Analysis on class participation based on artificial intelligence

Abstract

Artificial Intelligence (AI) frees humans from the heavy calculation burden in science and engineering. The AI can analyze the mass data on classroom behavior and the backend data of intelligent teaching platforms, and extract the useful features of student behaviors in class. Based on feature fusion, this paper develops a novel method to analyze class participation, which correlates class participation with classroom behaviors. Firstly, the basic data for the class participation analysis were processed, including the participation records of online classroom teaching and the data of classroom video, and the relevant calculation formulas were constructed. After that, the features of classroom behaviors were processed, and a training network for classroom behaviors was created. Next, the correlations between classroom behaviors and class participation were analyzed, and the similarity of facial features were evaluated, followed by the fusion of classroom behavior features. Experimental results show that our method can effectively and accurately evaluate class participation.