An Implementation of Hybrid PSO for Quality Evaluation of Higher Vocational Physical Education Teaching Techniques

Skill-Based Learning has an emphasis on modern education systems, which significantly increases the bright light of implementing higher vocational education in educational institutions. Hence, ensuring the quality of technical teaching within this domain is considered to essential for increasing the learning outcomes. This study proposes an intelligent ensemble of optimized learning model which leverages Particle Swarm Optimization and Neural Networks to systematically evaluate and fine-tune the effectiveness of various higher vocational physical training techniques. A novel fitness function is calculated, encompassing the various parameters such as instructional design, physical activity integration, learner engagement, assessment effectiveness, and skill acquisition. The comprehensive experimentation has been carried out using Student Educational Performance datasets from Kaggle and various performance metrics such as accuracy, precision, recall and F1-score are calculated. Experimental Outcomes demonstrates the proposed model has achieved accuracy of 99.2 % of accuracy, 99.1 % of precision and 99.1 % of recall and 99% F1 score, which clearly indicates the ability of model to understand and evaluate effectively. The suggested system promotes intelligent assessment across institutions and opens the door to automated, objective evaluation in vocational education.