Instance-Based Learning Algorithms
Machine Learning
Automatic Analysis of Multimodal Group Actions in Meetings
IEEE Transactions on Pattern Analysis and Machine Intelligence
The class imbalance problem: A systematic study
Intelligent Data Analysis
Locating case discussion segments in recorded medical team meetings
SSCS '09 Proceedings of the third workshop on Searching spontaneous conversational speech
Classification of patient case discussions through analysis of vocalisation graphs
Proceedings of the 2009 international conference on Multimodal interfaces
On the use of nonverbal speech sounds in human communication
COST 2102'07 Proceedings of the 2007 COST action 2102 international conference on Verbal and nonverbal communication behaviours
Automatic decision detection in meeting speech
MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
The nonverbal structure of patient case discussions in multidisciplinary medical team meetings
ACM Transactions on Information Systems (TOIS)
Multimodal prediction of expertise and leadership in learning groups
Proceedings of the 1st International Workshop on Multimodal Learning Analytics
Multimodal learning analytics: description of math data corpus for ICMI grand challenge workshop
Proceedings of the 15th ACM on International conference on multimodal interaction
Proceedings of the 15th ACM on International conference on multimodal interaction
ICMI 2013 grand challenge workshop on multimodal learning analytics
Proceedings of the 15th ACM on International conference on multimodal interaction
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An analysis of multiparty interaction in the problem solving sessions of the Multimodal Math Data Corpus is presented. The analysis focuses on non-verbal cues extracted from the audio tracks. Algorithms for expert identification and performance prediction (correctness of solution) are implemented based on patterns of speech activity among session participants. Both of these categorisation algorithms employ an underlying graph-based representation of dialogues for each individual problem solving activities. The proposed Bayesian approach to expert prediction proved quite effective, reaching accuracy levels of over 92\% with as few as 6 dialogues of training data. Performance prediction was not quite as effective. Although the simple graph-matching strategy employed for predicting incorrect solutions improved considerably over a Monte Carlo simulated baseline (F1 score increased by a factor of 2.3), there is still much room for improvement in this task.