A new approach and system for attentive mobile learning based on seamless migration
Applied Intelligence
Automated and flexible comparison of course sequencing algorithms in the LS-Lab framework
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Fuzzy cognitive map based student progress indicators
ICWL'11 Proceedings of the 10th international conference on Advances in Web-Based Learning
Evaluating the integration of fuzzy logic into the student model of a web-based learning environment
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A Unified Learning Style Model for Technology-Enhanced Learning: What, Why and How?
International Journal of Distance Education Technologies
Creating a Personalized Artificial Intelligence Course: WELSA Case Study
International Journal of Information Systems and Social Change
Review: Student modeling approaches: A literature review for the last decade
Expert Systems with Applications: An International Journal
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LS-Plan is a framework for personalization and adaptation in e-learning. In such framework an Adaptation Engine plays a main role, managing the generation of personalized courses from suitable repositories of learning nodes and ensuring the maintenance of such courses, for continuous adaptation of the learning material proposed to the learner. Adaptation is meant, in this case, with respect to the knowledge possessed by the learner and her learning styles, both evaluated prior to the course and maintained while attending the course. Knowledge and Learning styles are the components of the student model managed by the framework. Both the static, precourse, and dynamic, in-course, generation of personalized learning paths are managed through an adaptation algorithm and performed by a planner, based on Linear Temporal Logic. A first Learning Objects Sequence is produced based on the initial learner's Cognitive State and Learning Styles, as assessed through prenavigation tests. During the student's navigation, and on the basis of learning assessments, the adaptation algorithm can output a new Learning Objects Sequence to respond to changes in the student model. We report here on an extensive experimental evaluation, performed by integrating LS-Plan in an educational hypermedia, the LecompS web application, and using it to produce and deliver several personalized courses in an educational environment dedicated to Italian Neorealist Cinema. The evaluation is performed by mainly following two standard procedures: the As a Whole and the Layered approaches. The results are encouraging both for the system on the whole and for the adaptive components.