Social Robots and Young Children’s Early Language and Literacy Learning

Neumann, Michelle M.

doi:10.1007/s10643-019-00997-7

Cited by 59 publications

(37 citation statements)

References 44 publications

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“…Following the transfer to the Latin-based Kazakh alphabet and subsequent necessity for acquiring knowledge of the script, innovative approaches and instruments can facilitate a smooth Latin switchover for teaching and learning. Many innovative solutions are being implemented for the purposes of educational applicability (Mubin et al, 2013) for early language and literacy learning (Neumann, 2020), handwriting learning (Hood et al, 2015), or foreign language acquisition (Balkibekov et al, 2016). For instance, Sysoev et al (2017) presented SpeechBlocks, which is an application assisting young learners in their pursuit of mastering spelling strategies through listening to the differently positioned letters in a word.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Social Robot to Tablet and Teacher in a New Script Learning Context

et al. 2020

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This research occurred in a special context where Kazakhstan's recent decision to switch from Cyrillic to the Latin-based alphabet has resulted in challenges connected to teaching literacy, addressing a rare combination of research hypotheses and technical objectives about language learning. Teachers are not necessarily trained to teach the new alphabet, and this could result in a challenge for children with learning difficulties. Prior research studies in Human-Robot Interaction (HRI) have proposed the use of a robot to teach handwriting to children (Hood et al., 2015; Lemaignan et al., 2016). Drawing on the Kazakhstani case, our study takes an interdisciplinary approach by bringing together smart solutions from robotics, computer vision areas, and educational frameworks, language, and cognitive studies that will benefit diverse groups of stakeholders. In this study, a human-robot interaction application is designed to help primary school children learn both a newly-adopted script and also its handwriting system. The setup involved an experiment with 62 children between the ages of 7-9 years old, across three conditions: a robot and a tablet, a tablet only, and a teacher. Based on the paradigm-learning by teaching-the study showed that children improved their knowledge of the Latin script by interacting with a robot. Findings reported that children gained similar knowledge of a new script in all three conditions without gender effect. In addition, children's likeability ratings and positive mood change scores demonstrate significant benefits favoring the robot over a traditional teacher and tablet only approaches.

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Section: Introductionmentioning

confidence: 99%

A Comparison of Social Robot to Tablet and Teacher in a New Script Learning Context

et al. 2020

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“…This would explain the low number of studies with children of this age. Recent reviews on the interactions between neuro-typical children and a robot (Jamet et al, 2018;Neumann, 2020;van Straten et al, 2020) indicate that only one study was conducted using NAO and a group of children from 2 to 8 years-old (Yasumatsu et al, 2017). The few other studies conducted on 2 years-old either used the tiny humanoid robot QRIO that is smaller than a 2 years-old child (Tanaka et al, 2007), the iRobiQ robot that looks more like a toy (Hsiao et al, 2015), or robots specifically designed to be enjoyed by young children like the stuffed dragon robot Dragonbot (Kory Westlund et al, 2017) and the RUBI-4 (Movellan et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

Pragmatics in the False-Belief Task: Let the Robot Ask the Question!

et al. 2020

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The poor performances of typically developing children younger than 4 in the first-order false-belief task “Maxi and the chocolate” is analyzed from the perspective of conversational pragmatics. An ambiguous question asked by an adult experimenter (perceived as a teacher) can receive different interpretations based on a search for relevance, by which children according to their age attribute different intentions to the questioner, within the limits of their own meta-cognitive knowledge. The adult experimenter tells the child the following story of object-transfer: “Maxi puts his chocolate into the green cupboard before going out to play. In his absence, his mother moves the chocolate from the green cupboard to the blue one.” The child must then predict where Maxi will pick up the chocolate when he returns. To the child, the question from an adult (a knowledgeable person) may seem surprising and can be understood as a question of his own knowledge of the world, rather than on Maxi's mental representations. In our study, without any modification of the initial task, we disambiguate the context of the question by (1) replacing the adult experimenter with a humanoid robot presented as “ignorant” and “slow” but trying to learn and (2) placing the child in the role of a “mentor” (the knowledgeable person). Sixty-two typical children of 3 years-old completed the first-order false belief task “Maxi and the chocolate,” either with a human or with a robot. Results revealed a significantly higher success rate in the robot condition than in the human condition. Thus, young children seem to fail because of the pragmatic difficulty of the first-order task, which causes a difference of interpretation between the young child and the experimenter.

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“…Another study reported that senior citizens preferred Pepper compared to android robots [63]. On the other hand, robots for children such as therapy or educational support have cute and adorable designs [64] [65]; too realistic human-like appearances and adult size body may decrease children's acceptance.…”

Section: Gender Appearance and Age Effectsmentioning

confidence: 99%

Modeling the Timing and Duration of Grip Behavior to Express Emotions for a Social Robot

Zheng

Shiomi²,

Minato³

et al. 2021

IEEE Robot. Autom. Lett.

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This study addresses the effects of grip timing to express a robot's emotions to people while they are watching a video stimulus together. Past studies on human-robot touch interaction focused on the types of touch behaviors for expressing a robot's emotion, but timing factors have received less attention. In this study, we conducted data collection to investigate the appropriate grip timing to express heartwarming and horror emotions. Participants identified touch (grip) timing and durations by using a robot while they are watching video stimuli. Typically, they preferred a grip timing before a climax for horror videos but after a climax for heartwarming videos. We modeled the timing and durations by a fitting approach to probabilistic distribution to reproduce human-like touch behaviors, and also we implemented the models to an android robot.

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Social Robots and Young Children’s Early Language and Literacy Learning

Cited by 59 publications

References 44 publications

A Comparison of Social Robot to Tablet and Teacher in a New Script Learning Context

A Comparison of Social Robot to Tablet and Teacher in a New Script Learning Context

Pragmatics in the False-Belief Task: Let the Robot Ask the Question!

Modeling the Timing and Duration of Grip Behavior to Express Emotions for a Social Robot

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