Research on the data analysis system of student stress in English MOOC based on fuzzy C-means algorithm

Abstract

In order to improve the recognition accuracy of students’ psychological stress data in the English MOOC classroom teaching process, this paper improves the traditional fuzzy C-means algorithm, and uses the deviation value to represent the difference between the average algebraic distance of the neighborhood point and the center pixel. By calculating the deviation value, the influence of the neighborhood point on the center point can be measured, and the noise resistance of the algorithm can be improved. Moreover, this paper constructs a quantitative identification model of student stress data based on the needs of English MOOC teaching stress analysis, and uses image database to verify the basic performance of the algorithm, and constructs a data analysis system of student stress in English MOOC classroom, which is used in practice. In addition, this paper uses student facial expression recognition as a basis for quantitative identification of student stress, and designs experiments to analyze the reliability of the system. From the statistical results, it can be seen that the data analysis system of the students’ psychological stress in the English MOOC classroom teaching process constructed in this paper is effective.