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Development and Evaluation of a Real-Time Emotion Detection System to Enhance Student Interaction

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Title: Development and Evaluation of a Real-Time Emotion Detection System to Enhance Student Interaction
Language: English
Authors: Gerlan Apriandy Manu, Punaji Setyosari, Saida Ulfa, Henry Praherdhiono
Source: Journal of Educators Online. 2026 23(1).
Availability: Journal of Educators Online. Grand Canyon University, 23300 West Camelback Road, Phoenix, AZ 85017. e-mail: CIRT@gcu.edu. Web site: https://www.thejeo.com
Peer Reviewed: Y
Page Count: 15
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Foreign Countries, Undergraduate Students, Higher Education, Video Technology, Electronic Learning, Distance Education, Technology Uses in Education, Psychological Patterns, Artificial Intelligence, Emotional Adjustment, Synchronous Communication, Learner Engagement, Computer Mediated Communication, Identification
Geographic Terms: Indonesia
ISSN: 1547-500X
Abstract: This research explores the development of a real-time emotion detection system to improve engagement in online learning. The system uses Convolutional Neural Networks (CNN) to identify five emotions: happy, sad, angry, surprised, and neutral via webcam during virtual classes. Tested with 30 students in an Artificial Intelligence course, it achieved 86.4% accuracy, excelling in detecting happy and neutral states. Instructors used emotional feedback to adapt teaching dynamically, enhancing learning experiences and satisfaction. Feedback showed that 88% of students felt more motivated and engaged. This study highlights the potential of emotion-based tools in bridging gaps between online and traditional education.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1499233
Database: ERIC
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  Data: Development and Evaluation of a Real-Time Emotion Detection System to Enhance Student Interaction
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  Data: Journal of Educators Online. Grand Canyon University, 23300 West Camelback Road, Phoenix, AZ 85017. e-mail: CIRT@gcu.edu. Web site: https://www.thejeo.com
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  Data: <searchLink fieldCode="DE" term="%22Indonesia%22">Indonesia</searchLink>
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  Data: This research explores the development of a real-time emotion detection system to improve engagement in online learning. The system uses Convolutional Neural Networks (CNN) to identify five emotions: happy, sad, angry, surprised, and neutral via webcam during virtual classes. Tested with 30 students in an Artificial Intelligence course, it achieved 86.4% accuracy, excelling in detecting happy and neutral states. Instructors used emotional feedback to adapt teaching dynamically, enhancing learning experiences and satisfaction. Feedback showed that 88% of students felt more motivated and engaged. This study highlights the potential of emotion-based tools in bridging gaps between online and traditional education.
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      – Text: English
    PhysicalDescription:
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    Subjects:
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Undergraduate Students
        Type: general
      – SubjectFull: Higher Education
        Type: general
      – SubjectFull: Video Technology
        Type: general
      – SubjectFull: Electronic Learning
        Type: general
      – SubjectFull: Distance Education
        Type: general
      – SubjectFull: Technology Uses in Education
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      – SubjectFull: Psychological Patterns
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      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Emotional Adjustment
        Type: general
      – SubjectFull: Synchronous Communication
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      – SubjectFull: Learner Engagement
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      – SubjectFull: Computer Mediated Communication
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      – SubjectFull: Identification
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      – SubjectFull: Indonesia
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