The construction of an intelligent English grading teaching model based on partial least squares method

Building an intelligent English grading teaching model helps to improve English teaching and students’ English performance in colleges and universities. In this paper, the PLS-ELM model is constructed from partial least squares as the base algorithm, the concept of an extreme learning machine is introduced, and the principal component score of the PLS algorithm is used to the number of neuron nodes of ELM, and then the adjustment of weights is realized. The principal components of PLS were used to analyze the selection of independent variables for the English-graded teaching model. The effectiveness of the English-graded teaching model was verified by comparing students’ satisfaction with teaching evaluation and performance after teaching implementation. Regarding student satisfaction, the percentages of those evaluated as very satisfied, generally satisfied, and dissatisfied were 53.71%, 32.86%, and 13.43%, respectively. Regarding students’ performance, the average score of students in the experimental group was 88.6, 14.62% higher than the English performance of the control group. This shows that the English grading teaching mode can improve students’ English learning performance, enhancing students’ own quality and professional ability, improving university English teaching, and cultivating students’ English application ability.