Conversation Analysis for Facilitation in Children's Intercultural Collaboration

Abstract

In order to find solutions to various international problems, Global Citizenship Education (GCED) for children must consider the diversity of societies beyond basic issues of language and culture. For GCED, machine translation can be used as a tool to allow children to collaborate without a common language. However, low-resource language speakers often cannot enter the conversation and have difficulties in participating in a collaboration, because existing machine translations have poor translation quality in the low-resource languages. Messages from facilitators play an important role in encouraging children's responses and participation. We, therefore, have analyzed the role of the facilitator in a real-world intercultural children workshop. Specifically, we examine actual conversation log data that links the facilitator's utterances with children's utterances in adjacency pairing. We annotate the paired data with tags and then statistically analyze the tagged data. The analysis results show that some types of facilitator messages can significantly impact the responses of low-resource language speaking children. For example, "request"type utterances tended to encourage responses.