Supporting Children’s Metacognition with a Facial Emotion Recognition based Intelligent Tutor System

Xingran Ruan*, Charaka Palansuriya, Aurora Constantin, Konstantinos Tsiakas

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

The present study aims to investigate the relationship between emotions experienced during learning and metacognition in typically developing (TD) children and those with autism spectrum disorder (ASD). This will assist us in using machine learning (ML) to develop a facial emotion recognition (FER) based intelligent tutor system (ITS) to support children’s metacognitive monitoring process in order to enhance their learning outcomes. In this paper, we first report the results of our preliminary research, which utilized an ML-based FER algorithm to detect four spontaneous epistemic emotions (i.e., neutral, confused, frustrated, and boredom) and six spontaneous basic emotions (i.e., anger, disgust, fear, happiness, sadness, and surprise). Subsequently, we adapted an application (‘BrainHood’) to create the ‘Meta-BrainHood’, that embedded our proposed ML-based FER algorithm to examine the relationship between facial emotion expressions and metacognitive monitoring performance in TD children and those with ASD. Finally, we outline the future steps in our research, which adopts the outcomes of the first two steps to construct an ITS to improve children’s metacognitive monitoring performance and learning outcomes.
Original languageEnglish
Title of host publicationIDC '23: Proceedings of the 22nd Annual ACM Interaction Design and Children Conference
Number of pages5
ISBN (Electronic)9798400701313
Publication statusPublished - 19 Jun 2023
Event2023 ACM Interaction Design and Children (IDC) - Northwestern University , Chicago, United States
Duration: 19 Jun 202323 Jun 2023
Conference number: 22


Conference2023 ACM Interaction Design and Children (IDC)
Country/TerritoryUnited States
Internet address

Keywords / Materials (for Non-textual outputs)

  • metacognitive monitoring process
  • facial emotion recognition
  • Intelligent tutor system
  • learning outcomes


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