AI-driven Teacher Analytics: Informative Insights on Classroom Activities

Teachers require reliable feedback to refine their skills and to analyze their methods in the classroom. However, it can be challenging for teachers to be aware of the characterization and distribution of classroom activities while simultaneously delivering lectures. Traditional methods of achieving expertise through deliberate practice under the guidance of a human coach are not suitable, due to the long turnover time required for training coaches, observing classrooms, and coding activities. To address these challenges, automated approaches using artificial intelligence (AI) techniques to analyze audio recordings have been proposed to infer the climate in classrooms, model teacher discourse, and classify teaching activities. While these approaches have shown promising results inferring activities, there is a need for additional proposals that support tools and analytics enabling teachers to reflect on their practice and track their progress. In this paper, we present a novel framework that leverages deep learning for speaker diarization and machine learning algorithms for the classification of teaching practices and the analysis of different teaching styles. Our approach utilizes several non-verbal discursive features to provide informative insights. Specifically, we have defined 12 different features that are employed to classify up to three distinct practices: lecture, group discussion, and the use of audience response systems. We show that those features are also informative to analyze the behavior of the teachers for each teaching practice.