Effect of the Number of Conversations and Timings in Deciding the Number of Solved Problems Between the Learner and the Educational-Support Robot

In recent years, educational-support robots have attracted a lot of attention. In conventional educational-support robots, the number of problems to be solved by the learner (the number of solved problems) is determined by the system. This prevents learners from achieving self-motivated learning, in which they decide the number of solved problems. In the previous study, several conversational methods were proposed to determine the number of solved problems through a conversation with the robot before learning. The results suggested that letting the robot decide the number of solved problems before learning improved the impression of the robot and the number of solved problems of learner. However, in the conventional method, the conversation of learner is limited before learning. Therefore, the optimal number of negotiations and timing of the conversations have not been verified. In this study, we investigate the optimal number of negotiations and timing of conversations between the robot and the learner to determine the number of solved problems. Specifically, through two types of experiments, we compare robots that talk before learning, after learning, and before and after learning, and verify the differences in the number of solved problems and in the impressions of robot.