We increasingly view the world around us as a product of science and technology. Accordingly, we have begun to appreciate that science does not take its problems only from nature and then produces technological applications, but that the very problems of scientific research themselves are generated by science and technology. Simultaneously, problems like global warming, the toxicology of nanoparticles, or the use of renewable energies are constituted by many factors that interact with great complexity. Science in the context of application is challenged to gain new understanding and control of such complexity—it cannot seek shelter in the ivory tower or simply pursue its internal quest for understanding and gradual improvement of grand theories. Science in the Context of Application will identify, explore and assess these changes. Part I considers the "Changing Conditions of Scientific Research" and part II "Science, Values, and Society". Examples are drawn from pharmaceutical research, the information sciences, simulation modelling, nanotechnology, cancer research, the effects of commercialization, and many other fields. The book assembles papers from well-known European and American Science Studies scholars like Bernadette Bensaude-Vincent, Janet Kourany, Michael Mahoney, Margaret Morrison, Hans-Jörg Rheinberger, Arie Rip, Dan Sarewitz, Peter Weingart, and others. The individual chapters are written to address anyone who is concerned about the role of contemporary science in society, including scientists, philosophers, and policy makers.

Science and technology are embedded in virtually every aspect of modern life. As a result, people face an increasing need to integrate information from science with their personal values and other considerations as they make important life decisions about medical care, the safety of foods, what to do about climate change, and many other issues. Communicating science effectively, however, is a complex task and an acquired skill. Moreover, the approaches to communicating science that will be most effective for specific audiences and circumstances are not obvious. Fortunately, there is an expanding science base from diverse disciplines that can support science communicators in making these determinations. Communicating Science Effectively offers a research agenda for science communicators and researchers seeking to apply this research and fill gaps in knowledge about how to communicate effectively about science, focusing in particular on issues that are contentious in the public sphere. To inform this research agenda, this publication identifies important influences â€" psychological, economic, political, social, cultural, and media-related â€" on how science related to such issues is understood, perceived, and used.

This volume addresses context from three comprehensive perspectives: first, its importance, the issues surrounding context, and its value in the laboratory and the field; second, the theory guiding the AI used to model its context; and third, its applications in the field (e.g., decision-making). This breadth poses a challenge. The book analyzes how the environment (context) influences human perception, cognition and action. While current books approach context narrowly, the major contribution of this book is to provide an in-depth review over a broad range of topics for a computational context no matter its breadth. The volume outlines numerous strategies and techniques from world-class scientists who have adapted their research to solve different problems with AI, in difficult environments and complex domains to address the many computational challenges posed by context. Context can be clear, uncertain or an illusion. Clear contexts: A father praising his child; a trip to the post office to buy stamps; a policewoman asking for identification. Uncertain contexts: A sneak attack; a surprise witness in a courtroom; a shout of "Fire! Fire!" Contexts as illusion: Humans fall prey to illusions that machines do not (Adelson’s checkerboard illusion versus a photometer). Determining context is not easy when disagreement exists, interpretations vary, or uncertainty reigns. Physicists like Einstein (relativity), Bekenstein (holographs) and Rovelli (universe) have written that reality is not what we commonly believe. Even outside of awareness, individuals act differently whether alone or in teams. Can computational context with AI adapt to clear and uncertain contexts, to change over time, and to individuals, machines or robots as well as to teams? If a program automatically "knows" the context that improves performance or decisions, does it matter whether context is clear, uncertain or illusory? Written and edited by world class leaders from across the field of autonomous systems research, this volume carefully considers the computational systems being constructed to determine context for individual agents or teams, the challenges they face, and the advances they expect for the science of context.

In pre-publication, the authors were listed as editors.

Science, engineering, and technology permeate nearly every facet of modern life and hold the key to solving many of humanity's most pressing current and future challenges. The United States' position in the global economy is declining, in part because U.S. workers lack fundamental knowledge in these fields. To address the critical issues of U.S. competitiveness and to better prepare the workforce, A Framework for K-12 Science Education proposes a new approach to K-12 science education that will capture students' interest and provide them with the necessary foundational knowledge in the field. A Framework for K-12 Science Education outlines a broad set of expectations for students in science and engineering in grades K-12. These expectations will inform the development of new standards for K-12 science education and, subsequently, revisions to curriculum, instruction, assessment, and professional development for educators. This book identifies three dimensions that convey the core ideas and practices around which science and engineering education in these grades should be built. These three dimensions are: crosscutting concepts that unify the study of science through their common application across science and engineering; scientific and engineering practices; and disciplinary core ideas in the physical sciences, life sciences, and earth and space sciences and for engineering, technology, and the applications of science. The overarching goal is for all high school graduates to have sufficient knowledge of science and engineering to engage in public discussions on science-related issues, be careful consumers of scientific and technical information, and enter the careers of their choice. A Framework for K-12 Science Education is the first step in a process that can inform state-level decisions and achieve a research-grounded basis for improving science instruction and learning across the country. The book will guide standards developers, teachers, curriculum designers, assessment developers, state and district science administrators, and educators who teach science in informal environments.

Four leading experts convey the promise of data science and examine challenges in achieving its benefits and mitigating some harms.

This book presents an international perspective of the influence of educational context on science education. The focus is on the interactions between curriculum development and implementation, particularly in non-Western and non-English-speaking contexts (i.e., outside the UK, USA, Australia, NZ, etc. ). An important and distinguishing feature of the book is that it draws upon the experiences and research from local experts from an extremely diverse cohort across the world (26 countries in total). The book addresses topics such as: curriculum development; research or evaluation of an implemented curriculum; discussion of pressures driving curriculum reform or implementation of new curricula (e. g., technology or environmental education); the influence of political, cultural, societal or religious mores on education; governmental or ministerial drives for curriculum reform; economic or other pressures driving curriculum reform; the influence of external assessment regimes on curriculum; and so on.

Simulations, games, and real or virtual apprenticeships are the kinds of context-rich learning environments that leverage the power of episodic memory, boost student motivation, and create the conditions that make learning transfer more likely. Context-rich learning embodies the answer to the question, “Why do we need to learn this?” Many students, especially those who have dyslexia or otherwise neurodivergent brains, require the kind of context that is lacking in education. Designing Context-Rich Learning by Extending Reality is a collection of research and readings on the context-rich learning approach that examines the aspects of design, delivery, and assessments. The book proposes novel approaches to designing and delivering context-rich learning across all disciplines. Covering topics such as learning experiences, sustainable development, and user experience design, this premier reference source is a valuable resource of inspiration to learning and training facilitators, instructional designers, assessment developers, instructional technologists, psychometricians, educators and administrators of both higher and K-12 education, pre-service teachers, teacher educators, researchers, and academicians.

One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some fear that it may be a symptom of a lack of rigor in science, while others argue that such an observed inconsistency can be an important precursor to new discovery. Concerns about reproducibility and replicability have been expressed in both scientific and popular media. As these concerns came to light, Congress requested that the National Academies of Sciences, Engineering, and Medicine conduct a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research. Reproducibility and Replicability in Science defines reproducibility and replicability and examines the factors that may lead to non-reproducibility and non-replicability in research. Unlike the typical expectation of reproducibility between two computations, expectations about replicability are more nuanced, and in some cases a lack of replicability can aid the process of scientific discovery. This report provides recommendations to researchers, academic institutions, journals, and funders on steps they can take to improve reproducibility and replicability in science.