Predicting online lecture ratings based on gesturing and vocal behavior

Cheng, D. S., Salamin, H., Salvagnini, P., Cristani, M., Vinciarelli, A. and Murino, V. (2014) Predicting online lecture ratings based on gesturing and vocal behavior. Journal on Multimodal User Interfaces, 8(2), pp. 151-160. (doi:10.1007/s12193-013-0142-z)

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Publisher's URL: http://dx.doi.org/10.1007/s12193-013-0142-z

Abstract

Nonverbal behavior plays an important role in any human–human interaction. Teaching—an inherently social activity—is not an exception. So far, the effect of nonverbal behavioral cues accompanying lecture delivery was investigated in the case of traditional ex-cathedra lectures, where students and teachers are co-located. However, it is becoming increasingly more frequent to watch lectures online and, in this new type of setting, it is still unclear what the effect of nonverbal communication is. This article tries to address the problem and proposes experiments performed over the lectures of a popular web repository (“Videolectures”). The results show that automatically extracted nonverbal behavioral cues (prosody, voice quality and gesturing activity) predict the ratings that “Videolectures” users assign to the presentations.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Salamin, Mr Hugues and Vinciarelli, Professor Alessandro
Authors: Cheng, D. S., Salamin, H., Salvagnini, P., Cristani, M., Vinciarelli, A., and Murino, V.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Journal on Multimodal User Interfaces
Publisher:Springer
ISSN:1783-7677
ISSN (Online):1783-8738
Copyright Holders:Copyright © 2014 OpenInterface Association
First Published:First published in Journal on Multimodal User Interfaces 8(2):151-160
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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