Exploring the Relationship between Interactions and Learning Performance in Robot-Assisted Language Learning

Abstract

Robot-assisted language learning is a pattern that uses a social robot in a class to enhance learning performance through human-robot interaction. While this pattern receives increasing attention, few review articles generalize whether each interactive behavior improves learning performance. This study delves into the answers to three research questions. The results show that the synthesized and prerecorded human voices are appropriate for different teaching activities. Verbal communication except for egocentric small talk and L1 translation between the robot and learners are indispensable to improve students' confidence and learning gains. Moderate employment of nonverbal interactions helps students increase concentration, motivation, and retention of vocabulary, while undue interactions give rise to counterproductive effects. Based on the findings, future research on robot-assisted language learning is suggested to pay attention to the effectiveness of an independent interaction. Another valuable focus is the proper combination of interactive behaviors that suit different teaching tasks. A suitable level of robotic sociability is also worthy of exploration. Educators teaching with a robot should make full use of verbal and nonverbal interactive behaviors to boost students' confidence, motivation, engagement, and learning gains. Meanwhile, they need to be cautious about the excessive employment of interactions.