Adaptive Learning with the LS-Plan System: A Field Evaluation

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, pre-course, 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 pre-navigation 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