Multimodal Prediction of Expertise and Leadership in Learning Groups (bibtex)
by Stefan Scherer, Nadir Weibel, Sharon Oviatt, Louis-Philippe Morency
Abstract:
In this study, we investigate low level predictors from audio and writing modalities for the separation and identiā¬šcation of socially dominant leaders and experts within a study group. We use a multimodal dataset of situated computer assisted group learning tasks: Groups of three high-school students solve a number of mathematical problems in two separate sessions. In order to automatically identify the socially dominant student and expert in the group we analyze a number of prosodic and voice quality features as well as writing-based features. In this preliminary study we identify a number of promising acoustic and writing predictors for the disambiguation of leaders, experts and other students. We believe that this exploratory study reveals key opportunities for future analysis of multimodal learning analytics based on a combination of audio and writing signals.
Reference:
Multimodal Prediction of Expertise and Leadership in Learning Groups (Stefan Scherer, Nadir Weibel, Sharon Oviatt, Louis-Philippe Morency), In ACM International Conference on Multimodal Interaction (ICMI), 2012.
Bibtex Entry:
@inproceedings{scherer_multimodal_2012,
	address = {Santa Monica, CA},
	title = {Multimodal {Prediction} of {Expertise} and {Leadership} in {Learning} {Groups}},
	url = {http://ict.usc.edu/pubs/Multimodal%20Prediction%20of%20Expertise%20and%20Leadership%20in%20Learning%20Groups.pdf},
	abstract = {In this study, we investigate low level predictors from audio and writing modalities for the separation and identiā¬šcation of socially dominant leaders and experts within a study group. We use a multimodal dataset of situated computer assisted group learning tasks: Groups of three high-school students solve a number of mathematical problems in two separate sessions. In order to automatically identify the socially dominant student and expert in the group we analyze a number of prosodic and voice quality features as well as writing-based features. In this preliminary study we identify a number of promising acoustic and writing predictors for the disambiguation of leaders, experts and other students. We believe that this exploratory study reveals key opportunities for future analysis of multimodal learning analytics based on a combination of audio and writing signals.},
	booktitle = {{ACM} {International} {Conference} on {Multimodal} {Interaction} ({ICMI})},
	author = {Scherer, Stefan and Weibel, Nadir and Oviatt, Sharon and Morency, Louis-Philippe},
	month = oct,
	year = {2012},
	keywords = {Virtual Humans, UARC}
}
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