- Table View
- List View
Distributed Sensor Networks: Image and Sensor Signal Processing (Volume One) (Chapman And Hall/crc Computer And Information Science Ser. #26)
by S. Sitharama Iyengar Richard R. BrooksThe best-selling Distributed Sensor Networks became the definitive guide to understanding this far-reaching technology. Preserving the excellence and accessibility of its predecessor, Distributed Sensor Networks, Second Edition once again provides all the fundamentals and applications in one complete, self-contained source. Ideal as a tutorial for
Distributed Serverless Architectures on AWS: Design and Implement Serverless Architectures
by Jithin Jude PaulExplore the serverless world using Amazon Web Services (AWS) and develop various architectures, including those for event-driven and disaster recovery designs. This book will give you an understanding of different distributed serverless architectures and how to build them using AWS components. You will begin with an introduction to serverless components and architectures, before progressing to data platforms and containers. Next, you'll dig deeper into these serverless architectures and how they leverage AWS components through practical use cases. You will also explore designing systems in a multi-cloud paradigm. Author Jithin Jude Paul then demonstrates how efficient serverless architectures are, and the benefits of designing distributed systems globally in a cost-effective way while incorporating a microservices architectural style. Distributed Serverless Architectures with AWS concludes with a discussion of current and future trends in serverless frameworks. After completing this book, you'll be able to design distributed serverless architectures using AWS.What You'll LearnGain an overview of different serverless architectures Design and build distributed systems using serverless componentsBuild serverless data and container platforms on AWSPlan a multi-cloud strategy using serverless components Who This Book Is For Cloud engineers, DevOps engineers, and architects focused on the AWS ecosystem, as well as software engineers/developers working with AWS.
Distributed Simulation (Computer Science And Engineering Ser.)
by Udo W. Pooch John A. Hamilton David A. NashSimulation is a multi-disciplinary field, and significant simulation research is dispersed across multiple fields of study. Distributed computer systems, software design methods, and new simulation techniques offer synergistic multipliers when joined together in a distributed simulation. Systems of most interest to the simulation practitioner are often the most difficult to model and implement. Distributed Simulation brings together the many complex technologies for distributed simulation. There is strong emphasis on emerging simulation methodologies, including object-oriented, multilevel, and multi-resolution simulation. Finally, one concise text provides a strong foundation for the development of high fidelity simulations in heterogeneous distributed computing environments!
Distributed Simulation
by Okan Topçu Umut Durak Halit Oğuztüzün Levent YilmazThis uniquetext/reference provides a comprehensive review of distributed simulation (DS)from the perspective of Model Driven Engineering (MDE), illustrating how MDEaffects the overall lifecycle of the simulation development process. Numerouspractical case studies are included to demonstrate the utility andapplicability of the methodology, many of which are developed from toolsavailable to download from the public domain. Topics and features: Provides a thorough introduction to the fundamental concepts, principles and processes of modeling and simulation, MDE and high-level architecture Describes a road map for building a DS system in accordance with the MDE perspective, and a technical framework for the development of conceptual models Presents a focus on federate (simulation environment) architectures, detailing a practical approach to the design of federations (i. e. , simulation member design) Discusses the main activities related to scenario management in DS, and explores the process of MDE-based implementation, integration and testing Reviews approaches to simulation evolution and modernization, including architecture-driven modernization for simulation modernization Examines the potential synergies between the agent, DS, and MDE methodologies, suggesting avenues for future research at the intersection of these three fields Distributed Simulation -A Model Driven Engineering Approach is an important resource for allresearchers and practitioners involved in modeling and simulation, and softwareengineering, who may be interested in adopting MDE principles when developingcomplex DS systems.
Distributed Space-Time Coding
by Yindi JingDistributed Space-Time Coding (DSTC) is a cooperative relaying scheme that enables high reliability in wireless networks. This brief presents the basic concept of DSTC, its achievable performance, generalizations, code design, and differential use. Recent results on training design and channel estimation for DSTC and the performance of training-based DSTC are also discussed.
Distributed Storage Networks
by Thomas C. JepsenThe worldwide market for SAN and NAS storage is anticipated to grow from US $2 billion in 1999 to over $25 billion by 2004. As business-to-business and business-to-consumer e-commerce matures, even greater demands for management of stored data will arise.With the rapid increase in data storage requirements in the last decade, efficient management of stored data becomes a necessity for the enterprise. A recent UC-Berkeley study predicts that 150,000 terabytes of disk storage will be shipped in 2003. Most financial, insurance, healthcare, and telecommunications institutions are in the process of implementing storage networks that are distributed to some degree. For these institutions, data integrity is critical, and they will spend much time and money on planning.One of the primary obstacles to implementing a storage network cited by enterprise IT managers is a lack of knowledge about storage networking technology and the specific issues involved in extending a Storage Area Network (SAN) or Network Attached Storage (NAS) over the Metropolitan Area Networks (MAN) or Wireless Area Networks (WAN). Distributed Storage Networks : Architecture, Protocols and Management addresses the "terminology gap" between enterprise network planners and telecommunications engineers, who must understand the transport requirements of storage networks in order to implement distributed storage networks. Jepsen comprehensively provides IT managers, planners, and telecommunications professionals with the information they need in order to choose the technologies best suited for their particular environment.* Addresses a hot topic that will become increasingly important in the coming years* Enables high-level managers and planners to make intelligent decisions about network needs.* Includes example network configurations providing solutions to typical user scenarios* Fills the "terminology gap" between enterprise network managers and telecommunications engineers who must understand the transport requirements of storage networks in order to implement distributed storage area networksA fundamental resource for all network managers, planners and network design engineers, as well as telecommunications engineers and engineering, computer science, and information technology students.
Distributed System Design
by Jie WuFuture requirements for computing speed, system reliability, and cost-effectiveness entail the development of alternative computers to replace the traditional von Neumann organization. As computing networks come into being, one of the latest dreams is now possible - distributed computing. Distributed computing brings transparent access to as much computer power and data as the user needs for accomplishing any given task - simultaneously achieving high performance and reliability.The subject of distributed computing is diverse, and many researchers are investigating various issues concerning the structure of hardware and the design of distributed software. Distributed System Design defines a distributed system as one that looks to its users like an ordinary system, but runs on a set of autonomous processing elements (PEs) where each PE has a separate physical memory space and the message transmission delay is not negligible. With close cooperation among these PEs, the system supports an arbitrary number of processes and dynamic extensions.Distributed System Design outlines the main motivations for building a distributed system, including:inherently distributed applicationsperformance/costresource sharingflexibility and extendibilityavailability and fault tolerancescalabilityPresenting basic concepts, problems, and possible solutions, this reference serves graduate students in distributed system design as well as computer professionals analyzing and designing distributed/open/parallel systems.Chapters discuss:the scope of distributed computing systemsgeneral distributed programming languages and a CSP-like distributed control description language (DCDL)expressing parallelism, interprocess communication and synchronization, and fault-tolerant designtwo approaches describing a distributed system: the time-space view and the interleaving viewmutual exclusion and related issues, including election, bidding, and self-stabilizationprevention and detection of deadlockreliability, safety, and security as well as various methods of handling node, communication, Byzantine, and software faultsefficient interprocessor communication mechanisms as well as these mechanisms without specific constraints, such as adaptiveness, deadlock-freedom, and fault-tolerancevirtual channels and virtual networksload distribution problemssynchronization of access to shared data while supporting a high degree of concurrency
Distributed Systems: Theory and Applications
by Ratan K. Ghosh Hiranmay GhoshDistributed Systems Comprehensive textbook resource on distributed systems—integrates foundational topics with advanced topics of contemporary importance within the field Distributed Systems: Theory and Applications is organized around three layers of abstractions: networks, middleware tools, and application framework. It presents data consistency models suited for requirements of innovative distributed shared memory applications. The book also focuses on distributed processing of big data, representation of distributed knowledge and management of distributed intelligence via distributed agents. To aid in understanding how these concepts apply to real-world situations, the work presents a case study on building a P2P Integrated E-Learning system. Downloadable lecture slides are included to help professors and instructors convey key concepts to their students. Additional topics discussed in Distributed Systems: Theory and Applications include: Network issues and high-level communication tools Software tools for implementations of distributed middleware. Data sharing across distributed components through publish and subscribe-based message diffusion, gossip protocol, P2P architecture and distributed shared memory. Consensus, distributed coordination, and advanced middleware for building large distributed applications Distributed data and knowledge management Autonomy in distributed systems, multi-agent architecture Trust in distributed systems, distributed ledger, Blockchain and related technologies. Researchers, industry professionals, and students in the fields of science, technology, and medicine will be able to use Distributed Systems: Theory and Applications as a comprehensive textbook resource for understanding distributed systems, the specifics behind the modern elements which relate to them, and their practical applications.
Distributed Systems: An Algorithmic Approach, Second Edition (Chapman And Hall/crc Computer And Information Science Ser.)
by Sukumar GhoshDistributed Systems: An Algorithmic Approach, Second Edition provides a balanced and straightforward treatment of the underlying theory and practical applications of distributed computing. As in the previous version, the language is kept as unobscured as possible-clarity is given priority over mathematical formalism. This easily digestible text:Fea
Distributed Systems and Applications of Information Filtering and Retrieval
by Cristian Lai Alessandro Giuliani Giovanni SemeraroThis volume focuses on new challenges in distributed Information Filtering and Retrieval. It collects invited chapters and extended research contributions from the special session on Information Filtering and Retrieval: Novel Distributed Systems and Applications (DART) of the 4th International Conference on Knowledge Discovery and Information Retrieval (KDIR 2012), held in Barcelona, Spain, on 4-7 October 2012. The main focus of DART was to discuss and compare suitable novel solutions based on intelligent techniques and applied to real-world applications. The chapters of this book present a comprehensive review of related works and state of the art. Authors, both practitioners and researchers, shared their results in several topics such as "Multi-Agent Systems", "Natural Language Processing", "Automatic Advertisement", "Customer Interaction Analytics", "Opinion Mining". Contributions have been careful reviewed by experts in the area, who also gave useful suggestions to improve the quality of the volume.
Distributed Systems with Node.js
by Thomas Hunter IIMany companies, from startups to Fortune 500 companies alike, use Node.js to build performant backend services. And engineers love Node.js for its approachable API and familiar syntax. Backed by the world's largest package repository, Node's enterprise foothold is only expected to grow.In this hands-on guide, author Thomas Hunter II proves that Node.js is just as capable as traditional enterprise platforms for building services that are observable, scalable, and resilient. Intermediate to advanced Node.js developers will find themselves integrating application code with a breadth of tooling from each layer of a modern service stack.Learn why running redundant copies of the same Node.js service is necessaryKnow which protocol to choose, depending on the situationFine-tune your application containers for use in productionTrack down errors in a distributed setting to determine which service is at faultSimplify app code and increase performance by offloading work to a reverse proxyBuild dashboards to monitor service health and throughputFind out why so many different tools are required when operating in an enterprise environment
Distributed Tracing in Practice: Instrumenting, Analyzing, and Debugging Microservices
by Austin Parker Daniel Spoonhower Jonathan Mace Ben Sigelman Rebecca IsaacsMost applications today are distributed in some fashion. Monitoring the health and performance of these distributed architectures requires a new approach. Enter distributed tracing, a method of profiling and monitoring applications—especially those that use microservice architectures. There’s just one problem: distributed tracing can be hard. But it doesn’t have to be.With this practical guide, you’ll learn what distributed tracing is and how to use it to understand the performance and operation of your software. Key players at Lightstep walk you through instrumenting your code for tracing, collecting the data that your instrumentation produces, and turning it into useful, operational insights. If you want to start implementing distributed tracing, this book tells you what you need to know.You’ll learn:The pieces of a distributed tracing deployment: Instrumentation, data collection, and delivering valueBest practices for instrumentation (the methods for generating trace data from your service)How to deal with or avoid overhead, costs, and samplingHow to work with spans (the building blocks of request-based distributed traces) and choose span characteristics that lead to valuable tracesWhere distributed tracing is headed in the future
Distributed User Interfaces
by José A. Gallud Victor M.R. Penichet Ricardo TesorieroThe recent advances in display technologies and mobile devices is having an important effect on the way users interact with all kinds of devices (computers, mobile devices, laptops, tablets, and so on). These are opening up new possibilities for interaction, including the distribution of the UI (User Interface) amongst different devices, and implies that the UI can be split and composed, moved, copied or cloned among devices running the same or different operating systems. These new ways of manipulating the UI are considered under the emerging topic of Distributed User Interfaces (DUIs). DUIs are concerned with the repartition of one of many elements from one or many user interfaces in order to support one or many users to carry out one or many tasks on one or many domains in one or many contexts of use - each context of use consisting of users, platforms, and environments. The 20 chapters in the book cover between them the state-of-the-art, the foundations, and original applications of DUIs. Case studies are also included, and the book culminates with a review of interesting and novel applications that implement DUIs in different scenarios.
Distributed User Interfaces: Usability and Collaboration
by María D. Lozano José A. Gallud Ricardo Tesoriero Víctor M. R. PenichetWritten by international researchers in the field of Distributed User Interfaces (DUIs), this book brings together important contributions regarding collaboration and usability in Distributed User Interface settings. Throughout the thirteen chapters authors address key questions concerning how collaboration can be improved by using DUIs, including: in which situations a DUI is suitable to ease the collaboration among users; how usability standards can be used to evaluate the usability of systems based on DUIs; and accurately describe case studies and prototypes implementing these concerns. Under a collaborative scenario, users sharing common goals may take advantage of DUI environments to carry out their tasks more successfully because DUIs provide a shared environment where the users are allowed to manipulate information in the same space and at the same time. Under this hypothesis, collaborative DUI scenarios open new challenges to usability evaluation techniques and methods. Distributed User Interfaces: Collaboration and Usability presents an integrated view of different approaches related to Collaboration and Usability in Distributed User Interface settings, which demonstrate the state of the art, as well as future directions in this novel and rapidly evolving subject area.
Distributed Video Sensor Networks
by Bir Bhanu Demetri Terzopoulos Amit K. Roy-Chowdhury Hamid Aghajan Chinya V. RavishankarLarge-scale video networks are of increasing importance in a wide range of applications. However, the development of automated techniques for aggregating and interpreting information from multiple video streams in real-life scenarios is a challenging area of research. Collecting the work of leading researchers from a broad range of disciplines, this timely text/reference offers an in-depth survey of the state of the art in distributed camera networks. The book addresses a broad spectrum of critical issues in this highly interdisciplinary field: current challenges and future directions; video processing and video understanding; simulation, graphics, cognition and video networks; wireless video sensor networks, communications and control; embedded cameras and real-time video analysis; applications of distributed video networks; and educational opportunities and curriculum-development. Topics and features: presents an overview of research in areas of motion analysis, invariants, multiple cameras for detection, object tracking and recognition, and activities in video networks; provides real-world applications of distributed video networks, including force protection, wide area activities, port security, and recognition in night-time environments; describes the challenges in graphics and simulation, covering virtual vision, network security, human activities, cognitive architecture, and displays; examines issues of multimedia networks, registration, control of cameras (in simulations and real networks), localization and bounds on tracking; discusses system aspects of video networks, with chapters on providing testbed environments, data collection on activities, new integrated sensors for airborne sensors, face recognition, and building sentient spaces; investigates educational opportunities and curriculum development from the perspective of computer science and electrical engineering. This unique text will be of great interest to researchers and graduate students of computer vision and pattern recognition, computer graphics and simulation, image processing and embedded systems, and communications, networks and controls. The large number of example applications will also appeal to application engineers.
Distributional Reinforcement Learning
by Marc G. Bellemare Will Dabney Mark RowlandThe first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective.Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. Going beyond the common approach to reinforcement learning and expected values, it focuses on the total reward or return obtained as a consequence of an agent's choices—specifically, how this return behaves from a probabilistic perspective. In this first comprehensive guide to distributional reinforcement learning, Marc G. Bellemare, Will Dabney, and Mark Rowland, who spearheaded development of the field, present its key concepts and review some of its many applications. They demonstrate its power to account for many complex, interesting phenomena that arise from interactions with one's environment.The authors present core ideas from classical reinforcement learning to contextualize distributional topics and include mathematical proofs pertaining to major results discussed in the text. They guide the reader through a series of algorithmic and mathematical developments that, in turn, characterize, compute, estimate, and make decisions on the basis of the random return. Practitioners in disciplines as diverse as finance (risk management), computational neuroscience, computational psychiatry, psychology, macroeconomics, and robotics are already using distributional reinforcement learning, paving the way for its expanding applications in mathematical finance, engineering, and the life sciences. More than a mathematical approach, distributional reinforcement learning represents a new perspective on how intelligent agents make predictions and decisions.
DITA - der topic-basierte XML-Standard: Ein schneller Einstieg (essentials)
by Sissi ClossPrägnant und praxisorientiert erfahren Sie hier, auf welchen zentralen Prinzipien DITA beruht. Die wichtigsten DITA-Features werden anhand einfacher Beispiele erklärt, die direkt auf die eigene Umgebung übertragbar sind. Damit ist dieses essential ein guter Einstieg für alle, die DITA noch nicht kennen, und ideal als erste Entscheidungshilfe, wenn es um die Optimierung einer Informationslandschaft geht.
DITA for Practitioners Volume 1
by Eliot KimberDITA expert Eliot Kimber takes you inside the DITA XML standard, explaining the architecture and technology that make DITA unique. Volume 1 of his two-volume exploration of DITA starts with a hands-on explanation of end-to-end DITA processing that will get you up and running fast. Then, he explores the DITA architecture, explaining maps and topics, structural patterns, metadata, linking and addressing, keys and key references, relationship tables, conditional processing, reuse, and more. DITA for Practitioners Volume 1: Architecture and Technology is for engineers, tool builders, and content strategists: anyone who designs, implements, or supports DITA-based systems and needs a deeper understanding of DITA technology. Kimber's unique perspective unwraps the puzzle that is DITA, explaining the rationale for its design and structure, and giving you an unvarnished, detailed look inside this important technology.
Dive Into Algorithms: A Pythonic Adventure for the Intrepid Beginner
by Bradford TuckfieldDive Into Algorithms is a broad introduction to algorithms using the Python Programming Language.Dive Into Algorithms is a wide-ranging, Pythonic tour of many of the world's most interesting algorithms. With little more than a bit of computer programming experience and basic high-school math, you'll explore standard computer science algorithms for searching, sorting, and optimization; human-based algorithms that help us determine how to catch a baseball or eat the right amount at a buffet; and advanced algorithms like ones used in machine learning and artificial intelligence. You'll even explore how ancient Egyptians and Russian peasants used algorithms to multiply numbers, how the ancient Greeks used them to find greatest common divisors, and how Japanese scholars in the age of samurai designed algorithms capable of generating magic squares.You'll explore algorithms that are useful in pure mathematics and learn how mathematical ideas can improve algorithms. You'll learn about an algorithm for generating continued fractions, one for quick calculations of square roots, and another for generating seemingly random sets of numbers.You'll also learn how to: • Use algorithms to debug code, maximize revenue, schedule tasks, and create decision trees • Measure the efficiency and speed of algorithms • Generate Voronoi diagrams for use in various geometric applications • Use algorithms to build a simple chatbot, win at board games, or solve sudoku puzzles • Write code for gradient ascent and descent algorithms that can find the maxima and minima of functions • Use simulated annealing to perform global optimization • Build a decision tree to predict happiness based on a person's characteristicsOnce you've finished this book you'll understand how to code and implement important algorithms as well as how to measure and optimize their performance, all while learning the nitty-gritty details of today's most powerful algorithms.
Dive Into Data Science: Use Python To Tackle Your Toughest Business Challenges
by Bradford TuckfieldLearn how to use data science and Python to solve everyday business problems.Dive into the exciting world of data science with this practical introduction. Packed with essential skills and useful examples, Dive Into Data Science will show you how to obtain, analyze, and visualize data so you can leverage its power to solve common business challenges.With only a basic understanding of Python and high school math, you&’ll be able to effortlessly work through the book and start implementing data science in your day-to-day work. From improving a bike sharing company to extracting data from websites and creating recommendation systems, you&’ll discover how to find and use data-driven solutions to make business decisions.Topics covered include conducting exploratory data analysis, running A/B tests, performing binary classification using logistic regression models, and using machine learning algorithms.You&’ll also learn how to:Forecast consumer demand Optimize marketing campaignsReduce customer attritionPredict website trafficBuild recommendation systemsWith this practical guide at your fingertips, harness the power of programming, mathematical theory, and good old common sense to find data-driven solutions that make a difference. Don&’t wait; dive right in!
Dive Into Systems: A Gentle Introduction to Computer Systems
by Suzanne J. Matthews Tia Newhall Kevin C. WebbDive into Systems is a vivid introduction to computer organization, architecture, and operating systems that is already being used as a classroom textbook at more than 25 universities.This textbook is a crash course in the major hardware and software components of a modern computer system. Designed for use in a wide range of introductory-level computer science classes, it guides readers through the vertical slice of a computer so they can develop an understanding of the machine at various layers of abstraction. Early chapters begin with the basics of the C programming language often used in systems programming. Other topics explore the architecture of modern computers, the inner workings of operating systems, and the assembly languages that translate human-readable instructions into a binary representation that the computer understands. Later chapters explain how to optimize code for various architectures, how to implement parallel computing with shared memory, and how memory management works in multi-core CPUs. Accessible and easy to follow, the book uses images and hands-on exercise to break down complicated topics, including code examples that can be modified and executed.
Dive Into UDL: Immersive Practices to Develop Expert Learners
by Kendra Grant Luis Perez<p>Universal Design for Learning (UDL) is a framework for designing instruction that meets the needs of every learner. This book provides an overview of UDL, showing how to offer flexibility in methods of presentation, student participation and expression to support high achievement for all students, including those with disabilities or limited English proficiency. Dive into UDL shows K-12 educators how to incorporate UDL in their instructional design and engage in continuous professional growth. The book will also appeal to those in coaching positions and to administrators seeking to support their staff. <p>The book: offers three modes of entry to allow educators to "start where they are" in their understanding of UDL and how it applies to their areas of instruction; shows educators how to enhance and transform their instructional practices by applying a UDL lens to analyze and redesign lessons; illustrates how to design accessible materials and use technology to provide more options for learners; and highlights how UDL is foundational to inquiry-based, project-based and constructivist hands-on learning.</p>
Diventa esperto di Bitcoin
by Adidas Wilson Andrea GiampaoliIl Bitcoin è una criptovaluta ed un sistema di pagamento digitale ideato da uno sviluppatore sconosciuto, o da un gruppo di sviluppatori sotto il nome di Satoshi Nakamoto. Venne pubblicato come software open-source nel 2009. Il sistema è peer-to-peer e le transazioni vengono effettuate direttamente tra gli utenti, senza un intermediario. Queste transazioni vengono in seguito verificate dai nodi del network e registrate in un libro contabile pubblico chiamato blockchain. Poiché il sistema funziona senza un database centrale o un amministratore, il Bitcoin viene definito come la prima valuta digitale decentralizzata. Oltre ad essere creato come premio per il mining, il Bitcoin può essere scambiato con altre valute, prodotti e servizi nei mercati legali o nei mercati di contrabbando. Nel Febbraio 2015 oltre 100.000 commercianti e venditori accettavano pagamenti in Bitcoin. Secondo una ricerca dell'Università di Cambridge pubblicata nel 2017, ci sono da 2.9 a 5.8 milioni di utenti che usano un portafoglio di criptovalute, e la maggior parte di essi usano i Bitcoin.
Diventa Un Esperto di Apple HomePod: La Guida Ufficiale HomePod IOS 12
by Adidas WilsonIl nuovo dispositivo HomePod offre un servizio semplice e sorprendente per gli utenti Apple di godersi AirPlay, Apple Music e controllare i dispositivi HomeKit da qualsiasi luogo. HomePod non è stato creato per essere un concorrente di Google Home o Amazon Echo, così come il MacBook Air non era progettato per competere con il netbook. Certo, entrambi condividono un certo numero di caratteristiche . Ad esempio, l'altoparlante HomePod può essere controllato vocalmente e il MacBook Air è compatto e leggero. Tuttavia, HomePod è un assistente domestico da 350 dollari ; molto simile al MacBook, un computer portatile da 200 dollari. Il Fire Phone di Amazon non è stato un successo. Alexa, quindi, ha dovuto vendere molto in modo che Prime potesse restare nella vita di molti utenti. Questo risultò un successo grazie ai prezzi contenuti di Echo. Quando si tratta di HomePod, però, la questione cambia. AirPod è stato progettato per aiutarti a godere la tua musica preferita mentre sei in viaggio, mentre HomePod è stato progettato per ascoltare la tua musica preferita comodamente a casa tua.
Diversifying Digital Learning: Online Literacy and Educational Opportunity (Tech.edu: A Hopkins Series on Education and Technology)
by Edited by William G. Tierney, Zoë B. Corwin, and Amanda OchsnerHow does the digital divide affect the teaching and learning of historically underrepresented students?Many schools and programs in low-income neighborhoods lack access to the technological resources, including equipment and Internet service, that those in middle- and upper-income neighborhoods have at their fingertips. This inequity creates a persistent digital divide—not a simple divide in access to technology per se, but a divide in both formal and informal digital literacy that further marginalizes youths from low-income, minoritized, and first-generation communities.Diversifying Digital Learning outlines the pervasive problems that exist with ensuring digital equity and identifies successful strategies to tackle the issue. Bringing together top scholars to discuss how digital equity in education might become a key goal in American education, this book is structured to provide a framework for understanding how historically underrepresented students most effectively engage with technology—and how institutions may help or hinder students’ ability to develop and capitalize on digital literacies.This book will appeal to readers who are well versed in the diverse uses of social media and technologies, as well as less technologically savvy educators and policy analysts in educational organizations such as schools, afterschool programs, colleges, and universities. Addressing the intersection of digital media, race/ethnicity, and socioeconomic class in a frank manner, the lessons within this compelling work will help educators enable students in grades K–12, as well as in postsecondary institutions, to participate in a rapidly changing world framed by shifting new media technologies.Contributors: Young Whan Choi, Zoë B. Corwin, Christina Evans, Julie Flapan, Joanna Goode, Erica Hodgin, Joseph Kahne, Suneal Kolluri, Lynette Kvasny, David J. Leonard, Jane Margolis, Crystle Martin, Safiya Umoja Noble, Amanda Ochsner, Fay Cobb Payton, Antar A. Tichavakunda, William G. Tierney, S. Craig Watkins