Computationally Augmented Ethnography: Emotion Tracking and Learning in Museum Games

Abstract

In this paper, we describe a way of using multi-modal learning analytics to augment qualitative data. We extract facial expressions that may indicate particular emotions from videos of dyads playing an interactive table-top game built for a museum. From this data, we explore the correlation between students’ understanding of the biological and complex systems concepts showcased in the learning environment and their facial expressions. First, we show how information retrieval techniques can be used on facial expression features to investigate emotional variation during key moments of the interaction. Second, we connect these features to moments of learning identified by traditional qualitative methods. Finally, we present an initial pilot using these methods in concert to identify key moments in multiple modalities. We end with a discussion of our preliminary findings on interweaving machine and human analytical approaches.