Personalization and Collaboration in Adaptive E-Learning
Personalization and Collaboration in Adaptive E-Learning

Author: Tadlaoui, Mouenis Anouar

Publisher: IGI Global

Published: 2019-12-13

Total Pages: 348

ISBN-13: 1799814947

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As part of e-learning, adaptive systems are more specialized and focus on the adaptation of learning content and presentation of this content. An adaptive system focuses on how knowledge is learned and pays attention to the activities, cognitive structures, and context of the learning material. The adaptive term refers to the automatic adaptation of the system to the learner. The needs of the learner are borne by the system itself. The learner did not ask to change the parameters of the system to his own needs; it is rather the needs of the learner that will be supposed by the system. The system adapts according to this necessity. Personalization and Collaboration in Adaptive E-Learning is an essential reference book that aims to describe the specific steps in designing a scenario for a collaborative learning activity in the particular context of personalization in adaptive systems and the key decisions that need to be made by the teacher-learner. By applying theoretical and practical aspects of personalization in adaptive systems and applications within education, this collection features coverage on a broad range of topics that include adaptive teaching, personalized learning, and instructional design. This book is ideally designed for instructional designers, curriculum developers, educational software developers, IT specialists, educational administrators, professionals, professors, researchers, and students seeking current research on comparative studies and the pedagogical issues of personalized and collaborative learning.

Adaptive Behavior and Learning
Adaptive Behavior and Learning

Author: J. E. R. Staddon

Publisher: Cambridge University Press

Published: 2016-03-10

Total Pages: 619

ISBN-13: 1107082471

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Summarizes the current state of both theoretical and experimental knowledge about learning in animals.

Designing Adaptive and Personalized Learning Environments
Designing Adaptive and Personalized Learning Environments

Author: Kinshuk

Publisher: Routledge

Published: 2016-03-31

Total Pages: 172

ISBN-13: 1317747704

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Designing Adaptive and Personalized Learning Environments provides a theoretically-based yet practical guide to systematic design processes for learning environments that provide automatic customization of learning and instruction. The book consists of four main sections: In "Introduction and Overview," the concepts of adaptivity and personalization are introduced and explored in detail. In "Theoretical Perspectives with Example Applications," various theoretical concepts underlying adaptive and personalized learning are discussed, including cognitive profiling, content-based adaptivity, exploration-based adaptivity, and mobile and ubiquitous settings. In "Practical Perspectives with Example Applications," the implementation process for adaptive and personalized learning environments is described, followed by application in various contexts. In "Validation and Future Trends," various evaluation techniques for validating the efficiency and efficacy of adaptive and personalized learning systems are discussed. This final section concludes with a discussion of emerging trends in adaptive and personalized learning research. Based on cutting-edge research, Designing Adaptive and Personalized Learning Environments is appropriate as a primary textbook for both undergraduate and graduate courses focused on the design of learning systems, and as a secondary textbook for a variety of courses in programs such as educational technology, instructional design, learning sciences, digital literacy, computer based systems, and STEM content fields.

Adaptivity as a Transformative Disposition
Adaptivity as a Transformative Disposition

Author: David Hung

Publisher: Springer Science & Business Media

Published: 2013-11-19

Total Pages: 326

ISBN-13: 9814560170

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This volume introduces the concept of ‘adaptivity’ as occurring when, say, individuals cross boundaries. Through illustrations from both formal and informal learning, the book seeks to provide learning designs and frameworks for adaptivity. This book is unique as it ties together: a) social-individual dialectics; and b) adaptive learning as it relates to creativity and imagination. It highlights case studies from social / new media contexts, school learning milieux, and formal and informal situations. It approaches adaptive learning from the perspectives of students, teachers, school leaders, and participants in social media and other digitally mediated environments. The book is a valuable resource for practitioners and academics who are interested in adaptivity as a learning disposition.

Learning for Adaptive and Reactive Robot Control
Learning for Adaptive and Reactive Robot Control

Author: Aude Billard

Publisher: MIT Press

Published: 2022-02-08

Total Pages: 425

ISBN-13: 0262367017

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Methods by which robots can learn control laws that enable real-time reactivity using dynamical systems; with applications and exercises. This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when interacting with humans. It introduces a set of control laws that enable reactivity using dynamical systems, a widely used method for solving motion-planning problems in robotics. These control approaches can replan in milliseconds to adapt to new environmental constraints and offer safe and compliant control of forces in contact. The techniques offer theoretical advantages, including convergence to a goal, non-penetration of obstacles, and passivity. The coverage of learning begins with low-level control parameters and progresses to higher-level competencies composed of combinations of skills. Learning for Adaptive and Reactive Robot Control is designed for graduate-level courses in robotics, with chapters that proceed from fundamentals to more advanced content. Techniques covered include learning from demonstration, optimization, and reinforcement learning, and using dynamical systems in learning control laws, trajectory planning, and methods for compliant and force control . Features for teaching in each chapter: applications, which range from arm manipulators to whole-body control of humanoid robots; pencil-and-paper and programming exercises; lecture videos, slides, and MATLAB code examples available on the author’s website . an eTextbook platform website offering protected material[EPS2] for instructors including solutions.

Adaptive and Learning Systems
Adaptive and Learning Systems

Author: Kumpati S. Narendra

Publisher: Springer Science & Business Media

Published: 2013-11-22

Total Pages: 410

ISBN-13: 1475718950

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This volume offers a glimpse of the status of research in adaptive and learning systems in 1985. In recent years these areas have spawned a multiplicity of ideas so rapidly that the average research worker or practicing engineer is overwhelmed by the flood of information. The Yale Workshop on Applications of Adaptive Systems Theory was organized in 1979 to provide a brief respite from this deluge, wherein critical issues may be examined in a calm and collegial environment. The fourth of the series having been held in May 1985, it has now become well established as a biennial forum for the lively exchange of ideas in the ever changing domain of adaptive systems. The scope of this book is broad and ranges from theoretical investigations to practical applications. It includes twenty eight papers by leaders in the field, selected from the Pro ceedings of the Fourth Yale Workshop and divided into five sections. I have provided a brief introduction to each section so that it can be read as a self-contained unit. The first section, devoted to adaptive control theory, suggests the intensity of activity in the field and reveals signs of convergence towards some common themes by workers with rather different moti vation. Preliminary results concerning the reduced order model problem are dramatically changing the way we view the field and bringing it closer to other areas such as robust linear control where major advances have been recently reported.

The Master Adaptive Learner
The Master Adaptive Learner

Author: William Cutrer

Publisher: Elsevier Health Sciences

Published: 2019-09-29

Total Pages: 242

ISBN-13: 032371112X

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Tomorrow’s best physicians will be those who continually learn, adjust, and innovate as new information and best practices evolve, reflecting adaptive expertise in response to practice challenges. As the first volume in the American Medical Association’s MedEd Innovation Series, The Master Adaptive Learner is an instructor-focused guide covering models for how to train and teach future clinicians who need to develop these adaptive skills and utilize them throughout their careers. Explains and clarifies the concept of a Master Adaptive Learner: a metacognitive approach to learning based on self-regulation that fosters the success and use of adaptive expertise in practice. Contains both theoretical and practical material for instructors and administrators, including guidance on how to implement a Master Adaptive Learner approach in today’s institutions. Gives instructors the tools needed to empower students to become efficient and successful adaptive learners. Helps medical faculty and instructors address gaps in physician training and prepare new doctors to practice effectively in 21st century healthcare systems. One of the American Medical Association Change MedEd initiatives and innovations, written and edited by members of the ACE (Accelerating Change in Medical Education) Consortium – a unique, innovative collaborative that allows for the sharing and dissemination of groundbreaking ideas and projects.

Deep Learning
Deep Learning

Author: Ian Goodfellow

Publisher: MIT Press

Published: 2016-11-10

Total Pages: 801

ISBN-13: 0262337371

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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Advancing the Power of Learning Analytics and Big Data in Education
Advancing the Power of Learning Analytics and Big Data in Education

Author: Azevedo, Ana

Publisher: IGI Global

Published: 2021-03-19

Total Pages: 296

ISBN-13: 1799871045

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The term learning analytics is used in the context of the use of analytics in e-learning environments. Learning analytics is used to improve quality. It uses data about students and their activities to provide better understanding and to improve student learning. The use of learning management systems, where the activity of the students can be easily accessed, potentiated the use of learning analytics to understand their route during the learning process, help students be aware of their progress, and detect situations where students can give up the course before its completion, which is a growing problem in e-learning environments. Advancing the Power of Learning Analytics and Big Data in Education provides insights concerning the use of learning analytics, the role and impact of analytics on education, and how learning analytics are designed, employed, and assessed. The chapters will discuss factors affecting learning analytics such as human factors, geographical factors, technological factors, and ethical and legal factors. This book is ideal for teachers, administrators, teacher educators, practitioners, stakeholders, researchers, academicians, and students interested in the use of big data and learning analytics for improved student success and educational environments.

Managing Cognitive Load in Adaptive Multimedia Learning
Managing Cognitive Load in Adaptive Multimedia Learning

Author: Kalyuga, Slava

Publisher: IGI Global

Published: 2008-11-30

Total Pages: 336

ISBN-13: 1605660493

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"Provides theory and research-based recommendations on information presentation techniques for multimedia and e-learning environments. Focuses on extensively researched principles and methodologies, offering comprehensive research and practical implications while providing concrete examples on adaptive multimedia learning."--Publisher description.