Articles producció científica> Pedagogia

Classroom Emotion Monitoring Based on Image Processing

  • Datos identificativos

    Identificador: imarina:9351841
    Autores:
    Llurba, CFretes, GPalau, R
    Resumen:
    One challenge of teaching and learning the lack of information during these processes, including information about students’ emotions. Emotions play a role in learning and processing information, impacting accurate comprehension. Furthermore, emotions affect students’ academic engagement and performance. Consideration of students’ emotions, and therefore their well-being, contributes to building a more sustainable society. A new way of obtaining such information is by monitoring students’ facial emotions. Accordingly, the purpose of this study was to explore whether the use of such advanced technologies can assist the teaching–learning process while ensuring the emotional well-being of secondary school students. A model of Emotional Recognition (ER) was designed for use in a classroom. The model employs a custom code, recorded videos, and images to identify faces, follow action units (AUs), and classify the students’ emotions displayed on screen. We then analysed the classified emotions according to the academic year, subject, and moment in the lesson. The results revealed a range of emotions in the classroom, both pleasant and unpleasant. We observed significant variations in the presence of certain emotions based on the beginning or end of the class, subject, and academic year, although no clear patterns emerged. Our discussion focuses on the relationship between emotions, academic performance, and sustainability. We recommend that future research prioritise the study of how teachers can use ER-based tools to improve both the well-being and performance of students.
  • Otros:

    Autor según el artículo: Llurba, C; Fretes, G; Palau, R
    Departamento: Pedagogia
    Autor/es de la URV: Fretes Torruella, Maria Gabriela / Llurba Mort, Cèlia / Palau Martin, Ramon Felix
    Palabras clave: Students’ well-being Students’ emotions Secondary school students Py-feat Image processing Emotion recognition Achievement Academic performance students' well-being students' emotions students secondary school students py-feat emotion recognition context cognition academic performance
    Resumen: One challenge of teaching and learning the lack of information during these processes, including information about students’ emotions. Emotions play a role in learning and processing information, impacting accurate comprehension. Furthermore, emotions affect students’ academic engagement and performance. Consideration of students’ emotions, and therefore their well-being, contributes to building a more sustainable society. A new way of obtaining such information is by monitoring students’ facial emotions. Accordingly, the purpose of this study was to explore whether the use of such advanced technologies can assist the teaching–learning process while ensuring the emotional well-being of secondary school students. A model of Emotional Recognition (ER) was designed for use in a classroom. The model employs a custom code, recorded videos, and images to identify faces, follow action units (AUs), and classify the students’ emotions displayed on screen. We then analysed the classified emotions according to the academic year, subject, and moment in the lesson. The results revealed a range of emotions in the classroom, both pleasant and unpleasant. We observed significant variations in the presence of certain emotions based on the beginning or end of the class, subject, and academic year, although no clear patterns emerged. Our discussion focuses on the relationship between emotions, academic performance, and sustainability. We recommend that future research prioritise the study of how teachers can use ER-based tools to improve both the well-being and performance of students.
    Áreas temáticas: Zootecnia / recursos pesqueiros Renewable energy, sustainability and the environment Medicina i Management, monitoring, policy and law Interdisciplinar Historia Hardware and architecture Green & sustainable science & technology Geography, planning and development Geografía Geociências Environmental studies Environmental sciences Environmental science (miscellaneous) Ensino Engenharias iii Engenharias ii Engenharias i Enfermagem Energy engineering and power technology Education Computer science (miscellaneous) Computer networks and communications Ciências agrárias i Building and construction Biotecnología Biodiversidade Arquitetura, urbanismo e design Arquitetura e urbanismo
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: ramon.palau@urv.cat celia.llurba@urv.cat celia.llurba@urv.cat gabriela.fretes@estudiants.urv.cat
    Identificador del autor: 0000-0002-9843-3116
    Fecha de alta del registro: 2024-02-17
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.mdpi.com/2071-1050/16/2/916
    Referencia al articulo segun fuente origial: Sustainability. 16 (2):
    Referencia de l'ítem segons les normes APA: Llurba, C; Fretes, G; Palau, R (2024). Classroom Emotion Monitoring Based on Image Processing. Sustainability, 16(2), -. DOI: 10.3390/su16020916
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.3390/su16020916
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2024
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Computer Networks and Communications,Education,Energy Engineering and Power Technology,Environmental Science (Miscellaneous),Environmental Sciences,Environmental Studies,Geography, Planning and Development,Green & Sustainable Science & Technology,Hardware and Architecture,Management, Monitoring, Policy and Law,Renewable Energy, Sustainabil
    Students’ well-being
    Students’ emotions
    Secondary school students
    Py-feat
    Image processing
    Emotion recognition
    Achievement
    Academic performance
    students' well-being
    students' emotions
    students
    secondary school students
    py-feat
    emotion recognition
    context
    cognition
    academic performance
    Zootecnia / recursos pesqueiros
    Renewable energy, sustainability and the environment
    Medicina i
    Management, monitoring, policy and law
    Interdisciplinar
    Historia
    Hardware and architecture
    Green & sustainable science & technology
    Geography, planning and development
    Geografía
    Geociências
    Environmental studies
    Environmental sciences
    Environmental science (miscellaneous)
    Ensino
    Engenharias iii
    Engenharias ii
    Engenharias i
    Enfermagem
    Energy engineering and power technology
    Education
    Computer science (miscellaneous)
    Computer networks and communications
    Ciências agrárias i
    Building and construction
    Biotecnología
    Biodiversidade
    Arquitetura, urbanismo e design
    Arquitetura e urbanismo
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