A theoretical framework, called the Knowledge Mentoring framework (KMf), was developed to investigate how studies of dialogue and interaction can be exploited in a practical way by designers of computer-based teaching agents. The KMf provides a taxonomy and definitions of the pedagogical goals involved in a 'mentoring' style of teaching. Mentoring is an approach to teaching that aims to support learners' creative, metacognitive and critical thinking, these being essential to musical composition and other open-ended, problem-seeking domains.
This theoretical framework was used to guide the analysis and modelling of data produced by an empirical study of human teacher-learner interactions. Information on the temporal ordering of teacher-learner interactions was revealed (modelled as state transition networks and a mentoring script). Findings from the analysis also included a pause taxonomy (that provided evidence of a link between pause length and learner ability) and the occurrence of reciprocal modelling (where participants in learning interactions built up models of the other participants' expectations).
The theoretical framework and the analysis findings were then used to develop a computational model for teaching agents in problem-seeking domains. Aspects of our theory, analysis findings and computational model were incorporated into a computational implementation: a pre-prototype teaching agent called MetaMuse. A cooperative evaluation of MetaMuse with teacher-composers showed that it had the potential to promote creative reflection in learners.
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