Enhancing multimodal learning through personalized gesture recognition

Gestural recognition systems are important tools for leveraging movement-based interactions in multimodal learning environments but personalizing these interactions has proven difficult. We offer an adaptable model that uses multimodal analytics, enabling students to define their physical interactions with computer-assisted learning environments. We argue that these interactions are foundational to developing stronger connections between students' physical actions and digital representations within a multimodal space. Our model uses real time learning analytics for gesture recognition, training a hierarchical hidden-Markov model with a “one-shot” construct, learning from user-defined gestures, and accessing 3 different modes of data: skeleton positions, kinematics features, and internal model parameters. Through an empirical comparison with a “pretrained” model, we show that our model can achieve a higher recognition accuracy in repeatability and recall tasks. This suggests that our approach is a promising way to create productive experiences with gesture-based educational simulations, promoting personalized interfaces, and analytics of multimodal learning scenarios.