Evaluation model of English Informatization Teaching Quality in Universities Based on Particle Swarm Algorithm

Abstract

Teaching quality in the field of English education plays a crucial role in shaping students' language proficiency, communication skills, and overall understanding of the language. One of the most significant contributions of deep learning to English education is its ability to personalize instruction. Deep learning algorithms analyze individual students' learning patterns, strengths, and weaknesses. This paper designed a framework for enhancing teaching quality in English education by implementation of Non-Convex Particle Swarm Optimization and Optimization (NC-PSOO) with a Generative Adversarial Network (GAN). The proposed model NC-PSOO with GAN, presents the information related to the teaching and learning process. The features are computed based on the utilization of the non-convex estimation for the analysis of the variables. Through the implementation of an effective process of data pre-processing using the Fejer filter, feature selection and extraction with NC-PSOO, and classification with GAN, this model aims to improve student performance, elevate the quality of teaching materials, increase student participation, enhance homework quality, and balance exam difficulty. The performance of NC-PSOO with GAN is compared with conventional optimization techniques like Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) to demonstrate its superior efficacy. The findings highlight the model's capability to achieve higher accuracy, precision, recall, and F1 score, ultimately improving teaching quality in English education.