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

Abstract

English teaching in construction plays a vital role in facilitating effective communication and collaboration within the industry. By equipping construction professionals with proficiency in English, they can navigate global markets, engage with multinational clients, and access a wealth of resources and knowledge available in the English language. English instruction tailored to the construction sector emphasizes industry-specific terminology, communication skills for project management, and comprehension of technical documents and specifications. Additionally, English language proficiency enables construction workers to comply with safety protocols, understand complex engineering plans, and participate in international conferences and training programs. This paper introduces an innovative model of English teaching tailored for construction professionals, utilizing a Particle Swarm Algorithm Construction with Weighted Particle Swarm Classification (W-PSO). Acknowledging the significance of effective communication in the construction industry, especially in multinational contexts, this research endeavors to optimize English language instruction through advanced computational techniques. The proposed model combines traditional teaching methodologies with the W-PSO algorithm, a metaheuristic optimization technique inspired by the behavior of particles in a swarm. W-PSO dynamically adjusts the weightings assigned to various instructional components, such as vocabulary acquisition, grammar instruction, and communication skills development, to optimize learning outcomes for construction professionals. Simulation results a cohort of 100 construction professionals participating in the English teaching program, the average improvement in English language proficiency is measured at 25% after completing the W-PSO-enhanced curriculum. Furthermore, specific language skills exhibit notable enhancements, with vocabulary acquisition increasing by 30%, grammar comprehension by 20%, and communication skills by 35%. The W-PSO algorithm dynamically adjusts the weightings assigned to various instructional components, optimizing the learning process. For instance, vocabulary acquisition receives a weighting of 0.4, grammar comprehension 0.3, and communication skills 0.3, resulting in a balanced and comprehensive approach to English language instruction.