On the development of an AI performance and behavioural measures for teaching and classroom management
This work addresses the need for automated, non-judgmental tools to support teacher development and reduce manual workloads in educational settings, though it is incremental as it builds on existing AI-based educational analytics.
This paper tackled the problem of analyzing classroom dynamics by developing AI-driven measures using multimodal sensor data, resulting in a curated dataset, novel behavioral measures, and a proof-of-concept dashboard that was evaluated positively for clarity and usability by eight researchers.
This paper presents a two-year research project focused on developing AI-driven measures to analyze classroom dynamics, with particular emphasis on teacher actions captured through multimodal sensor data. We applied real-time data from classroom sensors and AI techniques to extract meaningful insights and support teacher development. Key outcomes include a curated audio-visual dataset, novel behavioral measures, and a proof-of-concept teaching review dashboard. An initial evaluation with eight researchers from the National Institute for Education (NIE) highlighted the system's clarity, usability, and its non-judgmental, automated analysis approach -- which reduces manual workloads and encourages constructive reflection. Although the current version does not assign performance ratings, it provides an objective snapshot of in-class interactions, helping teachers recognize and improve their instructional strategies. Designed and tested in an Asian educational context, this work also contributes a culturally grounded methodology to the growing field of AI-based educational analytics.