This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on supervised classification algorithms and their applications, including recent research on the combination of classifiers. The second part deals with unsupervised data mining and knowledge discovery, with special attention to text mining. Discovering the underlying structure on a data set has been a key research topic associated to unsupervised techniques with multiple applications and challenges, from web-content mining to the inference of cancer subtypes in genomic microarray data. Among those, the book focuses on a new application for dialog systems which can be thereby made adaptable and portable to different domains. Clustering evaluation metrics and new approaches, such as the ensembles of clustering algorithms, are also described.
Reinforcement and Systemic Machine Learning for Decision Making There are always difficulties in making machines that learn from experience. Complete information is not always available—or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the impact of decisions and actions on a system over that period of time. This book takes a holistic approach to addressing that need and presents a new paradigm—creating new learning applications and, ultimately, more intelligent machines. The first book of its kind in this new and growing field, Reinforcement and Systemic Machine Learning for Decision Making focuses on the specialized research area of machine learning and systemic machine learning. It addresses reinforcement learning and its applications, incremental machine learning, repetitive failure-correction mechanisms, and multiperspective decision making. Chapters include: Introduction to Reinforcement and Systemic Machine Learning Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning Systemic Machine Learning and Model Inference and Information Integration Adaptive Learning Incremental Learning and Knowledge Representation Knowledge Augmentation: A Machine Learning Perspective Building a Learning System With the potential of this paradigm to become one of the more utilized in its field, professionals in the area of machine and systemic learning will find this book to be a valuable resource.
Learning the City: Translocal Assemblage and Urban Politics critically examines the relationship between knowledge, learning, and urban politics, arguing both for the centrality of learning for political strategies and developing a progressive international urbanism. Presents a distinct approach to conceptualising the city through the lens of urban learning Integrates fieldwork conducted in Mumbai's informal settlements with debates on urban policy, political economy, and development Considers how knowledge and learning are conceived and created in cities Addresses the way knowledge travels and opportunities for learning about urbanism between North and South
How can you use technology for pedagogic purposes in the language classroom? Technology Enhanced Language Learning discusses how the use of technology opens up opportunities for learning, how it enables different types of learning, and how it affects language use.
В статье рассматриваются такие проекты, как «Постоянная среда для оценки качества в e-learning» (SEEQUEL), который координирует сеть MENON при содействии Еврокомиссии; «Поддержка усовершенствований в e-learning» (SEEL), который координирует Европейский институт e-learning (EIFeL), а также их совместный проект – TRIANGLE, призванный создать прочную систему и развивать исследования по качеству e-learning в Европе. Рассматривается работа Ассоциации гарантии качества e-learning (EFQUEL), которая занимается повышением качества программ e-learning в Европе, создав новую схему оказания услуг для членов образовательного сообщества и поддержав все заинтересованные стороны, категории которых представлены в данной статье, а также руководит работой ряда основных рабочих групп II Конференции EFQUEL в Париже в январе 2007 года. Автор обратил внимание на такой парадокс: с одной стороны, большинство участников образовательного процесса желают прийти к общему мнению о качестве e-learning, а с другой – универсальной модели качества не существует.
A real-world action plan for educators to create personalized learning experiences Learning Personalized: The Evolution of the Contemporary Classroom provides teachers, administrators, and educational leaders with a clear and practical guide to personalized learning. Written by respected teachers and leading educational consultants Allison Zmuda, Greg Curtis, and Diane Ullman, this comprehensive resource explores what personalized learning looks like, how it changes the roles and responsibilities of every stakeholder, and why it inspires innovation. The authors explain that, in order to create highly effective personalized learning experiences, a new instructional design is required that is based loosely on the traditional model of apprenticeship: learning by doing. Learning Personalized challenges educators to rethink the fundamental principles of schooling that honors students' natural willingness to play, problem solve, fail, re-imagine, and share. This groundbreaking resource: Explores the elements of personalized learning and offers a framework to achieve it Provides a roadmap for enrolling relevant stakeholders to create a personalized learning vision and reimagine new roles and responsibilities Addresses needs and provides guidance specific to the job descriptions of various types of educators, administrators, and other staff This invaluable educational resource explores a simple framework for personalized learning: co-creation, feedback, sharing, and learning that is as powerful for a teacher to re-examine classroom practice as it is for a curriculum director to reexamine the structure of courses.