John Komar

Andreea I. Niculescu, Jochen Ehnes, Chen Yi et al.

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.

John Komar, Romain Hérault, Ludovic Seifert

To answer the existence of optimal swimmer learning/teaching strategies, this work introduces a two-level clustering in order to analyze temporal dynamics of motor learning in breaststroke swimming. Each level have been performed through Sparse Fisher-EM, a unsupervised framework which can be applied efficiently on large and correlated datasets. The induced sparsity selects key points of the coordination phase without any prior knowledge.