Bridging the Gap Between Empirical Data on Open-Ended Tutorial Interactions and Computational Models
12 (1): "Part II of the Special Issue on Analysing Educational Dialogue Interaction"
In this paper we present an approach to using empirical data on human teacher-
learner interactions to guide the development of a pedagogical agent for supporting musical
composition learning. Our approach to bridging the gap between tutorial interaction analysis
and computational models, intended for use in learning support systems, is a new one. We
support our claim by pointing out that most of the previous work in the area of using human
tutors as models has been conducted in domains that are more procedural than the open-ended
subject area investigated here. However, the approach described in this paper seems applicable
to most, if not all, domains, whether open-ended or not. In the paper we describe how an
empirical study of teacher-learner interactions was linked, specifically modulated, to the
construction of a pedagogical agent called MetaMuse. Empirically derived state transition
networks were used to provide a semi-open, goal-oriented interaction plan. One distinctive
feature of MetaMuse is its use of student input, regarding whether their expectations were met,
to stimulate the pedagogical agent into action. The paper concludes with a discussion of the
utility of our approach.