Developing a model for tutoring strategy selection in intelligent tutoring systems.
Variation in tutoring strategy plays an important part in Intelligent Tutoring Systems
(ITSs). The potential for providing an adaptive ITS depends initially on having a range
of tutoring strategies to select from. However, in order to react effectively to the
student's needs, an ITS not only has to be able to simply offer different tutoring
strategies but to choose intelligently among them and determine which one is best for
an individual student at a particular moment.
This thesis first examines, through literature review and interactions with existing
systems, the current practices of ITSs regarding the provision of multiple tutoring
strategies and tutoring strategy selection. What stems from this examination are the
principles that underlie tutoring strategys election. These principles of tutoring strategy
selection serve as a foundation for the construction of the model for tutoring strategy
selection. To demonstrate the benefits of having such a model for formalising
selection, the model is then implemented in ARISTOTLE, an existing ITS for tutoring
zoology that includes several tutoring strategies but uses ad hoc mechanisms for
choosing among them.
This research is therefore contributing, through the principles of, and the model for
tutoring strategy selection, a formal basis for selecting among tutoring strategies in
ITSs that incorporate multiple tutoring strategies.