Evaluation of Physical Education Teaching Quality in Colleges Based on Hybrid Genetic Algorithm-Optimized Neural Network

The quality of physical education (PE) instruction in colleges has great effects on student health and the development of skills. In traditional ways, however, evaluation can be inefficient and subjective, which results in uneven judgments. Here, this research suggests a neural network-based method to assess PE instruction quality in order to overcome the shortcomings of traditional methods. The study considers critical performance indicators like instructor efficacy, student engagement, and course design, employing a Backpropagation Neural Network (BPNN) to analyse. For better accuracy, a hybrid Genetic Algorithm-Optimized Neural Network (GA-NN) is proposed, which enhances the convergence and reliability of prediction. The dataset includes structured questionnaires, expert scores, and student opinions across several colleges, which are Student Feedback & Course Evaluations, Expert & Peer Observation Scores, Wearable Sensor & Biometric Data, Institutional Academic Records, Video-Based Teaching Assessments, provides thorough analysis. Experimental results exhibit 98.3% classification accuracy, precision, recall, and F1score higher than 90 %, outperforming conventional methods. The originality comes from the adaptive optimization of neural networks for dynamic PE teaching settings, offering an intelligent, data-driven assessment framework. This system provides colleges with a sound tool for continuous physical education program improvement.