A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
The Ontolingua Server: a tool for collaborative ontology construction
International Journal of Human-Computer Studies - Special issue: innovative applications of the World Wide Web
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Building a Chemical Ontology Using Methontology and the Ontology Design Environment
IEEE Intelligent Systems
Knowledge Processes and Ontologies
IEEE Intelligent Systems
Courseware Authoring Tasks Ontology
ICCE '02 Proceedings of the International Conference on Computers in Education
The PROMPT suite: interactive tools for ontology merging and mapping
International Journal of Human-Computer Studies
Qualitative modeling in education
AI Magazine
Adaptive interaction multi-agent systems in E-learning/E-teaching on the web
ICWE'03 Proceedings of the 2003 international conference on Web engineering
Design of a performance-oriented workplace e-learning system using ontology
Expert Systems with Applications: An International Journal
A model for assessing the success of virtual talent communities
EC-TEL'11 Proceedings of the 6th European conference on Technology enhanced learning: towards ubiquitous learning
A semantic platform for the management of the educative curriculum
Expert Systems with Applications: An International Journal
Exploring the state of the art in adaptive distributed learning environments
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part II
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The essential elements of effective learning are control of students' skills and feedback between students and their tutor. The main idea behind the approach presented here is that a domain ontology is not only useful as a learning instrument but it can also be employed to assess students' skills. For it, each student is prompted to express his/her beliefs by building her/his own discipline-related ontology and then it is compared to a reference one. The analysis of students' mistakes allows to propose them personalized recommendations and to improve the course materials in general. In this work, we present a Semantic Web technologies-based multi-agent system that allows to automatically control students' acquired knowledge in e-learning frameworks.