By Scribd 2025 Chapter 1 5 Here The Expectations Perceptions Challenges And Solutions To Those Challenges Are Explained
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Corporate Social Responsibility
As a relatively young subject matter, corporate social responsibility has unsurprisingly developed and evolved in numerous ways since the first edition of this textbook was published. Retaining the features which made the first edition a top selling text in the field, the new edition continues to be the only textbook available which provides a ready-made, enhanced course pack for CSR classes. Authoritative editor introductions provide accessible entry points to the subjects covered - an approach which is particularly suited to advanced undergraduate and postgraduate teaching that emphasises a research-led approach. New case studies are integrated throughout the text to enable students to think and analyze the subject from every angle. The entire textbook reflects the global nature of CSR as a discipline and further pedagogical features include chapter learning outcomes; study questions; ‘challenges for practice’ boxes and additional ‘further reading’ features at the end of each chapter. This highly rated textbook now also benefits from a regularly updated companion website which features a brand new 'CSR Case Club' presenting students and lecturers with further case suggestions with which to enhance learning; lecture slides; updates from the popular Crane and Matten blog, links to further reading and career sites, YouTube clips and suggested answers to study questions. An Ivey CaseMate has also been created for this book at https://www.iveycases.com/CaseMateBookDetail.aspx?id=335.
How Learning Works
Author: Susan A. Ambrose
language: en
Publisher: John Wiley & Sons
Release Date: 2010-04-16
Praise for How Learning Works "How Learning Works is the perfect title for this excellent book. Drawing upon new research in psychology, education, and cognitive science, the authors have demystified a complex topic into clear explanations of seven powerful learning principles. Full of great ideas and practical suggestions, all based on solid research evidence, this book is essential reading for instructors at all levels who wish to improve their students' learning." —Barbara Gross Davis, assistant vice chancellor for educational development, University of California, Berkeley, and author, Tools for Teaching "This book is a must-read for every instructor, new or experienced. Although I have been teaching for almost thirty years, as I read this book I found myself resonating with many of its ideas, and I discovered new ways of thinking about teaching." —Eugenia T. Paulus, professor of chemistry, North Hennepin Community College, and 2008 U.S. Community Colleges Professor of the Year from The Carnegie Foundation for the Advancement of Teaching and the Council for Advancement and Support of Education "Thank you Carnegie Mellon for making accessible what has previously been inaccessible to those of us who are not learning scientists. Your focus on the essence of learning combined with concrete examples of the daily challenges of teaching and clear tactical strategies for faculty to consider is a welcome work. I will recommend this book to all my colleagues." —Catherine M. Casserly, senior partner, The Carnegie Foundation for the Advancement of Teaching "As you read about each of the seven basic learning principles in this book, you will find advice that is grounded in learning theory, based on research evidence, relevant to college teaching, and easy to understand. The authors have extensive knowledge and experience in applying the science of learning to college teaching, and they graciously share it with you in this organized and readable book." —From the Foreword by Richard E. Mayer, professor of psychology, University of California, Santa Barbara; coauthor, e-Learning and the Science of Instruction; and author, Multimedia Learning
Introduction to Artificial Intelligence
This accessible and engaging textbook presents a concise introduction to the exciting field of artificial intelligence (AI). The broad-ranging discussion covers the key subdisciplines within the field, describing practical algorithms and concrete applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks, and reinforcement learning. Fully revised and updated, this much-anticipated second edition also includes new material on deep learning. Topics and features: presents an application-focused and hands-on approach to learning, with supplementary teaching resources provided at an associated website; contains numerous study exercises and solutions, highlighted examples, definitions, theorems, and illustrative cartoons; includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; reports on developments in deep learning, including applications of neural networks to generate creative content such as text, music and art (NEW); examines performance evaluation of clustering algorithms, and presents two practical examples explaining Bayes’ theorem and its relevance in everyday life (NEW); discusses search algorithms, analyzing the cycle check, explaining route planning for car navigation systems, and introducing Monte Carlo Tree Search (NEW); includes a section in the introduction on AI and society, discussing the implications of AI on topics such as employment and transportation (NEW). Ideal for foundation courses or modules on AI, this easy-to-read textbook offers an excellent overview of the field for students of computer science and other technical disciplines, requiring no more than a high-school level of knowledge of mathematics to understand the material.