Exploring AI Techniques for Generalizable Teaching Practice Identification

Abstract

Using automated models to analyze classroom discourse is a valuable tool for educators to improve their teaching methods. In this paper, we focus on exploring alternatives to ensure the generalizability of models for identifying teaching practices across diverse teaching contexts. Our proposal utilizes artificial intelligence to analyze audio recordings of classroom activities. By leveraging deep learning for speaker diarization and traditional machine learning algorithms for classifying teaching practices, we extract features from the audio diarization using a processing pipeline to provide detailed insights into teaching dynamics. These features enable the classification of three distinct teaching practices: lectures, group discussions, and the use of audience response systems. Our findings demonstrate that these features effectively capture the nuances of teacher-student interactions, allowing for a refined analysis of teaching styles. To enhance the robustness and generalizability of our model, we explore various pipelines for audio processing, evaluating the model’s performance across diverse contexts involving different teachers and students. By comparing these practices and their associated features, we illustrate how AI-driven tools can support teachers in reflecting on and improving their teaching strategies.

Keywords

• Audio analysis
• deep learning
• machine learning
• multi-modal learning analytics
• speaker diarization
• teaching practices