| 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 |