The author examines the history of human communication, the disciplines used in multimedia development and the hardware/software involved. He offers expert advice on development and delivery techniques which will increase students' knowledge of the subject.

An evidence based, rigorous text reviewing 12 principles of experimental studies grounded in cognitive theory of multi-media learning.

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.

Collecting the latest developments in the field, Multimedia Data Mining: A Systematic Introduction to Concepts and Theory defines multimedia data mining, its theory, and its applications. Two of the most active researchers in multimedia data mining explore how this young area has rapidly developed in recent years.The book first discusses the theore

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.

Foundations of Critical Media and Information Studies lays down foundations for the analysis of media, information, and information technology in 21st century information society, as well as introducing the theoretical and empirical tools necessary for the critical study of media and information. Christian Fuchs shows the role classical critical theory can play for analyzing the information society and the information economy, as well as analyzing the role of the media and the information economy in economic development, the new imperialism, and the new economic crisis. The book critically discusses transformations of the Internet (‘web 2.0’), introduces the notion of alternative media as critical media, and shows the critical role media and information technology can play in contemporary society. This book provides an excellent introduction to the study of media, information technology, and information society, making it a valuable reference tool for both undergraduate and postgraduate students of subjects such as Media Studies, Sociology of Media, Social Theory, and New Media.

"Foundations of Large-Scale Multimedia Information Management and Retrieval: Mathematics of Perception" covers knowledge representation and semantic analysis of multimedia data and scalability in signal extraction, data mining, and indexing. The book is divided into two parts: Part I - Knowledge Representation and Semantic Analysis focuses on the key components of mathematics of perception as it applies to data management and retrieval. These include feature selection/reduction, knowledge representation, semantic analysis, distance function formulation for measuring similarity, and multimodal fusion. Part II - Scalability Issues presents indexing and distributed methods for scaling up these components for high-dimensional data and Web-scale datasets. The book presents some real-world applications and remarks on future research and development directions. The book is designed for researchers, graduate students, and practitioners in the fields of Computer Vision, Machine Learning, Large-scale Data Mining, Database, and Multimedia Information Retrieval. Dr. Edward Y. Chang was a professor at the Department of Electrical & Computer Engineering, University of California at Santa Barbara, before he joined Google as a research director in 2006. Dr. Chang received his M.S. degree in Computer Science and Ph.D degree in Electrical Engineering, both from Stanford University.