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.