Optimizing Teaching Methods and Student Learning Styles Using Computer Analysis

Abstract

With the development of big data, blockchain, and artificial intelligence technology, the actual application of artificial intelligence in the field of education has gradually emerged. Artificial intelligence has profoundly changed production methods, lifestyle, and thinking methods, especially providing new opportunities for the innovative development of education. However, at present, most school education models are still offline, and the evaluation of the quality of teaching of teachers' classroom teaching has always stayed at the original stage of the evaluation of classroom experts. It is a purely empirical observation and induction, lacks logic and comprehensiveness, and is not conducive to the optimization of teachers' teaching methods and student learning methods. Therefore, this article studies the computer visual analysis technology based on deep learning, uses its accurate and real-time characteristics, and applies it to the evaluation of classroom teaching to help teachers and students optimize teaching methods and student learning methods. The main tasks of this study are as follows: (1) Looking back on the development of computer vision analysis technology, analyzing and absorbing advanced methods at home and abroad, combined with the classroom teaching scene of Chinese schools, proposed deep learning based on deep learning, accurate and accurate Computer visual analysis method. This method uses a convolutional neural network RNN as the main network, combining methods such as S- T classroom teaching analysis and face recognition technology commonly used in target testing, to obtain more efficient and accurate results than traditional methods. (2) Analyze and summarize the lack of teaching effects and student learning methods of classroom education, especially for teachers 'classroom teaching methods, student classroom learning methods, teacher-student interactive exchanges, and students' lack of attention. Analysis and summary of the classroom teaching and automatic student behavior recognition system based on computer vision, and used this as a reference standard for teacher classroom teaching reform and student learning method optimization. (3) Using the data collected by computer videos in the classroom, the implementation path of this system is analyzed. The experiment shows that this study has certain feasibility. It has a certain reference value for the teaching methods of classroom teaching and the learning method of students. Key words: Teacher behavior; Student behavior; Classroom teaching behavior; Computer vision; S-T analysis