Personalized distance learning based on multiagent ontological system

Abstract

The paper presents architecture of a personalized distance learning system based on multiagent technology and ontological modelling of students' profiles. Delocalization of a student data in the system is achieved by software agents, which assumed to be distributed at different platforms. These platforms operate as separate Web services and use the ACL (agent communication language) for the data transfer. In this paper the algorithm is proposed, according to which the multiagent ontological system for personalized distance learning (MOSPDL) solves the tasks of distant learning process automation, which assume utilization of the ontological models of students' and learning resources' profiles.