Описание: So-called Intent-Based Networking (IBN) is founded on well-known SDN (Software-Defined Networking) and represents one of the most important emerging network infrastructure opportunities.
Описание: Simulation and game-based learning are essential applications in a learning environment as they provide learners an opportunity to apply the course material in real-life scenarios. Introducing real-life learning allows the learner to make critical decisions at different points within the simulation providing constructive education that leads to a cognitive understanding of the material. The use of simulations provides the learner with the ability to cognitively store and recall learning in real-life experiences. Therefore, it is crucial to not only provide course material but to have students apply what they have learned in simulations that replicate real-life scenarios. These learned skills are essential for students to be marketable and thrive in a career field where decision making, problem solving, and critical thinking are job requirements.
Simulation and Game-Based Learning in Emergency and Disaster Management is a cutting-edge research book that examines the best practices and holistic development when it comes to simulation learning within emergency and disaster management as well as global security. Drawing upon the neuroscience of learning, classroom instruction can be enhanced to incorporate active-experiential learning activities that positively impact a learner with long-term information retention. Each simulation project is carried out in different environments, with different goals in mind, and developed under various constraints. For these reasons, this book will provide insight into the simulation planning and development process, provide examples of online simulations and game-based learning activities, and provide insight on simulation development and implementation that can be used across disciplines in educational and training settings. As such, it is ideal for academicians, instructional designers, curriculum designers, education professionals, researchers, and students.
Автор: Russell G. Almond; Robert J. Mislevy; Linda S. Ste Название: Bayesian Networks in Educational Assessment ISBN: 1493938282 ISBN-13(EAN): 9781493938285 Издательство: Springer Рейтинг: Цена: 11878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Introduction.- An Introduction to Evidence-Centered Design.- Bayesian Probability and Statistics: a review.- Basic graph theory and graphical models.- Efficient calculations.- Some Example Networks.- Explanation and Test Construction.- Parameters for Bayesian Network Models.- Learning in Models with Fixed Structure.- Critiquing and Learning Model Structure.- An Illustrative Example.- The Conceptual Assessment Framework.- The Evidence Accumulation Process.- The Biomass Measurement Model.- The Future of Bayesian Networks in Educational Assessment.- Bayesian Network Resources.- References.
Описание: The prominence of social media, especially in the lives of teenagers and young adults, has long been regarded as a significant distraction from studies. However, the integration of these forms of media into the teaching experience can improve the engagement of students.Global Perspectives on Social Media in Tertiary Learning and Teaching: Emerging Research and Opportunities is an essential scholarly publication that embeds innovative, current pedagogical practices into new and redeveloped courses and introduces digital and online learning tools to best support teaching practices. Featuring coverage on a wide range of topics including collaborative learning, innovative learning environments, and blended teaching, this book provides essential research for educators, educational administrators, education stakeholders, academicians, researchers, and professionals within the realm of higher education.
Автор: Alejandro Pe?a-Ayala Название: Educational Networking ISBN: 3030299724 ISBN-13(EAN): 9783030299729 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is related to the educational networking (EN) domain, an incipient but disrupting trend engaged in extending and improving formal and informal academic practices by means of the support given by online social networks (OSNs) and Web 2.0 technologies. With the aim of contributing to spread the knowledge and development of the arena, this volume introduces ten recent works, whose content meets the quality criteria of formal scientific labor that is worthy to be published according to following five categories:· Reviews: gather three overviews that focus on K-12 EN practice, mixed methods approaches using social network analysis for learning and education, and a broad landscape of the recent accomplished labor.· Conceptual: presents a work where a theoretical framework is proposed to overcome barriers that constrain the use of OSNs for educational purposes by means of a Platform Adoption Model. · Projects: inform a couple of initiatives, where one fosters groups and networks for teachers involved in distance education, and the other encourages students the author academic videos to improve motivation and engagement.· Approaches: offer three experiences related to: Wiki and Blog usage for assessment affairs, application of a method that encourages OSNs users to actively post and repost valuable information for the learning community, and the recreation of learning spaces in context–aware to boost EN.· Study: applies an own method to ranking Mexican universities based on maximal clique, giving as a result a series of complex visual networks that characterize the tides among diverse features that describe academic institutions practice.In resume, this volume offers a fresh reference of an emergent field that contributes to spreading and enhancing the provision of education in classrooms and online settings through social constructivism and collaboration policy. Thus, it is expected the published content encourages researchers, practitioners, professors, and postgraduate students to consider their future contribution to extent the scope and impact of EN in formal and informal teaching and learning endeavors.
Описание: Teachers use e-learning systems to develop course notes and web-based activities to communicate with learners on one side and monitor and classify their progress on the other. Learners use it for learning, communication, and collaboration. Adaptive e-learning systems often employ learner models, and the behavior of an adaptive system varies depending on the data from the learner model and the learner's profile. Without knowing anything about the learner who uses the system, a system would behave in exactly the same way for all learners.Bayesian Networks for Managing Learner Models in Adaptive Hypermedia Systems: Emerging Research and Opportunities is a collection of research on the use of Bayesian networks and methods as a probabilistic formalism for the management of the learner model in adaptive hypermedia. It specifically discusses comparative studies, transformation rules, and case diagrams that support all phases of the learner model and the use of Bayesian networks and multi-entity Bayesian networks to manage dynamic aspects of this model. While highlighting topics such as developing the learner model, learning management systems, and modeling techniques, this book is ideally designed for instructional designers, course administrators, educators, researchers, and professionals.
Описание: Modern education has increased its reach through ICT tools and techniques. To manage educational data with the help of modern artificial intelligence, data and web mining techniques on dedicated cloud or grid platforms for educational institutes can be used. By utilizing data science techniques to manage educational data, the safekeeping, delivery, and use of knowledge can be increased for better quality education.
Utilizing Educational Data Mining Techniques for Improved Learning: Emerging Research and Opportunities is a critical scholarly resource that explores data mining and management techniques that promote the improvement and optimization of educational data systems. The book intends to provide new models, platforms, tools, and protocols in data science for educational data analysis and introduces innovative hybrid system models dedicated to data science. Including topics such as automatic assessment, educational analytics, and machine learning, this book is essential for IT specialists, data analysts, computer engineers, education professionals, administrators, policymakers, researchers, academicians, and technology experts.
Описание: Modern education has increased its reach through ICT tools and techniques. To manage educational data with the help of modern artificial intelligence, data and web mining techniques on dedicated cloud or grid platforms for educational institutes can be used. By utilizing data science techniques to manage educational data, the safekeeping, delivery, and use of knowledge can be increased for better quality education. Utilizing Educational Data Mining Techniques for Improved Learning: Emerging Research and Opportunities is a critical scholarly resource that explores data mining and management techniques that promote the improvement and optimization of educational data systems. The book intends to provide new models, platforms, tools, and protocols in data science for educational data analysis and introduces innovative hybrid system models dedicated to data science. Including topics such as automatic assessment, educational analytics, and machine learning, this book is essential for IT specialists, data analysts, computer engineers, education professionals, administrators, policymakers, researchers, academicians, and technology experts.
Автор: Paul Smeyers; Marc Depaepe Название: Educational Research: Networks and Technologies ISBN: 9048176816 ISBN-13(EAN): 9789048176816 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: It records the intellectual struggles of a group of scholars coming to grips with changes in knowledge production and research communication. Together these authors demonstrate how philosophical and historical approaches are relevant to the practice and theory of education.
Описание: The rise of technology within educational settings has allowed for a substantial shift in the way in which educators teach learners of all ages. In order to implement these new learning tools, school administrators and teachers alike must seek new research outlining the latest innovations in the field.Educational Technology Use and Design for Improved Learning Opportunities presents broad coverage of topics pertaining to the development and use of technology both in and out of the classroom. Including research on technology integration in K-12, higher education, and adult learning, this publication is ideal for use by school administrators, academicians, and upper-level students seeking the most up-to-date tools and methodologies surrounding educational technology.
Автор: Maria-Gabriella Di Benedetto; Andrea F. Cattoni; J Название: Cognitive Radio and Networking for Heterogeneous Wireless Networks ISBN: 3319378619 ISBN-13(EAN): 9783319378619 Издательство: Springer Рейтинг: Цена: 14365.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Combining expert European opinion from academia and industry, this overview of the latest developments in cognitive radio and networking technology considers a host of topics including air interfaces for spectrum-sharing and the evolving regulatory framework.
Автор: Babita Pandey, Aditya Khamparia Название: Hidden Link Prediction in Stochastic Social Networks ISBN: 152259096X ISBN-13(EAN): 9781522590965 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 28552.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Link prediction is required to understand the evolutionary theory of computing for different social networks. However, the stochastic growth of the social network leads to various challenges in identifying hidden links, such as representation of graph, distinction between spurious and missing links, selection of link prediction techniques comprised of network features, and identification of network types. Hidden Link Prediction in Stochastic Social Networks concentrates on the foremost techniques of hidden link predictions in stochastic social networks including methods and approaches that involve similarity index techniques, matrix factorization, reinforcement, models, and graph representations and community detections. The book also includes miscellaneous methods of different modalities in deep learning, agent-driven AI techniques, and automata-driven systems and will improve the understanding and development of automated machine learning systems for supervised, unsupervised, and recommendation-driven learning systems. It is intended for use by data scientists, technology developers, professionals, students, and researchers.
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