Action Learning In Action

Marquardt brings together the six essential elements with realistic advice, practical wisdom, and such tools as checklists and a comprehensive glossary of terms. Readers can learn to leverage action learning to solve problems, develop employees, enhance personal growth, and create organizational learning.

Previous editions of Action Learning in Practice established this authoritative overview of action learning around the world. Over the last decade the move towards action-based organizational learning and development has accelerated, and action learning is now an established part of the education and development mainstream in large and small organizations. Fully revised and updated, this fourth edition covers the origins of action learning with Reg Revans' ideas, and looks at their development and application today. Action learning is self-directed learning through tackling business and work problems with the support of peers and colleagues. A professional and diverse workforce, attracted, influenced and developed in this way is more able to deal effectively with the growing complexity and pressures of working life. As the limits of conventional training and development become more obvious, leaders are increasingly attracted to action-based approaches to learning when seeking better outcomes and returns on investment.

Action learning is a continued process of learning and reflection with the support of a group of colleagues, working on real issues. The action learning method is increasingly used to bring innovation to many different fields of work. The principles of action learning can achieve improvement and transformation in a wide range of applications and disciplines, including professional training and educational contexts. This book is a comprehensive guide to action learning which maintains an accessible, practical focus throughout. It is packed with useful resources, including case studies and ideas for workshop sessions. Key topics covered include: * action learning in professional and educational settings * setting up, facilitating and evaluating an action learning programme * the roles and skills required to practice successfully * use of action learning in relation to the individual, the group and the organization * the role of reflection; and action learning theory. Newcomers to the area of action learning will find this an essential introduction which can be put to use straight away, while more experienced practitioners seeking a deeper understanding will value the thorough analysis of action learning theory.

Most managers today understand the value of building a learning organization. Their goal is to leverage knowledge and make it a key corporate asset, yet they remain uncertain about how best to get started. What they lack are guidelines and tools that transform abstract theory—the learning organization as an ideal—into hands-on implementation. For the first time in Learning in Action, David Garvin helps managers make the leap from theory to proven practice. Garvin argues that at the heart of organizational learning lies a set of processes that can be designed, deployed, and led. He starts by describing the basic steps in every learning process—acquiring, interpreting, and applying knowledge—then examines the critical challenges facing managers at each of these stages and the various ways the challenges can be met. Drawing on decades of scholarship and a wealth of examples from a wide range of fields, Garvin next introduces three modes of learning—intelligence gathering, experience, and experimentation—and shows how each mode is most effectively deployed. These approaches are brought to life in complete, richly detailed case studies of learning in action at organizations such as Xerox, L. L. Bean, the U. S. Army, and GE. The book concludes with a discussion of the leadership role that senior executives must play to make learning a day-to-day reality in their organizations.

Shift to blended learning to transform education Blended learning has the power to reinvent education, but the transition requires a new approach to learning and a new skillset for educators. Loaded with research and examples, Blended Learning in Action demonstrates the advantages a blended model has over traditional instruction when technology is used to engage students both inside the classroom and online. Readers will find: Breakdowns of the most effective classroom setups for blended learning Tips for leaders Ideas for personalizing and differentiating instruction using technology Strategies for managing devices in schools Questions to facilitate professional development and deeper learning

The Third Edition of the field-defining book Originated by Reg Revans in the 1940s, the Action Learning Model was refined and then reintroduced by lead author Michael Marquardt to organizations globally as a powerful tool for improving organizational performance. Today, Marquardt is widely considered to be the modern "father" of the Action Learning approach. For this new edition, Marquardt has teamed up with three Action Learning experts from Asia and the UK who bring a broader global approach to what has become THE seminal book in the field. Each chapter has been updated for alignment with today's practice and implementation of Action Learning in organizations, including fresh information on virtual Action Learning, guidance on implementing Action Learning and becoming a Certified Action Learning Coach, and many new case studies.

Summary Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos Table of Contents PART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naïve Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce

First Published in 2001. Routledge is an imprint of Taylor & Francis, an informa company.