Exploring Indicators for Collaboration Quality and Its Dimensions in Classroom Settings Using Multimodal Learning Analytics

Multimodal Learning Analytics researchers have explored relationships between collaboration quality and multimodal data. However, the current state-of-art research works have scarcely investigated authentic settings and seldom used video data that can offer rich behavioral information. In this paper, we present our findings on potential indicators for collaboration quality and its underlying dimensions such as argumentation, and mutual understanding. We collected multimodal data (namely, video and logs) from 4 Estonian classrooms during authentic computer-supported collaborative learning activities. Our results show that vertical head movement (looking up and down) and mouth region features could be used as potential indicators for collaboration quality and its aforementioned dimensions. Also, our results from clustering provide indications of the potential of video data for identifying different levels of collaboration quality (e.g., high, low, medium). The findings have implications for building collaboration quality monitoring and guiding systems for authentic classroom settings.