探索学生的情绪识别和教师的教学反在大学的外语课堂,基于AFCNN模型
1School of Language, Literature & Law, Xi'an University of Architecture & Technology, Xi'an, 710055, Shaanxi, China. 2544551211@qq.com.
Scientific reports
|January 18, 2026
概括
这项研究引入了注意力特征卷积神经网络 (AFCNN),用于在外国语言课堂上实时识别学生的情绪. AFCNN模型通过提供及时的教学反来增强教师的专业发展,在准确性方面表现优于传统模型.
科学领域:
- 人工智能的人工智能
- 教育技术的教育技术
- 计算机视觉 计算机视觉
背景情况:
- 深度学习 (DL) 为教育情绪分析提供了新的可能性.
- 了解学生的情绪对于有效的教学和专业发展至关重要.
- 现有的模型缺乏教育工作者实时的情绪反机制.
研究的目的:
- 开发和验证注意力特征卷积神经网络 (AFCNN) 模型,用于大学外语课堂的实时情绪识别.
- 根据学生的情绪状态,为教师提供及时的教学反.
- 探索外语教师的专业发展道路,整合基于DL的情感分析.
主要方法:
- 实施注意力特征卷积神经网络 (AFCNN) 模型用于面部情绪识别.
- 使用VGG16和ResNet18等传统模型进行比较实验.
- 在封闭条件下测试模型的稳定性,以模拟真实的课堂场景.
主要成果:
- 在情感识别方面,AFCNN模型的准确性高达81%,超过了VGG16和ResNet18.
- 该模型展示了"快乐"和"中立"情绪的卓越识别准确性.
- 即使不包括面部数据,AFCNN也保持了比传统模型更高的识别率.
结论:
- 在外国语言课堂上,AFCNN模型是有效和合理的实时学生情绪识别.
- 通过DL模型整合情绪分析,支持外语教师的专业发展.
- 这项研究将教育技术与人机交互联系在一起,提供新的跨学科视角.
相关概念视频
Facial Feedback Hypothesis
Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role of...
Non-Verbal Cues
Non-verbal communication extends beyond gestures and facial expressions to include vocal elements known as paralanguage. Paralanguage consists of non-verbal vocal cues such as pitch, loudness, speech rate, pauses, and non-verbal vocalizations like laughter, sighs, and moans. These elements not only accompany speech but also provide critical emotional and contextual information.The Role of Paralanguage in CommunicationParalanguage adds depth to spoken language by conveying emotions and...


