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Nowadays, it is essential to capture and evaluate student action in the physical education classroom to assess their behavior. Every student’s performance is unique in physical activity. Every time, the staff or trainer cannot watch and evaluate the students individually. At the university level, the use of classroom capture systems is becoming more widespread. However, due to technology’s recent growth and application, the research on classroom capture systems’ efficacy in university classrooms has been minimal. This paper is proposed for the student action capture and evaluation system. Image preprocessing is the process of preparing pictures for use in model training and inference. This covers resizing, orienting, and color adjustments, among other things. As a result, a change that can be an augmentation in certain cases can be better served as a pretreatment step in others. The DL-IF uses cloud technology for data storage and evaluation. DL-IF uses the imaging technology to monitor students’ actions and responses in the classroom. The image data are evaluated based on the trained set of data provided in DL-IF’s Artificial Neural Network (ANN). The evaluation of unique individuality in every student’s performance is reported to the respective trainer. The simulation analysis of the proposed method DL-IF proves that it can monitor, capture and evaluate every student’s action in all physical activity classrooms. Hence, it proved that this framework could work with high accuracy and minimized mean square error rate.

Education is a dynamic system by which students perceive the factors necessary to fit them into the society. Education is mainly intentional learning that grooms individuals to achieve success in their adult lives. Evaluation of teaching techniques, course management (CM), communication, and student monitoring are the main characteristics of today’s education system. The aim to plan the curriculum of education management in both schools and colleges leads to the implementation of an MS-BDA. The development process for evaluation of teaching techniques and CM includes the use of the sentiment analysis method, which assesses the emotional feelings of students studying the course by managing curriculum quality. The big data analysis with MNN is developed by considering the communication and student monitoring system. This system evaluates the monitoring model provided in MS-BDA for assessing student communication on merging the voice-over with the communication language processing system. The simulation analysis is performed based on accessibility, adaptability, and efficiency, proving the proposed framework’s reliability. Therefore, the system outputs an accuracy of 99.1% when compared to the existing methods.