Semi-automated analysis of audio-recorded lessons: The case of teachers' engaging messages
This work addresses the problem of resource-intensive classroom discourse analysis for researchers and educators, though it is incremental as it applies existing techniques to a specific domain.
The study tackled the challenge of efficiently analyzing teachers' engaging messages in classrooms by developing a semi-automated method using audio recordings, which reduced analysis workload by 90% and found that message usage decreased over the academic year.
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.