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.

In traditional e-learning, teachers design sets of Learning Objects (LOs) and organize their sequencing; the material implementing the LOs could be either built anew or adopted from elsewhere (e.g. from standard-compliant repositories) and reused. This task is applicable also when the teacher works in a system for personalized e-learning. In this case, the burden actually increases: for instance, the LOs may need adaptation to the system, through additional metadata. This paper presents a module that gives some support to the operations of retrieving, analyzing, and importing LOs from a set of standard Learning Objects Repositories, acting as a recommending system. In particular, it is designed to support the teacher in the phases of (i) retrieval of LOs, through a keyword-based search mechanism applied to the selected repositories; (ii) analysis of the returned LOs, whose information is enriched by a concept of relevance metric, based on both the results of the searching operation and the data related to the previous use of the LOs in the courses managed by the Learning Management System; and (iii) LO importation into the course under construction.