A significant contribution to the scientific foundation of autonomous learning systems, this book contains clear, up-to-date coverage of three basic subtasks: active model abstraction, model application, and integration. It is the only textbook to offer a thorough discussion of active model abstraction.

This edited work presents a collection of papers on motivation research in education around the globe. Pursuing a uniquely international approach, it also features selected research studies conducted in Singapore under the auspices of the Motivation in Educational Research Lab, National Institute of Education, Singapore. A total of 15 chapters include some of the latest findings on theory and practical applications alike, prepared by internationally respected researchers in the field of motivation research in education. Each author provides his/her perspective and practical strategies on how to maximize motivation in the classroom. Individual chapters focus on theoretical and practical considerations, parental involvement, teachers’ motivation, ways to create a self-motivating classroom, use of ICT, and nurturing a passion for learning. The book will appeal to several different audiences: firstly, policymakers in education, school leaders and teachers will find it a valuable resource. Secondly, it offers a helpful guide for researchers and teacher educators in pre-service and postgraduate teacher education programmes. And thirdly, parents who want to help their children pursue lifelong learning will benefit from reading this book.

Traditionally, organizations and researchers have focused on learning that occurs through formal training and development programs. However, the realities of today’s workplace suggest that it is difficult, if not impossible, for organizations to rely mainly on formal programs for developing human capital. This volume offers a broad-based treatment of autonomous learning to advance our understanding of learner-driven approaches and how organizations can support them. Contributors in industrial/organizational psychology, management, education, and entrepreneurship bring theoretical perspectives to help us understand autonomous learning and its consequences for individuals and organizations. Chapters consider informal learning, self-directed learning, learning from job challenges, mentoring, Massive Open Online Courses (MOOCs), organizational communities of practice, self-regulation, the role of feedback and errors, and how to capture value from autonomous learning. This book will appeal to scholars, researchers, and practitioners in psychology, management, training and development, and educational psychology.

This volume focuses on how far the policies, principles and practices of foreign language teaching and learning are, or can be, informed by theoretical considerations and empirical findings from the linguistic disciplines. Part I deals with the nature of foreign language learning in general, while Part II explores issues arising from linguistic, socio-political, cultural and cognitive perspectives. Part III and IV then consider the different factors that have to be taken into account in designing the foreign language subject and the various approaches to pedagogy that have been proposed. Part V finally addresses questions concerning assessment of learner proficiency and the evaluation of courses designed to promote it. Key features: provides a state-of-the-art description of different areas in the context of foreign language communication and learning presents a critical appraisal of the relevance of the field offers solutions to everyday language-related problems with contributions from renowned experts

Dr. Greg Zacharias, former Chief Scientist of the United States Air Force (2015-18), explores next steps in autonomous systems (AS) development, fielding, and training. Rapid advances in AS development and artificial intelligence (AI) research will change how we think about machines, whether they are individual vehicle platforms or networked enterprises. The payoff will be considerable, affording the US military significant protection for aviators, greater effectiveness in employment, and unlimited opportunities for novel and disruptive concepts of operations. Autonomous Horizons: The Way Forward identifies issues and makes recommendations for the Air Force to take full advantage of this transformational technology.

TAKING CONTROL: Autonomy in Language Learning focuses on an area of language learning and teaching that is currently receiving an increasing amount of attention. The book, featuring 18 chapters from key figures around the world in the field of autonomous and self-access language learning, provides insightful coverage of the theoretical issues involved, and represents a significant contribution to research in this area. At the same time, it provides a variety of examples of current practice, in classrooms and self-access centres, at secondary and tertiary levels, and in a number of different cultural contexts. This volume is a timely publication which will be of interest to all those concerned with learner autonomy and self-directed language learning.

This book looks beyond the classroom, and focuses on out-of-class autonomous use of technology for language learning, discussing the theoretical frameworks, key findings and critical issues. The proliferation of digital language learning resources and tools is forcing language education into an era of unprecedented change. The book will stimulate discussions on how to support language learners to construct quality autonomous technology-mediated out-of-class learning experience outside the classroom and raise greater awareness of and research interest in this field. Out-of-class learning constitutes an important context for human development, and active engagement in out-of-class activities is associated with successful language development. With convenient access to expanded resources, venues and learning spaces, today's learners are not as dependent on in-class learning as they used to be. Thus, a deeper understanding of the terrain of out-of-class learning is of increasing significance in the current educational era. Technology is part and parcel of out-of-class language learning, and has been a primary source that learners actively use to construct language learning experience beyond the classroom. Language learners of all ages around the world have been found to actively utilize technological resources to support their language learning beyond formal language learning contexts. Insights into learners' out-of-class autonomous use of technology for language learning are essential to our understanding of out-of-class learning and inform educators on how language learners could be better supported to maximize the educational potentials of technology to construct quality out-of-class learning experience.

Autonomous Learner Model Resource Book includes activities and strategies to support the development of autonomous learners. More than 40 activities are included, all geared to the emotional, social, cognitive, and physical development of students. Teachers may use these activities and strategies with the entire class, small groups, or with individuals who are ready to be independent, self-directed, lifelong learners. These learners have the passions, abilities, skills, and attitudes to go beyond the regular curriculum and take control of their own educational pathways. Field-tested strategies and activities in the book include Find Someone Who, Teacher and Learner Questionnaires, Lifelong Notebook, Time Capsule, and Night of the Notables.

Theory, algorithms, and applications of machine learning techniques to overcome “covariate shift” non-stationarity. As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.

Traditionally, organizations and researchers have focused on learning that occurs through formal training and development programs. However, the realities of today’s workplace suggest that it is difficult, if not impossible, for organizations to rely mainly on formal programs for developing human capital. This volume offers a broad-based treatment of autonomous learning to advance our understanding of learner-driven approaches and how organizations can support them. Contributors in industrial/organizational psychology, management, education, and entrepreneurship bring theoretical perspectives to help us understand autonomous learning and its consequences for individuals and organizations. Chapters consider informal learning, self-directed learning, learning from job challenges, mentoring, Massive Open Online Courses (MOOCs), organizational communities of practice, self-regulation, the role of feedback and errors, and how to capture value from autonomous learning. This book will appeal to scholars, researchers, and practitioners in psychology, management, training and development, and educational psychology.