The Evaluation of College English Writing Teaching Ability in English Teaching Courses Based on Big Data

Abstract

Under the background of big data information fusion, the use of stepwise regression analysis method to evaluate the English writing teaching ability of college teachers usually results in inaccurate classification of data flow and great errors of constraint index parameters in information fusion. In this paper, k-means fuzzy clustering algorithm is proposed. By constructing parameter constraint index system and big data analysis model, various nonlinear English teaching parameters are clustered and integrated to achieve the extraction of entropy characteristics of English constraint information. On this basis, the recursive quantitative analysis method is used to intuitively analyze various parameters of English teaching ability and the law of data change in time series. Finally, the correlation of constraint parameters and disturbance data of various English teaching parameters is analyzed by Matlab simulation algorithm. Research shows that the use of k-means algorithm to carry out information fusion of English writing teaching ability data can further improve the efficiency and accuracy of English teaching ability assessment of teachers.