Handbook of Learning from Multiple Representations and Perspectives
Handbook of Learning from Multiple Representations and Perspectives

Author: Peggy Van Meter

Publisher: Routledge

Published: 2020-03-10

Total Pages: 696

ISBN-13: 0429813651

DOWNLOAD EBOOK

In and out of formal schooling, online and off, today’s learners must consume and integrate a level of information that is exponentially larger and delivered through a wider range of formats and viewpoints than ever before. The Handbook of Learning from Multiple Representations and Perspectives provides a path for understanding the cognitive, motivational, and socioemotional processes and skills necessary for learners across educational contexts to make sense of and use information sourced from varying inputs. Uniting research and theory from education, psychology, literacy, library sciences, media and technology, and more, this forward-thinking volume explores the common concerns, shared challenges, and thematic patterns in our capacity to make meaning in an information-rich society. Chapter 16 of this book is freely available as a downloadable Open Access PDF under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license available at http://www.taylorfrancis.com/books/e/9780429443961.

Use of Representations in Reasoning and Problem Solving
Use of Representations in Reasoning and Problem Solving

Author:

Publisher: Routledge

Published: 2010

Total Pages: 271

ISBN-13: 1136943994

DOWNLOAD EBOOK

Within an increasingly multimedia focused society, the use of external representations in learning, teaching and communication has increased dramatically. This book explores: how we can theorise the relationship between processing internal and external representations.

Use of Representations in Reasoning and Problem Solving
Use of Representations in Reasoning and Problem Solving

Author: Lieven Verschaffel

Publisher: Routledge

Published: 2010-09-13

Total Pages: 351

ISBN-13: 1136943986

DOWNLOAD EBOOK

Within an increasingly multimedia focused society, the use of external representations in learning, teaching and communication has increased dramatically. Whether in the classroom, university or workplace, there is a growing requirement to use and interpret a large variety of external representational forms and tools for knowledge acquisition, problem solving, and to communicate with others. Use of Representations in Reasoning and Problem Solving brings together contributions from some of the world’s leading researchers in educational and instructional psychology, instructional design, and mathematics and science education to document the role which external representations play in our understanding, learning and communication. Traditional research has focused on the distinction between verbal and non-verbal representations, and the way they are processed, encoded and stored by different cognitive systems. The contributions here challenge these research findings and address the ambiguity about how these two cognitive systems interact, arguing that the classical distinction between textual and pictorial representations has become less prominent. The contributions in this book explore: how we can theorise the relationship between processing internal and external representations what perceptual and cognitive restraints can affect the use of external representations how individual differences affect the use of external representations how we can combine external representations to maximise their impact how we can adapt representational tools for individual differences. Using empirical research findings to take a fresh look at the processes which take place when learning via external representations, this book is essential reading for all those undertaking postgraduate study and research in the fields of educational and instructional psychology, instructional design and mathematics and science education.

Strategy Representation
Strategy Representation

Author: Andrew S. Gordon

Publisher: Psychology Press

Published: 2004-07-16

Total Pages: 404

ISBN-13: 1135625255

DOWNLOAD EBOOK

Strategy Representation: An Analysis of Planning Knowledge describes an innovative methodology for investigating the conceptual structures that underlie human reasoning. This work explores the nature of planning strategies--the abstract patterns of planning behavior that people recognize across a broad range of real world situations. With a sense of scale that is rarely seen in the cognitive sciences, this book catalogs 372 strategies across 10 different planning domains: business practices, education, object counting, Machiavellian politics, warfare, scientific discovery, personal relationships, musical performance, and the anthropomorphic strategies of animal behavior and cellular immunology. Noting that strategies often serve as the basis for analogies that people draw across planning situations, this work attempts to explain these analogies by defining the fundamental concepts that are common across all instances of each strategy. By aggregating evidence from each of the strategy definitions provided, the representational requirements of strategic planning are identified. The important finding is that the concepts that underlie strategic reasoning are of incredibly broad scope. Nearly 1,000 fundamental concepts are identified, covering every existing area of knowledge representation research and many areas that have not yet been adequately formalized, particularly those related to common sense understanding of mental states and processes. An organization of these concepts into 48 fundamental areas of knowledge and representation is provided, offering an invaluable roadmap for progress within the field.

Graph-Based Representation and Reasoning
Graph-Based Representation and Reasoning

Author: Peter Chapman

Publisher: Springer

Published: 2018-06-07

Total Pages: 207

ISBN-13: 3319913794

DOWNLOAD EBOOK

This book constitutes the proceedings of the 23rd International Conference on Conceptual Structures, ICCS 2018, held in Edinburgh, UK, in June 2018. The 10 full papers, 2 short papers and 2 posters presented were carefully reviewed and selected from 21 submissions. They are organized in the following topical sections: graph- and concept-based inference; computer- human interaction and human cognition; and graph visualization.

Binary Representation Learning on Visual Images
Binary Representation Learning on Visual Images

Author: Zheng Zhang

Publisher: Springer Nature

Published: 2024

Total Pages: 212

ISBN-13: 9819721121

DOWNLOAD EBOOK

This book introduces pioneering developments in binary representation learning on visual images, a state-of-the-art data transformation methodology within the fields of machine learning and multimedia. Binary representation learning, often known as learning to hash or hashing, excels in converting high-dimensional data into compact binary codes meanwhile preserving the semantic attributes and maintaining the similarity measurements. The book provides a comprehensive introduction to the latest research in hashing-based visual image retrieval, with a focus on binary representations. These representations are crucial in enabling fast and reliable feature extraction and similarity assessments on large-scale data. This book offers an insightful analysis of various research methodologies in binary representation learning for visual images, ranging from basis shallow hashing, advanced high-order similarity-preserving hashing, deep hashing, as well as adversarial and robust deep hashing techniques. These approaches can empower readers to proficiently grasp the fundamental principles of the traditional and state-of-the-art methods in binary representations, modeling, and learning. The theories and methodologies of binary representation learning expounded in this book will be beneficial to readers from diverse domains such as machine learning, multimedia, social network analysis, web search, information retrieval, data mining, and others.

Heterogeneous Graph Representation Learning and Applications
Heterogeneous Graph Representation Learning and Applications

Author: Chuan Shi

Publisher: Springer Nature

Published: 2022-01-30

Total Pages: 329

ISBN-13: 9811661669

DOWNLOAD EBOOK

Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the need to incorporate heterogeneous structural (graph) information consisting of multiple types of node and edge, but also the need to consider heterogeneous attributes or types of content (e.g. text or image) associated with each node. Although considerable advances have been made in homogeneous (and heterogeneous) graph embedding, attributed graph embedding and graph neural networks, few are capable of simultaneously and effectively taking into account heterogeneous structural (graph) information as well as the heterogeneous content information of each node. In this book, we provide a comprehensive survey of current developments in HG representation learning. More importantly, we present the state-of-the-art in this field, including theoretical models and real applications that have been showcased at the top conferences and journals, such as TKDE, KDD, WWW, IJCAI and AAAI. The book has two major objectives: (1) to provide researchers with an understanding of the fundamental issues and a good point of departure for working in this rapidly expanding field, and (2) to present the latest research on applying heterogeneous graphs to model real systems and learning structural features of interaction systems. To the best of our knowledge, it is the first book to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from it, readers should have a basic grasp of computer science, data mining and machine learning.

Dynamic Knowledge Representation in Scientific Domains
Dynamic Knowledge Representation in Scientific Domains

Author: Pshenichny, Cyril

Publisher: IGI Global

Published: 2018-03-16

Total Pages: 420

ISBN-13: 1522552626

DOWNLOAD EBOOK

The main approach to understanding and creating knowledge engineering concepts is static knowledge. Currently, there is a need to approach knowledge through a dynamic lens and address changing relations on an elaborated syntactic and semantic basis. Dynamic Knowledge Representation in Scientific Domains provides emerging research on the internal and external changes in knowledge within various subject areas and their visual representations. While highlighting topics such as behavior diagrams, distribution analysis, and qualitative modeling, this publication explores the structural development and assessment of knowledge models. This book is an important resource for academicians, researchers, students, and practitioners seeking current research on information visualization in order to foster research and collaboration.

Robust Representation for Data Analytics
Robust Representation for Data Analytics

Author: Sheng Li

Publisher: Springer

Published: 2017-08-09

Total Pages: 229

ISBN-13: 3319601768

DOWNLOAD EBOOK

This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary. Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.

Visualization: Theory and Practice in Science Education
Visualization: Theory and Practice in Science Education

Author: John K. Gilbert

Publisher: Springer Science & Business Media

Published: 2007-12-05

Total Pages: 326

ISBN-13: 1402052677

DOWNLOAD EBOOK

External representations (pictures, diagrams, graphs, concrete models) have always been valuable tools for the science teacher. This book brings together the insights of practicing scientists, science education researchers, computer specialists, and cognitive scientists, to produce a coherent overview. It links presentations about cognitive theory, its implications for science curriculum design, and for learning and teaching in classrooms and laboratories.