An Approach to Embedding ITSs into Existing Systems
Thesis DisciplineComputer Science
Degree GrantorUniversity of Canterbury
Degree NameDoctor of Philosophy
Intelligent Tutoring Systems (ITSs) have proven their effectiveness in many domains, but very few attempts have been made to embed them with existing systems. This area of research has a lot of potential in providing life-long learning and work place training. This PhD project makes several significant contributions. This is the first attempt to embed a Constraint-Based Tutor (CBT) with an existing system, in order to investigate the benefits of providing on-the-job training. We also propose a framework for embedded ITSs, and develop DM-Tutor (Decision-Making Tutor) embedded with the MIS for palm oil. DM-Tutor is the first ITS for the domain of oil palm plantation decision making, and was developed in the ASPIRE authoring system. Our hypothesis was that DM-Tutor embedded with the MIS for palm oil would provide effective instruction and training for oil palm plantation decision making. We also wanted to investigate the role of feedback messages in helping to provide effective training.