Teaching Machines
Teaching Machines

Author: Audrey Watters

Publisher: MIT Press

Published: 2023-02-07

Total Pages: 325

ISBN-13: 026254606X

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How ed tech was born: Twentieth-century teaching machines--from Sidney Pressey's mechanized test-giver to B. F. Skinner's behaviorist bell-ringing box. Contrary to popular belief, ed tech did not begin with videos on the internet. The idea of technology that would allow students to "go at their own pace" did not originate in Silicon Valley. In Teaching Machines, education writer Audrey Watters offers a lively history of predigital educational technology, from Sidney Pressey's mechanized positive-reinforcement provider to B. F. Skinner's behaviorist bell-ringing box. Watters shows that these machines and the pedagogy that accompanied them sprang from ideas--bite-sized content, individualized instruction--that had legs and were later picked up by textbook publishers and early advocates for computerized learning. Watters pays particular attention to the role of the media--newspapers, magazines, television, and film--in shaping people's perceptions of teaching machines as well as the psychological theories underpinning them. She considers these machines in the context of education reform, the political reverberations of Sputnik, and the rise of the testing and textbook industries. She chronicles Skinner's attempts to bring his teaching machines to market, culminating in the famous behaviorist's efforts to launch Didak 101, the "pre-verbal" machine that taught spelling. (Alternate names proposed by Skinner include "Autodidak," "Instructomat," and "Autostructor.") Telling these somewhat cautionary tales, Watters challenges what she calls "the teleology of ed tech"--the idea that not only is computerized education inevitable, but technological progress is the sole driver of events.

Teaching Machines and Programming
Teaching Machines and Programming

Author: Kenneth Austwick

Publisher:

Published: 1964

Total Pages: 228

ISBN-13:

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Teaching Machines and Programming covers the significant developments in teaching machines and automated teaching, as well as the major theoretical issues and attributes involved in these procedures. After a brief introduction to teaching machine procedures, this six-chapter text goes on summarizing the industrial and military applications of teaching machines. The succeeding chapters consider the underlying theory, function, and schema of the adaptive teaching system, which are related to recognizable teaching functions performed by a human tutor. The last chapters discuss the development a.

The Technology of Teaching
The Technology of Teaching

Author: B. F. Skinner

Publisher: B. F. Skinner Foundation

Published: 2016-04-26

Total Pages: 268

ISBN-13: 099645392X

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On Parent's Day, in 1952, B. F. Skinner visited his daughter's fourth grade math class. As he watched the lesson, he became increasingly uncomfortable. Almost every principle of effective teaching that he had studied for more than 20 years was being violated in that classroom. Yet it was a typical class. The teacher showed how to solve the day's problems, then gave the students a worksheet to do. Some children began to work readily while others shifted uncomfortably in their chairs, or raised their hands for help. The teacher went from desk to desk, giving help and feedback. Skinner knew what was needed. Each student should be given a problem tailored precisely to his or her skill level, not to the class average, and every answer needed to be assessed immediately to determine the next step. The task was clearly impossible for one teacher. That afternoon, Skinner set to work on a teaching machine. Today's computers have made the mechanical machine obsolete, but the principles of how to design instruction in steps that lead from a basic level to competent performance are as valid today as they were in the 20th century. This book brings together Skinner's writings on education during the years he was most involved in improving education.

Programmed Learning in Perspective
Programmed Learning in Perspective

Author: I. K. Davies

Publisher: Transaction Publishers

Published: 2007-07-01

Total Pages: 222

ISBN-13: 0202368270

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The method of programming outlined in this book represents a major contribution to the growing body of literature in programmed learning. It is the first book in the field to present a carefully designed, complete and integrated system for analyzing, organizing and structuring learning materials in programmed form. Application of the system is illustrated through the step-by-step construction of two short programs. Starting with the analysis of the syllabus and course content, the authors take the reader through each phase of the programming process gathering and organizing the content material, construction of the program matrix and flow diagram and finally, the writing of frames. Every teacher and trainer can benefit from the application of this method to lesson plan preparation and to classroom teaching techniques. Such a method is essential, for all those who are writing programmed materials. In a new computer age classroom environment, programmed learning can be especially beneficial. C. A. Thomas, I. K. Davies, D. Openshaw, and J. B. Bird are instructors or directors at the British Royal Air Force School of Education. They are pioneers in the application of programmed learning in Britain and are highly regarded as forward looking and creative educational research workers. Their accomplishments include, in addition to this ingenious book, the design and development of the Empirical Tutor, one of Britain's major teaching machines, and the publication of a number of technical papers in the field of programmed learning. Lawrence M. Stolurow is professor emeritus of psychological & quantitative foundations at the University of Iowa.

Machine Learning for Kids
Machine Learning for Kids

Author: Dale Lane

Publisher: No Starch Press

Published: 2021-01-19

Total Pages: 290

ISBN-13: 1718500572

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A hands-on, application-based introduction to machine learning and artificial intelligence (AI) that guides young readers through creating compelling AI-powered games and applications using the Scratch programming language. Machine learning (also known as ML) is one of the building blocks of AI, or artificial intelligence. AI is based on the idea that computers can learn on their own, with your help. Machine Learning for Kids will introduce you to machine learning, painlessly. With this book and its free, Scratch-based, award-winning companion website, you'll see how easy it is to add machine learning to your own projects. You don't even need to know how to code! As you work through the book you'll discover how machine learning systems can be taught to recognize text, images, numbers, and sounds, and how to train your models to improve their accuracy. You'll turn your models into fun computer games and apps, and see what happens when they get confused by bad data. You'll build 13 projects step-by-step from the ground up, including: • Rock, Paper, Scissors game that recognizes your hand shapes • An app that recommends movies based on other movies that you like • A computer character that reacts to insults and compliments • An interactive virtual assistant (like Siri or Alexa) that obeys commands • An AI version of Pac-Man, with a smart character that knows how to avoid ghosts NOTE: This book includes a Scratch tutorial for beginners, and step-by-step instructions for every project. Ages 12+

Deep Learning for Coders with fastai and PyTorch
Deep Learning for Coders with fastai and PyTorch

Author: Jeremy Howard

Publisher: O'Reilly Media

Published: 2020-06-29

Total Pages: 624

ISBN-13: 1492045497

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Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Why They Can't Write
Why They Can't Write

Author: John Warner

Publisher: Johns Hopkins University Press

Published: 2020-03-17

Total Pages: 285

ISBN-13: 1421437988

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An important challenge to what currently masquerades as conventional wisdom regarding the teaching of writing. There seems to be widespread agreement that—when it comes to the writing skills of college students—we are in the midst of a crisis. In Why They Can't Write, John Warner, who taught writing at the college level for two decades, argues that the problem isn't caused by a lack of rigor, or smartphones, or some generational character defect. Instead, he asserts, we're teaching writing wrong. Warner blames this on decades of educational reform rooted in standardization, assessments, and accountability. We have done no more, Warner argues, than conditioned students to perform "writing-related simulations," which pass temporary muster but do little to help students develop their writing abilities. This style of teaching has made students passive and disengaged. Worse yet, it hasn't prepared them for writing in the college classroom. Rather than making choices and thinking critically, as writers must, undergraduates simply follow the rules—such as the five-paragraph essay—designed to help them pass these high-stakes assessments. In Why They Can't Write, Warner has crafted both a diagnosis for what ails us and a blueprint for fixing a broken system. Combining current knowledge of what works in teaching and learning with the most enduring philosophies of classical education, this book challenges readers to develop the skills, attitudes, knowledge, and habits of mind of strong writers.