The Model Of Domain Learning
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The Model Of Domain Learning
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Author : Helenrose Fives
language : en
Publisher: Routledge
Release Date : 2017-12-14
The Model Of Domain Learning written by Helenrose Fives and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-14 with Education categories.
The Model of Domain Learning is the first edited volume to provide a comprehensive overview of the Model of Domain Learning (MDL). Unique in its emphasis on development, this model examines both the cognitive and motivational forces behind expertise in academic domains. Chapters written by a variety of scholars, including those responsible for the model’s evolution, are tied together by commentaries that synthesize these varied perspectives. With dedicated sections focused on the foundations, current applications, and future potential of the MDL, this book is indispensable as an introduction to the theory and research associated with this topic and as a cutting-edge resource for established scholars.
Testing A Model Of Domain Learning In Music Therapy
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Author : Dianne Langan
language : en
Publisher:
Release Date : 2003
Testing A Model Of Domain Learning In Music Therapy written by Dianne Langan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Learning strategies categories.
First Framework 5 Domains 15 Principles Design Facilitate Active Deep Learner Experience Volume 1 1st Edition
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Author : Mohamed M. Bahgat
language : en
Publisher: Mohamed M. Bahgat
Release Date : 2018-02-08
First Framework 5 Domains 15 Principles Design Facilitate Active Deep Learner Experience Volume 1 1st Edition written by Mohamed M. Bahgat and has been published by Mohamed M. Bahgat this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-08 with Education categories.
No doubt learning is a subject that has been addressed by many books and workshops, with the core interest mostly revolving around the content and how to make it unique, relevant, concise, etc. Other books and courses would rather introduce new/creative techniques for better engaging or getting the best of the training. So where does this book stand? This book stands in the learner's shoes! It is standing as a cornerstone for a different approach, having an eye for every detail that might reflect on the learner's experience; hence the name, "Learner eXperience Facilitation". Who is this book for? This book is for learning facilitators and designers, so to get introduced to a new perspective and to learn new framework where the learner is the center of the whole process. This is considered a sound tool for professionals who work hard to leave an impact through trainings and face to face learning sessions. It targets both independent professionals and those working for specific organizations, teachers and education professionals. This book introduces FIRST framework, research based framework, which is holistic and backed by theories from different basic sciences like, educational psychology, neuroscience, cognitive psychology, design thinking; among others. These disciplines are combined together so to create an engaging framework; leading to creating positive Active Deep learner experience, and hence, positive change in mindset and behaviors. If you are a learning facilitator and you feel the need of a creative and innovative framework to highly influence trainees, then this book is for you; through which you add the active deep learning techniques to your facilitation style. FIRST framework This book introduces FIRST framework, which includes five main domains, and 15 principles. These principles act together and integrate together; creating the Active Deep Learner eXperience. FIRST framework is a holistic one; it is based on other models and theories, such as: experiential learning by Kolb and John Dewey; cooperative learning by Kagan; Carl Rogers’ facilitation skills, Roy’s 6Ds and learning transfer; as well as positive psychology principles. FIRST is also inspired by the spirit of group coaching, which aims at promoting deep change and is focused on the future. It is a scientific and research based framework, developed through our experience in learning and development field; as well as measuring the impact of implementing the model via SeGa or our learners. FIRST framework is not only aiming to create active learning experience, it also targets transforming learning into performance, because incorporating both active and deep strategies creates engagement and impact. The five domains of FIRST act as layers each of them build on the previous domain and add to it, all the principles integrate with each other to form the active deep learner experience. "The end result is a proven, practical, and priceless model with five domains and fifteen principles that you can use yourself in staging active deep learning experiences." B. Joseph Pine II. Author, The Experience Economy. "FIRST framework provides important insights, principles, and practical advice for doing so a travel guide, if you will, that will help you on your own learning journey and those on which you lead others." Roy V.H. Pollock, DVM, PhD. Author, The Six Disciplines of Breakthrough Learning
Model Based Approaches To Learning
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Author :
language : en
Publisher: BRILL
Release Date : 2019-02-11
Model Based Approaches To Learning written by and has been published by BRILL this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-11 with Education categories.
Model-Based Approaches to Learning provides a new perspective called learning by system modeling. This book explores the learning impact of students when constructing models of complex systems.
Modeling Reading Teacher Expertise
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Author : Tammy Mills
language : en
Publisher:
Release Date : 2016
Modeling Reading Teacher Expertise written by Tammy Mills and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Reading categories.
The purpose of this qualitative study was to describe and explain the manifestation of expertise among Reading Recovery teachers using the Model of Domain Learning. The focus of the inquiry was to description the components of the Model of Domain Learning, interest, knowledge, and strategic processing, as each existed among this group of Reading Recovery teachers. Highlighted is the interaction among the three components as well as how that interaction manifested as multiple stages of expertise, acclimation, early competence, mid- competence, late competence, and proficiency. The following research questions guided my inquiry: 1. When examined qualitatively and multidimensionally, how is expertise manifested among 8 RR teachers who are located in rural, Northeast, coastal elementary schools, sharing the same teacher leader and attending the same Continuing Contact sessions? 2. What is the capacity of the MDL to illuminate previously undiscovered aspects of the manifestation RR teacher expertise? 3. What constructs of the MDL may be in need of further conceptualization for the model to more thoroughly describe expertise in complex, ill-structured domains? I collected and analyzed questionnaire responses, conducted semi-structured interviews, and made observations and recorded field notes of Continuing Contact sessions and Behind-the-Glass presentations. These data were analyzed by qualitative methods. I reported my findings through an instrumental case study. My analysis of the data indicated that expertise in Reading Recovery teachers manifested as an interaction among interest, knowledge, and strategic processing. Contrary to previous studies using the Model of Domain learning in which authors suggested that learners would exhibit a lower level of interest at the start of their learning, I discovered that these teachers each possessed heightened interest in Reading Recovery at the beginning of their Reading Recovery career and that this heightened interest was sustained throughout their careers as Reading Recovery teachers. My data analysis also indicated that expertise among these teachers was dependent on their fluid, flexible, accessible Reading Recovery knowledge. Additionally, I found that Reading Recovery was represented by the teachers’ ability to scaffold instruction for each student. Further, analysis evidenced that the component of interest may need to be examined more broadly, with a closer look at the role of emotions on Reading Recovery teacher expertise development.
Learning Domain Driven Design
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Author : Vlad Khononov
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-10-08
Learning Domain Driven Design written by Vlad Khononov and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-08 with Computers categories.
Building software is harder than ever. As a developer, you not only have to chase ever-changing technological trends but also need to understand the business domains behind the software. This practical book provides you with a set of core patterns, principles, and practices for analyzing business domains, understanding business strategy, and, most importantly, aligning software design with its business needs. Author Vlad Khononov shows you how these practices lead to robust implementation of business logic and help to future-proof software design and architecture. You'll examine the relationship between domain-driven design (DDD) and other methodologies to ensure you make architectural decisions that meet business requirements. You'll also explore the real-life story of implementing DDD in a startup company. With this book, you'll learn how to: Analyze a company's business domain to learn how the system you're building fits its competitive strategy Use DDD's strategic and tactical tools to architect effective software solutions that address business needs Build a shared understanding of the business domains you encounter Decompose a system into bounded contexts Coordinate the work of multiple teams Gradually introduce DDD to brownfield projects
In Order To Learn
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Author : Frank E. Ritter
language : en
Publisher: Oxford University Press
Release Date : 2007-07-30
In Order To Learn written by Frank E. Ritter and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-30 with Psychology categories.
Order affects the results you get: Different orders of presenting material can lead to qualitatively and quantitatively different learning outcomes. These differences occur in both natural and artificial learning systems. In Order to Learn shows how order effects are crucial in human learning, instructional design, machine learning, and both symbolic and connectionist cognitive models. Each chapter explains a different aspect of how the order in which material is presented can strongly influence what is learned by humans and theoretical models of learning in a variety of domains. In addition to data, models are provided that predict and describe order effects and analyze how and when they will occur. The introductory and concluding chapters compile suggestions for improving learning through better sequences of learning materials, including how to take advantage of order effects that encourage learning and how to avoid order effects that discourage learning. Each chapter also highlights questions that may inspire further research. Taken together, these chapters show how order effects in different areas can and do inform each other. In Order to Learn will be of interest to researchers and students in cognitive science, education, machine learning.
Efficient Learning Across Multiple Domains With Deep Neural Networks
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Author : Yunhui Guo
language : en
Publisher:
Release Date : 2020
Efficient Learning Across Multiple Domains With Deep Neural Networks written by Yunhui Guo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Learning with data from multiple domains is a longstanding topic in machine learning research. In recent years, deep neural networks (DNN) have shown remarkable performance on different machine learning tasks. However, how to efficiently utilize deep neural networks for learning with multiple domains is largely unexploited. A model aware of the relationships between different domains can be trained to work on new domains with fewer resources and achieve better performance. However, to identify and leverage the transferable structure is challenging. In this dissertation, we propose novel methods which allow efficient learning across multiple domains in several different scenarios. First, we address the problem of learning across two image domains with deep neural networks. We propose two adaptive methods which allow different images to fine-tune and reuse different residual blocks and convolutional filters of the pre-trained model. Experimental results show that the proposed SpotTune outperforms the standard fine-tuning on 12 out of 14 datasets. Second, we consider the case that the target domain only has few examples per category which is referred to as the cross-domain few-shot problem. We establish a new benchmark for cross-domain few-shot learning and propose a multi-model selection algorithm which achieves an average improvement of 2% compared with the state-of-the-art approach on the proposed benchmark. Third, we consider learning with multiple domains simultaneously. We propose a multi--domain learning method based on depthwise separable convolution which achieves the highest score on the Visual Decathlon Challenge and reduces the number of parameters by 50% compared with the state-of-theart approach. We further propose an efficient multi-domain learning method for distributed training in sensor networks. The proposed method can reduce the communication cost by up to 53% and energy consumption by up to 67% without accuracy degradation compared with conventional approaches. Finally, we address learning with multiple domains sequentially. We propose an algorithm called mixed stochastic gradient descent (MEGA) which allows the model to maintain the performance on old domains while being trained on a new domain. MEGA achieves an average accuracy of 91.21±0.10% on Permuted MNIST, which is 2% better than the previous state-of-the-art model. On Split CIFAR, the proposed MEGA achieves an average accuracy of 66.12±1.93%, which is about 5% better than the state-of-the-art method.
Advances In Domain Adaptation Theory
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Author : Ievgen Redko
language : en
Publisher: Elsevier
Release Date : 2019-08-23
Advances In Domain Adaptation Theory written by Ievgen Redko and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-23 with Computers categories.
Advances in Domain Adaptation Theory gives current, state-of-the-art results on transfer learning, with a particular focus placed on domain adaptation from a theoretical point-of-view. The book begins with a brief overview of the most popular concepts used to provide generalization guarantees, including sections on Vapnik-Chervonenkis (VC), Rademacher, PAC-Bayesian, Robustness and Stability based bounds. In addition, the book explains domain adaptation problem and describes the four major families of theoretical results that exist in the literature, including the Divergence based bounds. Next, PAC-Bayesian bounds are discussed, including the original PAC-Bayesian bounds for domain adaptation and their updated version. Additional sections present generalization guarantees based on the robustness and stability properties of the learning algorithm. Gives an overview of current results on transfer learning Focuses on the adaptation of the field from a theoretical point-of-view Describes four major families of theoretical results in the literature Summarizes existing results on adaptation in the field Provides tips for future research
The Domain Theory
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Author : Alistair Sutcliffe
language : en
Publisher: CRC Press
Release Date : 2002-03-01
The Domain Theory written by Alistair Sutcliffe and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-03-01 with Computers categories.
Is this book about patterns? Yes and no. It is about software reuse and representation of knowledge that can be reapplied in similar situations; however, it does not follow the classic Alexandine conventions of the patterns community--i.e. Problem- solution- forces- context- example, etc. Chapter 6 on claims comes close to classic patterns, and the whole book can be viewed as a patterns language of abstract models for software engineering and HCI. So what sort of patterns does it contain? Specifications, conceptual models, design advice, but sorry not code. Plenty of other C++ code pattern books (see PLOP series). Nearest relative in published patterns books are Fowler's (1995) Analysis Patterns: Reusable object models and Coad, North and Mayfield. What do you mean by a Domain Theory? Not domains in the abstract mathematical sense, but domains in the knowledge--natural language sense, close to the everyday meaning when we talk about the application domain of a computer system, such as car rental, satellite tracking, whatever. The book is an attempt to answer the question ' what are the abstractions behind car rental, satellite tracking' so good design solutions for those problems can be reused. I work in industry, so what's in it for me? A new way of looking at software reuse, ideas for organizing a software and knowledge reuse program, new processes for reusing knowledge in requirements analysis, conceptual modeling and software specification. I am an academic, should I be interested? Yes if your research involves software engineering, reuse, requirements engineering, human computer interaction, knowledge engineering, ontologies and knowledge management. For teaching it may be useful for Master courses on reuse, requirements and knowledge engineering. More generally if you are interested in exploring what the concept of abstraction is when you extend it beyond programming languages, formal specificati