Modelling human tutors' feedback to inform natural language interfaces for learning

Modelling human tutors' socio-linguistic abilities computationally is necessary to underpin human-computer educational interactions with best pedagogic practices and to enhance the naturalness of such interactions. In this paper we present a computational model of tutorial feedback selection based on the context of the immediate situation for which the feedback is selected as well as on politeness considerations shown to be of importance to increasing pedagogical efficacy of computer assisted learning (Wang et al., 2008). The model is based on empirical studies with human tutors and on extensive linguistic analysis of human tutorial dialogues collected with the specific aim to inform the implementation of a natural language tutorial dialogue interface. The evaluation of the model, involving the comparison of its output with the linguistic responses produced by a human tutor, demonstrates the model's plausibility and highlights future directions for improving natural language human-computer interactions for educational purposes.