Toward collaboration sensing: applying network analysis techniques to collaborative eye-tracking data

In this paper we describe preliminary applications of network analysis techniques to eye-tracking data. In a previous study, the first author conducted a collaborative learning experiment in which subjects had access (or not) to a gaze-awareness tool: their task was to learn from neuroscience diagrams in a remote collaboration. In the treatment group, they could see the gaze of their partner displayed on the screen in real-time. In the control group, they could not. Dyads in the treatment group achieved a higher quality of collaboration and a higher learning gain. In this paper, we describe how network analysis techniques can further illuminate these results, and contribute to the development of 'collaboration sensing'. More specifically, we describe two contributions: first, one can use networks to visualize and explore eye-tracking data. Second, network metrics can be computed to interpret the properties of the graph. We conclude with comments on implementing this approach for formal learning environments.