DEVELOPMENT OF AN ADAPTIVE LEARNING SYSTEM FOR PHYSICAL EDUCATION LESSONS USING IOT TECHNOLOGIES AND DIGITAL PLATFORMS FOR REAL-TIME INDIVIDUAL LOAD MONITORING

Abstract

This study focuses on the development of an adaptive learning system for physical education lessons based on Internet of Things (IoT) technologies and digital platforms aimed at real-time monitoring of individual physical load. The increasing integration of digital technologies in education creates new opportunities for personalized learning, particularly in physical education where individual physiological differences significantly influence training outcomes. The proposed system utilizes wearable IoT devices to collect real-time biometric and activity data, which is then processed through a digital platform to adjust physical workloads according to each student’s capabilities and performance level.

The research emphasizes the design architecture of the adaptive system, including data acquisition, transmission, analysis, and feedback mechanisms. Special attention is given to ensuring accuracy, reliability, and scalability of the system in educational environments. The study also explores how real-time monitoring enhances student safety, motivation, and overall physical performance. The findings suggest that the integration of IoT and adaptive algorithms can significantly improve the efficiency of physical education by enabling personalized training programs and reducing the risk of overexertion.

The proposed model demonstrates the potential of smart educational systems in transforming traditional physical education into a data-driven, individualized learning process aligned with modern digital transformation trends in education.