A Study on Diagnosis and Improvement Path of Outdoor Play Guidance Skills for Early Childhood Teacher Trainees Based on Multi-Source Data Fusion
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Abstract
Outdoor play guidance in early childhood education involves rapid perception of children’s behavior, continuous visual attention allocation, and stable decision-making under uncertain outdoor conditions. To improve the objectivity and real-time capability of skill diagnosis for early childhood teacher trainees, this study proposes a multi-source data fusion framework integrating video-based behavior analysis, eye-tracking, physiological signal monitoring, and questionnaire data. A multi-angle camera system is used to capture guidance behaviors and movement trajectories, Tobii Pro eyetracking data characterize visual scanning and hazard attention, and wearable devices record heart-rate-variability indicators reflecting stress regulation. After temporal synchronization, feature normalization, and adaptive weighting, K -means clustering and an SVM classifier are employed to identify guidance-skill levels. Experimental analysis of 180 teacher trainees shows that the high-level group exceeds the low-level group by 27.4 percentage points in effective observation rate, and the SVM model reaches 87.6% classification accuracy with strong agreement with expert ratings. The proposed framework provides an engineering-oriented approach for multimodal sensing, physiological signal processing, and intelligent skill diagnosis in complex human–environment interaction scenarios.
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