ORIGINAL RESEARCH article
Front. Educ.
Sec. Teacher Education
Using Multimodal Learning Analytics as a Formative Assessment Tool in AI-Assisted Physical Education: A Case Study of Baduanjin Teaching
Provisionally accepted
- 1INTI International University, Nilai, Malaysia
- 2Minbei Vocational and Technical College, fujian, China
- 3Jimei University, Xiamen, China
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Traditional formative assessment in physical education (PE) often lacks objectivity and fails to capture the intricate multimodal dynamics of instructional behaviors. This study validates a Multimodal Learning Analytics (MMLA) framework to decode teaching effectiveness in a peer-coaching context using the traditional Chinese exercise, Baduanjin. We analyzed synchronous data from 20 instructional dyads (N=20), combining kinematic pose estimation (MediaPipe), speech fluency metrics (Whisper), and facial emotion recognition (OpenFace). Results from Spearman correlations and Mann-Whitney U tests revealed a "Precision-Economy" instructional archetype: high-performing instructors were characterized by superior kinematic fidelity (rs=.52) and verbal economy (rs=.47), rather than sheer feedback volume or emotional intensity. Counter-intuitively, excessive corrective feedback negatively correlated with learner skill gains (rs=−.41), suggesting a cognitive load interference effect. These findings challenge the "more-is-better" pedagogical assumption and demonstrate that AI-driven analytics can objectively quantify the tacit mechanisms of embodied instruction, offering a scalable tool for teacher training and developing countries seeking to modernize PE assessment.while acknowledging the necessity for further validation in open-skill sports contexts.
Keywords: embodied learning, formative assessment, Multimodal learning analytics (MMLA), Physical Education, teacher training and developing countries
Received: 17 Nov 2025; Accepted: 10 Feb 2026.
Copyright: © 2026 Guan, Chew, Wen and Huan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: TongKai Guan
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