知的学習支援システムによる協同学習の支援に向けて：

視線情報と言語情報を用いた学習プロセスの推定モデル

抄録

 One of the challenges on developing intelligent tutoring systems in collaborative learning is to providing adaptive feedbacks with adequate facilitations. This study focuses on collaborative learning involving a knowledge integration activity, whereby learner dyads explain each other’s expert knowledge supported by a Pedagogical Conversational Agent. The goal of this paper was to investigate how collaborative process and learning gain can be determined by the degree to which learners synchronize their gaze (gaze recurrence) and use overlapping language (lexical alignment) during their interaction. This study conducted a laboratory-based eye-tracking experiment, wherein thirty-four learners’ gazes and oral dialogs were analyzed. Through this experiment, the author investigated how gaze recurrence and lexical alignment can predict collaborative learning process and learning gain. Multiple regression analysis was conducted, wherein learning performance was regressed on the two independent variables and shows how the model predicts both collaborative process and gain. The results also showed that both gaze recurrence and lexical overlap significantly predicted learning performance. These results indicate that the two variables might be useful for developing detection modules that enable a better understanding of learner-learner collaborative learning.