A complete summary of the 15 most influential learning theories. Includes Vygotsky, Piaget, Bloom, Gagne, Maslow, Bruner, Kolb and many more.

Action Research in a Relational Perspective brings together an expert international academic team to present theoretical perspectives on social constructionist understandings of action research, as well as illustrative examples of action research practices within a wide range of sectors such as organizational learning, leadership development, education, mental health and health care. Building bridges between theory and practice, this book explores themes of dialogue, relationships, tensions, power and ethics in action research projects. It examines both the great potential, and the challenges and dilemmas, of action research. It aims to inspire readers with ideas and a practical "how-to" understanding of doing action research from a social constructionist standpoint. Action Research in a Relational Perspective will appeal to theoreticians and practitioners, senior researchers and PhD students, students, consultants, educators and managers who are interested in action research as an approach to organizational learning, team development, learning among professionals and citizens, or community development. | Author: Taylor & Francis Group | Publisher: Routledge | Publication Date: Mar 31, 2021 | Number of Pages: 208 pages | Language: English | Binding: Paperback | ISBN-10: 0367727021 | ISBN-13: 9780367727024

Motivation is that which moves us to action. Human motivation is thus a complex issue, as people are moved to action by both their evolved natures and by myriad familial, social and cultural influences. The Oxford Handbook of Human Motivation collects the top theorists and researchers of human motivation into a single volume, capturing the current state-of-the-art in this fast developing field. The book includes theoretical overviews from some of the best-known thinkers in this area, including chapters on Social Learning Theory, Control Theory, Self-determination theory, Terror Management theory, and the Promotion and Prevention perspective. Topical chapters appear on phenomena such as ego-depletion, flow, curiosity, implicit motives, and personal interests. A section specifically highlights goal research, including chapters on goal regulation, achievement goals, the dynamics of choice, unconscious goals and process versus outcome focus. Still other chapters focus on evolutionary and biological underpinnings of motivation, including chapters on cardiovascular dynamics, mood, and neuropsychology. Finally, chapters bring motivation down to earth in reviewing its impact within relationships, and in applied areas such as psychotherapy, work, education, sport, and physical activity. By providing reviews of the most advanced work by the very best scholars in this field, The Oxford Handbook of Human Motivation represents an invaluable resource for both researchers and practitioners, as well as any student of human nature.

Karl Popper's theory of falsification contends that scientific inquiry should aim not to verify hypotheses but to rigorously test and identify conditions under which they are false.

In this post, I share with you The 7 styles of learning illustrated with examples.

What is Adult Learning Theory, also known as Andragogy. Malcolm Knowles and the 5 Key principles in the workplace for using adult learning theory.

PRICES MAY VARY. Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook’s Artificial Intelligence Research Group. You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch. What You'll Learn Review machine learning fundamentals such as overfitting, underfitting, and regularization. - Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent. - Apply in-depth linear algebra with PyTorch - Explore PyTorch fundamentals andits building blocks - Work with tuning and optimizing models Who This Book Is For Beginners with a working knowledge of Python who want to understand Deep Learning in a practical, hands-on manner. Markiere, mache dir Notizen und suche im Buch

by Ashley Ahlbrand As I noted in a previous post, I am currently pursuing a graduate certificate in instructional design. The course I am taking this semester is heavily theory-based, and thus we h…