OntoSakai: On the optimization of a Learning Management System using semantics and user profiling

Abstract

This paper proposes recommendation services and user profiling features in Learning Management Systems (LMS) by means of a semantic intelligent system combining context information and expert knowledge. LMS users' context is represented through an ontology model called OntoSakai. It consists of four ontologies parceling different areas of the learning process: competences, users' profiles, learning tools and semantic classification of the elements in an LMS. Thus, we provide a standardized common vocabulary about LMS elements and academic tasks developed within these platforms. This model also enables inference processes about the behavior of LMS users. Indeed, our system incorporates an extensible set of expert rules to offer recommendation and user profiling services. This combination of context information and expert knowledge could be easily integrated with other systems in the academic world in order to promote the interoperability between them. Specifically, in this paper we integrate our proposal into Sakai, a well-known LMS for university-level. As a result of this integration, OntoSakai is able to generate users' profiles aimed at personalizing the use of LMS tools and to recommend resources to reach the optimum benefit in both lecturing and learning. As a proof of concept, a real case often detected in on-line students is shown as a running scenario where the services offered by OntoSakai could help them to improve their experiences and academic results.