A Preliminary Investigation of Hierarchical Hidden Markov Models for Tutorial Planning

For tutorial dialogue systems, selecting an appropriate dialogue move to support learners can significantly influence cognitive and affective outcomes. The strategies implemented in tutorial dialogue systems have historically been based on handcrafted rules derived from observing human tutors, but a data-driven model of strategy selection may increase the effectiveness of tutorial dialogue systems. Tutorial dialogue projects including CIRCSIM-TUTOR [1], ITSPOKE [2], and KSC-PAL [3] have utilized corpora to inform the behavior of a system. Our work builds on this line of research by directly learning a hierarchical hidden Markov model (HHMM) for predicting tutor dialogue acts within a corpus. The corpus was collected during a human-human tutoring study in the domain of introductory computer science [4]. We annotated the dialogue moves with dialogue acts (Table 1). The subtask structure and student problem-solving action correctness were also annotated manually.