students and scholars of epistemology, of game and decision theory, and of cognitive, economic, and computer sci

This book presents the theory of output-driven maps and provides a fresh perspective on the extent to which phonologies can be characterized in terms of restrictions on outputs. Closely related to traditional conceptions of process opacity, but differing in notable ways, the theory of output-driven maps applies equally to SPE-style ordered rules, Optimality Theory, and other phonological theories. It permits a formally rigorous analysis of the issues in Optimality Theory that is not possible with traditional process opacity. Also presented is a theory of phonological learning. Building on prior work on learning in Optimality Theory, the learning theory exploits the formal structure of output-driven maps to achieve learning that is far more computationally efficient than comparable prior approaches. In this book Bruce Tesar, one of the founders of the study of learnability in Optimality Theory, presents fresh perspectives in an accessible way for graduate students and academic researche

Addressing their colleagues in secondary and college writing instruction, Troy Hicks and Andy Schoenborn initiate a collaborative conversation about instructional decision-making that works to encoura

Through Positive Eyes is a collaborative photo-storytelling project by 130 people living with HIV and AIDS around the world. This global photographic collaboration with Gideon Mendel and the UCLA Art

Learning to See = Seeing to Learn: Vision, Learning & Behavior by Dr. Patrick Quaid et al, will unlock the connection between eyesight, education, and behaviors in children. How can you have 20/20

An essential tool for new teachers and trainees who want to use learning theories to develop their practice. The text explores key learning theories in a pragmatic way and encourages focused reflectio

Learning, innovation and adaptation are not concepts that we necessarily associate with the British army of the First World War. Yet the need to learn from mistakes, to exploit new opportunities and to adapt to complex situations are enduring and timeless. This revealing work is the first institutional examination of the army's process for learning during the First World War. Drawing on organisational learning and management theories, Aimée Fox critiques existing approaches to military learning in wartime. Focused around a series of case studies, the book ranges across multiple operational theatres and positions the army within a broader context in terms of its relationships with allies and civilians to reveal that learning was more complex and thoroughgoing than initially thought. It grapples with the army's failings and shortcomings, explores its successes and acknowledges the inherent difficulties of learning in a desperate and lethally competitive environment.

An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars.Deep learning is an artificial intellige

Variational Bayesian learning is one of the most popular methods in machine learning. Designed for researchers and graduate students in machine learning, this book summarizes recent developments in the non-asymptotic and asymptotic theory of variational Bayesian learning and suggests how this theory can be applied in practice. The authors begin by developing a basic framework with a focus on conjugacy, which enables the reader to derive tractable algorithms. Next, it summarizes non-asymptotic theory, which, although limited in application to bilinear models, precisely describes the behavior of the variational Bayesian solution and reveals its sparsity inducing mechanism. Finally, the text summarizes asymptotic theory, which reveals phase transition phenomena depending on the prior setting, thus providing suggestions on how to set hyperparameters for particular purposes. Detailed derivations allow readers to follow along without prior knowledge of the mathematical techniques specific to