Optimal Learning
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Optimal Learning
Author: Warren B. Powell
language: en
Publisher: John Wiley & Sons
Release Date: 2013-07-09
Learn the science of collecting information to make effective decisions Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive. Designed for readers with an elementary background in probability and statistics, the book presents effective and practical policies illustrated in a wide range of applications, from energy, homeland security, and transportation to engineering, health, and business. This book covers the fundamental dimensions of a learning problem and presents a simple method for testing and comparing policies for learning. Special attention is given to the knowledge gradient policy and its use with a wide range of belief models, including lookup table and parametric and for online and offline problems. Three sections develop ideas with increasing levels of sophistication: Fundamentals explores fundamental topics, including adaptive learning, ranking and selection, the knowledge gradient, and bandit problems Extensions and Applications features coverage of linear belief models, subset selection models, scalar function optimization, optimal bidding, and stopping problems Advanced Topics explores complex methods including simulation optimization, active learning in mathematical programming, and optimal continuous measurements Each chapter identifies a specific learning problem, presents the related, practical algorithms for implementation, and concludes with numerous exercises. A related website features additional applications and downloadable software, including MATLAB and the Optimal Learning Calculator, a spreadsheet-based package that provides an introduction to learning and a variety of policies for learning.
Optimal Learning Environments to Promote Student Engagement
Author: David J. Shernoff
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-05-29
Optimal Learning Environments to Promote Student Engagement analyzes the psychological, social, and academic phenomena comprising engagement, framing it as critical to learning and development. Drawing on positive psychology, flow studies, and theories of motivation, the book conceptualizes engagement as a learning experience, explaining how it occurs (or not) and how schools can adapt to maximize it among adolescents. Examples of empirically supported environments promoting engagement are provided, representing alternative high schools, Montessori schools, and extracurricular programs. The book identifies key innovations including community-school partnerships, technology-supported learning, and the potential for engaging learning opportunities during an expanded school day. Among the topics covered: Engagement as a primary framework for understanding educational and motivational outcomes. Measuring the malleability, complexity, multidimensionality, and sources of engagement. The relationship between engagement and achievement. Supporting and challenging: the instructor’s role in promoting engagement. Engagement within and beyond core academic subjects. Technological innovations on the engagement horizon. Optimal Learning Environments to Promote Student Engagement is an essential resource for researchers, professionals, and graduate students in child and school psychology; social work; educational psychology; positive psychology; family studies; and teaching/teacher education.
Optimal Learning
Author: Warren B. Powell
language: en
Publisher: John Wiley & Sons
Release Date: 2012-04-17
Learn the science of collecting information to make effective decisions Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive. Designed for readers with an elementary background in probability and statistics, the book presents effective and practical policies illustrated in a wide range of applications, from energy, homeland security, and transportation to engineering, health, and business. This book covers the fundamental dimensions of a learning problem and presents a simple method for testing and comparing policies for learning. Special attention is given to the knowledge gradient policy and its use with a wide range of belief models, including lookup table and parametric and for online and offline problems. Three sections develop ideas with increasing levels of sophistication: Fundamentals explores fundamental topics, including adaptive learning, ranking and selection, the knowledge gradient, and bandit problems Extensions and Applications features coverage of linear belief models, subset selection models, scalar function optimization, optimal bidding, and stopping problems Advanced Topics explores complex methods including simulation optimization, active learning in mathematical programming, and optimal continuous measurements Each chapter identifies a specific learning problem, presents the related, practical algorithms for implementation, and concludes with numerous exercises. A related website features additional applications and downloadable software, including MATLAB and the Optimal Learning Calculator, a spreadsheet-based package that provides an introduction to learning and a variety of policies for learning.