It is a book that models the types of issues that best suit different approaches and allows students to understand when to use mixed methods. Furthermore, its focus on theory and paradigms is done in a way that helps students decode their meaning." --MARTHA MONTERO-SIEBURTH, University of Massachusetts, BostonNew to the Second Edition:Because mixed methods research has come into its own since the publication of the first edition, every chapter now shows how to implement a mixed methods design in your proposal or plan as well as showing how to implement qualitative and quantitative approaches Ethical issues that may arise in quantitative, qualitative and mixed methods designs have been added to a new section in Chapter 3 Writing tips and considerations have been expanded and moved to the first part of the book to get your research plan started in the right direction The latest developments in qualitative inquiry, including advocacy, participatory, and emancipatory approaches have been added to Chapter 10 Mixed methods procedures (Chapter 11) show readers how to identify types of mixed methods strategy, select data collection and analysis approaches, and plan the overall structure of the study

    Clear explanations are detailed with many current examples.

    In it, Russell offers a nontechnical, undogmatic account of his philosophical criticism as it relates to arithmetic and logic. Rather than an exhaustive treatment, however, the influential philosopher and mathematician focuses on certain issues of mathematical logic that, to his mind, invalidated much traditional and contemporary philosophy.In dealing with such topics as number, order, relations, limits and continuity, propositional functions, descriptions, and classes, Russell writes in a clear, accessible manner, requiring neither a knowledge of mathematics nor an aptitude for mathematical symbolism. The result is a thought-provoking excursion into the fascinating realm where mathematics and philosophy meet — a philosophical classic that will be welcomed by any thinking person interested in this crucial area of modern thought.

    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

    With its unique combination of Japanese-style comics called manga and serious educational content, the EduManga format is already a hit in Japan.In The Manga Guide to Statistics, our heroine Rui is determined to learn about statistics to impress the dreamy Mr. Igarashi and begs her father for a tutor. Soon she's spending her Saturdays with geeky, bespectacled Mr. Yamamoto, who patiently teaches her all about the fundamentals of statistics: topics like data categorization, averages, graphing, and standard deviation.After all her studying, Rui is confident in her knowledge of statistics, including complex concepts like probability, coefficients of correlation, hypothesis tests, and tests of independence. But is it enough to impress her dream guy? Or maybe there's someone better, right in front of her?Reluctant statistics students of all ages will enjoy learning along with Rui in this charming, easy-to-read guide, which uses real-world examples like teen magazine quizzes, bowling games, test scores, and ramen noodle prices. Examples, exercises, and answer keys help you follow along and check your work. An appendix showing how to perform statistics calculations in Microsoft Excel makes it easy to put Rui's lessons into practice.This EduManga book is a translation from a bestselling series in Japan, co-published with Ohmsha, Ltd. of Tokyo, Japan.

    Its hands-on, completely accessible approach walks writers through each stage of the creative process, from the initial triggering idea to the revision of the final manuscript. It is unique in combing the three main aspects of creative writing instruction: process (finding inspiration, getting ideas on the page), craft (specific techniques like characterization), and anthology (learning by reading masters of the form). Succinct, clear definitions of basic terms of fiction are accompanied by examples, including excerpts from masterpieces of short fiction and essays as well as contemporary novels. A special highlight is Alice LaPlante's systematic debunking of many of the so-called rules of creative writing. This book is perfect for writers working alone as well as for creative writing classes, both introductory and advanced.

    This book will offer students a broad outline of essential mathematics and will help to fill in the gaps in their knowledge. The author explains the basic points and a few key results of all the most important undergraduate topics in mathematics, emphasizing the intuitions behind the subject. The topics include linear algebra, vector calculus, differential and analytical geometry, real analysis, point-set topology, probability, complex analysis, set theory, algorithms, and more. An annotated bibliography offers a guide to further reading and to more rigorous foundations.

    This is not the same as “doing math.” The latter usually involves the application of formulas, procedures, and symbolic manipulations; mathematical thinking is a powerful way of thinking about things in the world -- logically, analytically, quantitatively, and with precision. It is not a natural way of thinking, but it can be learned. Mathematicians, scientists, and engineers need to “do math,” and it takes many years of college-level education to learn all that is required. Mathematical thinking is valuable to everyone, and can be mastered in about six weeks by anyone who has completed high school mathematics. Mathematical thinking does not have to be about mathematics at all, but parts of mathematics provide the ideal target domain to learn how to think that way, and that is the approach taken by this short but valuable book. The book is written primarily for first and second year students of science, technology, engineering, and mathematics (STEM) at colleges and universities, and for high school students intending to study a STEM subject at university. Many students encounter difficulty going from high school math to college-level mathematics. Even if they did well at math in school, most are knocked off course for a while by the shift in emphasis, from the K-12 focus on mastering procedures to the “mathematical thinking” characteristic of much university mathematics. Though the majority survive the transition, many do not. To help them make the shift, colleges and universities often have a “transition course.” This book could serve as a textbook or a supplementary source for such a course. Because of the widespread applicability of mathematical thinking, however, the book has been kept short and written in an engaging style, to make it accessible to anyone who seeks to extend and improve their analytic thinking skills. Going beyond a basic grasp of analytic thinking that everyone can benefit from, the STEM student who truly masters mathematical thinking will find that college-level mathematics goes from being confusing, frustrating, and at times seemingly impossible, to making sense and being hard but doable. Dr. Keith Devlin is a professional mathematician at Stanford University and the author of 31 previous books and over 80 research papers. His books have earned him many awards, including the Pythagoras Prize, the Carl Sagan Award, and the Joint Policy Board for Mathematics Communications Award. He is known to millions of NPR listeners as “the Math Guy” on Weekend Edition with Scott Simon. He writes a popular monthly blog “Devlin’s Angle” for the Mathematical Association of America, another blog under the name “profkeithdevlin”, and also blogs on various topics for the Huffington Post.

    This long-awaited revision contains changes throughout the text. There are new implementations of most of the major programming systems in the book, including the interpreters and compilers, and the authors have incorporated many small changes that reflect their experience teaching the course at MIT since the first edition was published. A new theme has been introduced that emphasizes the central role played by different approaches to dealing with time in computational models: objects with state, concurrent programming, functional programming and lazy evaluation, and nondeterministic programming. There are new example sections on higher-order procedures in graphics and on applications of stream processing in numerical programming, and many new exercises. In addition, all the programs have been reworked to run in any Scheme implementation that adheres to the IEEE standard.

    We’ve combed through hundreds of articles in the Harvard Business Review archive and selected the most important ones to help you boost your emotional skills—and your professional success.This book will inspire you to:• Monitor and channel your moods and emotions• Make smart, empathetic people decisions• Manage conflict and regulate emotions within your team• React to tough situations with resilience• Better understand your strengths, weaknesses, needs, values, and goals• Develop emotional agility

    Throughout, the emphasis is on promoting a plain English style. This little book can help you communicate more effectively by showing you how to enliven your sentences.

    Providing a concise introduction to abstract algebra, this work unfolds some of the fundamental systems with the aim of reaching applicable, significant results.

    Since then, the ways we process written language have changed dramatically with many concerned about both their own changes and that of children. New research on the reading brain chronicles these changes in the brains of children and adults as they learn to read while immersed in a digitally dominated medium.Drawing deeply on this research, this book comprises a series of letters Wolf writes to us—her beloved readers—to describe her concerns and her hopes about what is happening to the reading brain as it unavoidably changes to adapt to digital mediums. Wolf raises difficult questions, including:Will children learn to incorporate the full range of "deep reading" processes that are at the core of the expert reading brain?Will the mix of a seemingly infinite set of distractions for children’s attention and their quick access to immediate, voluminous information alter their ability to think for themselves?With information at their fingertips, will the next generation learn to build their own storehouse of knowledge, which could impede the ability to make analogies and draw inferences from what they know?Will all these influences, in turn, change the formation in children and the use in adults of "slower" cognitive processes like critical thinking, personal reflection, imagination, and empathy that comprise deep reading and that influence both how we think and how we live our lives?Will the chain of digital influences ultimately influence the use of the critical analytical and empathic capacities necessary for a democratic society?How can we preserve deep reading processes in future iterations of the reading brain?Who are the "good readers" of every epoch?Concerns about attention span, critical reasoning, and over-reliance on technology are never just about children—Wolf herself has found that, though she is a reading expert, her ability to read deeply has been impacted as she has become, inevitably, increasingly dependent on screens.Wolf draws on neuroscience, literature, education, technology, and philosophy and blends historical, literary, and scientific facts with down-to-earth examples and warm anecdotes to illuminate complex ideas that culminate in a proposal for a biliterate reading brain. Provocative and intriguing, Reader, Come Home is a roadmap that provides a cautionary but hopeful perspective on the impact of technology on our brains and our most essential intellectual capacities—and what this could mean for our future.

    Never again will you order the Poisson Distribution in a French restaurant!This updated version features all new material.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases