用AI分析课堂行为，帮教师客观反思教学策略。
On the development of an AI performance and behavioural measures for teaching and classroom management
- 融合多模态传感器数据，实时捕捉教师课堂行为
- 构建可交互的教师评估看板，支持教学改进
- 适合教育研究者与一线教师提升教学反思能力
本研究历时两年，致力于开发基于AI的课堂动态分析方法，重点通过多模态传感数据捕捉教师行为。研究应用课堂传感器的实时数据与AI技术，提取有意义的教学洞察，支持教师专业发展。核心成果包括一个精心标注的音视频数据集、新型行为度量指标，以及一个教学评估看板的原型系统。八位来自新加坡国立教育学院（NIE）的研究人员参与初步评估，反馈显示系统清晰易用，分析过程非评判性，显著降低人工工作量，并促进建设性反思。当前版本不提供绩效评分，但可生成课堂互动的客观快照，帮助教师识别并优化教学策略。该系统在亚洲教育语境下设计与测试，为基于AI的教育分析领域贡献了具文化适应性的方法论。
原文摘要 · Abstract (English)
This paper presents a two-year research project focused on developing AI-driven measures to analyze classroom dynamics, with particular emphasis on teacher actions captured through multimodal sensor data. We applied real-time data from classroom sensors and AI techniques to extract meaningful insights and support teacher development. Key outcomes include a curated audio-visual dataset, novel behavioral measures, and a proof-of-concept teaching review dashboard. An initial evaluation with eight researchers from the National Institute for Education (NIE) highlighted the system's clarity, usability, and its non-judgmental, automated analysis approach -- which reduces manual workloads and encourages constructive reflection. Although the current version does not assign performance ratings, it provides an objective snapshot of in-class interactions, helping teachers recognize and improve their instructional strategies. Designed and tested in an Asian educational context, this work also contributes a culturally grounded methodology to the growing field of AI-based educational analytics.
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