Honest Signals: How They Shape Our World
Honest Signals: How They Shape Our World
Supporting an integrated paper-digital workflow for observational research
Proceedings of the 16th international conference on Intelligent user interfaces
ChronoViz: a system for supporting navigation of time-coded data
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Estimating Dominance in Multi-Party Meetings Using Speaker Diarization
IEEE Transactions on Audio, Speech, and Language Processing
Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies, and Emerging Applications, Third Edition
Computer Speech and Language
Proceedings of the 15th ACM on International conference on multimodal interaction
Expertise estimation based on simple multimodal features
Proceedings of the 15th ACM on International conference on multimodal interaction
Proceedings of the 15th ACM on International conference on multimodal interaction
Proceedings of the 15th ACM on International conference on multimodal interaction
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In his study, we investigate low level predictors from audio and writing modalities for the separation and identification 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.