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Robot interaction styles for conversation practice in second language learning
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH, Speech Communication and Technology.ORCID iD: 0000-0003-4532-014X
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.ORCID iD: 0000-0002-8773-9216
2020 (English)In: International Journal of Social Robotics, ISSN 1875-4791, E-ISSN 1875-4805Article in journal (Refereed) Accepted
Abstract [en]

Four different interaction styles for the social robot Furhat acting as a host in spoken conversation practice with two simultaneous language learners have been developed, based on interaction styles of human moderators of language cafés.We first investigated, through a survey and recorded sessions of three-party language café style conversations, how the interaction styles of human moderators are influenced by different factors (e.g., the participants language level and familiarity).Using this knowledge, four distinct interaction styles were developed for the robot: sequentially asking one participant questions at the time (Interviewer); the robot speaking about itself, robots and Sweden or asking quiz questions about Sweden (Narrator); attempting to make the participants talk with each other (Facilitator); and trying to establish a three-party robot-learner-learner interaction with equal participation (Interlocutor).A user study with 32 participants, conversing in pairs with the robot, was carried out to investigate how the post-session ratings of the robot's behavior along different dimensions (e.g., the robot's conversational skills and friendliness, the value of practice) are influenced by the robot's interaction style and participant variables (e.g., level in the target language, gender, origin).The general findings were that Interviewer received the highest mean rating, but that different factors influenced the ratings substantially, indicating that the preference of individual participants needs to be anticipated in order to improve learner satisfaction with the practice. We conclude with a list of recommendations for robot-hosted conversation practice in a second language.

Place, publisher, year, edition, pages
2020.
Keywords [en]
Robot-assisted language learning; multi-party human-robot interaction; collaborative language learning; conversational practice
National Category
Interaction Technologies
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-268194Scopus ID: 2-s2.0-85082549828OAI: oai:DiVA.org:kth-268194DiVA, id: diva2:1393957
Projects
Collaborative Robot-Assisted Language Learning
Funder
Swedish Research Council, 2016-03698
Note

QCR 20200512

Available from: 2020-02-17 Created: 2020-02-17 Last updated: 2020-05-25Bibliographically approved

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David Lopes, José

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