Abstract
In lecture with presentation slides such as e-Learning lecture on video, it is important for lecturers to control their non-verbal behavior involving gaze, gesture, and paralanguage to attract learners’ attention to slide or oral contents they intend to emphasize. However, it is not so easy even for well-experienced lecturers to properly use and maintain non-verbal behavior in their lecture to promote learners’ interest and understanding. This paper proposes robot lecture, in which a communication robot substitutes for human lecturers, and reconstructs their non-verbal behavior to enhance their lecture. Towards such reconstruction, we have designed a model of non-verbal behavior in lecture, and developed a robot lecture system, which follows the model to detect and reconstruct insufficient/inappropriate behavior, and which conducts the reconstructed lecture. This paper describes a case study with the system, whose purpose was to ascertain the benefits of robot lecture by comparing video lecture conducted by human, robot lecture simply reproducing the original one, and robot lecture reconstructing the original one. The results suggest that the robot lecture involving reconstruction promotes learners’ understanding of the lecture slides more than the video lecture and the robot lecture involving simple reproduction.
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This work is supported in part by JSPS KAKENHI Grant Number 17H01992.
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Kashihara, A., Ishino, T., Goto, M. (2019). Robot Lecture for Enhancing Non-verbal Behavior in Lecture. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science(), vol 11626. Springer, Cham. https://doi.org/10.1007/978-3-030-23207-8_24
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DOI: https://doi.org/10.1007/978-3-030-23207-8_24
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