Aligning for Learning offers faculty, instructional developers, administrators, and researchers a clear model through which to approach the complexities of effective teaching and learning. The alignment model is the culmination of 20 years of research done by Donald Wulff and others at the Center for Instructional Development and Research at the University of Washington. It helps instructors incorporate instructional components and communication strategies into a representation of teaching effectiveness related to rapport, structure, engagement, and interaction, in an effort to align themselves, their content, and their students in a consistent learning goal. This book combines the alignment model with the contributors’ years of experience as instructors, instructional developers, and administrators to produce a practical volume on teaching and learning that contains ideas applicable to a variety of institutions and instructional settings. Divided into five parts, this book Explains the alignment model as a tool to teaching effectiveness Addresses the importance of inclusive teaching and learning, the use of the alignment model in designing courses, the use of assessment in support of alignment, and alignment issues in evaluation Illustrates the application of alignment in various instructional contexts, such as large classes, team teaching, and mentoring Demonstrates how the alignment model can be used to advance the scholarship of teaching and learning and its role in faculty reward systems Provides key considerations in the application of alignment and some thoughts on the future of the alignment model

This book is a teachers’ and administrators’ guide for implementing and sustaining an educational system that ensures students are taught and learn what is required by benchmarks, assessments, and state standards, and to the learning needs of each individual student. This is accomplished by providing a tight alignment between the intended, taught, and tested curricula.

In order for a professional learning community to achieve its full potential, all levels districtwide must align with the three big ideas: ensuring a focus on learning, building a collaborative culture, and establishing a results orientation. This book breaks down the complex process of aligning the work of central office staff, building leadership, and teachers to increase student achievement.

Provides information for school administrators and curriculum specialists on ways to align the curriculum to state standards to improve student achievement and teacher effectiveness.

Over the past century, educational psychologists and researchers have posited many theories to explain how individuals learn, i.e. how they acquire, organize and deploy knowledge and skills. The 20th century can be considered the century of psychology on learning and related fields of interest (such as motivation, cognition, metacognition etc.) and it is fascinating to see the various mainstreams of learning, remembered and forgotten over the 20th century and note that basic assumptions of early theories survived several paradigm shifts of psychology and epistemology. Beyond folk psychology and its naïve theories of learning, psychological learning theories can be grouped into some basic categories, such as behaviorist learning theories, connectionist learning theories, cognitive learning theories, constructivist learning theories, and social learning theories. Learning theories are not limited to psychology and related fields of interest but rather we can find the topic of learning in various disciplines, such as philosophy and epistemology, education, information science, biology, and – as a result of the emergence of computer technologies – especially also in the field of computer sciences and artificial intelligence. As a consequence, machine learning struck a chord in the 1980s and became an important field of the learning sciences in general. As the learning sciences became more specialized and complex, the various fields of interest were widely spread and separated from each other; as a consequence, even presently, there is no comprehensive overview of the sciences of learning or the central theoretical concepts and vocabulary on which researchers rely. The Encyclopedia of the Sciences of Learning provides an up-to-date, broad and authoritative coverage of the specific terms mostly used in the sciences of learning and its related fields, including relevant areas of instruction, pedagogy, cognitive sciences, and especially machine learning and knowledge engineering. This modern compendium will be an indispensable source of information for scientists, educators, engineers, and technical staff active in all fields of learning. More specifically, the Encyclopedia provides fast access to the most relevant theoretical terms provides up-to-date, broad and authoritative coverage of the most important theories within the various fields of the learning sciences and adjacent sciences and communication technologies; supplies clear and precise explanations of the theoretical terms, cross-references to related entries and up-to-date references to important research and publications. The Encyclopedia also contains biographical entries of individuals who have substantially contributed to the sciences of learning; the entries are written by a distinguished panel of researchers in the various fields of the learning sciences.

This book describes the concept of mastery learning in the classroom and the various foundations upon which it is built. Five chapters discuss: (1) "Understanding Mastery Learning" (e.g., the learning/memory base, the measurement base, theoretical bases, and the brain base); (2) "Examining the Standards: Math, Science, Social Studies, and English Language Arts" (e.g., mastery as a beginning, beyond mastery, and standards); (3) "Planning Standards-Based Lessons using Mastery Learning" (e.g., overlap among state and national standards, enrichment and remediation, and planning lessons using six elements of mastery learning); (4) "Implementing Standards and Mastery Learning in the Classroom" (13 steps to implementing mastery learning); and (5) "Professional Development and Mastery Learning" (e.g., a plan for action and a professional development agenda and teaching for mastery). The appendix looks at what the literature can tell (e.g., two approaches to mastery learning, empirical evidence, research examples, and memory by fast and slow learners). (Contains approximately 160 references.) (SM).

A jaw-dropping exploration of everything that goes wrong when we build AI systems and the movement to fix them. Today’s “machine-learning” systems, trained by data, are so effective that we’ve invited them to see and hear for us—and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole—and appear to assess Black and White defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And as autonomous vehicles share our streets, we are increasingly putting our lives in their hands. The mathematical and computational models driving these changes range in complexity from something that can fit on a spreadsheet to a complex system that might credibly be called “artificial intelligence.” They are steadily replacing both human judgment and explicitly programmed software. In best-selling author Brian Christian’s riveting account, we meet the alignment problem’s “first-responders,” and learn their ambitious plan to solve it before our hands are completely off the wheel. In a masterful blend of history and on-the ground reporting, Christian traces the explosive growth in the field of machine learning and surveys its current, sprawling frontier. Readers encounter a discipline finding its legs amid exhilarating and sometimes terrifying progress. Whether they—and we—succeed or fail in solving the alignment problem will be a defining human story. The Alignment Problem offers an unflinching reckoning with humanity’s biases and blind spots, our own unstated assumptions and often contradictory goals. A dazzlingly interdisciplinary work, it takes a hard look not only at our technology but at our culture—and finds a story by turns harrowing and hopeful.