Facial Expression Recognition for Probing Students' Emotional Engagement in Science Learning
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| Title: | Facial Expression Recognition for Probing Students' Emotional Engagement in Science Learning |
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| Language: | English |
| Authors: | Xiaoyu Tang, Yayun Gong, Yang Xiao, Jianwen Xiong, Lei Bao (ORCID |
| Source: | Journal of Science Education and Technology. 2025 34(1):13-30. |
| Availability: | Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Peer Reviewed: | Y |
| Page Count: | 18 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Physics, Science Instruction, Nonverbal Communication, Science Achievement, Artificial Intelligence, Emotional Response, Affective Measures, Arousal Patterns, Predictive Validity, Learner Engagement, Teaching Methods, Instructional Effectiveness |
| DOI: | 10.1007/s10956-024-10143-7 |
| ISSN: | 1059-0145 1573-1839 |
| Abstract: | Student engagement in science classroom is an essential element for delivering effective instruction. However, the popular method for measuring students' emotional learning engagement (ELE) relies on self-reporting, which has been criticized for possible bias and lacking fine-grained time solution needed to track the effects of short-term learning interactions. Recent research suggests that students' facial expressions may serve as an external representation of their emotions in learning. Accordingly, this study proposes a machine learning method to efficiently measure students' ELE in real classroom. Specifically, a facial expression recognition system based on a multiscale perception network (MP-FERS) was developed by combining the pleasure-displeasure, arousal-nonarousal, and dominance-submissiveness (PAD) emotion models. Data were collected from videos of six physics lessons with 108 students. Meanwhile, students' academic records and self-reported learning engagement were also collected. The results show that students' ELE measured by MP-FERS was a significant predictor of academic achievement and a better indicator of true learning status than self-reported ELE. Furthermore, MP-FERS can provide fine-grained time resolution on tracking the changes in students' ELE in response to different teaching environments such as teacher-centered or student-centered classroom activities. The results of this study demonstrate the validity and utility of MP-FERS in studying students' emotional learning engagement. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1460783 |
| Database: | ERIC |
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| Abstract: | Student engagement in science classroom is an essential element for delivering effective instruction. However, the popular method for measuring students' emotional learning engagement (ELE) relies on self-reporting, which has been criticized for possible bias and lacking fine-grained time solution needed to track the effects of short-term learning interactions. Recent research suggests that students' facial expressions may serve as an external representation of their emotions in learning. Accordingly, this study proposes a machine learning method to efficiently measure students' ELE in real classroom. Specifically, a facial expression recognition system based on a multiscale perception network (MP-FERS) was developed by combining the pleasure-displeasure, arousal-nonarousal, and dominance-submissiveness (PAD) emotion models. Data were collected from videos of six physics lessons with 108 students. Meanwhile, students' academic records and self-reported learning engagement were also collected. The results show that students' ELE measured by MP-FERS was a significant predictor of academic achievement and a better indicator of true learning status than self-reported ELE. Furthermore, MP-FERS can provide fine-grained time resolution on tracking the changes in students' ELE in response to different teaching environments such as teacher-centered or student-centered classroom activities. The results of this study demonstrate the validity and utility of MP-FERS in studying students' emotional learning engagement. |
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| ISSN: | 1059-0145 1573-1839 |
| DOI: | 10.1007/s10956-024-10143-7 |
