Effects of Familiarity in Collaborative Learning between Human and Robot by Varying Accuracy Rate of a Robot

Abstract

The growth of robot technology has prompted growing interest in educational-support robots that assist in learning. We focus on a robot that prompted learners to alternately solve questions in collaborative learning. The previous study reported that robots that alternate between the roles of the speaker and listener can prompt college students to learn by alternately solving questions with the robot, possibly achieving the same effect as collaborative learning between two college students. As the speaker, the robot explained a solution to its partner and solved a question. Moreover, the robot improved its accuracy rate as learning progressed. As the listener, the robot did not solve questions itself, but instead payed attention to its partner that solves the questions. However, the previous study did not investigate the method by which the accuracy rate of the speaker robot was improved. Therefore, this study investigates the manner in which change in the accuracy rate in speaker robots prompts effective collaborative learning. Herein, we define collaborative learning as learning in which students and robots alternatively solve questions. We compare four methods of changing the speaker robot ’s accuracy rate in the experiment. In the first method, the accuracy rate changes in a manner similar to the learner ’s accuracy rate. In the second method, the accuracy rate is initially set to 0% but is gradually increased (in 10% increments) to 100% as learning progresses. In the third method, the accuracy rate is set to 100%; therefore, the robot always solves questions correctly. In the fourth method, the accuracy rate is set to 0%; therefore, the robot never solves questions correctly. The results of this study suggest that there was no difference in the learning effect of each group. However, we found that the robot that improved its accuracy rate as learning progressed could prompt learners to feel greater friendship for it than the robot which always solves questions correctly. Moreover, the learners could alternately solve questions with the robot that improved its accuracy rate as learning progressed more effectively than the robots of other groups. Therefore, we believe that a robot alternately solving questions with a human while constantly improving its accuracy rate may be the best for collaborative learning with a human.