Structural Learning (Volume 1)
Structural Learning (Volume 1)

Author: Joseph M. Scandura

Publisher: Routledge

Published: 2017-09-13

Total Pages: 309

ISBN-13: 1351815334

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Originally published in 1973, this book was published in two volumes. In the first volume, the author describes what he sees as the rudiments of three deterministic partial theories of structural learning. The first involves competence, partial theories which deal only with the problem of how to account for the various kinds of behavior of which people are typically capable. Special attention is given to mathematical competence. Nothing is said about learning or performance. The second partial theory is concerned with motivation, learning, and performance under idealized conditions, and is obtained from the first partial theory by imposing further structure on it. This theory says nothing about memory of the limited capacity of human subjects to process information. ... The final theory is obtained from the second by making additional assumptions, which bring memory and finite information processing into the picture. The theory is still partial, however, since no attempt is made to deal with certain ultra-short-term behavioral phenomena which appear to depend directly on particular physiological characteristics.

Structural Learning (Volume 2)
Structural Learning (Volume 2)

Author: Joseph M. Scandura

Publisher: Routledge

Published: 2017-09-13

Total Pages: 340

ISBN-13: 1351839403

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Originally published in 1976, this title is an edited volume and reflects the major approaches being taken in structural learning at the time. Chapter 1 deals with the basic question of whether competence (knowledge) should be characterized in terms of rules (automata), on the one hand, or associations on the other. The bulk of Chapter 2 is devoted to a series of earlier experiments on rule learning by the editor and his associates. The two contributions in Chapter 3 deal with graph theoretical models. Piagetian models constitute the subject of Chapter 4. Chapter 5 deals with attempts to stimulate human behaviour with a computer. Chapter 6 ranges over a wide variety of competence models, with particular reference to logic and mathematics. In Chapter 7 the editor proposes a new theory of structural learning, together with some empirical results.

Bringing Math Students Into the Formative Assessment Equation
Bringing Math Students Into the Formative Assessment Equation

Author: Susan Janssen Creighton

Publisher: Corwin Press

Published: 2015-01-21

Total Pages: 222

ISBN-13: 1483385965

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Make formative assessment work for you—and your math students! Finally, formative assessment that adds up! Bringing Math Students Into the Formative Assessment Equation is the ultimate resource for helping teachers implement formative assessment in the middle school mathematics classroom. And it’s much more than that. With this research-based, teacher-tested guide, you won’t just learn effective teaching strategies—you’ll turn your students into self-regulated learners. They’ll monitor and assess their own progress—and communicate to you about it! Features include: A clear and manageable six-aspect instructional model Detailed strategies for helping students own their successes Real-life examples from middle school mathematics teachers Useful resources and a companion website to help you implement formative assessment in your classroom Formative assessment isn’t just for teachers anymore. With the help of this essential resource, you’ll work together with your students toward a common goal of math success. "This book is outstanding. I would recommend it to any math educator. The depth of research integrated into practice is extensive and, as a result, it is the most practical book I have come across related to formative assessment and mathematics The self-regulation aspects, as well as the ownership and involvement emphasized in the book, went beyond the traditional cognitive strategies endorsed in most books." Marc Simmons, Principal Ilwaco Middle School, Ocean Beach School District, Long Beach, WA "The ideas in this book are brought to life with examples of teachers and students in the classroom. The teacher voices, comments, and quotes lend credibility and are a big component of the book’s strengths as well as the visuals and graphics." Rita Tellez, Math Coordinator Ysleta Independent School District, El Paso, TX

Organic Chemistry Workbook Series: Volume 1: Representations of Chemical Structures
Organic Chemistry Workbook Series: Volume 1: Representations of Chemical Structures

Author: Glenn Sammis

Publisher: ORCA Workbooks Publishing

Published: 2021-01-03

Total Pages: 86

ISBN-13: 1777203600

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Confused about Lewis bond representation of organic molecules? The octet rule got you down? This workbook, written by two award-winning instructors at the University of British Columbia, has been used to help organic chemistry students for years. Using a step-by-step approach, suitable to be used in conjunction with any textbook, this workbook helps students learn critical concepts at their own pace. It is suitable for any introductory-level organic student who wants to understand the smart approach to building correct structures and understanding reactivity.

Handbook Of Machine Learning - Volume 1: Foundation Of Artificial Intelligence
Handbook Of Machine Learning - Volume 1: Foundation Of Artificial Intelligence

Author: Tshilidzi Marwala

Publisher: World Scientific

Published: 2018-10-22

Total Pages: 329

ISBN-13: 9813271248

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This is a comprehensive book on the theories of artificial intelligence with an emphasis on their applications. It combines fuzzy logic and neural networks, as well as hidden Markov models and genetic algorithm, describes advancements and applications of these machine learning techniques and describes the problem of causality. This book should serves as a useful reference for practitioners in artificial intelligence.

Instructional Design Theories and Models
Instructional Design Theories and Models

Author: Charles M. Reigeluth

Publisher: Routledge

Published: 1983-11-01

Total Pages: 494

ISBN-13: 1136778276

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Instructional Design Theories and Models is a thorough yet concise overview of eight of the most comprehensive and best-known attempts to integrate knowledge about effective and appealing instruction. Chapters were written by the original theorists to provide a more accurate and behind-the-scenes look at the theories' development. Instructional Des

Deep Learning
Deep Learning

Author: Ian Goodfellow

Publisher: MIT Press

Published: 2016-11-10

Total Pages: 801

ISBN-13: 0262337371

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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.