Bridging the Gap Between Empirical Data on Open-Ended Tutorial Interactions and Computational Models

John Cook , Learning Technology Research Institute, University of North London, 166-220Holloway Road, London N7 8DB, UK.j.cook@unl.ac. ukAbstract. 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 musicalcomposition learning. Our approach to bridging the gap between tutorial interaction analysisand computational models, intended for use in learning support systems, is a new one. Wesupport our claim by pointing out that most of the previous work in the area of using humantutors as models has been conducted in domains that are more procedural than the open-endedsubject area investigated here. However, the approach described in this paper seems applicableto most, if not all, domains, whether open-ended or not. In the paper we describe how anempirical study of teacher-learner interactions was linked, specifically modulated, to theconstruction of a pedagogical agent called MetaMuse. Empirically derived state transitionnetworks were used to provide a semi-open, goal-oriented interaction plan. One distinctivefeature 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 theutility of our approach.INTRODUCTIONOne approach to building Intelligent Educational Systems is to base systems design on thestudy of human teacher-learner dialogue and interaction. Systems design based on such anapproach requires an appropriate teacher-learner interaction analysis technique that is able tobridge the gap between tutorial interaction analysis and computational models intended for usein learning support systems. Dialogue analysis results may lead to important insights that canguide systems development. For example, the results may be used to suggest useful tutoringstrategies that can be used in a particular learning situation. However, we suggest that when itcomes to using fine-grained dialogue analysis results to develop computational models forsupporting learning, most research has tended to investigate more procedural domains. Weclaim that existing research has not, until now, explored the link between open-ended domaindialogues and computational models.In an open-ended domain problems are typically ill-defined. A well formalised, closed,more procedural domain would be a subject area like arithmetic, where there are often clearlyspecified problem-solving goals, correct answers and clearly defined criteria for success atproblem-solving. The educational interactions most usually analysed are mathematics, scienceor computer science. For example, SHERLOCK (Lesgold, Lajoie, Bunzo and Eggan, 1992) is acoached practice environment for electronics troubleshooting in the navy. The SHERLOCKsystem makes use of empirical data from dialogue analysis in order to inform the types of hintsthat it gives to learners. Pilkington and Parker-Jones (1996) have explored the instructionaldomain of medical students interacting with a simulation of calcium homeostasis. They foundthat inquiry dialogues may be a significant factor in prompting conceptual change. Morerecently, the AutoTutor system (Graesser, Wiemer-Hastings, Wiemer-Hastings, Kreuz and theTutoring Research Group, 1999; Person, Graesser, Kreuz and Pomeroy and the TutoringResearch Group, 2001) has been designed to assist college students when learning the