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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.
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.
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.
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.
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