基于人工智能的英语教师课堂语言互动和情感行为的智能评估
Zhiming Liu1,2, Tajularipin Sulaiman3, Nur Raihan Che Nawi2
1Faculty of Foreign language Studies, Tianshui Normal University, Tianshui, 741000, China.
Scientific reports
|October 15, 2025
概括
本研究介绍了用于评估英语教师的双模式表示和融合网络 (BMRN).
科学领域:
- 多式模式机器学习
- 教育技术的教育技术
- 情感计算是一种情感计算.
背景情况:
- 评估教师表现对于专业发展和智能课堂环境至关重要.
- 传统方法通常依赖于主观观察,缺乏客观和全面的数据.
- 多模式分析为捕捉复杂的教学动态提供了一个有希望的途径.
研究的目的:
- 为英语教师的课堂行为提出一个智能评估模型.
- 整合文本,语音和视觉数据,以全面理解教学.
- 在多式调教互动中增强语义理解和情感识别.
主要方法:
- 开发了使用多式模式表示学习的双式模式表示和融合网络 (BMRN).
- 采用共享-私有功能解结构,以有效地整合信息.
- 使用四种类型的损失 (相似性,差异,循环一致性,重建) 进行交叉模式的融合.
主要成果:
- 在CMU-MOSI和CMU-MOSEI数据集 (MAE 0.547,PCC 0.781) 上,BMRN的表现优于现有的模型.
- 在自建的教师教学数据集上实现了66.1%的四分类准确性.
- 废弃研究确定了文本模式和循环一致性损失作为关键贡献者.
结论:
- BMRN模型在评估多式模式教学行为方面表现强.
- 该模型表现出强度,即使缺少模式,由于其跨模式重建机制.
- 在智能教室中为教师评估和专业发展提供了巨大的潜力.
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