Predicting Participation Styles using Co-occurrence Patterns of Nonverbal Behaviors in Collaborative Learning

With the goal of assessing participant attitudes and group activities in collaborative learning, this study presents models of participation styles based on co-occurrence patterns of nonverbal behaviors between conversational participants. First, we collected conversations among groups of three people in a collaborative learning situation, wherein each participant had a digital pen and wore a glasses-type eye tracker. We then divided the collected multimodal data into 0.1-second intervals. The discretized data were applied to an unsupervised method to find co-occurrence behavioral patterns. As a result, we discovered 122 multimodal behavioral motifs from more than 3,000 possible combinations of behaviors by three participants. Using the multimodal behavioral motifs as predictor variables, we created regression models for assessing participation styles. The multiple correlation coefficients ranged from 0.74 to 0.84, indicating a good fit between the models and the data. A correlation analysis also enabled us to identify a smaller set of behavioral motifs (fewer than 30) that are statistically significant as predictors of participation styles. These results show that automatically discovered combinations of multiple kinds of nonverbal information with high co-occurrence frequencies observed between multiple participants as well as for a single participant are useful in characterizing the participant's attitudes towards the conversation.