This book is appropriate for courses in First-Year Experience, College Success, and Study Skills. The focus of Foundations for Learning is on academic adjustment with personal development issues seamlessly integrated into the academic emphasis theme of 'claiming an education' and taking responsibility for one's own education. Foundations for Learning addresses both the attitudinal variables and personality traits that affect college achievement like locus of control, conceptions of intelligence, and intellectual curiosity in relation to specific study-related behaviors such as text annotation and active listening. At its core, this text is based on the psychology of adjustment. Students are pushed to consider how each mindset, perception, and attitude connects with their skill sets, and how one influences the other. The text encourages students to use this insight to make the necessary adjustments to their new role as college students. It offers an acute awareness of first-year student needs, an intellectual approach, and a tight framework. It is primarily focused on the development of academic adjustment issues and meta-cognitive strategies as they naturally unfold during the first semester, as opposed to primarily focusing on social adjustment issues or issues that aren't immediately relevant such as career development and is written in a challenging yet accessible way. This revision covers emerging technologies, broadens its audience, and more.

Describes fundamental differences in learning beliefs between the Western mind model and the East Asian virtue model of learning.

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.

Edited by a diverse group of expert collaborators, the Handbook of the Cultural Foundations of Learning is a landmark volume that brings together cutting-edge research examining learning as entailing inherently cultural processes. Conceptualizing culture as both a set of social practices and connected to learner identities, the chapters synthesize contemporary research in elaborating a new vision of the cultural nature of learning, moving beyond summary to reshape the field toward studies that situate culture in the learning sciences alongside equity of educational processes and outcomes. With the recent increased focus on culture and equity within the educational research community, this volume presents a comprehensive, innovative treatment of what has become one of the field’s most timely and relevant topics.

Foundations of Embodied Learning advances learning, instruction, and the design of educational technologies by rethinking the learner as an integrated system of mind, body, and environment. Body-based processes—direct physical, social, and environmental interactions—are constantly mediating intellectual performance, sensory stimulation, communication abilities, and other conditions of learning. This book’s coherent, evidence-based framework articulates principles of grounded and embodied learning for design and its implications for curriculum, classroom instruction, and student formative and summative assessment for scholars and graduate students of educational psychology, instructional design and technology, cognitive science, the learning sciences, and beyond.

The essential textbook in the field of blindness and visual impairment has been updated for the 21st c. Volume II contains new material on planning, assessment, instruction, and supporting students with visual impairments in the general education classroom. Icons in the book direct readers to supplemental materials in an online Learning Center.

Theoretical Foundations of Learning Environments describes the most contemporary psychological and pedagogical theories that are foundations for the conception and design of open-ended learning environments and new applications of educational technologies. In the past decade, the cognitive revolution of the 60s and 70s has been replaced or restructured by constructivism and its associated theories, including situated, sociocultural, ecological, everyday, and distributed conceptions of cognition. These theories represent a paradigm shift for educators and instructional designers, to a view of learning as necessarily more social, conversational, and constructive than traditional transmissive views of learning. Never in the history of education have so many different theories said the same things about the nature of learning and the means for supporting it. At the same time, although there is a remarkable amount of consonance among these theories, each also provides a distinct perspective on how learning and sense making occur. This book provides students, faculty, and instructional designers with a clear, concise introduction to these theories and their implications for the design of new learning environments for schools, universities, and corporations. It is well-suited as a required or supplementary text for courses in instructional design and theory, educational psychology, learning, theory, curriculum theory and design, and related areas.