Abstract
University English is one of the basic core courses for all university students. The course involves many difficult concepts and grammatical structures, so it is difficult to most students. The starting point in any teaching program is to determine whether teaching is needed to specify what that teaching should accomplish. So the students’ need is of vital importance to the success of university English teaching. Traditional university English teaching does not take into account students’ individual learning abilities and feedback, which would cause students to not grasp enough the key knowledge, and then make students lose their interest in English. The development of artificial intelligence technology is becoming more and more mature, especially in the application of English teaching, which promotes the reform, development, and modernization of English teaching. Hence, in this paper, we propose a hierarchical teaching method for the university English teaching platform and employ artificial intelligence to find the needs of university students and know about the mastering knowledge of students. Initially, the dataset is preprocessed using normalization, and then, the feature extraction is performed using principal component analysis (PCA). For classification of the data, we employ the K -means clustering algorithm. To enhance the evaluation system, the whale optimization algorithm (WOA) is used. The performance of the proposed system is analyzed, and it is shown that the average score of students who used the proposed platform to learn university English is far higher than those students who did not use the platform. Hence, the platform can improve students’ autonomous learning and English abilities.
Highlights
The traditional method of teaching English needs to be improved
The results show that to allocate English teaching resources, k-nearest neighbor (KNN) provides a feasible way
For the analyzing of hierarchical teaching methods based on language education, a constricted parametric index analysis model is formed, a quantitative recursive analysis is used to compare hierarchical teaching methods depending on big data knowledge analysis model in terms of improving the Create a starting population of whale optimization algorithm at random (i = 1, 2, ⋯, n) WOi
Summary
The traditional method of teaching English needs to be improved. The students are the major concern for the change in the standard of teaching methods, and the teachers became leaders to guides. (iv) Chen [9]: the technique of calculating an RBF regularization network was used to provide the knowledge fusion and optimization of the RBF neural net selection method for the performance assessment system of college English teaching. It employs an experimental research technique and. The performance of the method is evaluated using example verification It demonstrates that the suggested method’s result has a high accuracy rate and can be used to classify text in English teaching material and offer a reference point for relevant research. The study findings show that the suggested artificial intelligence model in this article has a positive impact
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