Ontological model for representation of learning objectives

Learning objects have been used to provide personalized learning experiences. In particular, sequenced learning objects are recommended according to unique individual learning objectives. The opportunity for personalization by learning objectives is not fully exploited due to limited and duplicated efforts in creating learning objectives and connecting them with learning objects. Additionally, current standardization efforts do not offer sufficient support of automatic discovery of learning objects. This thesis proposes an ontological representation model of learning objective description that aims to improve the effectiveness of consistent learning objective description, sequencing rules representation, and the availability of learning objects for personalization by learning objectives. An evaluation of the model showed that it improves the discoverability of learning objects by learning objective through annotating learning objectives in the metadata of learning objects and qualitative representation of learning objectives.