Implement standards-based grading practices that help students succeed! Classroom assessment methods should help students develop to their full potential, but meshing traditional grading practices with students’ achievement on standards has been difficult. Making lasting changes to grading practices requires both knowledge and willpower. Discover eight guidelines for good grading, recommendations for practical applications, and suggestions for implementing new grading practices as well as: ? The why’s and the how-to’s of implementing standards-based grading practices ? Tips from 48 nationally and internationally known authors and consultants ? Additional information on utilizing level scores rather than percentages ? Reflective exercises ? Techniques for managing grading more efficiently

Solidly grounded in theory and research, but concise and practice-oriented, Adult Learning: Linking Theory and Practice is perfect for master’s-level students and practitioners alike. Sharan Merriam and Laura Bierema have infused each chapter with practical applications for instruction which will help readers personally relate to the material. The contents covers: Adult Learning in Today’s World Traditional Learning Theories Andragogy Self-Directed Learning Transformative Learning Experience and Learning Body and Spirit in Learning Motivation and Learning The Brain and Cognitive Functioning Adult Learning in the Digital Age Critical Thinking and Critical Perspectives Culture and Context Discussion questions and activities for reflection are included at the end of each chapter.

Table of Contents and Executive Summary

Linking Leadership to Student Learning Linking Leadership to Student Learning clearly shows how school leadership improves student achievement. The book is based on an ambitious five-year study on educational leadership that was sponsored by The Wallace Foundation. The authors studied 43 districts, across 9 states and 180 elementary, middle, and secondary schools. In this book, Kenneth Leithwood, Karen Seashore Louis, and their colleagues report on what they found. They examined leadership at each organizational level in the school system—classroom, school, district, community, and state. Their comprehensive approach to investigating school leadership offers a balanced understanding of how the structures within which leaders operate shape what they do. The results within will have significant implications for future policy and practice. Praise for Linking Leadership to Student Learning "Kenneth Leithwood and Karen Seashore Louis offer a seminal new contribution to the leadership field. They provide a rich and authoritative evidence base that demonstrates clearly just why school leadership is so important and how it promotes successful student learning." —PAMELA SAMMONS, Ph.D., Professor of Education, Department of Education, University of Oxford, Oxford "This ambitious, groundbreaking, and thought provoking treatment of the link between school leadership and student learning is a testament to the outstanding work of these exemplary scholars. This is a 'must read' for academics and practitioners alike." —MARTHA McCARTHY, President's Professor, Loyola Marymount University, and Chancellor's Professor Emeritus, Indiana University "The question is no longer whether school and district leader's impact student learning, but rather how they do it. The authors provide a convincing answer, one that recognizes the crucial interaction between leader and locality." —DANIEL L. DUKE, Professor of Educational Leadership, University of Virginia

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Updated standards for school libraries in Illinois

The first comprehensive examination of the field, this book brings together stakeholders representing a variety of perspectives to explore how educators actually use data and technology tools to achieve lasting improvement in student performance. Contributors: David V. Abbott, Carrie Amon, Jonathan Bertfield, Cornelia Brunner, Fred Carrigg, Jere Confrey, Katherine Conoly, Valerie M. Crawford, Chris Dede, John Gasko, Greg Gunn, Juliette Heinze, Naomi Hupert, Sherry P. King, Mary Jane Kurabinski, Daniel Light, Lisa Long, Michael Merrill, Liane Moody, William R. Penuel, Luz M. Rivas, Mark S. Schlager, John Stewart, Sam Stringfield, Ronald Thorpe, Yukie Toyama, Jeffrey C. Wayman, and Viki M. Young. “If you want to understand usable knowledge, read Data-Driven School Improvement.” —Ellen Condliffe Lagemann, Harvard University “It is reassuring to know that at least some of the data being generated in our data-driven age are being used to make wiser decisions. We can all learn from these illustrative accounts.” —David C. Berliner, Mary Lou Fulton College of Education, Arizona State University “Replete with examples from real schools and districts, this volume provides a multi-layered portrait of what it takes to establish a culture of data use. Readers will come away with an appreciation of the systemic changes needed to reap the full potential of data-driven decision making.” —Barbara Means, Center for Technology in Learning, SRI International