Research on Classroom Teaching Quality Evaluation Method Based on Machine Vision Analysis

Abstract

Concentration is one of the key factors of human intelligent behaviour. Especially in recent years, our attention to learners has received extensive attention. Many evaluation methods are put forward for the evaluation of concentration, including questionnaire, physiological observation and computer vision. In the classroom of primary and secondary schools, students' concentration in class is an important factor that affects students' achievement and teaching effect, so it is a major concern of many parents. Firstly, the analytic hierarchy process is used to construct the evaluation index system, then the multi-population genetic algorithm is used to optimize the design of BP neural network, and finally the BP neural network is used to evaluate the teaching quality. In the traditional classroom environment, students' classroom participation research is mostly obtained through questionnaires designed after class. The lack of real-time learning monitoring and analysis in the classroom is far from meeting the needs of modern education development. Therefore, there is an urgent need for Research on an automatic learning state evaluation method that uses artificial intelligence technology to evaluate students' full and whole process, assist teachers to understand and master students' learning state, adopt targeted teaching methods, and improve students' personalized training level.