An Architecture and a Domain Ontology for Personalized Multi-agent e-Learning Systems

Abstract

Semantic web and intelligent agents are main technologies to implement personalization for e-learning systems. Ontology is the key concept in semantic web; and it plays an important role in knowledge representation, sharing of didactic material and content personalization. Furthermore, ontologies are machine-readable. Therefore they are faster and more convenient to query and retrieval educational material with the Semantic Web services. Intelligent agents can provide the learners with personal assistants to carry out learning activities according to their learning styles and their knowledge levels. In this paper, we present the architecture of a multi-agent e-learning system that supports pre-defining students' learning styles and re-estimating them during the course for a better personalization. We also present a domain ontology that is suitable for adaptive e-learning environments. The ontology describes learning material that composes a course as well as describes learners and their learning styles.

An ontology-based adaptive personalized e-learning system, assisted by software agents on cloud storage

“…Dung et al propose an architecture in 2011 which is to represent domain ontology for e-learning course content and agents interact on it to provide personalized elearning content to learners [42]. Subsequently, in 2013 Bokhari et al propose an architecture for interactive multi-agent based learning system for distance education [43].…”

Intelligent e-learning system model for maintenance of updates courses

“…The research work of Pham et al [7] identified an architecture for personalized multi-agent e-learning system. The proposed system provide to the learner personal assistants to accomplish their activities and learning according to their preferences, learning styles and level knowledge.…”

Uma Revisão Sistemática das Tecnologias da Web Semântica em Ambientes Educacionais

“…Isso pode ser verificado pela falta de guidelines e pela diversidade de frameworks distintos e também pelas diferenças existentes entre os domínios de aplicação dos ambientes de ensino encontrados. Dentre os estudos avaliados, os principais tópicos que têm atraído a atenção da comunidade são ligados à melhoria de agentes inteligentes para a recomendação de conteúdo apropriados aos alunos, à melhoria dos módulos pedagógicos de sistemas adaptativos e ao reuso e compartilhamento de conteúdos entre modelos de domínios de sistemas distintos (Mitrovic and Devedzic, 2002;Dung and Florea, 2011;Strujic and Sendelj, 2012).…”