How Teachers’ Gestural Culture Influences Japanese Students’ Emotions: A Machine Learning Approach

This study analyzes differences in teachers’ gestural styles based on their culture and investigates how these differences are perceived to influence Japanese students’ emotional responses by active observers. Classroom videos of Japanese- and English-native instructors were analyzed using MediaPipe for gesture tracking and DeepFace for facial emotion recognition. Ground-truth emotion labels were collected from four Japanese observers. Results show that Japanese and non-Japanese teachers’ gesture dynamics differ in terms of range, rhythm, and symmetry. Japanese student observers perceived each group’s gestures differently, with cultural familiarity playing a role in their shifts in emotion. Machine learning models trained on gesture features, facial emotion scores, and teacher background successfully predicted students’ affective reactions. These findings highlight the importance of culturally sensitive nonverbal communication in education and demonstrate the potential of AI-based approaches for modeling student emotion in cross-cultural contexts. This study contributes a novel multimodal framework that integrates gesture dynamics, facial emotion recognition, and teacher cultural background to predict student affect, thereby highlighting the necessity of culturally adaptive affective computing in education.

@article{Nishi2025,
  title     = {How Teachers’ Gestural Culture Influences Japanese Students’ Emotions: A Machine Learning Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Yuka Nishi and Olivia Kennedy and Choi Dongeun and Noriaki Kuwahara},
  doi       = {10.14569/IJACSA.2025.0160802},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160802}
}