Real-Time Feedback Based on Emotion Recognition for Improving Children’s Metacognitive Monitoring Skill

Xingran Ruan; Charaka Palansuriya; Aurora Constantin

doi:10.1145/3501712.3538831

Real-Time Feedback Based on Emotion Recognition for Improving Children’s Metacognitive Monitoring Skill

Xingran Ruan, Charaka Palansuriya, Aurora Constantin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this extended abstract, we outline a PhD project which investigates the relationship between emotion and metacognition in children with Autism Spectrum Disorder (ASD) in order to design and develop an automatic Machine Learning based tool with real-time feedback to support metacognitive process of both Typically Developing (TD) children and children with ASD.

Original language	English
Title of host publication	Proceedings of Interaction Design and Children 2022
Place of Publication	New York, NY, USA
Publisher	Association for Computing Machinery, Inc
Pages	672–675
Number of pages	4
ISBN (Print)	9781450391979
DOIs	https://doi.org/10.1145/3501712.3538831
Publication status	Published - 27 Jun 2022
Event	The 21st ACM Interaction Design and Children Conference 2022 - Braga, Portugal Duration: 27 Jun 2022 → 30 Jun 2022 Conference number: 21 https://idc.acm.org/2022/

Publication series

Name	IDC '22
Publisher	Association for Computing Machinery

Conference

Conference	The 21st ACM Interaction Design and Children Conference 2022
Abbreviated title	IDC 2022
Country/Territory	Portugal
City	Braga
Period	27/06/22 → 30/06/22
Internet address	https://idc.acm.org/2022/

Keywords

Self-regulated learning
Metacognitive monitoring process
Emotion
Children
Automatic tool

Access to Document

10.1145/3501712.3538831Licence: All Rights Reserved

Real-time Feedback_RUAN_DOA25042022_AFVAccepted author manuscript, 467 KBLicence: All Rights Reserved

https://dl.acm.org/doi/abs/10.1145/3501712.3538831Licence: All Rights Reserved

Cite this

@inproceedings{63d4c8d31de5401e82f16e9de00f966f,

title = "Real-Time Feedback Based on Emotion Recognition for Improving Children{\textquoteright}s Metacognitive Monitoring Skill",

abstract = "In this extended abstract, we outline a PhD project which investigates the relationship between emotion and metacognition in children with Autism Spectrum Disorder (ASD) in order to design and develop an automatic Machine Learning based tool with real-time feedback to support metacognitive process of both Typically Developing (TD) children and children with ASD.",

keywords = "Self-regulated learning, Metacognitive monitoring process, Emotion, Children, Automatic tool",

author = "Xingran Ruan and Charaka Palansuriya and Aurora Constantin",

note = "Publisher Copyright: {\textcopyright} 2022 Owner/Author.; The 21st ACM Interaction Design and Children Conference 2022, IDC 2022 ; Conference date: 27-06-2022 Through 30-06-2022",

year = "2022",

month = jun,

day = "27",

doi = "10.1145/3501712.3538831",

language = "English",

isbn = "9781450391979",

series = "IDC '22",

publisher = "Association for Computing Machinery, Inc",

pages = "672–675",

booktitle = "Proceedings of Interaction Design and Children 2022",

url = "https://idc.acm.org/2022/",

}

Ruan, X, Palansuriya, C & Constantin, A 2022, Real-Time Feedback Based on Emotion Recognition for Improving Children’s Metacognitive Monitoring Skill. in Proceedings of Interaction Design and Children 2022. IDC '22, Association for Computing Machinery, Inc, New York, NY, USA, pp. 672–675, The 21st ACM Interaction Design and Children Conference 2022, Braga, Portugal, 27/06/22. https://doi.org/10.1145/3501712.3538831

Real-Time Feedback Based on Emotion Recognition for Improving Children’s Metacognitive Monitoring Skill. / Ruan, Xingran; Palansuriya, Charaka ; Constantin, Aurora.

Proceedings of Interaction Design and Children 2022. New York, NY, USA : Association for Computing Machinery, Inc, 2022. p. 672–675 (IDC '22).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Real-Time Feedback Based on Emotion Recognition for Improving Children’s Metacognitive Monitoring Skill

AU - Ruan, Xingran

AU - Palansuriya, Charaka

AU - Constantin, Aurora

N1 - Conference code: 21

PY - 2022/6/27

Y1 - 2022/6/27

N2 - In this extended abstract, we outline a PhD project which investigates the relationship between emotion and metacognition in children with Autism Spectrum Disorder (ASD) in order to design and develop an automatic Machine Learning based tool with real-time feedback to support metacognitive process of both Typically Developing (TD) children and children with ASD.

AB - In this extended abstract, we outline a PhD project which investigates the relationship between emotion and metacognition in children with Autism Spectrum Disorder (ASD) in order to design and develop an automatic Machine Learning based tool with real-time feedback to support metacognitive process of both Typically Developing (TD) children and children with ASD.

KW - Self-regulated learning

KW - Metacognitive monitoring process

KW - Emotion

KW - Children

KW - Automatic tool

U2 - 10.1145/3501712.3538831

DO - 10.1145/3501712.3538831

M3 - Conference contribution

SN - 9781450391979

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BT - Proceedings of Interaction Design and Children 2022

PB - Association for Computing Machinery, Inc

CY - New York, NY, USA

T2 - The 21st ACM Interaction Design and Children Conference 2022

Y2 - 27 June 2022 through 30 June 2022

ER -

Real-Time Feedback Based on Emotion Recognition for Improving Children’s Metacognitive Monitoring Skill

Abstract

Publication series

Conference

Keywords

Access to Document

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Cite this