Construction of intelligent PE classroom and innovation of teaching mode in colleges and universities based on machine learning and Internet of Things

Physical Education (PE) teaching at colleges and universities has historically faced issues such as limited personalization, ineffective monitoring, and low teaching efficiency. Advances in the Internet of Things (IoT) and Machine Learning (ML) offer smarter classrooms by combining real-time data collection and intelligent analysis. Research offers an innovative intelligent PE classroom system that includes a revolutionary Levy-Driven Polar Bear Boltzmann Machine (Levy-PB-BM), a hybrid approach that combines improved polar bear optimization (IPBO) and Boltzmann Machine (BM) to increase PE instruction accuracy and efficiency. Data were acquired from 1,000 samples of the Intelligent PE Classroom Dataset, encompassing physiological signals, motion dynamics, and ambient factors. Z-score normalization and Fourier analysis are employed to eliminate noise and extract significant information such as heart rate variability, movement dynamics, and postural signals. The data features are then optimized using an IPBO algorithm with Levy flight behaviours to improve the accuracy and reliability of selected data features for use as predictors. The optimized data features are evaluated using a BM to create probabilistic predictions of student physical active performance, and to provide adaptive feedback to each student in a personalized manner. The experimental data revealed that the prediction ratios of the proposed model were 97.14%, indicating a high accuracy in recognizing student performance behaviours and generating appropriate instructional feedback. The pedagogical evaluations reveal notable improvements in student engagement, motor skills, and instructional effectiveness. The intelligent PE classroom framework based on the proposed data-driven, personalized, and efficient approach to adapt PE pedagogy, resulting in better educational outcomes. IoT and ML-based system personalizes PE feedback, improving student engagement and outcomes. Levy-PB-BM boosts prediction accuracy (97.14%) and efficiency (95.98%) over traditional models. Enhances motor skills, fitness progress, and teacher feedback for better PE instruction.