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An evidence based, rigorous text reviewing 12 principles of experimental studies grounded in cognitive theory of multi-media learning.
This book introduces the concept of multimedia in education, and how multimedia technology could be implemented to impart digital education to university students. The book emphasizes the versatile use of technology enabled education through the research papers from distinguished academicians and researchers who are specifically working in this area. It benefits all those researchers who are enthusiastic about learning online and also for those academicians who are interested to work on various aspects of learning and teaching through technology.
This book offers a primary focus on the meaning and importance of multimedia learning theory and is application in educator preparation. Integrating multimedia learning theory into preparing the next generation of educators for their role in the education of the next generation of students is presented as an important consideration for the future of our educational systems and society. As the use of digital technologies and Web 2.0 becomes more prevalent and the world becomes more infused with multimedia, it is important to ask to what extent, if at all, such developments change the forms and nature of knowledge. Teaching and learning in this digital, multimedia environment is increasingly challenged as the neomillennial generation enters schools and colleges having grown up with digital technologies defining their culture and shaping their cognitive and social interactions. Multimedia, for the neomillennial generation, is deeply embedded in their sensory and cognitive patterns; the neomillennials see and understand media in more sophisticated ways than their parents and the generations of society that preceded them.
Now established as an effective tool in the instructional process, multimedia has penetrated educational systems at almost every level of study. In their quest to maximize educational outcomes and identify best practices, multimedia researchers are now expanding their examinations to extend towards the cognitive functionality of multimedia.""Cognitive Effects of Multimedia Learning"" identifies the role and function of multimedia in learning through a collection of research studies focusing on cognitive functionality. An advanced collection of critical theories and practices, this much needed contribution to the research is an essential holding for academic libraries, and will benefit researchers, practitioners and students in basic and applied fields ranging from education to cognitive sciences.
The updated second edition of the only handbook to offer a comprehensive analysis of research and theory in the field of multimedia learning, or learning from words and images. It examines research-based principles to determine the most effective methods of multimedia instruction and uses cognitive theory to explain how these methods work.
Multimedia-Based Instructional Design is a thoroughly revised and updated second edition of the best-selling book that provided a complete guide to designing and developing interactive multimedia training. While most training companies develop their training programs in many different technological delivery media—computer-based, web-based, and distance learning technologies—this unique book demonstrates that the same instructional design process can be used for all media. Using just one process reduces cycle time for course development—and also reduces costs.
Processing multimedia content has emerged as a key area for the application of machine learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it. Arising from the EU MUSCLE network, this multidisciplinary book provides a comprehensive coverage of the most important machine learning techniques used and their application in this domain.
Digital and online learning is more prevalent than ever, making multimedia learning a primary objective for many instructors. The Cambridge Handbook of Multimedia Learning examines cutting-edge research to guide creative teaching methods in online classrooms and training. Recognized as the field's major reference work, this research-based handbook helps define and shape this area of study. This third edition provides the latest progress report from the world's leading multimedia researchers, with forty-six chapters on how to help people learn from words and pictures, particularly in computer-based environments. The chapters demonstrate what works best and establishes optimized practices. It systematically examines well-researched principles of effective multimedia instruction and pinpoints exactly why certain practices succeed by isolating the boundary conditions. The volume is founded upon research findings in learning theory, giving it an informed perspective in explaining precisely how effective teaching practices achieve their goals or fail to engage.
Provides comprehensive articles on significant issues, methods, and theories currently combining the studies of technology and literacy.
The field of multimedia is unique in offering a rich and dynamic forum for researchers from “traditional” fields to collaborate and develop new solutions and knowledge that transcend the boundaries of individual disciplines. Despite the prolific research activities and outcomes, however, few efforts have been made to develop books that serve as an introduction to the rich spectrum of topics covered by this broad field. A few books are available that either focus on specific subfields or basic background in multimedia. Tutorial-style materials covering the active topics being pursued by the leading researchers at frontiers of the field are currently lacking. In 2015, ACM SIGMM, the special interest group on multimedia, launched a new initiative to address this void by selecting and inviting 12 rising-star speakers from different subfields of multimedia research to deliver plenary tutorial-style talks at the ACM Multimedia conference for 2015. Each speaker discussed the challenges and state-of-the-art developments of their prospective research areas in a general manner to the broad community. The covered topics were comprehensive, including multimedia content understanding, multimodal human-human and human-computer interaction, multimedia social media, and multimedia system architecture and deployment. Following the very positive responses to these talks, the speakers were invited to expand the content covered in their talks into chapters that can be used as reference material for researchers, students, and practitioners. Each chapter discusses the problems, technical challenges, state-of-the-art approaches and performances, open issues, and promising direction for future work. Collectively, the chapters provide an excellent sampling of major topics addressed by the community as a whole. This book, capturing some of the outcomes of such efforts, is well positioned to fill the aforementioned needs in providing tutorial-style reference materials for frontier topics in multimedia. At the same time, the speed and sophistication required of data processing have grown. In addition to simple queries, complex algorithms like machine learning and graph analysis are becoming common. And in addition to batch processing, streaming analysis of real-time data is required to let organizations take timely action. Future computing platforms will need to not only scale out traditional workloads, but support these new applications too. This book, a revised version of the 2014 ACM Dissertation Award winning dissertation, proposes an architecture for cluster computing systems that can tackle emerging data processing workloads at scale. Whereas early cluster computing systems, like MapReduce, handled batch processing, our architecture also enables streaming and interactive queries, while keeping MapReduce's scalability and fault tolerance. And whereas most deployed systems only support simple one-pass computations (e.g., SQL queries), ours also extends to the multi-pass algorithms required for complex analytics like machine learning. Finally, unlike the specialized systems proposed for some of these workloads, our architecture allows these computations to be combined, enabling rich new applications that intermix, for example, streaming and batch processing. We achieve these results through a simple extension to MapReduce that adds primitives for data sharing, called Resilient Distributed Datasets (RDDs). We show that this is enough to capture a wide range of workloads. We implement RDDs in the open source Spark system, which we evaluate using synthetic and real workloads. Spark matches or exceeds the performance of specialized systems in many domains, while offering stronger fault tolerance properties and allowing these workloads to be combined. Finally, we examine the generality of RDDs from both a theoretical modeling perspective and a systems perspective. This version of the dissertation makes corrections throughout the text and adds a new section on the evolution of Apache Spark in industry since 2014. In addition, editing, formatting, and links for the references have been added.