Proceedings of the Annual Meeting of the Cognitive Science Society

This study focuses on collaborative learning involving a knowledge integration activity, whereby learner dyads explain each other’s expert knowledge. It was hypothesized that learn- ing gain can be determined by the degree to which learn- ers synchronize their gaze (gaze recurrence) and use overlap- ping language (information overlap) during their interaction. Thirty-four learners participated in a laboratory-based eye- tracking experiment, wherein learners’ gazes and oral dialogs were analyzed. Multiple regression analysis was conducted, wherein learning performance was regressed on the two inde- pendent variables. Then, a simulation was conducted to view how the model predicts performance based on the collabora- tive process. The results showed that both gaze recurrence and lexical overlap significantly predicted learning performance in the current task. Furthermore, the suggested model success- fully predicted learning performance in the simulation. These results indicate that the two variables might be useful for de- veloping detection modules that enable a better understanding of learner-learner collaborative learning.