Applying Network Analysis and Machine Learning Techniques to Exploit Social Eye-Tracking Data
2012 CSS Fellowship
I plan to use computational techniques to make sense of existing eye-tracking data. I previously conducted a study where dyads remotely worked on a set of contrasting cases (Schwartz & Bransford, 1998) to learn how the human brain processes visual information. In one condition, members of the dyads saw the gaze of their partner on the screen; in a control group, they did not have access to this information. This intervention helped students achieve a higher quality of collaboration and a higher learning gain compared to the control group. To my knowledge, this is the first study that was able to highlight the learning benefits of this kind of “gaze-awareness” tool. The eye-tracking data of this study, however, has been largely unexploited so far; I plan to develop skills in 1)network analysis, to describe how this effect took place; and 2), in machine learning, to predict how well future students will do based on their gaze patterns.