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Using Machine Learning to Support Behavioral Data Analysis in Physical Education
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DOI：10.1123/jtpe.2025-0392.png)
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Abstract
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Behavioral data analysis in physical education has traditionally relied on manual coding, a process that is time consuming, resource intensive, and prone to observer bias. To address these limitations, researchers and practitioners increasingly turn to machine learning methods to support the analysis of behavioral data. This paper outlines how two machine learning approaches, Computer Vision and Natural Language Processing, can be applied in physical education. Computer Vision enables the detection and tracking of students, identification of body landmarks and equipment, and assessment of movements as proxies of learning, useful in both classroom research and teacher training. Natural Language Processing methods are applied to transcribe, segment, and classify teacher utterances, providing scalable insights into verbal behavior. These methods enhance efficiency, analytical depth, and scalability while reducing the limitations of manual coding. Future progress will depend on improving model performance and building domain-specific data sets to fully unlock the potential of machine learning in naturalistic physical education settings.
Keywords:
quantitative research
student-level analysis
individualized learning
teacher and student behavior
motor skill development
lesson-based research
field observations
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