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<p>Physical education instruction efficacy evaluation is difficult owing to subjective observation, delayed feedback, and inadequate physiological and behavioral data. The smart wearable feedback system for teaching effectiveness evaluation (SWaF-TEE), a secure cloud-based architecture that merges wearable sensor networks with virtualized analytics, addresses these issues. SWaF-TEE collects fine-grained student activity data during organized physical training sessions using inertial motion sensors, heart rate monitors, and posture-tracking devices. The first layer of a dual-layer virtual load balancer architecture acquires and normalizes student physiological and motion signals, while the second layer analyzes instructional patterns and aggregated learner responses to estimate teaching effectiveness. A cloud dashboard allows real-time viewing and data-driven educational feedback. Pilot deployments were undertaken in undergraduate physical education training sessions under controlled institutional circumstances using predetermined activity protocols. Experimental findings suggest that the proposed framework may accurately capture instructional delivery and student involvement, aligning with performance evaluation outcomes. In physical education, cloud-enabled wearable analytics may provide more objective, continuous, and scalable instructional effectiveness assessment.</p>

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Secure Cloud-Based Wearable Sensor Networks for Teaching Effectiveness Evaluation in Physical Education Through Virtualized Load Balancer

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月23日

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  • S. Manikandan,
  • D. Vijayakumar

摘要

Physical education instruction efficacy evaluation is difficult owing to subjective observation, delayed feedback, and inadequate physiological and behavioral data. The smart wearable feedback system for teaching effectiveness evaluation (SWaF-TEE), a secure cloud-based architecture that merges wearable sensor networks with virtualized analytics, addresses these issues. SWaF-TEE collects fine-grained student activity data during organized physical training sessions using inertial motion sensors, heart rate monitors, and posture-tracking devices. The first layer of a dual-layer virtual load balancer architecture acquires and normalizes student physiological and motion signals, while the second layer analyzes instructional patterns and aggregated learner responses to estimate teaching effectiveness. A cloud dashboard allows real-time viewing and data-driven educational feedback. Pilot deployments were undertaken in undergraduate physical education training sessions under controlled institutional circumstances using predetermined activity protocols. Experimental findings suggest that the proposed framework may accurately capture instructional delivery and student involvement, aligning with performance evaluation outcomes. In physical education, cloud-enabled wearable analytics may provide more objective, continuous, and scalable instructional effectiveness assessment.