This paper presents LS-Plan, a system capable of providing Educational Hypermedia with adaptation and personalization. The architecture of LS-Plan is based on three main components: the Adaptation Engine, the Planner and the Teacher Assistant. Dynamic course generation is driven by an adaptation algorithm, based on Learning Styles, as defined by Felder-Silverman’s model. The Planner, based on Linear Temporal Logic, produces a first Learning Objects Sequence, starting from the student’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 propose a new Learning Objects Sequence. In particular, the algorithm can suggest different learning materials either trying to fill possible cognitive gaps or by re-planning a newly adapted Learning Objects Sequence. A first experimental evaluation, performed on a prototype version of the system, has shown encouraging results.
Limongelli, C., Sciarrone, F., Vaste, G. (2008). LS-Plan: An Effective Combination of Dynamic Courseware Generation and Learning Styles in Web-Based Education. In Proceedings of the 5th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (pp.133-142). BERLIN, HEIDELBERG : Springer [10.1007/978-3-540-70987-9_16].
LS-Plan: An Effective Combination of Dynamic Courseware Generation and Learning Styles in Web-Based Education
LIMONGELLI, Carla;
2008-01-01
Abstract
This paper presents LS-Plan, a system capable of providing Educational Hypermedia with adaptation and personalization. The architecture of LS-Plan is based on three main components: the Adaptation Engine, the Planner and the Teacher Assistant. Dynamic course generation is driven by an adaptation algorithm, based on Learning Styles, as defined by Felder-Silverman’s model. The Planner, based on Linear Temporal Logic, produces a first Learning Objects Sequence, starting from the student’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 propose a new Learning Objects Sequence. In particular, the algorithm can suggest different learning materials either trying to fill possible cognitive gaps or by re-planning a newly adapted Learning Objects Sequence. A first experimental evaluation, performed on a prototype version of the system, has shown encouraging results.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.