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Child-Robot Interactions for Second Language Tutoring to Preschool Children

Overview of attention for article published in Frontiers in Human Neuroscience, March 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (67th percentile)

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8 X users

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178 Mendeley
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Title
Child-Robot Interactions for Second Language Tutoring to Preschool Children
Published in
Frontiers in Human Neuroscience, March 2017
DOI 10.3389/fnhum.2017.00073
Pubmed ID
Authors

Paul Vogt, Mirjam de Haas, Chiara de Jong, Peta Baxter, Emiel Krahmer

Abstract

In this digital age social robots will increasingly be used for educational purposes, such as second language tutoring. In this perspective article, we propose a number of design features to develop a child-friendly social robot that can effectively support children in second language learning, and we discuss some technical challenges for developing these. The features we propose include choices to develop the robot such that it can act as a peer to motivate the child during second language learning and build trust at the same time, while still being more knowledgeable than the child and scaffolding that knowledge in adult-like manner. We also believe that the first impressions children have about robots are crucial for them to build trust and common ground, which would support child-robot interactions in the long term. We therefore propose a strategy to introduce the robot in a safe way to toddlers. Other features relate to the ability to adapt to individual children's language proficiency, respond contingently, both temporally and semantically, establish joint attention, use meaningful gestures, provide effective feedback and monitor children's learning progress. Technical challenges we observe include automatic speech recognition (ASR) for children, reliable object recognition to facilitate semantic contingency and establishing joint attention, and developing human-like gestures with a robot that does not have the same morphology humans have. We briefly discuss an experiment in which we investigate how children respond to different forms of feedback the robot can give.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 178 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Netherlands 1 <1%
Unknown 176 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 19%
Student > Master 32 18%
Student > Doctoral Student 9 5%
Researcher 8 4%
Student > Bachelor 7 4%
Other 25 14%
Unknown 63 35%
Readers by discipline Count As %
Computer Science 33 19%
Psychology 21 12%
Social Sciences 17 10%
Arts and Humanities 9 5%
Linguistics 7 4%
Other 26 15%
Unknown 65 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 March 2017.
All research outputs
#5,678,172
of 22,952,268 outputs
Outputs from Frontiers in Human Neuroscience
#2,310
of 7,179 outputs
Outputs of similar age
#91,643
of 310,716 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#60
of 187 outputs
Altmetric has tracked 22,952,268 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,179 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.6. This one has gotten more attention than average, scoring higher than 67% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 310,716 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 187 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.