Learner Characteristics and Feedback in Tutorial Dialogue

Tutorial dialogue has been the subject of increasing attention in recent years, and it has become evident that empirical studies of human-human tutorial dialogue can contribute important insights to the design of computational models of dialogue. This paper reports on a corpus study of human-human tutorial dialogue transpiring in the course of problemsolving in a learning environment for introductory computer science. Analyses suggest that the choice of corrective tutorial strategy makes a significant difference in the outcomes of both student learning gains and selfefficacy gains. The findings reveal that tutorial strategies intended to maximize student motivational outcomes (e.g., self-efficacy gain) may not be the same strategies that maximize cognitive outcomes (i.e., learning gain). In light of recent findings that learner characteristics influence the structure of tutorial dialogue, we explore the importance of understanding the interaction between learner characteristics and tutorial dialogue strategy choice when designing tutorial dialogue systems.