Features of learner corpus collected with joining-in type robot assisted language learning system

Abstract

Computer assisted language learning (CALL) is going to be more attractive, more interactive and more realistic by introducing humanoid robots. We propose a novel joining-in type robot assisted language learning (RALL) system using two humanoid robots, one playing a role of a teacher and the other playing a role of an advanced learner. The system has a seamless scenario of a natural dialogue flow among two robots and a learner to draw the learner into the dialogue. We designed the system to switch two modes of learning: tutoring and peer learning. As a characteristic behavior of the peer learning, we expect an “alignment” phenomenon between the robot's sample answer and the learner's utterance. That is to say, the learner learns a sentence pattern by mimicking a sample answer presented by the advanced learner robot. We created a learner corpus of over 60 participants, carried out an analysis on how much alignment occurred in the interactions.