Foundations For Learning
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| Author | : Laurie L. Hazard |
| Publisher | : Prentice Hall |
| Total Pages | : 208 |
| Release | : 2012-08-12 |
| Genre | : Education |
| ISBN | : 9780321871206 |
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.
| Author | : Laurie L. Hazard |
| Publisher | : Prentice Hall |
| Total Pages | : 0 |
| Release | : 2011-02-20 |
| Genre | : Academic achievement |
| ISBN | : 9780132318068 |
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.
| Author | : Jin Li |
| Publisher | : Cambridge University Press |
| Total Pages | : 401 |
| Release | : 2012-03-26 |
| Genre | : Education |
| ISBN | : 0521768292 |
Describes fundamental differences in learning beliefs between the Western mind model and the East Asian virtue model of learning.
| Author | : Mehryar Mohri |
| Publisher | : MIT Press |
| Total Pages | : 505 |
| Release | : 2018-12-25 |
| Genre | : Computers |
| ISBN | : 0262351366 |
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.
| Author | : Mitchell J. Nathan |
| Publisher | : Taylor & Francis |
| Total Pages | : 361 |
| Release | : 2021-09-27 |
| Genre | : Education |
| ISBN | : 1000430073 |
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.
| Author | : Na'ilah Suad Nasir |
| Publisher | : Routledge |
| Total Pages | : 476 |
| Release | : 2020-05-01 |
| Genre | : Education |
| ISBN | : 1135039305 |
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.
| Author | : Allan C. Ornstein |
| Publisher | : Houghton Mifflin |
| Total Pages | : 584 |
| Release | : 2006 |
| Genre | : Business & Economics |
| ISBN | : |
Foundations of Education incorporates relevant interdisciplinary perspectives and emphasizes coverage of key issues in education, with up-to-date research, primary resources, and documentation. This text provides comprehensive and substantive coverage of all foundational areas--including social, philosophical, historical, political, economic, curricular, and legal--for students who are preparing for a career in teaching and for those who simply wish to learn more about significant contemporary issues in education. The authors have included strong, thought-provoking pedagogy, and have emphasized the growing role of technology in education, especially in the new Technology@School feature.New! In This Case boxed features contain brief, fictional case scenarios that describe situations in which a new teacher might find themselves. Readers are asked to think critically about concepts discussed in each chapter to answer questions that encourage critical and applied analysis.New! Expanded topical overview charts in each chapter summarize and compare key developments and topics in education.New! Information about standards addresses the growing emphasis on holding students, teachers and schools accountable for performing at levels specified by local, state, and national standards. New sections focus on preparing teachers for the stress related to state/district standards, and legal issues, including the No Child Left Behind act, are addressed.Technology@School, a popular feature in every chapter, updates students on relevant developments in educational technology and provides information that may prove useful in their teaching careers. Topics include, Usingthe Internet to Learn About World Schools (Chapter 3), Helping Students Develop Media Literacy (Chapter 9), and Protecting Students from Undesirable Material on the Web (Chapter 13).Focus Questions appear at the beginning of each chapter and provide students with an organized reference to chapter material--Refocus Questions appear after major text sections and are designed to help reinforce and improve the students' comprehension by reviewing the information in the chapter.Taking issue charts appear in each chapter to present controversial issues in the field of education, offering arguments on both sides of a question to illustrate why the topic is important and how it affects contemporary schools. Instructors can use these charts as the basis for class discussions or essay assignments.Eduspace, a customizable, powerful, interactive platform, provides instructors with text-specific online courses and content in multiple disciplines. Eduspace gives an instructor the ability to create all or part of their course online using the widely recognized tools of Blackboard and quality text-specific content from HMCo. Instructors can quickly and easily assign homework exercises, quizzes, tests, tutorials and supplemental study materials and can modify that content or even add their own.
| Author | : M. Cay Holbrook |
| Publisher | : American Printing House for the Blind |
| Total Pages | : 982 |
| Release | : 2017 |
| Genre | : Education |
| ISBN | : 9780891286967 |
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.
| Author | : Faye Ong |
| Publisher | : |
| Total Pages | : 132 |
| Release | : 2008 |
| Genre | : Education |
| ISBN | : |
| Author | : Johannes Fürnkranz |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 345 |
| Release | : 2012-11-06 |
| Genre | : Computers |
| ISBN | : 3540751971 |
Rules – the clearest, most explored and best understood form of knowledge representation – are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning. The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data.
