The Capture and Assessment system of Student Activity-Based State Recognition for Physical Education

Abstract

In physical education classes, students get bored and distracted at some point in time. The current physical education programs to reach popularity among many universities are massive open online courses (MOOCs). The MOOC for physical education is recognized globally and provides a chance for physical education change as successful online learning. Educating students in physical education develop online instruction and virtual physical education, especially constructing a supplementation of courses and online courses-based learning. The significant drawbacks of teaching physical education with fewer content to cover and inspiring instructor applicants for time use are open-ended schedules. In this paper, MOOC assisted Teaching Framework (MOOCTF) with an artificial neural network (ANN) to analyze the student knowledge, attention level, and student performance in education is proposed. The proposed MOOCTF utilizes ANN and imaging technology to predict students’ activities during physical education online class. The collected image data are processed in ANN layers with a trained set of students’ behaviour. The simulation analysis proved that online courses for general education could be reformed education courses by taking MOOC’s form. The MOOCTF proposed for MOOC shows high performance, scalability, and high probable predictability.