This book speaks about physics discoveries that intertwine mathematical reasoning, modeling, and scientific inquiry. It offers ways of bringing together the structural domain of mathematics and the content of physics in one coherent inquiry. Teaching and learning physics is challenging because students lack the skills to merge these learning paradigms. The purpose of this book is not only to improve access to the understanding of natural phenomena but also to inspire new ways of delivering and understanding the complex concepts of physics. To sustain physics education in college classrooms, authentic training that would help develop high school students' skills of transcending function modeling techniques to reason scientifically is needed and this book aspires to offer such training The book draws on current research in developing students' mathematical reasoning. It identifies areas for advancements and proposes a conceptual framework that is tested in several case studies designed using that framework. Modeling Newton's laws using limited case analysis, Modeling projectile motion using parametric equations and Enabling covariational reasoning in Einstein formula for the photoelectric effect represent some of these case studies. A wealth of conclusions that accompany these case studies, drawn from the realities of classroom teaching, is to help physics teachers and researchers adopt these ideas in practice.

This book speaks about physics discoveries that intertwine mathematical reasoning, modeling, and scientific inquiry. It offers ways of bringing together the structural domain of mathematics and the content of physics in one coherent inquiry. Teaching and learning physics is challenging because students lack the skills to merge these learning paradigms. The purpose of this book is not only to improve access to the understanding of natural phenomena but also to inspire new ways of delivering and understanding the complex concepts of physics. To sustain physics education in college classrooms, authentic training that would help develop high school students' skills of transcending function modeling techniques to reason scientifically is needed and this book aspires to offer such training The book draws on current research in developing students' mathematical reasoning. It identifies areas for advancements and proposes a conceptual framework that is tested in several case studies designed using that framework. Modeling Newton's laws using limited case analysis, Modeling projectile motion using parametric equations and Enabling covariational reasoning in Einstein formula for the photoelectric effect represent some of these case studies. A wealth of conclusions that accompany these case studies, drawn from the realities of classroom teaching, is to help physics teachers and researchers adopt these ideas in practice.

This valuable resource provides an overview of recent research and strategies in developing and applying modelling to promote practice-based research in STEM education. In doing so, it bridges barriers across academic disciplines by suggesting activities that promote integration of qualitative science concepts with the tools of mathematics and engineering. The volume's three parts offer a comprehensive review, by 1) Presenting a conceptual background of how scientific inquiry can be induced in mathematics classes considering recommendations of prior research, 2) Collecting case studies that were designed using scientific inquiry process designed for math classes, and 3) Exploring future possibilities and directions for the research included within. Among the topics discussed: * STEM education: A platform for multidisciplinary learning. * Teaching and learning representations in STEM. * Formulating conceptual framework for multidisciplinary STEM modeling. * Exploring function continuity in context. * Exploring function transformations using a dynamic system. Scientific Inquiry in Mathematics - Theory and Practice delivers hands-on and concrete strategies for effective STEM teaching in practice to educators within the fields of mathematics, science, and technology. It will be of interest to practicing and future mathematics teachers at all levels, as well as teacher educators, mathematics education researchers, and undergraduate and graduate mathematics students interested in research based methods for integrating inquiry-based learning into STEM classrooms.

This valuable resource provides an overview of recent research and strategies in developing and applying modelling to promote practice-based research in STEM education. In doing so, it bridges barriers across academic disciplines by suggesting activities that promote integration of qualitative science concepts with the tools of mathematics and engineering. The volume's three parts offer a comprehensive review, by 1) Presenting a conceptual background of how scientific inquiry can be induced in mathematics classes considering recommendations of prior research, 2) Collecting case studies that were designed using scientific inquiry process designed for math classes, and 3) Exploring future possibilities and directions for the research included within. Among the topics discussed: * STEM education: A platform for multidisciplinary learning. * Teaching and learning representations in STEM. * Formulating conceptual framework for multidisciplinary STEM modeling. * Exploring function continuity in context. * Exploring function transformations using a dynamic system. Scientific Inquiry in Mathematics - Theory and Practice delivers hands-on and concrete strategies for effective STEM teaching in practice to educators within the fields of mathematics, science, and technology. It will be of interest to practicing and future mathematics teachers at all levels, as well as teacher educators, mathematics education researchers, and undergraduate and graduate mathematics students interested in research based methods for integrating inquiry-based learning into STEM classrooms.

This book deals with recent developments in classification and data analysis and presents new topics which are of central interest to modern statistics. In particular, these include: classification models and clustering methods, multivariate data analysis, symbolic data, neural networks and learning devices, phylogeny and bioinformatics, new software systems for classification and data analysis, as well as applications in social, economic, biological, medical and other sciences. The book presents a long list of useful methods for classification, clustering and data analysis. By combining theoretical aspects with practical problems it is designed for researchers as well as for applied statisticians and will support the fast transfer of new methodological advances to a wide range of applications.