Semi-automated analysis of audio-recorded lessons: The case of teachers' engaging messages

TL;DR

This study introduces a semi-automated method for analyzing teachers' engaging messages in audio-recorded lessons, significantly reducing analysis time and providing insights into communication patterns over an academic year.

Contribution

The paper presents a novel methodology combining automatic transcription and keyword filtering to efficiently analyze large datasets of classroom audio recordings.

Findings

Most engaging messages emphasized future benefits of activities

Use of engaging messages decreased over the academic year

Method reduced analysis effort by 90%

Abstract

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.