Estimating collaborative attitudes based on non-verbal features in collaborative learning interaction

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.