Simulations, games, and real or virtual apprenticeships are the kinds of context-rich learning environments that leverage the power of episodic memory, boost student motivation, and create the conditions that make learning transfer more likely. Context-rich learning embodies the answer to the question, "Why do we need to learn this?" Many students, especially those who have dyslexia or otherwise neurodivergent brains, require the kind of context that is lacking in education. Designing Context-Rich Learning by Extending Reality is a collection of research and readings on the context-rich learning approach that examines the aspects of design, delivery, and assessments. The book proposes novel approaches to designing and delivering context-rich learning across all disciplines. Covering topics such as learning experiences, sustainable development, and user experience design, this premier reference source is a valuable resource of inspiration to learning and training facilitators, instructional designers, assessment developers, instructional technologists, psychometricians, educators and administrators of both higher and K-12 education, pre-service teachers, teacher educators, researchers, and academicians.

Research in the domains of learning analytics and educational data mining has prototyped an approach where methodologies from data science and machine learning are used to gain insights into the learning process by using large amounts of data. As many training and academic institutions are maturing in their data-driven decision making, useful, scalable, and interesting trends are emerging. Organizations can benefit from sharing information on those efforts. Applying Data Science and Learning Analytics Throughout a Learner's Lifespan examines novel and emerging applications of data science and sister disciplines for gaining insights from data to inform interventions into learners' journeys and interactions with academic institutions. Data is collected at various times and places throughout a learner's lifecycle, and the learners and the institution should benefit from the insights and knowledge gained from this data. Covering topics such as learning analytics dashboards, text network analysis, and employment recruitment, this book is an indispensable resource for educators, computer scientists, faculty of higher education, government officials, educational administration, students of higher education, pre-service teachers, business professionals, researchers, and academicians.

In the modern data-driven era, artificial intelligence (AI) and machine learning (ML) technologies that allow a computer to mimic intelligent human behavior are essential for organizations to achieve business excellence and assist organizations in extracting useful information from raw data. AI and ML have existed for decades, but in the age of big data, this sort of analysis is in higher demand than ever, especially for customer support and analytics. AI and Machine Learning Applications and Implications in Customer Support and Analytics investigates the applications of AI and ML and how they can be implemented to enhance customer support and analytics at various levels of organizations. This book is ideal for marketing professionals, managers, business owners, researchers, practitioners, academicians, instructors, university libraries, and students, and covers topics such as artificial intelligence, machine learning, supervised learning, deep learning, customer sentiment analysis, data mining, neural networks, and business analytics.

As technology becomes further meshed into our culture and everyday lives, new mediums and outlets for creative expression and innovation are necessary. The Handbook of Research on Computational Arts and Creative Informatics covers a comprehensive range of topics regarding the interaction of the sciences and the arts. Exploring new uses of technology and investigating creative insights into concepts of art and expression, this cutting-edge Handbook of Research offers a valuable resource to academicians, researchers, and field practitioners.

As interest in computer, cognitive, and social sciences grow, the need for alternative approaches to models in related-disciplines thrives. ""An Imitation-Based Approach to Modeling Homogeneous Agents Societies"" offers a framework for modeling societies of autonomous agents that is heavily based on fuzzy algebraic tools. This publication overviews platforms developed with the purpose of simulating hypotheses or harvesting data from human subjects in efforts for calibration of the model of early learning in humans. ""An Imitation-Based Approach to Modeling Homogeneous Agents Societies"" reaches out to the cognitive sciences, psychology, and anthropology providing a different perspective on a few ""classical"" problems within these fields.

In a given environment, agents interact with each other, imitating, communicating, exchanging, and competing. Based on these heterogeneous modalities of interaction, a variety of socialities may emerge including language and communication, identities, economies, and cultures. ""The Handbook of Research on Agent-Based Societies: Social and Cultural Interactions"" addresses the emergence of societal phenomena in the interactions of systems of agents. Comprising authoritative chapters by numerous international authors, this reference book goes well beyond describing the next generation of multi-agent systems in simulations and system engineering and analyzes existing systems to stimulate the development of new ones. It features: 23 authoritative contributions by over 40 of the world's leading experts on agent-based societies from 7 countries; comprehensive coverage of each specific topic, highlighting recent trends and describing the latest advances in the field; more than 830 references to existing literature and research on agent-based societies; and a compendium of over 260 key terms with detailed definitions. It is organized by topic and indexed, making it a convenient method of reference for all IT/IS scholars and professionals. It includes cross-referencing of key terms, figures, and information pertinent to agent-based societies.

Simulations, games, and real or virtual apprenticeships are the kinds of context-rich learning environments that leverage the power of episodic memory, boost student motivation, and create the conditions that make learning transfer more likely. Context-rich learning embodies the answer to the question, "Why do we need to learn this?" Many students, especially those who have dyslexia or otherwise neurodivergent brains, require the kind of context that is lacking in education. Designing Context-Rich Learning by Extending Reality is a collection of research and readings on the context-rich learning approach that examines the aspects of design, delivery, and assessments. The book proposes novel approaches to designing and delivering context-rich learning across all disciplines. Covering topics such as learning experiences, sustainable development, and user experience design, this premier reference source is a valuable resource of inspiration to learning and training facilitators, instructional designers, assessment developers, instructional technologists, psychometricians, educators and administrators of both higher and K-12 education, pre-service teachers, teacher educators, researchers, and academicians.

Research in the domains of learning analytics and educational data mining has prototyped an approach where methodologies from data science and machine learning are used to gain insights into the learning process by using large amounts of data. As many training and academic institutions are maturing in their data-driven decision making, useful, scalable, and interesting trends are emerging. Organizations can benefit from sharing information on those efforts. Applying Data Science and Learning Analytics Throughout a Learner's Lifespan examines novel and emerging applications of data science and sister disciplines for gaining insights from data to inform interventions into learners' journeys and interactions with academic institutions. Data is collected at various times and places throughout a learner's lifecycle, and the learners and the institution should benefit from the insights and knowledge gained from this data. Covering topics such as learning analytics dashboards, text network analysis, and employment recruitment, this book is an indispensable resource for educators, computer scientists, faculty of higher education, government officials, educational administration, students of higher education, pre-service teachers, business professionals, researchers, and academicians.