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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