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Author: Jimmie Leppink
Publisher: Springer Nature
ISBN: 3030430820
Category : Education
Languages : en
Pages : 276
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Book Description
By uniting key concepts and methods from education, psychology, statistics, econometrics, medicine, language, and forensic science, this textbook provides an interdisciplinary methodological approach to study human learning processes longitudinally. This longitudinal approach can help to acquire a better understanding of learning processes, can inform both future learning and the revision of educational content and formats, and may help to foster self-regulated learning skills. The initial section of this textbook focuses on different types of research questions as well as practice-driven questions that may refer to groups or to individual learners. This is followed by a discussion of different types of outcome variables in educational research and practice, such as pass/fail and other dichotomies, multi-category nominal choices, ordered performance categories, and different types of quantifiable (i.e., interval or ratio level of measurement) variables. For each of these types of outcome variables, single-measurement and repeated-measurements scenarios are offered with clear examples. The book then introduces cross-sectional and longitudinal interdependence of learning-related variables through emerging network-analytic methods and in the final part the learned concepts are applied to different types of studies involving time series. The book concludes with some general guidelines to give direction to future (united) educational research and practice. This textbook is a must-have for all applied researchers, teachers and practitioners interested in (the teaching of) human learning, instructional design, assessment, life-long learning or applications of concepts and methods commonly encountered in fields such as econometrics, psychology, and sociology to educational research and practice.
Author: Jimmie Leppink
Publisher: Springer Nature
ISBN: 3030430820
Category : Education
Languages : en
Pages : 276
Get Book Here
Book Description
By uniting key concepts and methods from education, psychology, statistics, econometrics, medicine, language, and forensic science, this textbook provides an interdisciplinary methodological approach to study human learning processes longitudinally. This longitudinal approach can help to acquire a better understanding of learning processes, can inform both future learning and the revision of educational content and formats, and may help to foster self-regulated learning skills. The initial section of this textbook focuses on different types of research questions as well as practice-driven questions that may refer to groups or to individual learners. This is followed by a discussion of different types of outcome variables in educational research and practice, such as pass/fail and other dichotomies, multi-category nominal choices, ordered performance categories, and different types of quantifiable (i.e., interval or ratio level of measurement) variables. For each of these types of outcome variables, single-measurement and repeated-measurements scenarios are offered with clear examples. The book then introduces cross-sectional and longitudinal interdependence of learning-related variables through emerging network-analytic methods and in the final part the learned concepts are applied to different types of studies involving time series. The book concludes with some general guidelines to give direction to future (united) educational research and practice. This textbook is a must-have for all applied researchers, teachers and practitioners interested in (the teaching of) human learning, instructional design, assessment, life-long learning or applications of concepts and methods commonly encountered in fields such as econometrics, psychology, and sociology to educational research and practice.
Author: Tom Verguts
Publisher: MIT Press
ISBN: 0262362317
Category : Science
Languages : en
Pages : 265
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Book Description
An introduction to computational modeling for cognitive neuroscientists, covering both foundational work and recent developments. Cognitive neuroscientists need sophisticated conceptual tools to make sense of their field’s proliferation of novel theories, methods, and data. Computational modeling is such a tool, enabling researchers to turn theories into precise formulations. This book offers a mathematically gentle and theoretically unified introduction to modeling cognitive processes. Theoretical exercises of varying degrees of difficulty throughout help readers develop their modeling skills. After a general introduction to cognitive modeling and optimization, the book covers models of decision making; supervised learning algorithms, including Hebbian learning, delta rule, and backpropagation; the statistical model analysis methods of model parameter estimation and model evaluation; the three recent cognitive modeling approaches of reinforcement learning, unsupervised learning, and Bayesian models; and models of social interaction. All mathematical concepts are introduced gradually, with no background in advanced topics required. Hints and solutions for exercises and a glossary follow the main text. All code in the book is Python, with the Spyder editor in the Anaconda environment. A GitHub repository with Python files enables readers to access the computer code used and start programming themselves. The book is suitable as an introduction to modeling cognitive processes for students across a range of disciplines and as a reference for researchers interested in a broad overview.
Author: Tibor Müller
Publisher: Springer Science & Business Media
ISBN: 9783540001539
Category : Science
Languages : en
Pages : 480
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Book Description
This book defines the wide application of the art of modelling. The main emphasis is on the imaging of dynamic processes which are analysed and subdivided into their atomic constituents by means of systems analysis. The cyclic structure and the stages of models’ set-up are explained. The evaluation of a model’s quality is regarded as a stochastic process. The aspects of grade used in different fields of sciences are brought into perspective. Thus, a quantitative concept of validity on the basis of conditional degrees of rational belief can be developed.
Author: Panel on Modeling Human Behavior and Command Decision Making: Representations for Military Simulations
Publisher: National Academies Press
ISBN: 0309523893
Category : Business & Economics
Languages : en
Pages : 433
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Book Description
Simulations are widely used in the military for training personnel, analyzing proposed equipment, and rehearsing missions, and these simulations need realistic models of human behavior. This book draws together a wide variety of theoretical and applied research in human behavior modeling that can be considered for use in those simulations. It covers behavior at the individual, unit, and command level. At the individual soldier level, the topics covered include attention, learning, memory, decisionmaking, perception, situation awareness, and planning. At the unit level, the focus is on command and control. The book provides short-, medium-, and long-term goals for research and development of more realistic models of human behavior.
Author: Gloria Ann Stillman
Publisher: Springer Nature
ISBN: 3030376737
Category : Education
Languages : en
Pages : 502
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Book Description
This volume documents on-going research and theorising in the sub-field of mathematics education devoted to the teaching and learning of mathematical modelling and applications. Mathematical modelling provides a way of conceiving and resolving problems in people’s everyday lives as well as sophisticated new problems for society at large. Mathematical modelling and real world applications are considered as having potential for cultivating sense making in classroom settings. This book focuses on the educational perspective, researching the complexities encountered in effective teaching and learning of real world modelling and applications for sense making is only beginning. All authors of this volume are members of the International Community of Teachers of Mathematical Modelling (ICTMA), the peak research body into researching the teaching and learning of mathematical modelling at all levels of education from the early years to tertiary education as well as in the workplace.
Author: Margaret Berry Wilson
Publisher: Center for Responsive Schools, Inc.
ISBN: 1892989530
Category : Education
Languages : en
Pages : 210
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Book Description
Be a more effective teacher by using this simple, yet transformative, technique for teaching essential academic and social skills, routines, and behaviors. Through Interactive Modeling, your students actively observe, model, and practice skills that can lead to higher, lasting achievements and kinder classrooms. You'll save time; they'll gain mastery!, You can use Interactive Modeling to help your students achieve success in: math, reading, writing, social studies, science, working in groups, making smooth transitions, using supplies carefully, test-taking, and more! Book jacket.
Author: Keith S. Taber
Publisher: Springer Science & Business Media
ISBN: 9400776489
Category : Science
Languages : en
Pages : 371
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Book Description
This book sets out the necessary processes and challenges involved in modeling student thinking, understanding and learning. The chapters look at the centrality of models for knowledge claims in science education and explore the modeling of mental processes, knowledge, cognitive development and conceptual learning. The conclusion outlines significant implications for science teachers and those researching in this field. This highly useful work provides models of scientific thinking from different field and analyses the processes by which we can arrive at claims about the minds of others. The author highlights the logical impossibility of ever knowing for sure what someone else knows, understands or thinks, and makes the case that researchers in science education need to be much more explicit about the extent to which research onto learners’ ideas in science is necessarily a process of developing models. Through this book we learn that research reports should acknowledge the role of modeling and avoid making claims that are much less tentative than is justified as this can lead to misleading and sometimes contrary findings in the literature. In everyday life we commonly take it for granted that finding out what another knows or thinks is a relatively trivial or straightforward process. We come to take the ‘mental register’ (the way we talk about the ‘contents’ of minds) for granted and so teachers and researchers may readily underestimate the challenges involved in their work.
Author: Peter L. Galbraith
Publisher: Springer Science & Business Media
ISBN: 0387298223
Category : Education
Languages : en
Pages : 524
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Book Description
The book aims at showing the state-of-the-art in the field of modeling and applications in mathematics education. This is the first volume to do this. The book deals with the question of how key competencies of applications and modeling at the heart of mathematical literacy may be developed; with the roles that applications and modeling may play in mathematics teaching, making mathematics more relevant for students.
Author: Gilbert Greefrath
Publisher: Springer
ISBN: 3319450042
Category : Education
Languages : en
Pages : 49
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Book Description
This survey provides an overview of the German discussion on modelling and applications in schools. It considers the development from the beginning of the 20th century to the present, and discusses the term “mathematical model” as well as different representations of the modelling process as modelling cycles. Different trends in the historical and current debate on applications and modelling can be differentiated as perspectives of modelling. Modelling is now one of the six general mathematical competencies defined in the educational standards for mathematics introduced in Germany in 2003, and there have been several initiatives to implement modelling in schools, as well as a whole range of empirical research projects focusing on teachers or students in modelling processes. As a special kind for implementing modelling into school, modelling weeks and days carried out by various German universities have been established.
Author: Sofian Audry
Publisher: MIT Press
ISBN: 0262367106
Category : Art
Languages : en
Pages : 215
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Book Description
An examination of machine learning art and its practice in new media art and music. Over the past decade, an artistic movement has emerged that draws on machine learning as both inspiration and medium. In this book, transdisciplinary artist-researcher Sofian Audry examines artistic practices at the intersection of machine learning and new media art, providing conceptual tools and historical perspectives for new media artists, musicians, composers, writers, curators, and theorists. Audry looks at works from a broad range of practices, including new media installation, robotic art, visual art, electronic music and sound, and electronic literature, connecting machine learning art to such earlier artistic practices as cybernetics art, artificial life art, and evolutionary art. Machine learning underlies computational systems that are biologically inspired, statistically driven, agent-based networked entities that program themselves. Audry explains the fundamental design of machine learning algorithmic structures in terms accessible to the nonspecialist while framing these technologies within larger historical and conceptual spaces. Audry debunks myths about machine learning art, including the ideas that machine learning can create art without artists and that machine learning will soon bring about superhuman intelligence and creativity. Audry considers learning procedures, describing how artists hijack the training process by playing with evaluative functions; discusses trainable machines and models, explaining how different types of machine learning systems enable different kinds of artistic practices; and reviews the role of data in machine learning art, showing how artists use data as a raw material to steer learning systems and arguing that machine learning allows for novel forms of algorithmic remixes.
