The role of domain ontology in knowledge acquisition for ITSs
Knowledge acquisition is the major hurdle in building intelligent tutoring systems. There have been several attempts to automate knowledge acquisition for ITSs that teach procedural tasks. The goal of our project is to automate the acquisition of domain models for constraint-based tutors for both procedural and non-procedural tasks. We propose a three-phase approach: building a domain ontology, acquiring syntactic constraints directly from the ontology, and engaging the author in a dialog, in order to induce semantic constraints using machine learning techniques. An ontology contains a lot of knowledge about the domain, but is arguably easier to create than the final domain model. Furthermore, our hypothesis is that the domain ontology is also useful for reflecting on the instructional domain, so would be of great importance for building constraint sets manually. This paper reports on an experiment performed in order to test this hypothesis. The results show that constraints sets built using a domain ontology are superior, and the authors who developed the ontology before working on constraints acknowledge the usefulness of an ontology in the knowledge acquisition process. Further work on this project will focus on the automatic acquisition of constraints.