Maximizing English Teaching Efficacy With Particle Swarm Optimization-Driven Neural Network Training - Fei Wu; Yu Chen (IEEE Access, 2024)
This study investigates the potential of Particle Swarm Optimization (PSO)-based Neural Network (NN) training to enhance the efficacy of English language instruction. Recognizing English as a global language vital for international communication and economic interaction, effective language training is imperative. Leveraging PSO as a guiding mechanism for NN training presents a novel approach to refine instructional strategies. This study critically examines existing literature and identifies shortcomings in conventional algorithms employed in English teaching methodologies. Subsequently, a sophisticated algorithmic framework integrating PSO with NN is proposed to address these deficiencies and augment instructional outcomes. The adaptability of this methodology to conventional teaching models in English language education is explored. Experimentation and simulation validate the effectiveness of this innovative approach, illustrating its potential to revolutionize English education by optimizing learning outcomes. The findings underscore the significance of PSO-driven NN training in enhancing teaching effectiveness, paving the way for advancing English language education methodologies.