Informed Learning Applications is the latest volume of rigorous research in the Advances in Librarianship series. Edited by experienced librarian Kim L. Ranger, the eight contributions to this volume describe various practices extending Christine Bruce's informed learning theory across a range of educational spaces.

Informed Learning Applications is the latest volume of rigorous research in the Advances in Librarianship series. Edited by experienced librarian Kim L. Ranger, the eight contributions to this volume describe various practices extending Christine Bruce's informed learning theory across a range of educational spaces.

Learning and Development (L&D) programmes are too often based on fads, the latest trends or learning designers' personal preferences without critical evaluation. Evidence-Informed Learning Design allows learning professionals to move away from this type of approach by showing them how to assess and apply relevant scientific literature, learning science research and proven learning techniques to design their training in a way that will make a measurable difference to employee performance and overall business success. Packed with tips, tools and examples, Evidence-Informed Learning Design enables L&D and training professionals to save both time and money by ensuring that efforts are focused on designing learning that's proven to be effective. Covering techniques like interleaving and self-directed and self-regulated learning, as well as debunking myths and fallacies in the field, it covers how best to test, measure and reinforce learning in both online, offline and face-to-face scenarios. To ensure that employees develop the skills the business needs to succeed and that the L&D function is recognised as adding true organizational value, this book is essential reading for anyone responsible for designing learning.

"This book is written for a diverse audience of educators from many disciplines, curriculum designers, researchers, and administrators. While this book establishes both a new approach to learning design and an associated research agenda, it is also intended to be practical." "In this book you will find many examples of how people experience information use as they go about learning in different contexts.' --From the preface.

These specially-commissioned studies cover corpus-informed approaches to researching, teaching and learning English for Specific Purposes (ESP). The corpora used range from very large published corpora to small tailor-made collections of written and spoken text, as well as parallel and contrastive corpora, in both the hard and softer sciences. Designed to tackle the problems faced by a variety of first- and second-language ESP users (specialised translators, undergraduates, junior and experienced researchers, and language trainers), the breadth of approaches enables treatment of issues central to ESP and corpus research, from corpus compilation and analysis to new applications and data-driven learning. The first full-length book on applied corpus use in France, Corpus-Informed Research and Learning in ESP will be of interest not only to those working in the French context, but to a wide variety of language professionals – teachers, researchers or course designers – in many countries looking at ESP from different linguistic, cultural and educational perspectives.

Educators must both respond to the impact of trauma, and prevent trauma at school. Trauma-informed initiatives tend to focus on the challenging behaviors of students and ascribe them to circumstances that students are facing outside of school. This approach ignores the reality that inequity itself causes trauma, and that schools often heighten inequities when implementing trauma-informed practices that are not based in educational equity. In this fresh look at trauma-informed practice, Alex Shevrin Venet urges educators to shift equity to the center as they consider policies and professional development. Using a framework of six principles for equity-centered trauma-informed education, Venet offers practical action steps that teachers and school leaders can take from any starting point, using the resources and influence at their disposal to make shifts in practice, pedagogy, and policy. Overthrowing inequitable systems is a process, not an overnight change. But transformation is possible when educators work together, and teachers can do more than they realize from within their own classrooms.

The utilization of machine learning (ML) techniques to understand hidden patterns and build data-driven predictive models from complex multivariate datasets is rapidly increasing in many applied science and engineering disciplines, including geo-energy. Motivated by these developments, Machine Learning Applications in Subsurface Energy Resource Management presents a current snapshot of the state of the art and future outlook for ML applications to manage subsurface energy resources (e.g., oil and gas, geologic carbon sequestration, and geothermal energy). Covers ML applications across multiple application domains (reservoir characterization, drilling, production, reservoir modeling, and predictive maintenance) Offers a variety of perspectives from authors representing operating companies, universities, and research organizations Provides an array of case studies illustrating the latest applications of several ML techniques Includes a literature review and future outlook for each application domain This book is targeted at practicing petroleum engineers or geoscientists interested in developing a broad understanding of ML applications across several subsurface domains. It is also aimed as a supplementary reading for graduate-level courses and will also appeal to professionals and researchers working with hydrogeology and nuclear waste disposal.

Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field. Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers. KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML

Risk-informed Methods and Applications in Nuclear and Energy Engineering: Modelling, Experimentation, and Validation presents a comprehensive view of the latest technical approaches and experimental capabilities in nuclear energy engineering. Based on Idaho National Laboratory's popular summer school series, this book compiles a collection of entries on the cutting-edge research and knowledge presented by proponents and developers of current and future nuclear systems, focusing on the connection between modelling and experimental approaches. Included in this book are key topics such as probabilistic concepts for risk analysis, the survey of legacy reliability and risk analysis tools, and newly developed tools supporting dynamic probabilistic risk-assessment. This book is an insightful and inspiring compilation of work from top nuclear experts from INL. Industry professionals, researchers and academics working in nuclear engineering, safety, operations and training will gain a board picture of the current state-of-practice and be able to apply that to their own risk-assessment studies. - Based on Idaho National Laboratory's summer school series, this book is a collection of entries from proponents and developers of current and future nuclear systems - Provides an up-to-date view of current technical approaches and experimental capabilities in nuclear energy engineering, covering modeling and validation, and focusing on risk-informed methods and applications - Equips the reader with an understanding of various case studies and experimental validations to enable them to carry out a risk-assessment study

This book focuses on inquiry-based teaching, one of the five vital aspects of the instructional work of school librarians identified in the second edition of the IFLA School Library Guidelines (2015). Effective implementation of inquiry-based teaching and learning requires a consistent instructional approach, based on a model of inquiry that is built upon foundations of research and best practice. The book explains the importance and significance of inquiry as a process of learning; outlines the research underpinning this process of learning; describes ways in which models of inquiry have been developed; provides recommendations for implementing the use of such models; and demonstrates how the other core instructional activities of school librarians, such as literacy and reading promotion, media and information literacy instruction, technology integration and professional development of teachers, can be integrated into inquiry. Inquiry-based learning is part of “learning to be a learner,” a lifelong pursuit involving finding and using information. Inquiry develops the skills and understandings that learners need in new information environments, whether that be as students in post-secondary institutions, as producers and creators in workplaces, or as citizens in communities. Through inquiry-based teaching, school librarians help students to build the essential skills and understandings needed for dealing with complex learning challenges, including analysis, critical thinking, and problem solving. In this book, special attention is given to the development of students’ metacognitive abilities, which are essential to their becoming life-long and life-wide learners.