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】

Computing and Technology Ethics: Engaging Through Science Fiction (1版)

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【簡介】 A new approach to teaching computing and technology ethics using science fiction stories.Should autonomous weapons be legal? Will we be cared for by robots in our old age? Does the efficiency of online banking outweigh the risk of theft? From communication to travel to medical care, computing technologies have transformed our daily lives, for better and for worse. But how do we know when a new development comes at too high a cost? Using science fiction stories as case studies of ethical ambiguity, this engaging textbook offers a comprehensive introduction to ethical theory and its application to contemporary developments in technology and computer science. Computing and Technology Ethics: Engaging through Science Fiction first introduces the major ethical frameworks: deontology, utilitarianism, virtue ethics, communitarianism, and the modern responses of responsibility ethics, feminist ethics, and capability ethics. It then applies these frameworks to many of the modern issues arising in technology ethics including privacy, computing, and artificial intelligence. A corresponding anthology of science fiction brings these quandaries to life and challenges students to ask ethical questions of themselves and their work. Uses science fiction case studies to make ethics education engaging and fun Trains students to recognize, evaluate, and respond to ethical problems as they ariseFeatures anthology of short stories from internationally acclaimed writers including Ken Liu, Elizabeth Bear, Paolo Bacigalupi, and T. C. Boyle to animate ethical challenges in computing technology Written by interdisciplinary author team of computer scientists and ethical theoristsIncludes a robust suite of instructor resources, such as pedagogy guides, story frames, and reflection questions

Big Data Science in Finance: Mathematics and Applications (1版)

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【簡介】 Explains the mathematics, theory, and methods of Big Data as applied to finance and investingData science has fundamentally changed Wall Street--applied mathematics and software code are increasingly driving finance and investment-decision tools. Big Data Science in Finance examines the mathematics, theory, and practical use of the revolutionary techniques that are transforming the industry. Designed for mathematically-advanced students and discerning financial practitioners alike, this energizing book presents new, cutting-edge content based on world-class research taught in the leading Financial Mathematics and Engineering programs in the world. Marco Avellaneda, a leader in quantitative finance, and quantitative methodology author Irene Aldridge help readers harness the power of Big Data.Comprehensive in scope, this book offers in-depth instruction on how to separate signal from noise, how to deal with missing data values, and how to utilize Big Data techniques in decision-making. Key topics include data clustering, data storage optimization, Big Data dynamics, Monte Carlo methods and their applications in Big Data analysis, and more. This valuable book: Provides a complete account of Big Data that includes proofs, step-by-step applications, and code samplesExplains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD)Covers vital topics in the field in a clear, straightforward mannerCompares, contrasts, and discusses Big Data and Small DataIncludes Cornell University-tested educational materials such as lesson plans, end-of-chapter questions, and downloadable lecture slidesBig Data Science in Finance: Mathematics and Applications is an important, up-to-date resource for students in economics, econometrics, finance, applied mathematics, industrial engineering, and business courses, and for investment managers, quantitative traders, risk and portfolio managers, and other financial practitioners.

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