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在中学教育中使用深度学习,将图像识别整合到语言分析技术中
Liqing Chu1, Yanlan Liu2, Yixi Zhai2
1School of Foreign Studies, Tangshan Normal University, Tangshan City, 063000, China. chuliqing@tstc.edu.cn.
Scientific reports
|February 4, 2024
概括
先进的AI和深度学习增强了中学教育的语言分析. 本研究确定了有效的在线教学行为,以改善学生的支持和指导.
科学领域:
- 人工智能的人工智能
- 教育技术的教育技术
- 自然语言处理自然语言处理.
背景情况:
- 中学教育在分析学生的语言行为方面面临挑战,特别是在在线环境中.
- 现有的语言分析工具缺乏复杂性,无法为教育工作者提供可操作的见解.
- 人工智能和深度学习的整合为增强教育支持提供了潜在的解决方案.
研究的目的:
- 研究深度学习和图像识别在中学高级语言分析中的应用.
- 开发一个框架来分析在线中学教育中的语言行为.
- 识别有效的教学行为,改善在线课程教学.
主要方法:
- 使用评估评论和定义的指标构建一个分析语言行为的框架.
- 采用数据挖掘,图像和字符识别来处理在线教学视频数据.
- 设计一个实验来验证拟议的语言行为分析框架.
主要成果:
- 该研究确定了分析指标分组评估得分的具体差异.
- 在在线话语中,发言速度 (-0.56),语音可理解性 (-0.71),平均句子长度 (-0.71) 和内容相似性 (-0.74) 发现了显著的p值.
- 结果表明在线课堂沟通中存在不同的模式.
结论:
- 开发的框架有效地分析在线中学教育中的语言行为.
- 人工智能驱动的洞察力可以帮助教育工作者识别和支持有效的教学策略.
- 这项研究为更复杂的人工智能驱动的教育工具提供了基础.
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