通过自动分析录音课，高效识别教师激励性话语。
Semi-automated analysis of audio-recorded lessons: The case of teachers' engaging messages
- 用语音转录+关键词过滤提取课堂激励话语。
- 信息量减少90%，分析效率大幅提升。
- 适合教育研究者与教学改进实践者。
教师在课堂中传递的激励性话语是影响学生学习成果的重要因素，但因难以获取观察数据而难于提升。本研究提出一种高效方法，从2,477节音频课程中提取实际的激励性话语。研究覆盖75名教师，历时两个学年。通过自动语音转录与关键词过滤分析，成功识别并分类了激励性话语。该方法使需分析的信息量减少90%，显著节省时间和资源。后续描述性分析发现，最常用的话语强调参与学校活动的未来益处；且随着学年推进，激励性话语使用频率下降。研究为自然情境下教师话语分析提供了新路径，并为改进教师沟通策略提供了实证依据。
原文摘要 · Abstract (English)
Engaging messages delivered by teachers are a key aspect of the classroom discourse that influences student outcomes. However, improving this communication is challenging due to difficulties in obtaining observations. This study presents a methodology for efficiently extracting actual observations of engaging messages from audio-recorded lessons. We collected 2,477 audio-recorded lessons from 75 teachers over two academic years. Using automatic transcription and keyword-based filtering analysis, we identified and classified engaging messages. This method reduced the information to be analysed by 90%, optimising the time and resources required compared to traditional manual coding. Subsequent descriptive analysis revealed that the most used messages emphasised the future benefits of participating in school activities. In addition, the use of engaging messages decreased as the academic year progressed. This study offers insights for researchers seeking to extract information from teachers' discourse in naturalistic settings and provides useful information for designing interventions to improve teachers' communication strategies.
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