Cukurova, M; Kent, C; Luckin, R; (2019) The value of multimodal data in classification of social and emotional aspects of tutoring. In: Proceedings of the International Conference on Artificial Intelligence in Education AIED 2019. (pp. pp. 46-51). Springer, Cham: Chicago, IL, USA.

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Abstract

There are many aspects of tutoring that are associated with social and emotional learning. These are complex processes that involve dynamic combinations of skills, abilities and knowledge. Here, we present the results of our investigation on the particular personal, emotional, and experience traits of tutors who are likely to be successful at social and emotional aspects of tutoring. In particular, we present our approach to measure the social and emotional aspects of tutoring through classification models of 47 candidates’ multimodal data from audio and psychometric measures. Moreover, we compare the accuracy of models with unimodal and multimodal data, and show that multimodal data leads to more accurate classifications of the candidates. We argue that when evaluating the social and emotional aspects of tutoring, multimodal data might be more preferrable.

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