Evaluation Model of College English Teaching Effect Based on Particle Swarm Algorithm and Support Vector Machine

Abstract

Based on the principle of particle swarm algorithm and support vector machine, this article aims to improve the classification performance of college English teaching effect and explores the best support vector machine parameter optimization algorithm to promote college English teaching for the theory and application research of data analysis. First, the advantages and disadvantages of common support vector machine parameter selection methods such as grid search algorithm, gradient descent method, and swarm intelligence algorithm are studied. Secondly, this article has a detailed analysis and comparison of various other algorithms. Finally, the study analyzed the advantages and disadvantages of the quantum particle swarm algorithm, introduced the dual-center idea into the quantum particle swarm algorithm, and proposed an improved quantum particle swarm algorithm. Through simulation experiments, it is proved that the improved quantum particle swarm algorithm is more superior in optimizing the parameters of support vector machine. In general, this paper uses the PSO algorithm to simultaneously solve the SVM feature selection and parameter optimization problems and has achieved good results. Within the scope of the literature that the author has, there is still a lack of work in this area. Compared with the existing algorithms, the algorithm proposed in this paper has stronger feature selection ability and higher efficiency.

Construction of College English teaching effect evaluation model based on artificial intelligence and output-oriented approach

“…Figure 5 and Figure 6 show the comparison results of teaching effect evaluation errors of different methods in class A and class B respectively. It can be seen from Figure 5 and Figure 6 that the teaching effect evaluation error gap between the method of reference [7] and the method of reference [8] is small and basically maintained at the same level, while the teaching effect evaluation error of this method is significantly lower than that of the method of reference [7] and the method of reference [8]. It shows that the evaluation result of this method is more reliable and feasible.…”

“…(2) Application effect analysis In order to further verify the effectiveness of this method, reference [7] method and reference [8] method are used as comparison methods to compare the evaluation effects of different methods from the perspective of evaluation efficiency and evaluation error. The comparison results of evaluation efficiency are shown in Figure 3 From the data in Figure 3 and Figure 4, it can be seen that during the experiment, the evaluation time required for the evaluation of teaching effect by using the method of reference [7] and reference [8] is higher than 4 minutes, while in the same experimental environment, the evaluation time required for the teaching effect evaluation using the method in this paper is all less than 2.5min. It can be seen that the method in this paper effectively improves the efficiency of teaching effect evaluation, and also improves its application performance in practical work.…”

“…Reference [7] designed a university theoretical and experimental teaching quality evaluation model based on RVM machine learning method, improved the kernel function and parameter determination method of RVM, established a new MK -RVM model for university theoretical and experimental teaching quality evaluation by using the MK -RVM model optimized by genetic algorithm, and constructed a result reliability analysis method based on confidence interval. The experimental results show that the confidence of the evaluation results obtained by this method is high, indicating that the evaluation results of teaching quality have high reliability, but this method has the problem of long evaluation time.…”

An Innovative Model of English Teaching Based on Particle Swarm Algorithm Construction

“…Yang and Huang (2022) present a classification technique for English teaching resources and merging using a swarm intelligence algorithm, highlighting the importance of categorizing and organizing teaching materials for effective instruction. Similarly, Wei and Tsai (2022) propose an evaluation model for college English teaching effectiveness based on particle swarm and support vector machine algorithms, emphasizing the significance of data-driven assessment methodologies in educational settings. Zhang (2023) explores the application of IoT-based English translation and teaching using particle swarm optimization and neural network algorithms, indicating the integration of cutting-edge technologies to enhance language learning experiences.…”

“…Teaching English is an art that combines linguistic expertise with effective communication strategies [1]. It involves not only imparting knowledge of grammar rules and vocabulary but also fostering language proficiency through interactive activities such as discussions, role-plays, and language games [2]. A skilled English teacher understands the diverse needs and learning styles of students and adapts their teaching approach accordingly, whether they're teaching grammar to beginners or refining advanced speaking skills.…”