Emotion Recognition in Self-Regulated Learning: Advancing Metacognition Through AI-Assisted Reflections

Si Chen, Haocong Cheng, Yun Huang

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Metacognition, understood as the awareness of one’s own learning processes, plays a pivotal role in achieving effective learning outcomes. Recent work has shown the potential of AI-based emotion recognition technology to enhance learners’ metacognitive abilities during post-learning reflections. In this article, we present a novel interaction design that seamlessly integrates emotion recognition technology into the reflection process. The proposed solution captures and visualizes learners’ emotional states during self-regulated video-based learning activities, thereby facilitating deeper metacognition by offering insights into learners’ own emotions and those of their peers. We discuss the theoretical foundations of the proposed approach, its benefits, and ethical considerations for future research.

Original languageEnglish (US)
Title of host publicationTrust and Inclusion in AI-Mediated Education
Subtitle of host publicationWhere Human Learning Meets Learning Machines
EditorsDoura Kourkoulou, Anastasia Olga Tzirides, Bill Cope, Mary Kalantzis
PublisherSpringer
Pages185-212
Number of pages28
ISBN (Electronic)9783031644870
ISBN (Print)9783031644863, 9783031644894
DOIs
StatePublished - Sep 28 2024

Publication series

NamePostdigital Science and Education
VolumePart F3835
ISSN (Print)2662-5326
ISSN (Electronic)2662-5334

Keywords

  • Automatic emotion recognition
  • Educational technology
  • Metacognition
  • Reflections
  • Self-regulated learning

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Philosophy
  • Social Sciences (miscellaneous)
  • Education

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