Interactive Event: The Rimac Tutor - A Simulation of the Highly Interactive Nature of Human Tutorial Dialogue

摘要

Rimac is a natural-language intelligent tutoring system that engages students in dialogues that address physics concepts and principles, after they have solved quantitative physics problems. Much research has been devoted to identifying features of tutorial dialogue that can explain its effectiveness (e.g., [1]), so that these features can be simulated in natural-language tutoring systems. One hypothesis is that the highly interactive nature of tutoring itself promotes learning. Several studies indicate that our understanding of interactivty needs refinement because it cannot be defined simply by the amount of interaction nor the granularity of the interaction but must also take into consideration how well the interaction is carried out (e.g., [2]). This need for refinement suggests that we should more closely examine the linguistic mechanisms evident in tutorial dialogue. Towards this end, we first identified which of a subset of co-constructed discourse relations correlate with learning and operationalized our findings with a set of nine decision rules which we implemented in Rimac [3]. To test for causality, we are conducting pilot tests that compare learning outcomes for two versions of Rimac: an experimental version that deliberately executes the nine decision rules within a Knowledge Construction Dialogue (KCD) framework, and a control KCD system that does not intentionally execute these rules.