A Multi-level Fusion System for Intelligent Capture and Assessment of Student Activity in Physical Training based on Machine Learning

Abstract

To record and evaluate students' physical education class participation, this study proposes using a Machine Learning aided Physical Training Framework (ML-PTF). Improve student achievement in physical education with the help of the Multi-level Fusion System that employs machine learning strategies. The system integrates sensor data, video data, and contextual data to deliver a holistic and precise evaluation of student engagement. This study's simulation analysis shows that the ML-PTF improves the reliability of evaluating universities' physical education programs. A important reference path and paradigm for advancing tertiary-level physical education for graduates, the multi-level fusion system also provides an investigation of information technology and language education integration. The experimental findings demonstrate that the ML-PTF is superior to other approaches in terms of learning rate, f1-score, precision, and probability, as well as student engagement, involvement, and recognition accuracy.