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Author: Joseph M. Scandura
Publisher: Routledge
ISBN: 1351815334
Category : Psychology
Languages : en
Pages : 309
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Book Description
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.
Author: Joseph M. Scandura
Publisher: Routledge
ISBN: 1351815334
Category : Psychology
Languages : en
Pages : 309
Get Book Here
Book Description
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.
Author: Keith Hudson
Publisher: Springer
ISBN: 1489931902
Category : Education
Languages : en
Pages : 182
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Book Description
It is often the case - perhaps more often than not - that new ideas arrive long before there is the me ans to clothe and deli ver them. We can think ofLeonardo da Vinci's drawings of helicopters and submarines among many other examples. Computer-Assisted Learning (CAL) is an example of an idea which has had a particularly long gestation. As I will illustrate early in the book, the principles of CAL were really first discovered by Socrates. As a formal method of teaching, the Socratic method disappeared for over two millennia until the 1950s. It was then revived in the form ofProgrammed Learning (PL) which resulted from the researches ofB. F. Skinner at Harvard University. Even then, PL was premature. In the 1950s and 60s, methods were devised, such as teaching machines and various sorts ofPL text books, and there was a mushrooming of PL publishing at that time. For a complex of reason- economic, logistical and technical-PL also largely disappeared from the mid- 60s, although it continued in a few specialized areas ofteaching and industrial training. However, during the same period, PL quietly transformed itselfinto CAL. But the computerized form was not capable of mass dissemination until recently hecause personal microcomputers did not have sufficient internal memory sizes. That situation has now changed very dramatically and 128K microcomputers are becoming cheap and widely available. Cheap memory chips of256K and 1024K cannot be far away, either.
Author: Doug Fisher
Publisher: ASCD
ISBN: 1416612297
Category : Education
Languages : en
Pages : 157
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Book Description
Better Learning Through Structured Teaching describes how teachers can help students develop stronger learning skills by ensuring that instruction moves from modeling and guided practice (situations where the teacher has most of the responsibility) to collaborative learning and, finally, to independent tasks. You'll find out how to use the four components of this approach to help meet critical challenges, including differentiating instruction and making effective use of class time: 1. Focus Lessons: Establishing the lesson’s purpose and then modeling your own thinking for students.2. Guided Instruction: Working with small groups of students who have similar results on performance assessments. 3. Collaborative Learning: Enabling students to discuss and negotiate with one another to create independent work, not simply one project. 4. Independent Tasks: Requiring students to use their previous knowledge to create new and authentic products. The authors explore each component using student dialogues and examples from a variety of disciplines and grade levels. They provide tips and tools for successfully implementing this instructional approach in your own classroom, including checklists for classroom setup and routines, critical questions, real-world lesson plans, and more. No matter what grade level you teach, Better Learning Through Structured Teaching is your essential guide to helping students develop and expand their capacity for authentic and long-lasting learning.
Author: Louise Spear-Swerling
Publisher: Guilford Publications
ISBN: 1462548792
Category : Education
Languages : en
Pages : 266
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Book Description
"In this book, structured literacy is conceptualized as an umbrella term encompassing a variety of intervention methods, instructional approaches, and commercial programs. In addition to focusing on SL approaches to intervention, this book is organized around common poor reader profiles that have been identified in research. The chapters in this volume are written by experts who are well known as researchers but who are also highly skilled at writing for practitioners. Chapters were written with a strong foundation of research that is summarized, but with a concentration on translating research into practice, including case studies, sample intervention activities, and lesson plans. Each chapter includes application activities at the end to check for and extend readers' understanding"--
Author: Joseph M. Scandura
Publisher: Routledge
ISBN: 1351839403
Category : Psychology
Languages : en
Pages : 340
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Book Description
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.
Author: Arnold P. Goldstein
Publisher: Research Press (IL)
ISBN:
Category : Education
Languages : en
Pages : 236
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Book Description
Offers the methods to instill appropriate behavior in an adolescent population often considered unmanageable. These three audiocassettes prepare teachers and other trainers to effectively conduct structured learning sessions.
Author: Mark Ryan
Publisher: Simon and Schuster
ISBN: 163835717X
Category : Computers
Languages : en
Pages : 262
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Book Description
Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Summary Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world businesses depend on. Filled with practical, relevant applications, this book teaches you how deep learning can augment your existing machine learning and business intelligence systems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Here’s a dirty secret: Half of the time in most data science projects is spent cleaning and preparing data. But there’s a better way: Deep learning techniques optimized for tabular data and relational databases deliver insights and analysis without requiring intense feature engineering. Learn the skills to unlock deep learning performance with much less data filtering, validating, and scrubbing. About the book Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Get started using a dataset based on the Toronto transit system. As you work through the book, you’ll learn how easy it is to set up tabular data for deep learning, while solving crucial production concerns like deployment and performance monitoring. What's inside When and where to use deep learning The architecture of a Keras deep learning model Training, deploying, and maintaining models Measuring performance About the reader For readers with intermediate Python and machine learning skills. About the author Mark Ryan is a Data Science Manager at Intact Insurance. He holds a Master's degree in Computer Science from the University of Toronto. Table of Contents 1 Why deep learning with structured data? 2 Introduction to the example problem and Pandas dataframes 3 Preparing the data, part 1: Exploring and cleansing the data 4 Preparing the data, part 2: Transforming the data 5 Preparing and building the model 6 Training the model and running experiments 7 More experiments with the trained model 8 Deploying the model 9 Recommended next steps
Author: Shai Shalev-Shwartz
Publisher: Cambridge University Press
ISBN: 1107057132
Category : Computers
Languages : en
Pages : 415
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Book Description
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Author: Ian Goodfellow
Publisher: MIT Press
ISBN: 0262337371
Category : Computers
Languages : en
Pages : 801
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Book Description
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.
Author: Charles R. Farrar
Publisher: John Wiley & Sons
ISBN: 1118443217
Category : Technology & Engineering
Languages : en
Pages : 735
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Book Description
Written by global leaders and pioneers in the field, this book is a must-have read for researchers, practicing engineers and university faculty working in SHM. Structural Health Monitoring: A Machine Learning Perspective is the first comprehensive book on the general problem of structural health monitoring. The authors, renowned experts in the field, consider structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm, first explaining the paradigm in general terms then explaining the process in detail with further insight provided via numerical and experimental studies of laboratory test specimens and in-situ structures. This paradigm provides a comprehensive framework for developing SHM solutions. Structural Health Monitoring: A Machine Learning Perspective makes extensive use of the authors’ detailed surveys of the technical literature, the experience they have gained from teaching numerous courses on this subject, and the results of performing numerous analytical and experimental structural health monitoring studies. Considers structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm Emphasises an integrated approach to the development of structural health monitoring solutions by coupling the measurement hardware portion of the problem directly with the data interrogation algorithms Benefits from extensive use of the authors’ detailed surveys of 800 papers in the technical literature and the experience they have gained from teaching numerous short courses on this subject.
