Significant Educational Content Based Learning Model Using Public Ontologies and Multiagent Systems

The Semantic Web offers a structure to support generation of significant learning contents for Web based intelligent learning environments by using public knowledge bases known as public ontologies, available on the Web. Prior research has therefore been undertaken into allowing agents societies to navigate through these knowledge bases, in search of answers to queries. In addition, research is emerging into using this knowledge to contribute to the area of education, in terms of creating virtual learning environments. This work proposes a model for agents that allows access to ontologies related to a given domain of knowledge available on the Web, allowing these agents to use this knowledge in the construction and formulation of questions for the production of relevant and updated content for the student. Several efforts have been made to integrate agents with ontologies, which allow a greater knowledge for the agent based on a local ontology. However, no proposal has yet combined the ability to use the semantic data available on the Web in conjunction with a consolidated BDI agent framework for the production of meaningful content for virtual learning environments. Therefore, this work proposes a model for a virtual learning environment that uses agents developed using the Jason interpreter, with its ability to access ontologies available on the Web to update its belief base and generate significant content for the student. To validate this approach, a case study of an educational quiz is presented that uses this information to identify questions and check the answers obtained.