Remote Learning of Speaking in Syntactic Forms with Robot-Avatar-Assisted Language Learning System

Abstract

To help second language (L2) learners acquire oral communication skills, dialogue-based computer-assisted language learning (DB-CALL) systems are attracting more interest than ever. When robot-assisted language learning (RALL) is used for realizing such systems, L2 learners are provided with a sense of reality and tension similar to that in a real L2 conversation. At the same time, there are increasing demands for remote learning, accelerated in part by the spread of the novel coronavirus. We have therefore developed a robot-avatar-assisted language learning system that simulates a trialogue in English with two robot avatars and a learner for remote learning. The conversation scenarios deal with various daily topics to keep the learner’s interest and the system prompts the learner to acquire oral skills by using specific syntactic forms in conversation. We conducted a six-day remote learning experiment with ten Japanese university students to evaluate the learning effect, using eye gaze as an index of the learners’ degree of concentration. Our findings demonstrated the effectiveness of our system for remote learning and showed that the learners’ eye gaze activities changed between question answering and repeating tasks.