[3Q5-GS-8] Robot and real worlds:

[3Q5-GS-8-03] Feasibility of Autonomous Learning Support Provided by Teacher-Type Robots Using Spatio-Temporal Perplexion Estimation Method

〇Koki Sato¹, Felix Jimenez², Shuichi Akizuki³, Tomohiro Yoshikawa⁴ (1. Graduate School of Aichi Prefectural University, 2. Aichi Prefectural University, 3. Chukyo University, 4. Suzuka University of Medical Science)

Keywords:Spatio-Temporal Perplexion Estimation Method, Deep learning, Educational Support Robot

In recent years, educational support robots have attracted attention. This study focuses on teacher-type robots that provide learning support for solving problems. In a previous study of teacher-type robots, a perplexion estimation method was proposed to estimate the state of the learners' perplexion (i.e., the state of being unable to solve a problem) from the learners' facial expression. However, the estimation accuracy is 61.0%, which is not practical. In this study, we proposed a spatio-temporal perplexion estimation method that uses time-series data of learners' facial expression changes and achieved an accuracy of 88.1%. In this paper, to investigate whether the robot equipped with the spatio-temporal perplexion estimation method can autonomously provide learning support, we conducted an experiment in which the robot and university students learned together. The results suggest that the proposed method can provide the same level of learning support as the conventional button-operated learning support.