Learning From Textbooks
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Learning From Textbooks
It is surprising that there is so little research on textbooks, given their centrality to teaching and learning in elementary and secondary schools. Textbooks have become a focus of political and cultural controversy, advocating a multicultural curriculum that has sparked some vigorous protests. Research is absent in this debate; therefore, questions of legitimate knowledge, the role of textbooks, textbook design, policy selection issues, and economic issues concerning the marketplace are not part of the current debate. Without insights of research on considerate text, mentioning, illustrations and so forth, the current controversy will result in publishers responding to demands for more content not less; thus, textbooks will become compendia of information that on the surface satisfy everyone. This volume demonstrates how research on important issues relative to textbook design can advance our knowledge about what makes textbooks effective learning tools, and thus inform policymakers, publishers, and those involved in textbook selection. Representing pure and applied approaches, researchers present papers on the quality of writing, the role of questions, the role of pictures and illustrations, and the role of auxiliary materials in the design of effective textbooks. The chapters provide insight into research and its application to textbook design and improvement -- stimulating others to follow this lead.
Deep Learning
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.
Instructional-Design Theories and Models, Volume III
Instructional-Design Theories and Models, Volume III: Building a Common Knowledge Base is perhaps best described by its new subtitle. Whereas Volume II sought to comprehensively review the proliferating theories and models of instruction of the 1980’s and 1990’s, Volume III takes on an even more daunting task: starting to build a common knowledge base that underlies and supports the vast array of instructional theories, models and strategies that constitute the field of Instructional Design. Unit I describes the need for a common knowledge base, offers some universal principles of instruction, and addresses the need for variation and detailed guidance when implementing the universal principles. Unit II describes how the universal principles apply to some major approaches to instruction such as direct instruction or problem-based instruction. Unit III describes how to apply the universal principles to some major types of learning such as understandings and skills. Unit IV provides a deeper understanding of instructional theory using the structural layers of a house as its metaphor and discusses instructional theory in the broader context of paradigm change in education.