Predictability of understanding in explanatory interactions based on multimodal cues

In explanatory interactions, explainees are expected to continuously provide feedback to explainers by signaling whether they understand an ongoing explanation. The study presented in this paper is based on the hypothesis that explainees use a set of multimodal cues, including vocalizations, facial expressions, and movements of the torso, head, and hands, to do so. We test this hypothesis by building a random forest classifier based on a multimodal corpus of dyadic explanations (21 explainers and explainees), in which windows of understanding or non-understanding were identified by participants in a retrospective video recall task. Results show that sequences of understanding can indeed be differentiated from those of non-understanding, and that a diverse set of predictors covering a wide range of modalities contributes to this classification. Due to data sparsity and a high degree of individual variation, the generalizability of our results is currently limited, but they support our hypothesis of the relevance of multimodal display in explanatory interactions.