Expressing the Personality of a Humanoid Robot as a Talking Partner in an Elementary School Classroom

Omokawa, Reika; Kobayashi, Makoto; Matsuura, Shinya

doi:10.1007/978-3-030-23560-4_36

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…These studies also suggest that timely feedback during human-robot communication has been regarded as a success factor contributing to the quality of interaction. By presenting NAO as a talking partner, Omokawa et al (2019) distinguished between two dialog types for verbal interaction: query type is a questionand-answer format, and phatic type is a casual format that involves small talk and/or personal feelings (e.g. acceptance).…”

Section: Verbal Communication With Naomentioning

confidence: 99%

10 Years of Human-NAO Interaction Research: A Scoping Review

et al. 2021

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The evolving field of human-robot interaction (HRI) necessitates that we better understand how social robots operate and interact with humans. This scoping review provides an overview of about 300 research works focusing on the use of the NAO robot from 2010 to 2020. This study presents one of the most extensive and inclusive pieces of evidence on the deployment of the humanoid NAO robot and its global reach. Unlike most reviews, we provide both qualitative and quantitative results regarding how NAO is being used and what has been achieved so far. We analyzed a wide range of theoretical, empirical, and technical contributions that provide multidimensional insights, such as general trends in terms of application, the robot capabilities, its input and output modalities of communication, and the human-robot interaction experiments that featured NAO (e.g. number and roles of participants, design, and the length of interaction). Lastly, we derive from the review some research gaps in current state-of-the-art and provide suggestions for the design of the next generation of social robots.

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Section: Verbal Communication With Naomentioning

confidence: 99%

10 Years of Human-NAO Interaction Research: A Scoping Review

et al. 2021

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“…As Dewey indicated, teachers never teach students directly, but teach them indirectly by means of the educative environment (Hansen, 2002). By utilizing emerging technologies in TRCs like education robot, virtual reality, 3D avatars, and wireless sensors, diversified innovative teaching and learning activities have been held in TRCs (Fuad, Deb, Etim, & Gloster, 2018;Hwang, Chu, Shih, Huang, & Tsai, 2010;Lemmon, Lui, Cottrell, & Hamilton, 2012;Liao, Sunq, Wang, & Lin, 2019;Omokawa, Kobayashi, & Matsuura, 2019). These activities can stimulate students' learning motivation, improve students' learning experience, promote students' active learning behaviour, help students achieve good learning performance, and cultivate the 21st-century skills (Liu, Horton, Olmanson, & Toprac, 2011;O'Grady, Simmie, & Kennedy, 2014).…”

Section: Introductionmentioning

confidence: 99%

Expectations and Experiences of Technology-Rich Classrooms of Preservice and In-service Teachers in China

Zhao

2019

JED

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Technology-Rich Classrooms (TRCs) have been increasingly constructed in K12 schools. Existing studies revealed that to what extent TRCs play a role in shaping teaching and learning depends on what teachers expect to do and what they do with TRCs. This study aimed at exploring the gap between teachers’ experiences of existing TRCs and their expectations of ideal TRCs. A total of 194 preservice teachers studying at Beijing Normal University (BNU) and 149 in-service teachers who enrolled in a master program at BNU participated in the study. The Smart Classroom Inventory (SCI) a ten-component inventory revised from the previous study, was used to collect the data. Independent-samples t-tests and paired-samples t-tests revealed that: (a) both preservice teachers and in-service teachers reported significantly lower experiences of TRCs than their expectations of ideal ones in terms of most dimensions, with the only exception that preservice teachers’ Technology usage. (b) Whether in existing TRCs, or in ideal ones, both preservice teachers and in-service teachers reported relative lower scores of Flexibility, Learning data, Differentiation, Investigation, and Cooperation than other dimensions. (c) Preservice teachers reported significantly higher scores of Flexibility, Technology usage, Learning data, Differentiation, Investigation, and Cooperation than in-service teachers, while in-service teachers reported significantly higher scores of Physical design, Student Cohesiveness, Equity, and Learning experiences than preservice teachers.4) Preservice teachers reported significantly higher expectation of ideal Physical design, Differentiation than in-service teachers, while they reported significantly lower expectations of ideal Flexibility, Student Cohesiveness, Equity, and Learning experience than in-service teachers. Implications for preservice teachers’ education and in-service teachers’ professional development were discussed.

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Expressing the Personality of a Humanoid Robot as a Talking Partner in an Elementary School Classroom

Cited by 2 publications

References 7 publications

10 Years of Human-NAO Interaction Research: A Scoping Review

10 Years of Human-NAO Interaction Research: A Scoping Review

Expectations and Experiences of Technology-Rich Classrooms of Preservice and In-service Teachers in China

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