Teaching Primary Science Constructively helps readers to create effective science learning experiences for primary students by using a constructivist approach to learning. This best-selling text explains the principles of constructivism and their implications for learning and teaching, and discusses core strategies for developing science understanding and science inquiry processes and skills. Chapters also provide research-based ideas for implementing a constructivist approach within a number of content strands. Throughout there are strong links to the key ideas, themes and terminology of the revised Australian Curriculum: Science. This sixth edition includes a new introductory chapter addressing readers' preconceptions and concerns about teaching primary science.

The book focuses on different variants of decision tree induction but also describes the meta-learning approach in general which is applicable to other types of machine learning algorithms. The book discusses different variants of decision tree induction and represents a useful source of information to readers wishing to review some of the techniques used in decision tree learning, as well as different ensemble methods that involve decision trees. It is shown that the knowledge of different components used within decision tree learning needs to be systematized to enable the system to generate and evaluate different variants of machine learning algorithms with the aim of identifying the top-most performers or potentially the best one. A unified view of decision tree learning enables to emulate different decision tree algorithms simply by setting certain parameters. As meta-learning requires running many different processes with the aim of obtaining performance results, a detailed description of the experimental methodology and evaluation framework is provided. Meta-learning is discussed in great detail in the second half of the book. The exposition starts by presenting a comprehensive review of many meta-learning approaches explored in the past described in literature, including for instance approaches that provide a ranking of algorithms. The approach described can be related to other work that exploits planning whose aim is to construct data mining workflows. The book stimulates interchange of ideas between different, albeit related, approaches.

Winner of the Pulitzer Prize in Fiction Winner of the William Dean Howells Medal Shortlisted for the Man Booker Prize Over One Year on the New York Times Bestseller List A New York Times Notable Book and a Washington Post, Time, Oprah Magazine, Newsweek, Chicago Tribune, and Kirkus Reviews Best Book of the Year "The best novel ever written about trees, and really just one of the best novels, period." —Ann Patchett The Overstory, winner of the 2019 Pulitzer Prize in Fiction, is a sweeping, impassioned work of activism and resistance that is also a stunning evocation of—and paean to—the natural world. From the roots to the crown and back to the seeds, Richard Powers’s twelfth novel unfolds in concentric rings of interlocking fables that range from antebellum New York to the late twentieth-century Timber Wars of the Pacific Northwest and beyond. There is a world alongside ours—vast, slow, interconnected, resourceful, magnificently inventive, and almost invisible to us. This is the story of a handful of people who learn how to see that world and who are drawn up into its unfolding catastrophe.