APLe: Agents for Personalized Learning in Distance Learning.

This work presents an intelligent tutoring system that supports personalized learning especially in a distance learning setting. The proposed architecture is based on multi-agents which facilitate the communication between the different components and on ontologies that provide a sound and complete representation of the knowledge domain. The operational procedure of the multi-agent system is described and the overall functions of its fundamental components are illustrated. The prototype, called APLe, provides dynamic learning path sequencing in a bottom up fashion using direct information about the student preferences or learning styles and relative information about the student learning process as part of a group. The preliminary evaluation of our system indicated positive feedback by the users in terms of the usability of the system, completeness of the educational content and ability of the system to adapt to the individual's educational needs and preferences and informed for further developments required.