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The purpose of this book is to introduce some of the array of models of teaching that have been developed, polished & studied over the last twenty five years. Teachers, advisers, inspectors, teacher educators & educational researchers who study these models will discover elegant modes of teaching that have great power for learners. The book also contains peer coaching guides.
Book Synopsis Models of Learning by : Bruce R. Joyce
Download or read book Models of Learning written by Bruce R. Joyce and published by . This book was released on 1997 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to introduce some of the array of models of teaching that have been developed, polished & studied over the last twenty five years. Teachers, advisers, inspectors, teacher educators & educational researchers who study these models will discover elegant modes of teaching that have great power for learners. The book also contains peer coaching guides.
The pioneering research and theories of Norbert Seel have had a profound impact on educational thought in mathematics. In this special tribute, an international panel of researchers presents the current state of model-based education: its research, methodology, and technology. Fifteen stimulating, sometimes playful chapters link the multiple ways of constructing knowledge to the complex real world of skill development. This synthesis of latest innovations and fresh perspectives on classic constructs makes the book cutting-edge reading for the researchers and educators in mathematics instruction building the next generation of educational models.
Book Synopsis Understanding Models for Learning and Instruction: by : Dirk Ifenthaler
Download or read book Understanding Models for Learning and Instruction: written by Dirk Ifenthaler and published by Springer Science & Business Media. This book was released on 2008-02-22 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: The pioneering research and theories of Norbert Seel have had a profound impact on educational thought in mathematics. In this special tribute, an international panel of researchers presents the current state of model-based education: its research, methodology, and technology. Fifteen stimulating, sometimes playful chapters link the multiple ways of constructing knowledge to the complex real world of skill development. This synthesis of latest innovations and fresh perspectives on classic constructs makes the book cutting-edge reading for the researchers and educators in mathematics instruction building the next generation of educational models.
Cognitive psychologists have found the production systems class of computer simulation models to be one of the most direct ways to cast complex theories of human intelligence. There have been many scattered studies on production systems since they were first proposed as computational models of human problem-solving behavior by Allen Newell some twenty years ago, but this is the first book to focus exclusively on these important models of human cognition, collecting and giving many of the best examples of current research. In the first chapter, Robert Neches, Pat Langley, and David Klahr provide an overview of the fundamental issues involved in using production systems as a medium for theorizing about cognitive processes, emphasizing their theoretical power. The remaining chapters take up learning by doing and learning by understanding, discrimination learning, learning through incremental refinement, learning by chunking, procedural earning, and learning by composition. A model of cognitive development called BAIRN is described, and a final chapter reviews John Anderson's ACT theory and discusses how it can be used in intelligent tutoring systems, including one that teaches LISP programming skills. In addition to the editors, the contributors are Yuichiro Anzai (Hokkaido University, Japan), Paul Rosenbloom (Stanford) and Allen Newell (Carnegie-Mellon), Stellan Ohlsson (University of Pittsburgh), Clayton Lewis (University of Colorado, Boulder), Iain Wallace and Kevin Bluff (Deakon University, Australia), and John Anderson (Carnegie-Mellon). David Klahr is Professor and Head of the Department of Psychology at Carnegie-Mellon University. Pat Langley is Associate Professor, Department ofInformation and Computer Science, University of California, Irvine, and Robert Neches is Research Computer Scientist at University of Southern California Information Sciences Institute. "Production System Models of Learning and Development" is included in the series Computational Models of Cognition and Perception, edited by Jerome A. Feldman, Patrick J. Hayes, and David E.Rumelhart. A Bradford Book.
Book Synopsis Production System Models of Learning and Development by : David Klahr
Download or read book Production System Models of Learning and Development written by David Klahr and published by MIT Press. This book was released on 1987 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cognitive psychologists have found the production systems class of computer simulation models to be one of the most direct ways to cast complex theories of human intelligence. There have been many scattered studies on production systems since they were first proposed as computational models of human problem-solving behavior by Allen Newell some twenty years ago, but this is the first book to focus exclusively on these important models of human cognition, collecting and giving many of the best examples of current research. In the first chapter, Robert Neches, Pat Langley, and David Klahr provide an overview of the fundamental issues involved in using production systems as a medium for theorizing about cognitive processes, emphasizing their theoretical power. The remaining chapters take up learning by doing and learning by understanding, discrimination learning, learning through incremental refinement, learning by chunking, procedural earning, and learning by composition. A model of cognitive development called BAIRN is described, and a final chapter reviews John Anderson's ACT theory and discusses how it can be used in intelligent tutoring systems, including one that teaches LISP programming skills. In addition to the editors, the contributors are Yuichiro Anzai (Hokkaido University, Japan), Paul Rosenbloom (Stanford) and Allen Newell (Carnegie-Mellon), Stellan Ohlsson (University of Pittsburgh), Clayton Lewis (University of Colorado, Boulder), Iain Wallace and Kevin Bluff (Deakon University, Australia), and John Anderson (Carnegie-Mellon). David Klahr is Professor and Head of the Department of Psychology at Carnegie-Mellon University. Pat Langley is Associate Professor, Department ofInformation and Computer Science, University of California, Irvine, and Robert Neches is Research Computer Scientist at University of Southern California Information Sciences Institute. "Production System Models of Learning and Development" is included in the series Computational Models of Cognition and Perception, edited by Jerome A. Feldman, Patrick J. Hayes, and David E.Rumelhart. A Bradford Book.
Models of Teaching: Connecting Student Learning with Standards features classic and contemporary models of teaching appropriate to elementary and secondary settings. Authors Jeanine M. Dell'Olio and Tony Donk use detailed case studies to discuss 10 models of teaching and demonstrate how they can be connected to state content standards and benchmarks, as well as technology standards. This book provides readers with the theoretical and practical understandings of how to use models of teaching to both meet and exceed the growing expectations for research based instructional practices and student achievement.
Book Synopsis Models of Teaching by : Jeanine M. Dell'Olio
Download or read book Models of Teaching written by Jeanine M. Dell'Olio and published by SAGE Publications. This book was released on 2007-02-26 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: Models of Teaching: Connecting Student Learning with Standards features classic and contemporary models of teaching appropriate to elementary and secondary settings. Authors Jeanine M. Dell'Olio and Tony Donk use detailed case studies to discuss 10 models of teaching and demonstrate how they can be connected to state content standards and benchmarks, as well as technology standards. This book provides readers with the theoretical and practical understandings of how to use models of teaching to both meet and exceed the growing expectations for research based instructional practices and student achievement.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Book Synopsis Interpretable Machine Learning by : Christoph Molnar
Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
In the past decade, a number of different research communities within the computational sciences have studied learning in networks, starting from a number of different points of view. There has been substantial progress in these different communities and surprising convergence has developed between the formalisms. The awareness of this convergence and the growing interest of researchers in understanding the essential unity of the subject underlies the current volume. Two research communities which have used graphical or network formalisms to particular advantage are the belief network community and the neural network community. Belief networks arose within computer science and statistics and were developed with an emphasis on prior knowledge and exact probabilistic calculations. Neural networks arose within electrical engineering, physics and neuroscience and have emphasised pattern recognition and systems modelling problems. This volume draws together researchers from these two communities and presents both kinds of networks as instances of a general unified graphical formalism. The book focuses on probabilistic methods for learning and inference in graphical models, algorithm analysis and design, theory and applications. Exact methods, sampling methods and variational methods are discussed in detail. Audience: A wide cross-section of computationally oriented researchers, including computer scientists, statisticians, electrical engineers, physicists and neuroscientists.
Book Synopsis Learning in Graphical Models by : M.I. Jordan
Download or read book Learning in Graphical Models written by M.I. Jordan and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 658 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the past decade, a number of different research communities within the computational sciences have studied learning in networks, starting from a number of different points of view. There has been substantial progress in these different communities and surprising convergence has developed between the formalisms. The awareness of this convergence and the growing interest of researchers in understanding the essential unity of the subject underlies the current volume. Two research communities which have used graphical or network formalisms to particular advantage are the belief network community and the neural network community. Belief networks arose within computer science and statistics and were developed with an emphasis on prior knowledge and exact probabilistic calculations. Neural networks arose within electrical engineering, physics and neuroscience and have emphasised pattern recognition and systems modelling problems. This volume draws together researchers from these two communities and presents both kinds of networks as instances of a general unified graphical formalism. The book focuses on probabilistic methods for learning and inference in graphical models, algorithm analysis and design, theory and applications. Exact methods, sampling methods and variational methods are discussed in detail. Audience: A wide cross-section of computationally oriented researchers, including computer scientists, statisticians, electrical engineers, physicists and neuroscientists.
First released in the Spring of 1999, How People Learn has been expanded to show how the theories and insights from the original book can translate into actions and practice, now making a real connection between classroom activities and learning behavior. This edition includes far-reaching suggestions for research that could increase the impact that classroom teaching has on actual learning. Like the original edition, this book offers exciting new research about the mind and the brain that provides answers to a number of compelling questions. When do infants begin to learn? How do experts learn and how is this different from non-experts? What can teachers and schools do-with curricula, classroom settings, and teaching methods--to help children learn most effectively? New evidence from many branches of science has significantly added to our understanding of what it means to know, from the neural processes that occur during learning to the influence of culture on what people see and absorb. How People Learn examines these findings and their implications for what we teach, how we teach it, and how we assess what our children learn. The book uses exemplary teaching to illustrate how approaches based on what we now know result in in-depth learning. This new knowledge calls into question concepts and practices firmly entrenched in our current education system. Topics include: How learning actually changes the physical structure of the brain. How existing knowledge affects what people notice and how they learn. What the thought processes of experts tell us about how to teach. The amazing learning potential of infants. The relationship of classroom learning and everyday settings of community and workplace. Learning needs and opportunities for teachers. A realistic look at the role of technology in education.
Book Synopsis How People Learn by : National Research Council
Download or read book How People Learn written by National Research Council and published by National Academies Press. This book was released on 2000-08-11 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: First released in the Spring of 1999, How People Learn has been expanded to show how the theories and insights from the original book can translate into actions and practice, now making a real connection between classroom activities and learning behavior. This edition includes far-reaching suggestions for research that could increase the impact that classroom teaching has on actual learning. Like the original edition, this book offers exciting new research about the mind and the brain that provides answers to a number of compelling questions. When do infants begin to learn? How do experts learn and how is this different from non-experts? What can teachers and schools do-with curricula, classroom settings, and teaching methods--to help children learn most effectively? New evidence from many branches of science has significantly added to our understanding of what it means to know, from the neural processes that occur during learning to the influence of culture on what people see and absorb. How People Learn examines these findings and their implications for what we teach, how we teach it, and how we assess what our children learn. The book uses exemplary teaching to illustrate how approaches based on what we now know result in in-depth learning. This new knowledge calls into question concepts and practices firmly entrenched in our current education system. Topics include: How learning actually changes the physical structure of the brain. How existing knowledge affects what people notice and how they learn. What the thought processes of experts tell us about how to teach. The amazing learning potential of infants. The relationship of classroom learning and everyday settings of community and workplace. Learning needs and opportunities for teachers. A realistic look at the role of technology in education.
Changing student profiles and the increasing availability of mainstream and specialized learning technologies are stretching the traditional face-to-face models of teaching and learning in higher education. Institutions, too, are facing far-reaching systemic changes which are placing strains on existing resources and physical infrastructure and calling into question traditional ways of teaching through lectures and tutorials. And, with an ever-increasing scrutiny on teaching and teachers’ accountability for positive educational outcomes, the call for closer attention to learning, teaching and, most especially, to the design and delivery of the curriculum is given increasing relevance and importance. Research provides strong evidence of the potential for technologies to facilitate not only cognition and learning but also to become integral components in the redesign of current curriculum models. Some Universities and individual academics have moved along this pathway, developing new and innovative curriculum, blending pedagogies and technologies to suit their circumstances. Yet, there are others, unsure of the possibilities, the opportunities and constraints in these changing times. Curriculum Models for the 21st Century gives insights into how teaching and learning can be done differently. The focus is on a whole of curriculum approach, looking at theoretical models and examples of practice which capitalize on the potential of technologies to deliver variations and alternatives to the more traditional lecture-based model of University teaching.
Book Synopsis Curriculum Models for the 21st Century by : Maree Gosper
Download or read book Curriculum Models for the 21st Century written by Maree Gosper and published by Springer Science & Business Media. This book was released on 2013-08-28 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: Changing student profiles and the increasing availability of mainstream and specialized learning technologies are stretching the traditional face-to-face models of teaching and learning in higher education. Institutions, too, are facing far-reaching systemic changes which are placing strains on existing resources and physical infrastructure and calling into question traditional ways of teaching through lectures and tutorials. And, with an ever-increasing scrutiny on teaching and teachers’ accountability for positive educational outcomes, the call for closer attention to learning, teaching and, most especially, to the design and delivery of the curriculum is given increasing relevance and importance. Research provides strong evidence of the potential for technologies to facilitate not only cognition and learning but also to become integral components in the redesign of current curriculum models. Some Universities and individual academics have moved along this pathway, developing new and innovative curriculum, blending pedagogies and technologies to suit their circumstances. Yet, there are others, unsure of the possibilities, the opportunities and constraints in these changing times. Curriculum Models for the 21st Century gives insights into how teaching and learning can be done differently. The focus is on a whole of curriculum approach, looking at theoretical models and examples of practice which capitalize on the potential of technologies to deliver variations and alternatives to the more traditional lecture-based model of University teaching.
This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.
Book Synopsis Machine Learning Models and Algorithms for Big Data Classification by : Shan Suthaharan
Download or read book Machine Learning Models and Algorithms for Big Data Classification written by Shan Suthaharan and published by Springer. This book was released on 2015-10-20 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.
This Handbook provides a comprehensive and up-to-date examination of lifelong learning. Across 38 chapters, including twelve that are brand new to this edition, the approach is interdisciplinary, spanning human resources development, adult learning (educational perspective), psychology, career and vocational learning, management and executive development, cultural anthropology, the humanities, and gerontology. This volume covers trends that contribute to the need for continuous learning, considers psychological characteristics that relate to the drive to learn, reviews existing theory and research on adult learning, describes training methods and learning technologies for instructional design, and explores current and future challenges to support continuous learning.
Book Synopsis The Oxford Handbook of Lifelong Learning by :
Download or read book The Oxford Handbook of Lifelong Learning written by and published by Oxford University Press. This book was released on 2021-06-01 with total page 813 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Handbook provides a comprehensive and up-to-date examination of lifelong learning. Across 38 chapters, including twelve that are brand new to this edition, the approach is interdisciplinary, spanning human resources development, adult learning (educational perspective), psychology, career and vocational learning, management and executive development, cultural anthropology, the humanities, and gerontology. This volume covers trends that contribute to the need for continuous learning, considers psychological characteristics that relate to the drive to learn, reviews existing theory and research on adult learning, describes training methods and learning technologies for instructional design, and explores current and future challenges to support continuous learning.
