Learning Online


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Learning Online


Learning Online

Author: Barbara Means

language: en

Publisher: Routledge

Release Date: 2014-04-03


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At a time when more and more of what people learn both in formal courses and in everyday life is mediated by technology, Learning Online provides a much-needed guide to different forms and applications of online learning. This book describes how online learning is being used in both K-12 and higher education settings as well as in learning outside of school. Particular online learning technologies, such as MOOCs (massive open online courses), multi-player games, learning analytics, and adaptive online practice environments, are described in terms of design principles, implementation, and contexts of use. Learning Online synthesizes research findings on the effectiveness of different types of online learning, but a major message of the book is that student outcomes arise from the joint influence of implementation, context, and learner characteristics interacting with technology--not from technology alone. The book describes available research about how best to implement different forms of online learning for specific kinds of students, subject areas, and contexts. Building on available evidence regarding practices that make online and blended learning more effective in different contexts, Learning Online draws implications for institutional and state policies that would promote judicious uses of online learning and effective implementation models. This in-depth research work concludes with a call for an online learning implementation research agenda, combining education institutions and research partners in a collaborative effort to generate and share evidence on effective practices.

Learning How to Learn


Learning How to Learn

Author: Barbara Oakley, PhD

language: en

Publisher: Penguin

Release Date: 2018-08-07


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A surprisingly simple way for students to master any subject--based on one of the world's most popular online courses and the bestselling book A Mind for Numbers A Mind for Numbers and its wildly popular online companion course "Learning How to Learn" have empowered more than two million learners of all ages from around the world to master subjects that they once struggled with. Fans often wish they'd discovered these learning strategies earlier and ask how they can help their kids master these skills as well. Now in this new book for kids and teens, the authors reveal how to make the most of time spent studying. We all have the tools to learn what might not seem to come naturally to us at first--the secret is to understand how the brain works so we can unlock its power. This book explains: Why sometimes letting your mind wander is an important part of the learning process How to avoid "rut think" in order to think outside the box Why having a poor memory can be a good thing The value of metaphors in developing understanding A simple, yet powerful, way to stop procrastinating Filled with illustrations, application questions, and exercises, this book makes learning easy and fun.

Deep Learning for Coders with fastai and PyTorch


Deep Learning for Coders with fastai and PyTorch

Author: Jeremy Howard

language: en

Publisher: O'Reilly Media

Release Date: 2020-06-29


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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