一个基于人工智能的框架来评估教师在课堂互动中的表现:一种深度学习方法.
Arwa Almubarak1,2,3, Wadee Alhalabi1,4, Ibrahim Albidewi1,2
1Department of Computer Sciences, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
Frontiers in artificial intelligence
|September 19, 2025
概括
本研究介绍了一种使用计算机视觉的AI框架,通过分析课堂互动来客观评估教师表现. YOLOv8在识别相互作用类别方面表现出卓越的准确性,为传统方法提供了更可靠的替代方案.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 教育技术的教育技术
背景情况:
- 传统的教师绩效评估依赖于主观,劳动密集型的观察方法.
- 当前的方法往往缺乏一致的可靠性和专业发展的客观数据.
- 需要客观和可扩展的解决方案来评估课堂动态.
研究的目的:
- 开发和评估一个人工智能驱动的计算机视觉框架,用于客观地评估课堂互动.
- 为了比较最先进的物体检测模型在识别教师和学生之间的互动中的性能.
- 为基于证据的教师评估和反提供基础.
主要方法:
- 使用YOLOv8,Faster R-CNN和RetinaNet对象检测模型开发了一个计算机视觉框架.
- 一个包含7259张课堂图像的数据集被注释,以确定11个不同的交互类别.
- 使用平均平均精度 (mAP) 量化评估模型性能.
主要成果:
- YOLOv8实现了最高的性能,平均平均精度 (mAP) 为85.8%.
- 人工智能框架在检测多样化的课堂互动方面表现出强大的准确性.
- 与YOLOv8.8相比,更快的R-CNN和RetinaNet显示出具有竞争力的,但最终性能较低的性能.
结论:
- 基于深度学习的计算机视觉提供了一种更客观和可靠的方法来评估教师和学生之间的互动.
- 拟议的AI框架支持基于证据的教育评估.
- 这项技术有可能显著增强教师反,改善教育成果.
相关概念视频
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