An Ontology-based Learning System for Supporting Self-directed Learning in Online Environments
Abstract: Recent research in online distance education indicates that students need to have a high level of self-direction to succeed in an online learning environment. This study proposes an ontology-based learning supporting system which offers adaptive scaffolding using the domain ontology and the user model ontology so that learners’ self directed learning can be supported. The concepts of learning content and the relations among them are visually represented using ontology. This feature helps learners understand more easily the knowledge of learning content. In addition, the system supports reasoning so that learners can do intelligent query when they want to learn more or they are curious about the high-level knowledge while they are learning a learning topic. This can be done based on the attributes of relations among learning concepts and the inference rules expressed in SWRL. Furthermore, it analyzes learning characteristics of students, diagnosing their learning problems, and provides proper advice accordingly.
Presider: Sophie Peter, The University of Greenwich