@techreport{oai:ipsj.ixsq.nii.ac.jp:00231225,
 author = {島崎, 俊介 and 柏原, 昭博 and Toshiyuki, Shimazaki and Akihiro, Kashihara},
 issue = {23},
 month = {Nov},
 note = {長時間の講義になるほど，学習者の受講状態に応じて，講義に対する注意制御と理解追従を行なうことは，人間講師でも容易ではない．本研究では，人間講師の講義をロボットが代行し，学習者の姿勢データおよび視線データの講義センシングに基づく学習状態推定に応じて，モデルベースにインタラクティブな講義を展開し，長時間にわたる学習者の注意維持支援を実現するインタラクティブロボット講義システムの開発を目的としている．本稿では，先行研究の講義センシングを改良し，理工系大学生を被験者として実施したケーススタディを報告する．その結果，ロボットが行う注意の回復（注意リカバリ）のインタラクションが，講義における注意維持に有効であることが示唆された．, In lecturing, lecturers need to control the attention of learners to maintain while monitoring their learning states. We have claimed that a robot as lecturer can properly conduct nonverbal lecture behavior to control learners' attention and enhance their engagement compared with human lecturers. Although attention control is possible in shorter lectures, it would be more difficult for the robot to maintain learners' attention in a longer lecture. This prevents them from becoming aware of an important part of the lecture contents to understand. In this work, we have been developing an interactive robot lecture system that interacts with learners to recover their attention and understanding when they overlook the lecture contents based on lecture sensing. A preliminary case study has been conducted with participants to evaluate the accuracy of sensing data for learning state estimation and the interactive lecture with NAO. The results suggest that the learning state estimation using posture and gaze sensing data is similar to that of a human lecturer, and that the interaction with NAO contributes to maintaining learner's attention and recovering their understanding of the lecture contents.},
 title = {インタラクティブロボット講義における注意維持支援の評価},
 year = {2023}
}