In this book you will read stories told by faculty who have redesigned their university courses using the Decision-Based Learning pedagogy and the impact this powerful strategy can have on student learning. It should be of use to anyone teaching and designing curricula in higher education settings.

"Gathering data and using it to inform instruction is a requirement for many schools, yet educators are not necessarily formally trained in how to do it. This book helps bridge the gap between classroom practice and the principles of educational psychology. Teachers will find cutting-edge advances in research and theory on human learning and teaching in an easily understood and transferable format. The text's integrated model shows teachers, school leaders, and district administrators how to establish a data culture and transform quantitative and qualitative data into actionable knowledge based on: assessment; statistics; instructional and differentiated psychology; classroom management."--Publisher's description.

In a context where schools are held more and more accountable for the education they provide, data-based decision making has become increasingly important. This book brings together scholars from several countries to examine data-based decision making. Data-based decision making in this book refers to making decisions based on a broad range of evidence, such as scores on students’ assessments, classroom observations etc. This book supports policy-makers, people working with schools, researchers and school leaders and teachers in the use of data, by bringing together the current research conducted on data use across multiple countries into a single volume. Some of these studies are ‘best practice’ studies, where effective data use has led to improvements in student learning. Others provide insight into challenges in both policy and practice environments. Each of them draws on research and literature in the field.

Goal-Directed Decision Making: Computations and Neural Circuits examines the role of goal-directed choice. It begins with an examination of the computations performed by associated circuits, but then moves on to in-depth examinations on how goal-directed learning interacts with other forms of choice and response selection. This is the only book that embraces the multidisciplinary nature of this area of decision-making, integrating our knowledge of goal-directed decision-making from basic, computational, clinical, and ethology research into a single resource that is invaluable for neuroscientists, psychologists and computer scientists alike. The book presents discussions on the broader field of decision-making and how it has expanded to incorporate ideas related to flexible behaviors, such as cognitive control, economic choice, and Bayesian inference, as well as the influences that motivation, context and cues have on behavior and decision-making. - Details the neural circuits functionally involved in goal-directed decision-making and the computations these circuits perform - Discusses changes in goal-directed decision-making spurred by development and disorders, and within real-world applications, including social contexts and addiction - Synthesizes neuroscience, psychology and computer science research to offer a unique perspective on the central and emerging issues in goal-directed decision-making

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.

This book, jointly authored by two distinguished philosophers and two prominent social scientists, has an ambitious aim: to improve decision-making in education policy. First they dive into the goals of education policy and explain the terms "educational goods" and "childhood goods," adding precision and clarity to the discussion of the distributive values that are essential for good decision-making about education. Then they provide a framework for individual decision-makers that enables them to combine values and evidence in the evaluation of educational policy options. Finally they delve into the particular policy issues of school finance, school accountability, and school choice, and they show how decision makers might approach them in the light of this decision-making framework. The authors are not advocated particular policy choices, however. The focus instead is a smart framework that will make it easier for policymakers (and readers) to identify and think through what they disagree with others about.

This well-established international series examines major areas of basic and clinical research within neuroscience, as well as emerging and promising subfields.This volume explores interdisciplinary research on decision making taking a neural and behavioural approach - Leading authors review the state-of-the-art in their field of investigation, and provide their views and perspectives for future research - Chapters are extensively referenced to provide readers with a comprehensive list of resources on the topics covered - All chapters include comprehensive background information and are written in a clear form that is also accessible to the non-specialist

This book is written for anyone who has been inspired by the idea of Team-Based Learning (TBL) through his or her reading, a workshop, or a colleague’s enthusiasm, and then asks the inevitable question: how do I start?Written by five authors who use TBL in their teaching and who are internationally recognized as mentors and trainers of faculty making the switch to TBL, the book also presents the tips and insights of 46 faculty members from around the world who have adopted this teaching method.TBL is a uniquely powerful form of small group learning. It harnesses the power of teams and social learning with accountability structures and instructional sequences. This book provides the guidance, from first principles to examples of practice, together with concrete advice, suggestions, and tips to help you succeed in the TBL classroom. This book will help you understand what TBL is and why it is so powerful. You will find what you need to plan, build, implement, and use TBL effectively. This book will appeal to both the novice and the expert TBL teacher.

Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing to give you an overview of how these modern areas of computing relate to each other. This book introduces a collection of the most important concepts of machine learning and sets them in context with other vital technologies that decision makers need to know about. These concepts span the process from envisioning the problem to applying machine-learning techniques to your particular situation. This discussion also provides an insight to help deploy the results to improve decision-making. The book uses case studies and jargon busting to help you grasp the theory of machine learning quickly. You'll soon gain the big picture of machine learning and how it fits with other cutting-edge IT services. This knowledge will give you confidence in your decisions for the future of your business. What You Will Learn Discover the machine learning, big data, and cloud and cognitive computing technology stack Gain insights into machine learning concepts and practices Understand business and enterprise decision-making using machine learning Absorb machine-learning best practices Who This Book Is For Managers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them.