Learning Science for Instructional Designers: From Cognition to Application (1版)

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【簡介】 Ensure Your Instructional Design Stands Up to Learning ScienceLearning science is a professional imperative for instructional designers. In fact, instructional design is applied learning science. To create effective learning experiences that engage, we need to know how learning works and what facilitates and hinders it. We need to track the underlying research and articulate how our designs reflect what is known. Otherwise, how can we claim to be scrutable in our approaches? Learning Science for Instructional Designers: From Cognition to Application distills the current scope of learning science into an easy-to-read primer.Good instructional design makes learning as simple as possible by removing distractions, minimizing the cognitive load, and chunking necessary information into digestible bits. But our aim must go beyond enabling learners to recite facts to empowering them to make better decisions--decisions about what to do, when, and how. This book prepares you to design learning experiences that ensure retention over time and transfer to the appropriate situations. Gain insights into: -Providing spaced practice and reflection -Tapping into motivation and challenge to build learner confidence -Using performance-support tools, social learning, and humor appropriatelyPrompts at the end of each chapter will spark your thinking about how to use these concepts and more in your daily work.Written by Clark N. Quinn, author of Millennials, Goldfish & Other Training Misconceptions: Debunking Learning Myths and Superstitions, this book is perfect for anyone who strives for their instruction to stand up to learning science. 【目錄】

Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making (1版)

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【簡介】 Using real-world data case studies, this innovative and accessible textbook introduces an actionable framework for conducting trustworthy data science.Most textbooks present data science as a linear analytic process involving a set of statistical and computational techniques without accounting for the challenges intrinsic to real-world applications. Veridical Data Science, by contrast, embraces the reality that most projects begin with an ambiguous domain question and messy data; it acknowledges that datasets are mere approximations of reality while analyses are mental constructs. Bin Yu and Rebecca Barter employ the innovative Predictability, Computability, and Stability (PCS) framework to assess the trustworthiness and relevance of data-driven results relative to three sources of uncertainty that arise throughout the data science life cycle: the human decisions and judgment calls made during data collection, cleaning, and modeling. By providing real-world data case studies, intuitive explanations of common statistical and machine learning techniques, and supplementary R and Python code, Veridical Data Science offers a clear and actionable guide for conducting responsible data science. Requiring little background knowledge, this lucid, self-contained textbook provides a solid foundation and principled framework for future study of advanced methods in machine learning, statistics, and data science. Presents the Predictability, Computability, and Stability (PCS) methodology for producing trustworthy data-driven resultsTeaches how a data science project should be conducted from beginning to end, including extensive discussion of the data scientist’s decision-making processCultivates critical thinking throughout the entire data science life cycleProvides practical examples and illuminating case studies of real-world data analysis problems with associated code, exercises, and solutionsSuitable for advanced undergraduate and graduate students, domain scientists, and practitioners