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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.
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.
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.
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.
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.
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