Title
Estimating Collaborative Attitudes based on Non-verbal Features in Collaborative Learning Interaction.
Abstract
To understand collaborative learning interaction, it is important to analyze not only argument processes based on verbal information but also non-verbal interaction. In order to analyze learning situations in collaborative learning, our previous work proposed an estimation method for learning attitudes based on participants' non-verbal features. Because the method used limited features, this research enhances the method of the participants' collaborative attitudes by analyzing non-verbal features in detail. The model also considers participants' knowledge of their learning subject in the analysis. The estimation model detects three levels of the participants' collaborative attitudes based on multinomial logistic regression analysis. The results of the analysis show that the speech interval feature, in particular, affects the participants' collaborative attitudes. In addition, the results indicate that speakers with knowledge of the learning subject receive more attention from participants with insufficient knowledge. The results of the model evaluation find that the f-measure for classifying the participants' collaborative attitudes is 0.569; for participants with knowledge, the f-measure is 0.647. (C) 2014 The Authors. Published by Elsevier B. V.
Year
DOI
Venue
2014
10.1016/j.procs.2014.08.184
Procedia Computer Science
Keywords
Field
DocType
Collaborative learning,collaborative attitudes,nonverbal features,multinominal logistic regression
Collaborative learning,Multinomial logistic regression,Computer science,Knowledge management,Cognitive psychology,Nonverbal communication,Artificial intelligence,Machine learning
Conference
Volume
ISSN
Citations 
35
1877-0509
1
PageRank 
References 
Authors
0.36
4
3
Name
Order
Citations
PageRank
Yuki Hayashi13811.12
Haruka Morita210.36
Yukiko Nakano350162.37