Automatic Identification of Experts and Performance Prediction in the Multimodal Math Data Corpus through Analysis of Speech Interaction
Citation:
Saturnino Luz, Automatic Identification of Experts and Performance Prediction in the Multimodal Math Data Corpus through Analysis of Speech Interaction, Proceedings of the 15th ACM on International conference on multimodal interaction, ICMI'13 - Grand Challenge on Multimodal Learning Analytics, ICMI'13, ACM Press, 2013, 575 - 582Download Item:
Abstract:
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.
Sponsor
Grant Number
Science Foundation Ireland (SFI)
07/CE/I1142
Author's Homepage:
http://people.tcd.ie/luzsDescription:
PUBLISHED
Author: LUZ, SATURNINO
Other Titles:
Proceedings of the 15th ACM on International conference on multimodal interaction, ICMI'13 - Grand Challenge on Multimodal Learning AnalyticsICMI'13
Publisher:
ACM PressType of material:
Conference PaperCollections
Availability:
Full text availableKeywords:
Machine Learning, Multimodal Interaction, Dialogue, Learning AnalyticsSubject (TCD):
Intelligent Content & CommunicationsMetadata
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